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None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544139, "website_id": 1237, "url": "https://pubmed.ncbi.nlm.nih.gov/35060915/", "title": "Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature - PubMed", "snippet": "The Fitbit Charge and Fitbit Charge HR were consistently shown to have a good accuracy for step counts and the Apple Watch for measuring heart rate. None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://cdn.ncbi.nlm.nih.gov/coreutils/nwds/img/favicons/favicon.ico", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 2, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This systematic review assesses the accuracy and acceptability of various wrist-wearable activity-tracking devices. It directly addresses the topic of wearable accuracy."}, {"id": 544159, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7509623/", "slug": "nih", "title": "Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review - PMC", "snippet": "Of the 542 that remained for full-text ... not peer reviewed (n=2, 0.4%), and conference paper (n=1, 0.2%). As a result, a total of 158 publications were included in this systematic review (Figure 1) [14]. Table 1 shows the details of the device brand, model, year, and status (current model or discontinued) in the included studies. ... PRISMA flow chart for systematic review of the reliability and validity of commercial wearable ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544159, "website_id": 34203, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7509623/", "title": "Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review - PMC", "snippet": "Of the 542 that remained for full-text ... not peer reviewed (n=2, 0.4%), and conference paper (n=1, 0.2%). As a result, a total of 158 publications were included in this systematic review (Figure 1) [14]. Table 1 shows the details of the device brand, model, year, and status (current model or discontinued) in the included studies. ... 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The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544174, "website_id": 21284, "url": "https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-015-0314-1", "title": "Systematic review of the validity and reliability of consumer-wearable activity trackers | International Journal of Behavioral Nutrition and Physical Activity | Full Text", "snippet": "Background Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) ...", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://ijbnpa.biomedcentral.com/static/img/favicons/bmc/apple-touch-icon-582ef1d0f5.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 9, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This paper presents a systematic review on wearable devices that measure various physiological metrics and their relevance to accuracy."}, {"id": 544182, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-023-01473-7", "slug": "biomedcentral", "title": "Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions | International Journal of Behavioral Nutrition and Physical Activity | Full Text", "snippet": "Background Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544182, "website_id": 21284, "url": "https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-023-01473-7", "title": "Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions | International Journal of Behavioral Nutrition and Physical Activity | Full Text", "snippet": "Background Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://ijbnpa.biomedcentral.com/static/img/favicons/bmc/apple-touch-icon-582ef1d0f5.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 6, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "The source provides validation studies regarding wearable devices measuring various physical activities while emphasizing accuracy and reliability."}, {"id": 544163, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://link.springer.com/article/10.1007/s40279-024-02077-2", "slug": "springer", "title": "Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement | Sports Medicine", "snippet": "For instance, the International ... consumer wearables to measure direct and derived metrics [25,26,27]. However, the pace of academic research struggles to keep up with the more agile commercial ecosystem: primary research studies are slowed by the need to secure funding, develop validation protocols, recruit and test participants and navigate the peer review process [16], ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544163, "website_id": 219, "url": "https://link.springer.com/article/10.1007/s40279-024-02077-2", "title": "Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement | Sports Medicine", "snippet": "For instance, the International ... consumer wearables to measure direct and derived metrics [25,26,27]. However, the pace of academic research struggles to keep up with the more agile commercial ecosystem: primary research studies are slowed by the need to secure funding, develop validation protocols, recruit and test participants and navigate the peer review process [16], ...", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://link.springer.com/oscar-static/img/favicons/darwin/apple-touch-icon-92e819bf8a.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 7, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This source offers a systematic review of the accuracy of consumer wearable technologies, providing a broad perspective on the topic."}, {"id": 544176, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01773-w", "slug": "biomedcentral", "title": "Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review | BMC Medicine | Full Text", "snippet": "Background Wearable biometric monitoring devices (BMDs) have the potential to transform the conduct of randomized controlled trials (RCTs) by shifting the collection of outcome data from single measurements at predefined time points to dense continuous measurements.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544176, "website_id": 14167, "url": "https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01773-w", "title": "Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review | BMC Medicine | Full Text", "snippet": "Background Wearable biometric monitoring devices (BMDs) have the potential to transform the conduct of randomized controlled trials (RCTs) by shifting the collection of outcome data from single measurements at predefined time points to dense continuous measurements.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://bmcmedicine.biomedcentral.com/static/img/favicons/bmc/apple-touch-icon-582ef1d0f5.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 8, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This source emphasizes accuracy in quantifying wearable device performance for biometric monitoring within randomized controlled trials."}, {"id": 544174, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-015-0314-1", "slug": "biomedcentral", "title": "Systematic review of the validity and reliability of consumer-wearable activity trackers | International Journal of Behavioral Nutrition and Physical Activity | Full Text", "snippet": "Background Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544174, "website_id": 21284, "url": "https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-015-0314-1", "title": "Systematic review of the validity and reliability of consumer-wearable activity trackers | International Journal of Behavioral Nutrition and Physical Activity | Full Text", "snippet": "Background Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) ...", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://ijbnpa.biomedcentral.com/static/img/favicons/bmc/apple-touch-icon-582ef1d0f5.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 9, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This paper presents a systematic review on wearable devices that measure various physiological metrics and their relevance to accuracy."}, {"id": 544175, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://bjsm.bmj.com/content/55/14/767", "slug": "bmj", "title": "Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network | British Journal of Sports Medicine", "snippet": "Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544175, "website_id": 23612, "url": "https://bjsm.bmj.com/content/55/14/767", "title": "Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network | British Journal of Sports Medicine", "snippet": "Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. 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Accuracy of wearable technologies has been a hotly ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 544184, "website_id": 1759, "url": "https://www.nature.com/articles/s41746-020-0226-6", "title": "Investigating sources of inaccuracy in wearable optical heart rate sensors | npj Digital Medicine", "snippet": "As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. 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In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 527462, "website_id": 34203, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9931360/", "title": "Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness - PMC", "snippet": "Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, ...", "cleaned_text": "Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledge-related) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity.\n\nDevices that can be worn on our bodies and track several activities and parameters\u2014wearable devices\u2014are increasingly sold and used in the general population. One of the main areas of use of wearable devices is health, including biomedical research, clinical care, personal health practices and tracking, technology development, and engineering. In this context, the use of wearables for health has been connected to several promises and benefits for a more digital, personalised, preventive medicine [1\u20133]. At the same time, crucial work has identified and discussed technical and ethical challenges in the extended use of wearables for health, including accuracy, privacy, security, cyber risks [4\u20137]. Yet, most analyses have focused on either of these areas of discussions, thus framing technical and ethical considerations as largely separate issues. As a result, the connections between specific technical solutions and ethical considerations remain underdiscussed: This is a problem, as we will show that many challenges of the wearable context can be addressed only partially through technical solutions. In addition, the epistemic (knowledge-related) contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. As a result, there is a lack of understanding of the specific uses and functions that wearables can and should fulfil for digital health\u2014yet this is crucial to identify the role of wearables in digital health and beyond and assess their ethical and social impact in relation to specific uses.\n\nIn response to these considerations, in this article we start by providing an epistemic overview of the main functions of wearable technology for health: we discuss monitoring, screening, detection, and prediction. The role of these functions is clear when looking at the use of wearables in concrete cases (Table 1), for example, the context of Coronavirus Disease 2019 (COVID-19). The COVID-19 pandemic has been discussed as a crucial catalyst for the use of wearable technologies in the biomedical and health domain and we will use it as a source of uses and cases to illustrate our points throughout the article [8,9]. On the basis of this overview, we discuss specific issues and concerns that are connected to the use of wearables for the identified functions of monitoring, screening, detection, and prediction. We focus on 4 main areas of concern (data quality, balanced estimations, health equity, and fairness) and propose recommendations and possible solutions (local standards of quality, interoperability, access, and representativity). On the basis of our overview and analysis of these challenges, the recommendations we propose enable us to better understand the actual impact, benefits, and risks of wearables and improve their application for digital health (Table 2). In this way, as a group of researchers with different areas of expertise (biomedical engineering and research, philosophy and ethics of science and technology) but working in the same department, we develop an interdisciplinary account of wearable technology and its contribution to digital health.\n\nAn overview of wearable technology for digital health There is currently an abundance of uses of wearable devices for health. Focusing on the epistemic contribution of wearables to the collection, development, application of biomedical knowledge, we develop an overview that looks at the current context of wearable technology. While this is not a systematic representation of all possible or future applications of wearable devices, we identify 4 main functions that wearables are currently used to serve in the health context: monitoring, screening, detection, and prediction (Table 1). We identify monitoring as the basic and fundamental function served by wearables, performed by wristbands, patches, watches, clothing. Monitoring is the practice of continuous data collection, focused on members of a population, which can be the general population or a specific subset of individuals. Wearables are considered particularly efficient to fulfil this function because they can track a number of various biomedical processes depending on the types of sensors available and can be used for continuous and remote monitoring\u2014as wearables can be worn constantly, they are ideally placed to collect data continuously. In this way, wearables can deliver a significant improvement to remote and tele-monitoring [10,11] and have been used in this sense to monitor crucial physiological metrics for COVID-19 such as heart rate, physical activity, oxygen saturation, as well as long-term effects [8,12]. In this context, wearable devices have also been applied in coordination with other tele-health systems for remote monitoring for individuals at risk that could easily shift to hospitalisation and to assist remote diagnosis. On the basis of these monitoring capabilities, we identify 3 other main functions that wearables can serve. Screening is the identification of specific conditions and individuals associated with this condition within datasets collected through monitoring. The use of wearables for this function is usually based on passive sensors that measure motion, steps, light, pressure, sound, etc. [3]. For example, wearable garments have been used to monitor individuals during sleep and screen for individuals suffering from sleep apnea [13]. A close function related to screening is detection. When wearables monitor specific conditions in populations, they are often used to detect conditions and alert individual users. Detection is the analysis of wearable data collected through monitoring in order to investigate possible patterns and features that can be interpreted as indicators and markers of specific biomedical conditions. For example, a combination of smartwatches and dedicated bands has been used for heart rate monitoring and automatic detection of atrial fibrillation [14]. The integration of wearable data with symptom data has been presented as a way to improve the identification of COVID-19 positive versus negative cases [15]. Detection is also the function where we see an intersection with both monitoring and screening: for example, smartwatches have been used to monitor populations for irregular pulse and, on this basis, screen for individuals potentially suffering from atrial fibrillation as well as identify the condition [10]. A final diagnosis of a condition can thus be based on detections performed by wearables, although wearables currently cannot perform diagnosis as a consequence to technical and regulatory limitations. The fourth function we identify is prediction, the inference of future trends and/or events of interest for the biomedical study of populations based on monitoring. Just a few wearable devices are currently used for prediction in the health context, for example, to predict mortality, readmissions, and clinical risk [16]. In the context of COVID-19, wearables have been tested for the retrospective detection of infection and prediction of COVID-19, days before the presence of symptoms [17]. Other examples include the use of accelerometer data from wearable devices to predict biological age and mortality [18] and respiratory rate data to predict exacerbations of chronic obstructive pulmonary disease [19]. These 4 functions are often intertwined and interconnected in concrete contexts and in many cases the same device can perform more than 1 function. Still, identifying different functions is a crucial step to understand the actual impact and goals of using wearable technology for health. Depending on whether we use wearables to predict or monitor health, different assumptions, uses, and standards will be necessary. In addition, understanding which functions wearables can and do serve currently helps us to make sense of their possible limitations. As we will see in the next sections, this overview enables us to see how the use of wearables for health is currently limited by crucial challenges, which impact different functions in different ways. The remainder of the paper will be dedicated to a discussion of these challenges, as they emerge in concrete uses of wearables to serve the functions we have identified.\n\nIn our overview, we have identified monitoring as the fundamental feature at the basis of the functions of wearable technology for health. Monitoring is a promising application of wearables thanks to their abilities for constant and personal data collection, but a key concern is data quality. Quality is a crucial feature of scientific data, which needs to be evaluated to warrant the reliability of scientific claims. Data quality is also one of the fundamental values of research ethics and the social goals of biomedical research\u2014high-quality data are considered the basis for benefits at the clinical level and beyond [25]. Yet, the variability of sensors and lack of consistency of data collection in the wearable context make it difficult to coordinate and assess quality. In addition, the lack of contextual information on the ways in which wearable data are collected, classified, and interpreted raise concerns on the possibility of assessing quality. A first issue that makes it difficult to assess data quality in the wearable context is variability. Wearable data are usually collected by different types of devices or different sensors, if not through different data collection practices. For example, the measurement of metrics such as oxygen saturation can vary substantially in terms of location (e.g., wrist, finger, ear) and types of devices (e.g., watches, rings, earphones) employed for measurement [1,11]. This level of variability makes it difficult to have common standards to assess data quality: The same parameter is often measured with very different sensors, which employ different processing techniques, and may even render different results [3]. One way of responding to these concerns is regulation, which should make sure that wearables can be used as reliable and high-quality sources of data. In this context, the push is to regulate wearables as medical devices on the basis of clinical validity [26]. Clinical validity is a crucial step for the adoption of wearable technologies for health and also for the regulation of the quality of wearable data. Yet, clinical validity as an intrinsic feature of quality is not enough on its own. Extensive work in philosophy of science has shown that quality is not only an intrinsic feature of data. Quality is a contextual component of data: depending on a specific use and context of use, considerations of data quality might change [27]. For example, it is clearly crucial to know that a wearable device has been clinically validated to collect high-quality data on heart rate [22]. However, using a wearable to monitor heart rate and detect COVID-19 infection on this basis constitutes a new context of use, where considerations of data quality may be different. For example, a certain number of false positives might be considered good enough for fitness tracking or even remote monitoring of heart patients, but it might not be enough when wearables are used to detect COVID-19 and suggest isolation and quarantines. The ethical and social burdens of poor quality or unreliable data change depending on the context. For patients who rely on wearables to track the health of their heart after bypass surgery, the quality of data is more serious than for users tracking fitness activities: In the context of heart patients, the collection of low-quality data about severe health problems can be a very serious burden, leading to unnecessary anxiety [28]. For users with limited financial resources, wearables can be an inexpensive tool to keep track of their health when other health services are too expensive and difficult to access [2]. As such, wearables can improve access to healthcare, but wearable technology might constitute the main health service available for these users and poor data quality will unequally impact them more than others. This is why data quality should be considered a contextual property of data that needs to be constantly considered as the context of use changes significantly. In turn, in order to assess data quality for a specific use, knowledge of contextual features of data collection is crucial. For example, it is crucial to know which experimental procedures and protocols were applied, which sensors and techniques were used, and which questions and hypotheses were investigated during data collection. As questions and hypotheses change from general heart monitoring to COVID-19 detection, for example, it is crucial to know the original experimental procedures and questions to understand whether data quality remains the same\u2014access to contextual features of data practices is crucial to assess quality and ensure the reliability and trustworthiness of data [29]. The problem is that access to these contextual features is often not available in the wearable context. For example, the collection of heart rate data from wearable devices is usually covered by the opt-in of users to general medical studies, which are organised by private companies and large research bodies, such as the Apple Heart Study created by the collaboration between Stanford University and Apple. While this type of study was of course validated and regulated as a clinical trial [30], there is little information on data collection, analysis, storing, and access and this makes it difficult to assess quality. In addition, in many cases biomedical researchers cannot even download data directly from the device and have to go through proprietary archives. As a result, because of commercial interests, very little information on how the data are collected, classified, and interpreted by the device is shared throughout the process. This is an issue for researchers, but also users and patients. The lack of access to contextual information about data collection makes it difficult for users to interpret the data to take action on their health and can eventually lead them not to trust and use the technology [28,31,32]. On this basis, we need more contextual information and coherence for wearable data quality [33]. Contextual information can be used to understand the specific features and needs for the assessment of data quality in the wearable context. In this direction, common and local standards of data quality can be developed to overcome current limitations and gaps in the wearable market. For example, the framework provided by FAIR (Findability, Accessibility, Interoperability, and Reuse) can be used as a basis to discuss future developments in this direction [34\u201336]. We do not see these as hard compliance standards set by standard organisations, but rather the result of a bottom-up process of coordination and assessment, as a way of fully appreciating the contextual dimensions of data quality. As we have seen, depending on the specific context of use, standards, requirements, and burdens of data quality can change. This is why data quality standards need to be local and could first be created for the research context, where knowledge of the contextual components of data collection is crucial to assess data quality. Yet, standards of data quality can clearly be crucial for regulators, institutions, industry, and users too, and standards could be adopted by the institutions, journals, repositories of specific research communities. Again similarly to FAIR data, the institutions of specific research communities could be in charge of managing, updating, and assessing standards and informing individual users of their existence and application, thus presenting data quality as a fundamental issue for digital health.\n\nAs we have seen in our overview, detection and prediction are among the main functions for which wearable devices are currently used in health. In turn, detection and prediction are fundamental activities at the basis of the production and use of scientific knowledge. Yet, issues affecting balanced estimations in screening and prediction raise concerns on the grounding and validity of wearables as detection and prediction tools. In the COVID-19 pandemic, several models have been used to predict the development, spreading, and impact of the pandemic, but they have also been at the centre of several critiques concerning their uncertain assumptions and limitations [37\u201340]. Wearable devices have been proposed as potential solutions to some of these issues [8]. For example, data from Fitbits have been used to detect elevated signals at the level of heart rate and temperature\u2014these are possible symptoms of COVID-19 that can be identified in advance or just when more explicit symptoms surfaced [17]. This is an extremely promising use of wearables, but the status of predictions based on wearable data raises challenges. Applications of wearables for the detection of COVID-19 are severely affected by overestimation, the issue where non-problematic conditions and abnormalities are systematically detected or predicted as problematic. For example, it is often difficult to differentiate between COVID-19 and seasonal influenza and cases of standard influenza on the basis of wearable data\u2014elevated heart rate can be interpreted as a symptom of respiratory illness more generally and, as a result, wearables have wrongly detected and predicted COVID-19 infections [17,24]. This is a crucial epistemic issue for testing the validity of using wearable data to perform or assist prediction, but is also significant from an ethical point of view. For example, health resources and personnel may be diverted from actually problematic situations towards overestimated issues, thus creating imbalance in health treatment and access [41]. Erroneous prediction and detection can also create unnecessary stress in patients, raising concerns on the implementation of wearables for health [7,42]. In addition, the burdens of overestimation may also be unequally distributed over different types of social groups, policy contexts, healthcare services. For example, estimation issues in the context of COVID-19 might be overcome with access to molecular or antigen tests, which can differentiate between influenza and COVID-19. In this sense, it could be argued that overestimating infections might thus be better in light of the precautionary principle. However, access to fast COVID-19 testing is not equally available and distributed in the world and has often become expansive, especially when infections surge. Policy decisions might require a person to isolate if their wearable device has detected a possible infection (as we have seen with the use of contact-tracing smartphone apps), which is potentially harmful for them and their family, especially if remote work is not an option and wages might be lost. These issues are even more severe in the context of wearables and other digital health solutions. These technologies are presented as key opportunities for parts of the world with limited or non-existing health services [43]. However, if other technologies and services that might help overcome overestimation are limited and not available (e.g., fast COVID-19 testing), this poses even more significant constraints on the accuracy and estimation of wearables and other digital health technologies. Unsurprisingly, recent work by political institutions, such as the EU Commission, on the internet of things technologies such as wearables has concluded that overestimation is among the main issues for the adoption of wearable technology [2]. In order to overcome these concerns, we propose to focus on the interoperability of wearable data as a crucial way forward. Interoperability is the possibility that data can be integrated and used together with other types of data [35]. Several philosophical, historical, and sociological studies of the role of data in science have highlighted that the value of large volumes of data for research lies in the possibility of integrating and linking different datasets [44]. In this sense, a high level of interoperability is key to exploit the benefits of new and large datasets, such as those collected with wearable technology. A low level of interoperability makes it difficult to integrate wearable data with other health data and thus compare and balance results collected by different devices, sensors, approaches. In turn, making sure that wearable data are interoperable can make it easier to compare results obtained through other means and assess the extent to which overestimation might be a problem. Data interoperability is also connected to interoperability at the software and hardware level of wearable technology. For example, the integration of wearables into health services is currently challenging because the additional staff required to assist patients with the technology might need to be trained differently, as software and hardware solutions are different between devices [45]. In turn, interoperability standards are also crucial for data storage and thus to include wearables in health services, for example through personal and electronic health records, which is currently very costly [3], and to deal with cyber risks, for example by highlighting transparency and accountability in healthcare infrastructures [46,47]. Ensuring that a wearable device is interoperable is thus an essential way to approach overestimation and the promise of providing more personal and precise healthcare in digital health.\n\nOne of the defining features of wearables is their ability to be worn on our bodies. This means that they can often be personal devices, in the sense that they might fulfil a personal need of the user (such as tracking fitness activities and exercise) as well as be used as personal and fashion objects (such as rings and wristwatches). As such, wearables play a crucial role towards an increasingly personalised, precise, and person-centred medicine. In this way, wearable technology is uniquely positioned to move in the direction of one of the goals of digital health: expanding access to health services and thus improving health equity. Health equity is about making sure that different users are equally provided with services and care as part of their interactions with the health system, as defined in several policy initiatives such as the Thirteenth General Programme of Work of the World Health Organization (WHO). While we agree the contribution of wearables to these goals is promising, issues connected to access raise significant concerns. As we have seen, wearable devices can clearly provide data that are personal to user needs, issues, and concerns [48]. However, the extent to which individual users can access the benefits of this data collection seems unequally distributed. Users with more digital literacy and socioeconomic resources are disproportionately advantaged to access benefits from the use of wearables as tools to detect and predict states of health and disease [49,50]. In addition, the use of wearables and other digital health tools for monitoring in the context of public health efforts might raise concerns about surveillance, in different ways for different social groups. Historically, members of marginalised social groups have been targeted by health surveillance and monitoring with unclear benefits and sometimes harmful results. For example, COVID-19 surveillance and policy restrictions have disproportionately affected structurally disadvantaged social groups [51]. If wearables as digital health technologies are to be made part of public health policy and campaigns, access to the technology needs to be ensured as much as access to clear benefits from the use of the technology. Currently, the benefits of health monitoring through wearables are disproportionately available to consumer technology companies, rather than individual users. Most wearables available on the market are developed and sold by some of the largest corporations in the world, such as Apple and Google. The increasing collection of health data through wearables by consumer technology creates clear economic and political benefits for these corporations, which can use the data for marketing and advertising. Individual users do not necessarily have access to these benefits of data collection or at least not at the same level [52]. In addition, even for those who can and do use wearables, other issues of access raise concerns on health equity. As we have seen, contextual information on the collection, classification, interpretation of wearable data is usually not shared by data providers and device manufacturers. This is an issue for health equity: epistemically, information of the ways in which wearable data are analysed for detection and screening is crucial to interpret data and translate results into significant actions of health promotion for individual users. Without this information, users can struggle to understand why the analysis of wearable data leads to the detection of a condition and how they can act upon this function. This can also create doubt and anxiety, as users do not know the extent to which the data are reliable and are unsure about the actions they can take to counter possibly alarming conclusions [28]. In other words, this creates a situation of health inequity. For some users, the collection of wearable data can be a source of actions to improve their health, but for others barriers to data access can create new burdens. Several approaches have been proposed in recent years to counteract the burdens of health inequity [53]. A way forward for these challenges in the wearable context is an expansion of both the access to the data and related interpretation tools. More access to data can partially counter the economic and political power of technology corporations [54]. Access to interpretation, in turn, can empower users, enabling them to make sense of the trustworthiness, quality, and actionability of the functions provided by wearables. We see these as goals that should be part of health campaigns and public health policy involving wearables and other digital health technologies. Crucially for health equity, however, access to technology should be approached critically, in light of considerations of the specific social and political context of use. For example, some members of the general population might not be interested in tracking their health or might find it confusing, alienating, guilt-inducing, stressful. The specific use and position of wearables as digital health technology needs to be openly and critically discussed to ensure that those who choose not to be part of the movement are not unequally treated and loose access to other health services.\n\nWearables are at the centre of several attempts to make health more mobile and digital. As we have seen in our overview, wearables are technologies that can track and collect digital data on various daily activities and provide users with individual monitoring and screening in connection to other digital tools and services. Wearables can also be ways of further developing remote detection and prediction, without the need to interact with other health services. In the digital health context, this use of digital devices and services such as wearables is connected to various benefits. For example, digital health is often framed explicitly as an opportunity to shift the medical knowledge system towards the representation of the majority that is typically excluded from more traditional research methodology [55]. While wearables are clearly promising tools to achieve these crucial goals, we raise concerns on their fairness. In the health context, fairness is close to the notion of equity and related attempts for the equal distribution of services and care. Yet, fairness is also about the just treatment of individuals when they interact with health services and thus about making sure that people are not treated in unjust ways in healthcare because of bias, discrimination, lack of consideration [56]. We argue that current use and features of wearables disproportionately target some members of the general population and exclude others, thus creating issues of fairness. Thanks to wearable and other digital devices, data points such as steps have been tracked for almost a decade, at a scale that is unprecedented when compared to more traditional and preceding data practices. In these ways, more generally, wearables are contributing to the increasing datafication of activities and aspects of our lives. But they are contributing to their medicalisation too, as the possibility of quantifying and measuring these activities and aspects renders them as new areas of research and intervention. In the health context, current processes of datafication and medicalisation are contributing to a re-configuration of health, by expanding the limits and remits of biomedical research, producing new markers of health and disease, redefining what counts as health data, broadening the categories of influential stakeholders, and involving and empowering more individuals [32]. Datafication and medicalisation through wearables can thus create various benefits by uncovering new health needs and issues of specific communities. Consider, for example, the role that patient groups have played throughout the COVID-19 pandemic in raising concerns on the limitations and diverse impact of public health interventions and raising awareness on the long-lasting effects of COVID-19 infection, which are now known as long COVID. Enabling patients to track their own health individually and actively can provide them with more powerful tools and empowerment in this direction. However, current uses and applications of wearable technology for health focus only on some members and groups of the general population, thus rendering the use of the technology unfair. For example, consider the framing of wearable technology as a crucial tool for the remote and constant monitoring of the elderly and patients that need to practice social distancing, avoid hospital visits but require monitoring [8]. Looking at current figures on the adoption of wearable devices, members of the population that fit into these categories are severely underrepresented and excluded by the application of this technology [2]. This is highly problematic from the point of view of fairness: Wearable technology seems to exclude the users that arguably would benefit the most from the use of wearables. Children are also an interesting type of users in this sense. Age groups including young adolescents and children have increasing access to digital technologies, including wearables. Yet, the adoption of wearable technology in children can vary substantially, for example depending on whether they use other technologies (e.g., smartphones are normally gateways for wearables), where they live, and the socioeconomic status of their family. The new contribution of these age groups to biomedical research is an exciting opportunity of wearable technology, potentially enabling the retooling of medical knowledge system to represent groups that are currently excluded and underrepresented [55]. At the same time, the opportunity of further introducing wearable technology in these age groups needs to be balanced against ethical reflections about security, privacy, intrusiveness. More generally, the cases of the elderly and children suggest that, however, large and extended wearable datasets may be, wearables usually target some social, economic, age groups more than others. This is crucial because excluding important and large parts of the general population can lead to biased and underrepresentative datasets, which do not give us a good picture of population health, thus creating a weak and unsound basis for knowledge claims and focusing health policy only on few members of the population. Thus, issues of fairness raise concerns on the legitimacy of using and recommending wearable technology for health in the general population. To overcome these challenges, more focus needs to be given on the representativity of various members of the general population in wearable technology and digital health. We see the focus on representativity as one of the steps of the assessment of data quality and fairness [36], which should be one of the first steps for discussions on using wearables as part of health promotion and public health programmes too. In addition, focusing on representativity can also be a way of taking into account the context around the use and introduction of wearable technology. In communities and parts of the world with limited availability of fast and inexpensive testing, for example, early detection of pre-symptomatic COVID-19 is not as useful or might be useful only for some members of the population, thus creating issues of fairness. Consider one of the prime areas of application of wearables for health: the tracking of physical activity to suggest interventions and behavioural change [57,58]. Wearables can be powerful tools in this context\u2014yet alerting a person that they have been sedentary might not be as useful, if they do not have opportunities or services that can make them more active.", "cleaned_text_custom_parser": "Official websites use .govA.govwebsite belongs to an official\n government organization in the United States.\nSecure .gov websites use HTTPSAlock(LockLocked padlock icon) orhttps://means you've safely\n connected to the .gov website. Share sensitive\n information only on official, secure websites.\nPERMALINK\nChallenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness\nStefano Canali\nViola Schiaffonati\nAndrea Aliverti\nRoles\nThis is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.\nAbstract\nWearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledge-related) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity.\nIntroduction\nDevices that can be worn on our bodies and track several activities and parameters\u2014wearable devices\u2014are increasingly sold and used in the general population. One of the main areas of use of wearable devices is health, including biomedical research, clinical care, personal health practices and tracking, technology development, and engineering. In this context, the use of wearables for health has been connected to several promises and benefits for a more digital, personalised, preventive medicine [1\u20133]. At the same time, crucial work has identified and discussed technical and ethical challenges in the extended use of wearables for health, including accuracy, privacy, security, cyber risks [4\u20137]. Yet, most analyses have focused on either of these areas of discussions, thus framing technical and ethical considerations as largely separate issues. As a result, the connections between specific technical solutions and ethical considerations remain underdiscussed: This is a problem, as we will show that many challenges of the wearable context can be addressed only partially through technical solutions. In addition, the epistemic (knowledge-related) contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. As a result, there is a lack of understanding of the specific uses and functions that wearables can and should fulfil for digital health\u2014yet this is crucial to identify the role of wearables in digital health and beyond and assess their ethical and social impact in relation to specific uses.\nIn response to these considerations, in this article we start by providing an epistemic overview of the main functions of wearable technology for health: we discussmonitoring,screening,detection, andprediction. The role of these functions is clear when looking at the use of wearables in concrete cases (Table 1), for example, the context of Coronavirus Disease 2019 (COVID-19). The COVID-19 pandemic has been discussed as a crucial catalyst for the use of wearable technologies in the biomedical and health domain and we will use it as a source of uses and cases to illustrate our points throughout the article [8,9]. On the basis of this overview, we discuss specific issues and concerns that are connected to the use of wearables for the identified functions of monitoring, screening, detection, and prediction. We focus on 4 main areas of concern (data quality, balanced estimations, health equity, and fairness) and propose recommendations and possible solutions (local standards of quality, interoperability, access, and representativity). On the basis of our overview and analysis of these challenges, the recommendations we propose enable us to better understand the actual impact, benefits, and risks of wearables and improve their application for digital health (Table 2). In this way, as a group of researchers with different areas of expertise (biomedical engineering and research, philosophy and ethics of science and technology) but working in the same department, we develop an interdisciplinary account of wearable technology and its contribution to digital health.\nTable 1. The main functions served by wearables for health, with examples.\nTable 2. Summary of the identified areas of concern and key issues and proposed recommendations.\nAn overview of wearable technology for digital health\nThere is currently an abundance of uses of wearable devices for health. Focusing on the epistemic contribution of wearables to the collection, development, application of biomedical knowledge, we develop an overview that looks at the current context of wearable technology. While this is not a systematic representation of all possible or future applications of wearable devices, we identify 4 main functions that wearables are currently used to serve in the health context: monitoring, screening, detection, and prediction (Table 1).\nWe identifymonitoringas the basic and fundamental function served by wearables, performed by wristbands, patches, watches, clothing. Monitoring is the practice of continuous data collection, focused on members of a population, which can be the general population or a specific subset of individuals. Wearables are considered particularly efficient to fulfil this function because they can track a number of various biomedical processes depending on the types of sensors available and can be used for continuous and remote monitoring\u2014as wearables can be worn constantly, they are ideally placed to collect data continuously. In this way, wearables can deliver a significant improvement to remote and tele-monitoring [10,11] and have been used in this sense to monitor crucial physiological metrics for COVID-19 such as heart rate, physical activity, oxygen saturation, as well as long-term effects [8,12]. In this context, wearable devices have also been applied in coordination with other tele-health systems for remote monitoring for individuals at risk that could easily shift to hospitalisation and to assist remote diagnosis.\nOn the basis of these monitoring capabilities, we identify 3 other main functions that wearables can serve.Screeningis the identification of specific conditions and individuals associated with this condition within datasets collected through monitoring. The use of wearables for this function is usually based on passive sensors that measure motion, steps, light, pressure, sound, etc. [3]. For example, wearable garments have been used to monitor individuals during sleep and screen for individuals suffering from sleep apnea [13]. A close function related to screening isdetection. When wearables monitor specific conditions in populations, they are often used to detect conditions and alert individual users. Detection is the analysis of wearable data collected through monitoring in order to investigate possible patterns and features that can be interpreted as indicators and markers of specific biomedical conditions. For example, a combination of smartwatches and dedicated bands has been used for heart rate monitoring and automatic detection of atrial fibrillation [14]. The integration of wearable data with symptom data has been presented as a way to improve the identification of COVID-19 positive versus negative cases [15]. Detection is also the function where we see an intersection with both monitoring and screening: for example, smartwatches have been used to monitor populations for irregular pulse and, on this basis, screen for individuals potentially suffering from atrial fibrillation as well as identify the condition [10]. A final diagnosis of a condition can thus be based on detections performed by wearables, although wearables currently cannot perform diagnosis as a consequence to technical and regulatory limitations.\nThe fourth function we identify isprediction, the inference of future trends and/or events of interest for the biomedical study of populations based on monitoring. Just a few wearable devices are currently used for prediction in the health context, for example, to predict mortality, readmissions, and clinical risk [16]. In the context of COVID-19, wearables have been tested for the retrospective detection of infection and prediction of COVID-19, days before the presence of symptoms [17]. Other examples include the use of accelerometer data from wearable devices to predict biological age and mortality [18] and respiratory rate data to predict exacerbations of chronic obstructive pulmonary disease [19].\nThese 4 functions are often intertwined and interconnected in concrete contexts and in many cases the same device can perform more than 1 function. Still, identifying different functions is a crucial step to understand the actual impact and goals of using wearable technology for health. Depending on whether we use wearables to predict or monitor health, different assumptions, uses, and standards will be necessary. In addition, understanding which functions wearables can and do serve currently helps us to make sense of their possible limitations. As we will see in the next sections, this overview enables us to see how the use of wearables for health is currently limited by crucial challenges, which impact different functions in different ways. The remainder of the paper will be dedicated to a discussion of these challenges, as they emerge in concrete uses of wearables to serve the functions we have identified.\nLocal standards for data quality\nIn our overview, we have identified monitoring as the fundamental feature at the basis of the functions of wearable technology for health. Monitoring is a promising application of wearables thanks to their abilities for constant and personal data collection, but a key concern isdata quality. Quality is a crucial feature of scientific data, which needs to be evaluated to warrant the reliability of scientific claims. Data quality is also one of the fundamental values of research ethics and the social goals of biomedical research\u2014high-quality data are considered the basis for benefits at the clinical level and beyond [25]. Yet, the variability of sensors and lack of consistency of data collection in the wearable context make it difficult to coordinate and assess quality. In addition, the lack of contextual information on the ways in which wearable data are collected, classified, and interpreted raise concerns on the possibility of assessing quality.\nA first issue that makes it difficult to assess data quality in the wearable context is variability. Wearable data are usually collected by different types of devices or different sensors, if not through different data collection practices. For example, the measurement of metrics such as oxygen saturation can vary substantially in terms of location (e.g., wrist, finger, ear) and types of devices (e.g., watches, rings, earphones) employed for measurement [1,11]. This level of variability makes it difficult to have common standards to assess data quality: The same parameter is often measured with very different sensors, which employ different processing techniques, and may even render different results [3]. One way of responding to these concerns is regulation, which should make sure that wearables can be used as reliable and high-quality sources of data. In this context, the push is to regulate wearables as medical devices on the basis of clinical validity [26]. Clinical validity is a crucial step for the adoption of wearable technologies for health and also for the regulation of the quality of wearable data.\nYet, clinical validity as an intrinsic feature of quality is not enough on its own. Extensive work in philosophy of science has shown that quality is not only an intrinsic feature of data. Quality is a contextual component of data: depending on a specific use and context of use, considerations of data quality might change [27]. For example, it is clearly crucial to know that a wearable device has been clinically validated to collect high-quality data on heart rate [22]. However, using a wearable to monitor heart rate and detect COVID-19 infection on this basis constitutes a new context of use, where considerations of data quality may be different. For example, a certain number of false positives might be considered good enough for fitness tracking or even remote monitoring of heart patients, but it might not be enough when wearables are used to detect COVID-19 and suggest isolation and quarantines. The ethical and social burdens of poor quality or unreliable data change depending on the context. For patients who rely on wearables to track the health of their heart after bypass surgery, the quality of data is more serious than for users tracking fitness activities: In the context of heart patients, the collection of low-quality data about severe health problems can be a very serious burden, leading to unnecessary anxiety [28]. For users with limited financial resources, wearables can be an inexpensive tool to keep track of their health when other health services are too expensive and difficult to access [2]. As such, wearables can improve access to healthcare, but wearable technology might constitute the main health service available for these users and poor data quality will unequally impact them more than others. This is why data quality should be considered a contextual property of data that needs to be constantly considered as the context of use changes significantly.\nIn turn, in order to assess data quality for a specific use, knowledge of contextual features of data collection is crucial. For example, it is crucial to know which experimental procedures and protocols were applied, which sensors and techniques were used, and which questions and hypotheses were investigated during data collection. As questions and hypotheses change from general heart monitoring to COVID-19 detection, for example, it is crucial to know the original experimental procedures and questions to understand whether data quality remains the same\u2014access to contextual features of data practices is crucial to assess quality and ensure the reliability and trustworthiness of data [29]. The problem is that access to these contextual features is often not available in the wearable context. For example, the collection of heart rate data from wearable devices is usually covered by the opt-in of users to general medical studies, which are organised by private companies and large research bodies, such as the Apple Heart Study created by the collaboration between Stanford University and Apple. While this type of study was of course validated and regulated as a clinical trial [30], there is little information on data collection, analysis, storing, and access and this makes it difficult to assess quality. In addition, in many cases biomedical researchers cannot even download data directly from the device and have to go through proprietary archives. As a result, because of commercial interests, very little information on how the data are collected, classified, and interpreted by the device is shared throughout the process. This is an issue for researchers, but also users and patients. The lack of access to contextual information about data collection makes it difficult for users to interpret the data to take action on their health and can eventually lead them not to trust and use the technology [28,31,32].\nOn this basis, we need more contextual information and coherence for wearable data quality [33]. Contextual information can be used to understand the specific features and needs for the assessment of data quality in the wearable context. In this direction, common and local standards of data quality can be developed to overcome current limitations and gaps in the wearable market. For example, the framework provided by FAIR (Findability,Accessibility,Interoperability, andReuse) can be used as a basis to discuss future developments in this direction [34\u201336]. We do not see these as hard compliance standards set by standard organisations, but rather the result of a bottom-up process of coordination and assessment, as a way of fully appreciating the contextual dimensions of data quality. As we have seen, depending on the specific context of use, standards, requirements, and burdens of data quality can change. This is why data quality standards need to be local and could first be created for the research context, where knowledge of the contextual components of data collection is crucial to assess data quality. Yet, standards of data quality can clearly be crucial for regulators, institutions, industry, and users too, and standards could be adopted by the institutions, journals, repositories of specific research communities. Again similarly to FAIR data, the institutions of specific research communities could be in charge of managing, updating, and assessing standards and informing individual users of their existence and application, thus presenting data quality as a fundamental issue for digital health.\nInteroperability for balanced estimations\nAs we have seen in our overview, detection and prediction are among the main functions for which wearable devices are currently used in health. In turn, detection and prediction are fundamental activities at the basis of the production and use of scientific knowledge. Yet, issues affectingbalanced estimationsin screening and prediction raise concerns on the grounding and validity of wearables as detection and prediction tools.\nIn the COVID-19 pandemic, several models have been used to predict the development, spreading, and impact of the pandemic, but they have also been at the centre of several critiques concerning their uncertain assumptions and limitations [37\u201340]. Wearable devices have been proposed as potential solutions to some of these issues [8]. For example, data from Fitbits have been used to detect elevated signals at the level of heart rate and temperature\u2014these are possible symptoms of COVID-19 that can be identified in advance or just when more explicit symptoms surfaced [17]. This is an extremely promising use of wearables, but the status of predictions based on wearable data raises challenges. Applications of wearables for the detection of COVID-19 are severely affected by overestimation, the issue where non-problematic conditions and abnormalities are systematically detected or predicted as problematic. For example, it is often difficult to differentiate between COVID-19 and seasonal influenza and cases of standard influenza on the basis of wearable data\u2014elevated heart rate can be interpreted as a symptom of respiratory illness more generally and, as a result, wearables have wrongly detected and predicted COVID-19 infections [17,24].\nThis is a crucial epistemic issue for testing the validity of using wearable data to perform or assist prediction, but is also significant from an ethical point of view. For example, health resources and personnel may be diverted from actually problematic situations towards overestimated issues, thus creating imbalance in health treatment and access [41]. Erroneous prediction and detection can also create unnecessary stress in patients, raising concerns on the implementation of wearables for health [7,42]. In addition, the burdens of overestimation may also be unequally distributed over different types of social groups, policy contexts, healthcare services. For example, estimation issues in the context of COVID-19 might be overcome with access to molecular or antigen tests, which can differentiate between influenza and COVID-19. In this sense, it could be argued that overestimating infections might thus be better in light of the precautionary principle. However, access to fast COVID-19 testing is not equally available and distributed in the world and has often become expansive, especially when infections surge. Policy decisions might require a person to isolate if their wearable device has detected a possible infection (as we have seen with the use of contact-tracing smartphone apps), which is potentially harmful for them and their family, especially if remote work is not an option and wages might be lost. These issues are even more severe in the context of wearables and other digital health solutions. These technologies are presented as key opportunities for parts of the world with limited or non-existing health services [43]. However, if other technologies and services that might help overcome overestimation are limited and not available (e.g., fast COVID-19 testing), this poses even more significant constraints on the accuracy and estimation of wearables and other digital health technologies. Unsurprisingly, recent work by political institutions, such as the EU Commission, on the internet of things technologies such as wearables has concluded that overestimation is among the main issues for the adoption of wearable technology [2].\nIn order to overcome these concerns, we propose to focus on the interoperability of wearable data as a crucial way forward. Interoperability is the possibility that data can be integrated and used together with other types of data [35]. Several philosophical, historical, and sociological studies of the role of data in science have highlighted that the value of large volumes of data for research lies in the possibility of integrating and linking different datasets [44]. In this sense, a high level of interoperability is key to exploit the benefits of new and large datasets, such as those collected with wearable technology. A low level of interoperability makes it difficult to integrate wearable data with other health data and thus compare and balance results collected by different devices, sensors, approaches. In turn, making sure that wearable data are interoperable can make it easier to compare results obtained through other means and assess the extent to which overestimation might be a problem. Data interoperability is also connected to interoperability at the software and hardware level of wearable technology. For example, the integration of wearables into health services is currently challenging because the additional staff required to assist patients with the technology might need to be trained differently, as software and hardware solutions are different between devices [45]. In turn, interoperability standards are also crucial for data storage and thus to include wearables in health services, for example through personal and electronic health records, which is currently very costly [3], and to deal with cyber risks, for example by highlighting transparency and accountability in healthcare infrastructures [46,47]. Ensuring that a wearable device is interoperable is thus an essential way to approach overestimation and the promise of providing more personal and precise healthcare in digital health.\nAccess for health equity\nOne of the defining features of wearables is their ability to be worn on our bodies. This means that they can often be personal devices, in the sense that they might fulfil a personal need of the user (such as tracking fitness activities and exercise) as well as be used as personal and fashion objects (such as rings and wristwatches). As such, wearables play a crucial role towards an increasingly personalised, precise, and person-centred medicine. In this way, wearable technology is uniquely positioned to move in the direction of one of the goals of digital health: expanding access to health services and thus improvinghealth equity. Health equity is about making sure that different users are equally provided with services and care as part of their interactions with the health system, as defined in several policy initiatives such as the Thirteenth General Programme of Work of the World Health Organization (WHO). While we agree the contribution of wearables to these goals is promising, issues connected to access raise significant concerns.\nAs we have seen, wearable devices can clearly provide data that are personal to user needs, issues, and concerns [48]. However, the extent to which individual users can access the benefits of this data collection seems unequally distributed. Users with more digital literacy and socioeconomic resources are disproportionately advantaged to access benefits from the use of wearables as tools to detect and predict states of health and disease [49,50]. In addition, the use of wearables and other digital health tools for monitoring in the context of public health efforts might raise concerns about surveillance, in different ways for different social groups. Historically, members of marginalised social groups have been targeted by health surveillance and monitoring with unclear benefits and sometimes harmful results. For example, COVID-19 surveillance and policy restrictions have disproportionately affected structurally disadvantaged social groups [51]. If wearables as digital health technologies are to be made part of public health policy and campaigns, access to the technology needs to be ensured as much as access to clear benefits from the use of the technology. Currently, the benefits of health monitoring through wearables are disproportionately available to consumer technology companies, rather than individual users. Most wearables available on the market are developed and sold by some of the largest corporations in the world, such as Apple and Google. The increasing collection of health data through wearables by consumer technology creates clear economic and political benefits for these corporations, which can use the data for marketing and advertising. Individual users do not necessarily have access to these benefits of data collection or at least not at the same level [52].\nIn addition, even for those who can and do use wearables, other issues of access raise concerns on health equity. As we have seen, contextual information on the collection, classification, interpretation of wearable data is usually not shared by data providers and device manufacturers. This is an issue for health equity: epistemically, information of the ways in which wearable data are analysed for detection and screening is crucial to interpret data and translate results into significant actions of health promotion for individual users. Without this information, users can struggle to understand why the analysis of wearable data leads to the detection of a condition and how they can act upon this function. This can also create doubt and anxiety, as users do not know the extent to which the data are reliable and are unsure about the actions they can take to counter possibly alarming conclusions [28]. In other words, this creates a situation of health inequity. For some users, the collection of wearable data can be a source of actions to improve their health, but for others barriers to data access can create new burdens.\nSeveral approaches have been proposed in recent years to counteract the burdens of health inequity [53]. A way forward for these challenges in the wearable context is an expansion of both the access to the data and related interpretation tools. More access to data can partially counter the economic and political power of technology corporations [54]. Access to interpretation, in turn, can empower users, enabling them to make sense of the trustworthiness, quality, and actionability of the functions provided by wearables. We see these as goals that should be part of health campaigns and public health policy involving wearables and other digital health technologies. Crucially for health equity, however, access to technology should be approached critically, in light of considerations of the specific social and political context of use. For example, some members of the general population might not be interested in tracking their health or might find it confusing, alienating, guilt-inducing, stressful. The specific use and position of wearables as digital health technology needs to be openly and critically discussed to ensure that those who choose not to be part of the movement are not unequally treated and loose access to other health services.\nRepresentativity for fairness\nWearables are at the centre of several attempts to make health more mobile and digital. As we have seen in our overview, wearables are technologies that can track and collect digital data on various daily activities and provide users with individual monitoring and screening in connection to other digital tools and services. Wearables can also be ways of further developing remote detection and prediction, without the need to interact with other health services. In the digital health context, this use of digital devices and services such as wearables is connected to various benefits. For example, digital health is often framed explicitly as an opportunity to shift the medical knowledge system towards the representation of the majority that is typically excluded from more traditional research methodology [55]. While wearables are clearly promising tools to achieve these crucial goals, we raise concerns on theirfairness. In the health context, fairness is close to the notion of equity and related attempts for the equal distribution of services and care. Yet, fairness is also about the just treatment of individuals when they interact with health services and thus about making sure that people are not treated in unjust ways in healthcare because of bias, discrimination, lack of consideration [56]. We argue that current use and features of wearables disproportionately target some members of the general population and exclude others, thus creating issues of fairness.\nThanks to wearable and other digital devices, data points such as steps have been tracked for almost a decade, at a scale that is unprecedented when compared to more traditional and preceding data practices. In these ways, more generally, wearables are contributing to the increasing datafication of activities and aspects of our lives. But they are contributing to their medicalisation too, as the possibility of quantifying and measuring these activities and aspects renders them as new areas of research and intervention. In the health context, current processes of datafication and medicalisation are contributing to a re-configuration of health, by expanding the limits and remits of biomedical research, producing new markers of health and disease, redefining what counts as health data, broadening the categories of influential stakeholders, and involving and empowering more individuals [32]. Datafication and medicalisation through wearables can thus create various benefits by uncovering new health needs and issues of specific communities. Consider, for example, the role that patient groups have played throughout the COVID-19 pandemic in raising concerns on the limitations and diverse impact of public health interventions and raising awareness on the long-lasting effects of COVID-19 infection, which are now known as long COVID. Enabling patients to track their own health individually and actively can provide them with more powerful tools and empowerment in this direction.\nHowever, current uses and applications of wearable technology for health focus only on some members and groups of the general population, thus rendering the use of the technology unfair. For example, consider the framing of wearable technology as a crucial tool for the remote and constant monitoring of the elderly and patients that need to practice social distancing, avoid hospital visits but require monitoring [8]. Looking at current figures on the adoption of wearable devices, members of the population that fit into these categories are severely underrepresented and excluded by the application of this technology [2]. This is highly problematic from the point of view of fairness: Wearable technology seems to exclude the users that arguably would benefit the most from the use of wearables. Children are also an interesting type of users in this sense. Age groups including young adolescents and children have increasing access to digital technologies, including wearables. Yet, the adoption of wearable technology in children can vary substantially, for example depending on whether they use other technologies (e.g., smartphones are normally gateways for wearables), where they live, and the socioeconomic status of their family. The new contribution of these age groups to biomedical research is an exciting opportunity of wearable technology, potentially enabling the retooling of medical knowledge system to represent groups that are currently excluded and underrepresented [55]. At the same time, the opportunity of further introducing wearable technology in these age groups needs to be balanced against ethical reflections about security, privacy, intrusiveness. More generally, the cases of the elderly and children suggest that, however, large and extended wearable datasets may be, wearables usually target some social, economic, age groups more than others. This is crucial because excluding important and large parts of the general population can lead to biased and underrepresentative datasets, which do not give us a good picture of population health, thus creating a weak and unsound basis for knowledge claims and focusing health policy only on few members of the population.\nThus, issues of fairness raise concerns on the legitimacy of using and recommending wearable technology for health in the general population. To overcome these challenges, more focus needs to be given on the representativity of various members of the general population in wearable technology and digital health. We see the focus on representativity as one of the steps of the assessment of data quality and fairness [36], which should be one of the first steps for discussions on using wearables as part of health promotion and public health programmes too. In addition, focusing on representativity can also be a way of taking into account the context around the use and introduction of wearable technology. In communities and parts of the world with limited availability of fast and inexpensive testing, for example, early detection of pre-symptomatic COVID-19 is not as useful or might be useful only for some members of the population, thus creating issues of fairness. Consider one of the prime areas of application of wearables for health: the tracking of physical activity to suggest interventions and behavioural change [57,58]. Wearables can be powerful tools in this context\u2014yet alerting a person that they have been sedentary might not be as useful, if they do not have opportunities or services that can make them more active.\nConclusions\nIn this paper, we have discussed various implications of wearable technology for digital health. First, we have identified functions that wearable technology currently serves in biomedical research and clinical care as a way of specifying the epistemic contribution of wearables to the development and application of biomedical knowledge through monitoring, screening, detection, and prediction (Table 1). On this basis, we have discussed a number of challenges that are connected to these functions, particularly at the level of data quality, estimations, equity, and fairness. As a way to overcome these challenges, we have introduced recommendations and possible solutions based on local standards of quality, interoperability, access, and representativity (Table 2). Our analysis has thus been aimed at improving our understanding of the position and relations between wearables and other biomedical technologies and data sources, as well as ways to approach their adoption and regulation.\nThroughout the article, we have applied an integrated approach for the discussion of wearables for health, which we see as a starting point for more work. In recent years, philosophers, sociologists, and ethicists of science and technology have started to work more closely in collaboration with biomedical scientists, engineers, and practitioners. An increasing number of publications is the result of collaborations between science scholars and scientists; philosophical work is increasingly relevant and cited in science journals [59]. In this context, approaches such as ELSI (Ethical, Legal and Social Issues) and E2LSI show the need for a systematic integration of epistemic, ethical, legal, and social considerations [60]. This is a particularly important step to take in the context of new and evolving technologies for digital health, as important decisions are being taken now on their regulation, inclusion in healthcare programmes, and use in research. Our work in this article provides a first step for thinking about these as integrated issues.\nFunding Statement\nThis research was funded by the Fondazione Silvio Tronchetti Provera (Project: Etica e tecnologie emergenti) and Regione Lombardia (Project: BASE5G, ID 1155850). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\nReferences\nACTIONS\nPERMALINK\nRESOURCES\nSimilar articles\nCited by other articles\nLinks to NCBI Databases\nCite\nAdd to Collections", "cleaned_text_custom_parser_2": "\n\n \n \n Skip to main content\n \n

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\n Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness\n

\n \n

\n \n Stefano Canali\n \n

\n Stefano Canali\n

\n ^{\n 1\n}\n Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy\n

\n Find articles by\n \n Stefano Canali\n \n

\n ^{\n 1,\n}\n ^{\n *\n}\n ,\n \n Viola Schiaffonati\n \n

\n Viola Schiaffonati\n

\n ^{\n 1\n}\n Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy\n

\n Find articles by\n \n Viola Schiaffonati\n \n

\n ^{\n 1\n}\n ,\n \n Andrea Aliverti\n \n

\n Andrea Aliverti\n

\n ^{\n 1\n}\n Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy\n

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\n ^{\n 1\n}\n Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy\n

\n ^{\n 2\n}\n Vanderbilt University, UNITED STATES\n

\n The authors have declared that no competing interests exist.\n

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\n Abstract\n

\n Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledge-related) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity.\n

\n Introduction\n

\n Devices that can be worn on our bodies and track several activities and parameters\u2014\n \n wearable devices\n \n \u2014are increasingly sold and used in the general population. One of the main areas of use of wearable devices is health, including biomedical research, clinical care, personal health practices and tracking, technology development, and engineering. In this context, the use of wearables for health has been connected to several promises and benefits for a more digital, personalised, preventive medicine [\n \n 1\n \n \u2013\n \n 3\n \n ]. At the same time, crucial work has identified and discussed technical and ethical challenges in the extended use of wearables for health, including accuracy, privacy, security, cyber risks [\n \n 4\n \n \u2013\n \n 7\n \n ]. Yet, most analyses have focused on either of these areas of discussions, thus framing technical and ethical considerations as largely separate issues. As a result, the connections between specific technical solutions and ethical considerations remain underdiscussed: This is a problem, as we will show that many challenges of the wearable context can be addressed only partially through technical solutions. In addition, the epistemic (knowledge-related) contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. As a result, there is a lack of understanding of the specific uses and functions that wearables can and should fulfil for digital health\u2014yet this is crucial to identify the role of wearables in digital health and beyond and assess their ethical and social impact in relation to specific uses.\n

\n In response to these considerations, in this article we start by providing an epistemic overview of the main functions of wearable technology for health: we discuss\n \n monitoring\n \n ,\n \n screening\n \n ,\n \n detection\n \n , and\n \n prediction\n \n . The role of these functions is clear when looking at the use of wearables in concrete cases (\n \n Table 1\n \n ), for example, the context of Coronavirus Disease 2019 (COVID-19). The COVID-19 pandemic has been discussed as a crucial catalyst for the use of wearable technologies in the biomedical and health domain and we will use it as a source of uses and cases to illustrate our points throughout the article [\n \n 8\n \n ,\n \n 9\n \n ]. On the basis of this overview, we discuss specific issues and concerns that are connected to the use of wearables for the identified functions of monitoring, screening, detection, and prediction. We focus on 4 main areas of concern (data quality, balanced estimations, health equity, and fairness) and propose recommendations and possible solutions (local standards of quality, interoperability, access, and representativity). On the basis of our overview and analysis of these challenges, the recommendations we propose enable us to better understand the actual impact, benefits, and risks of wearables and improve their application for digital health (\n \n Table 2\n \n ). In this way, as a group of researchers with different areas of expertise (biomedical engineering and research, philosophy and ethics of science and technology) but working in the same department, we develop an interdisciplinary account of wearable technology and its contribution to digital health.\n

\n Table 1. The main functions served by wearables for health, with examples.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

\n Functions\n	\n Examples\n
\n Monitoring\n	\n - Pulse monitoring [\n \n 10\n \n ]\n \n - Advanced tele-monitoring [\n \n 20\n \n ]\n \n - COVID-19 symptoms and long-term effects monitoring [\n \n 21\n \n ]\n
\n Screening\n	\n - Atrial fibrillation screening [\n \n 14\n \n ]\n \n - Sleep apnea screening [\n \n 13\n \n ]\n \n - Cardiovascular disease screening [\n \n 22\n \n ]\n
\n Detection\n	\n - Physical activity levels detection [\n \n 23\n \n ]\n \n - Pre-symptomatic detection of COVID-19 infections [\n \n 17\n \n ]\n \n - Seasonal influenza detection [\n \n 24\n \n ]\n
\n Prediction\n	\n - Prediction of mortality and clinical risk [\n \n 16\n \n ]\n \n - Prediction of COVID-19 infections [\n \n 17\n \n ]\n \n - Prediction of exacerbations of chronic obstructive pulmonary disease [\n \n 19\n \n ]\n

\n \n Open in a new tab\n \n

\n Table 2. Summary of the identified areas of concern and key issues and proposed recommendations.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

\n Areas of concern\n	\n Key issues\n	\n Recommendations\n	\n References\n
\n Data quality\n	\n - Variability of sensors, data collection practices\n \n - Lack of contextual information\n	\n Local standards of data quality\n	\n [\n \n 27\n \n ,\n \n 29\n \n ,\n \n 34\n \n ]\n
\n Balanced estimations\n	\n - Overestimation\n \n - Overprediction\n	\n Interoperability of wearable data\n	\n [\n \n 35\n \n ,\n \n 41\n \n ,\n \n 44\n \n ]\n
\n Health equity\n	\n - Unequal access to benefits\n \n - Digital and technological divides\n	\n Access to wearable data and interpretation\n	\n [\n \n 28\n \n ,\n \n 50\n \n ,\n \n 52\n \n ]\n
\n Fairness\n	\n - Exclusion of portions of the general population\n \n - Unfair wearable datasets\n	\n Representativity of wearable data\n	\n [\n \n 2\n \n ,\n \n 9\n \n ,\n \n 36\n \n ]\n

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\n An overview of wearable technology for digital health\n

\n There is currently an abundance of uses of wearable devices for health. Focusing on the epistemic contribution of wearables to the collection, development, application of biomedical knowledge, we develop an overview that looks at the current context of wearable technology. While this is not a systematic representation of all possible or future applications of wearable devices, we identify 4 main functions that wearables are currently used to serve in the health context: monitoring, screening, detection, and prediction (\n \n Table 1\n \n ).\n

\n We identify\n \n monitoring\n \n as the basic and fundamental function served by wearables, performed by wristbands, patches, watches, clothing. Monitoring is the practice of continuous data collection, focused on members of a population, which can be the general population or a specific subset of individuals. Wearables are considered particularly efficient to fulfil this function because they can track a number of various biomedical processes depending on the types of sensors available and can be used for continuous and remote monitoring\u2014as wearables can be worn constantly, they are ideally placed to collect data continuously. In this way, wearables can deliver a significant improvement to remote and tele-monitoring [\n \n 10\n \n ,\n \n 11\n \n ] and have been used in this sense to monitor crucial physiological metrics for COVID-19 such as heart rate, physical activity, oxygen saturation, as well as long-term effects [\n \n 8\n \n ,\n \n 12\n \n ]. In this context, wearable devices have also been applied in coordination with other tele-health systems for remote monitoring for individuals at risk that could easily shift to hospitalisation and to assist remote diagnosis.\n

\n On the basis of these monitoring capabilities, we identify 3 other main functions that wearables can serve.\n \n Screening\n \n is the identification of specific conditions and individuals associated with this condition within datasets collected through monitoring. The use of wearables for this function is usually based on passive sensors that measure motion, steps, light, pressure, sound, etc. [\n \n 3\n \n ]. For example, wearable garments have been used to monitor individuals during sleep and screen for individuals suffering from sleep apnea [\n \n 13\n \n ]. A close function related to screening is\n \n detection\n \n . When wearables monitor specific conditions in populations, they are often used to detect conditions and alert individual users. Detection is the analysis of wearable data collected through monitoring in order to investigate possible patterns and features that can be interpreted as indicators and markers of specific biomedical conditions. For example, a combination of smartwatches and dedicated bands has been used for heart rate monitoring and automatic detection of atrial fibrillation [\n \n 14\n \n ]. The integration of wearable data with symptom data has been presented as a way to improve the identification of COVID-19 positive versus negative cases [\n \n 15\n \n ]. Detection is also the function where we see an intersection with both monitoring and screening: for example, smartwatches have been used to monitor populations for irregular pulse and, on this basis, screen for individuals potentially suffering from atrial fibrillation as well as identify the condition [\n \n 10\n \n ]. A final diagnosis of a condition can thus be based on detections performed by wearables, although wearables currently cannot perform diagnosis as a consequence to technical and regulatory limitations.\n

\n The fourth function we identify is\n \n prediction\n \n , the inference of future trends and/or events of interest for the biomedical study of populations based on monitoring. Just a few wearable devices are currently used for prediction in the health context, for example, to predict mortality, readmissions, and clinical risk [\n \n 16\n \n ]. In the context of COVID-19, wearables have been tested for the retrospective detection of infection and prediction of COVID-19, days before the presence of symptoms [\n \n 17\n \n ]. Other examples include the use of accelerometer data from wearable devices to predict biological age and mortality [\n \n 18\n \n ] and respiratory rate data to predict exacerbations of chronic obstructive pulmonary disease [\n \n 19\n \n ].\n

\n These 4 functions are often intertwined and interconnected in concrete contexts and in many cases the same device can perform more than 1 function. Still, identifying different functions is a crucial step to understand the actual impact and goals of using wearable technology for health. Depending on whether we use wearables to predict or monitor health, different assumptions, uses, and standards will be necessary. In addition, understanding which functions wearables can and do serve currently helps us to make sense of their possible limitations. As we will see in the next sections, this overview enables us to see how the use of wearables for health is currently limited by crucial challenges, which impact different functions in different ways. The remainder of the paper will be dedicated to a discussion of these challenges, as they emerge in concrete uses of wearables to serve the functions we have identified.\n

\n Local standards for data quality\n

\n In our overview, we have identified monitoring as the fundamental feature at the basis of the functions of wearable technology for health. Monitoring is a promising application of wearables thanks to their abilities for constant and personal data collection, but a key concern is\n \n data quality\n \n . Quality is a crucial feature of scientific data, which needs to be evaluated to warrant the reliability of scientific claims. Data quality is also one of the fundamental values of research ethics and the social goals of biomedical research\u2014high-quality data are considered the basis for benefits at the clinical level and beyond [\n \n 25\n \n ]. Yet, the variability of sensors and lack of consistency of data collection in the wearable context make it difficult to coordinate and assess quality. In addition, the lack of contextual information on the ways in which wearable data are collected, classified, and interpreted raise concerns on the possibility of assessing quality.\n

\n A first issue that makes it difficult to assess data quality in the wearable context is variability. Wearable data are usually collected by different types of devices or different sensors, if not through different data collection practices. For example, the measurement of metrics such as oxygen saturation can vary substantially in terms of location (e.g., wrist, finger, ear) and types of devices (e.g., watches, rings, earphones) employed for measurement [\n \n 1\n \n ,\n \n 11\n \n ]. This level of variability makes it difficult to have common standards to assess data quality: The same parameter is often measured with very different sensors, which employ different processing techniques, and may even render different results [\n \n 3\n \n ]. One way of responding to these concerns is regulation, which should make sure that wearables can be used as reliable and high-quality sources of data. In this context, the push is to regulate wearables as medical devices on the basis of clinical validity [\n \n 26\n \n ]. Clinical validity is a crucial step for the adoption of wearable technologies for health and also for the regulation of the quality of wearable data.\n

\n Yet, clinical validity as an intrinsic feature of quality is not enough on its own. Extensive work in philosophy of science has shown that quality is not only an intrinsic feature of data. Quality is a contextual component of data: depending on a specific use and context of use, considerations of data quality might change [\n \n 27\n \n ]. For example, it is clearly crucial to know that a wearable device has been clinically validated to collect high-quality data on heart rate [\n \n 22\n \n ]. However, using a wearable to monitor heart rate and detect COVID-19 infection on this basis constitutes a new context of use, where considerations of data quality may be different. For example, a certain number of false positives might be considered good enough for fitness tracking or even remote monitoring of heart patients, but it might not be enough when wearables are used to detect COVID-19 and suggest isolation and quarantines. The ethical and social burdens of poor quality or unreliable data change depending on the context. For patients who rely on wearables to track the health of their heart after bypass surgery, the quality of data is more serious than for users tracking fitness activities: In the context of heart patients, the collection of low-quality data about severe health problems can be a very serious burden, leading to unnecessary anxiety [\n \n 28\n \n ]. For users with limited financial resources, wearables can be an inexpensive tool to keep track of their health when other health services are too expensive and difficult to access [\n \n 2\n \n ]. As such, wearables can improve access to healthcare, but wearable technology might constitute the main health service available for these users and poor data quality will unequally impact them more than others. This is why data quality should be considered a contextual property of data that needs to be constantly considered as the context of use changes significantly.\n

\n In turn, in order to assess data quality for a specific use, knowledge of contextual features of data collection is crucial. For example, it is crucial to know which experimental procedures and protocols were applied, which sensors and techniques were used, and which questions and hypotheses were investigated during data collection. As questions and hypotheses change from general heart monitoring to COVID-19 detection, for example, it is crucial to know the original experimental procedures and questions to understand whether data quality remains the same\u2014access to contextual features of data practices is crucial to assess quality and ensure the reliability and trustworthiness of data [\n \n 29\n \n ]. The problem is that access to these contextual features is often not available in the wearable context. For example, the collection of heart rate data from wearable devices is usually covered by the opt-in of users to general medical studies, which are organised by private companies and large research bodies, such as the Apple Heart Study created by the collaboration between Stanford University and Apple. While this type of study was of course validated and regulated as a clinical trial [\n \n 30\n \n ], there is little information on data collection, analysis, storing, and access and this makes it difficult to assess quality. In addition, in many cases biomedical researchers cannot even download data directly from the device and have to go through proprietary archives. As a result, because of commercial interests, very little information on how the data are collected, classified, and interpreted by the device is shared throughout the process. This is an issue for researchers, but also users and patients. The lack of access to contextual information about data collection makes it difficult for users to interpret the data to take action on their health and can eventually lead them not to trust and use the technology [\n \n 28\n \n ,\n \n 31\n \n ,\n \n 32\n \n ].\n

\n On this basis, we need more contextual information and coherence for wearable data quality [\n \n 33\n \n ]. Contextual information can be used to understand the specific features and needs for the assessment of data quality in the wearable context. In this direction, common and local standards of data quality can be developed to overcome current limitations and gaps in the wearable market. For example, the framework provided by FAIR (\n \n F\n \n indability,\n \n A\n \n ccessibility,\n \n I\n \n nteroperability, and\n \n R\n \n euse) can be used as a basis to discuss future developments in this direction [\n \n 34\n \n \u2013\n \n 36\n \n ]. We do not see these as hard compliance standards set by standard organisations, but rather the result of a bottom-up process of coordination and assessment, as a way of fully appreciating the contextual dimensions of data quality. As we have seen, depending on the specific context of use, standards, requirements, and burdens of data quality can change. This is why data quality standards need to be local and could first be created for the research context, where knowledge of the contextual components of data collection is crucial to assess data quality. Yet, standards of data quality can clearly be crucial for regulators, institutions, industry, and users too, and standards could be adopted by the institutions, journals, repositories of specific research communities. Again similarly to FAIR data, the institutions of specific research communities could be in charge of managing, updating, and assessing standards and informing individual users of their existence and application, thus presenting data quality as a fundamental issue for digital health.\n

\n Interoperability for balanced estimations\n

\n As we have seen in our overview, detection and prediction are among the main functions for which wearable devices are currently used in health. In turn, detection and prediction are fundamental activities at the basis of the production and use of scientific knowledge. Yet, issues affecting\n \n balanced estimations\n \n in screening and prediction raise concerns on the grounding and validity of wearables as detection and prediction tools.\n

\n In the COVID-19 pandemic, several models have been used to predict the development, spreading, and impact of the pandemic, but they have also been at the centre of several critiques concerning their uncertain assumptions and limitations [\n \n 37\n \n \u2013\n \n 40\n \n ]. Wearable devices have been proposed as potential solutions to some of these issues [\n \n 8\n \n ]. For example, data from Fitbits have been used to detect elevated signals at the level of heart rate and temperature\u2014these are possible symptoms of COVID-19 that can be identified in advance or just when more explicit symptoms surfaced [\n \n 17\n \n ]. This is an extremely promising use of wearables, but the status of predictions based on wearable data raises challenges. Applications of wearables for the detection of COVID-19 are severely affected by overestimation, the issue where non-problematic conditions and abnormalities are systematically detected or predicted as problematic. For example, it is often difficult to differentiate between COVID-19 and seasonal influenza and cases of standard influenza on the basis of wearable data\u2014elevated heart rate can be interpreted as a symptom of respiratory illness more generally and, as a result, wearables have wrongly detected and predicted COVID-19 infections [\n \n 17\n \n ,\n \n 24\n \n ].\n

\n This is a crucial epistemic issue for testing the validity of using wearable data to perform or assist prediction, but is also significant from an ethical point of view. For example, health resources and personnel may be diverted from actually problematic situations towards overestimated issues, thus creating imbalance in health treatment and access [\n \n 41\n \n ]. Erroneous prediction and detection can also create unnecessary stress in patients, raising concerns on the implementation of wearables for health [\n \n 7\n \n ,\n \n 42\n \n ]. In addition, the burdens of overestimation may also be unequally distributed over different types of social groups, policy contexts, healthcare services. For example, estimation issues in the context of COVID-19 might be overcome with access to molecular or antigen tests, which can differentiate between influenza and COVID-19. In this sense, it could be argued that overestimating infections might thus be better in light of the precautionary principle. However, access to fast COVID-19 testing is not equally available and distributed in the world and has often become expansive, especially when infections surge. Policy decisions might require a person to isolate if their wearable device has detected a possible infection (as we have seen with the use of contact-tracing smartphone apps), which is potentially harmful for them and their family, especially if remote work is not an option and wages might be lost. These issues are even more severe in the context of wearables and other digital health solutions. These technologies are presented as key opportunities for parts of the world with limited or non-existing health services [\n \n 43\n \n ]. However, if other technologies and services that might help overcome overestimation are limited and not available (e.g., fast COVID-19 testing), this poses even more significant constraints on the accuracy and estimation of wearables and other digital health technologies. Unsurprisingly, recent work by political institutions, such as the EU Commission, on the internet of things technologies such as wearables has concluded that overestimation is among the main issues for the adoption of wearable technology [\n \n 2\n \n ].\n

\n In order to overcome these concerns, we propose to focus on the interoperability of wearable data as a crucial way forward. Interoperability is the possibility that data can be integrated and used together with other types of data [\n \n 35\n \n ]. Several philosophical, historical, and sociological studies of the role of data in science have highlighted that the value of large volumes of data for research lies in the possibility of integrating and linking different datasets [\n \n 44\n \n ]. In this sense, a high level of interoperability is key to exploit the benefits of new and large datasets, such as those collected with wearable technology. A low level of interoperability makes it difficult to integrate wearable data with other health data and thus compare and balance results collected by different devices, sensors, approaches. In turn, making sure that wearable data are interoperable can make it easier to compare results obtained through other means and assess the extent to which overestimation might be a problem. Data interoperability is also connected to interoperability at the software and hardware level of wearable technology. For example, the integration of wearables into health services is currently challenging because the additional staff required to assist patients with the technology might need to be trained differently, as software and hardware solutions are different between devices [\n \n 45\n \n ]. In turn, interoperability standards are also crucial for data storage and thus to include wearables in health services, for example through personal and electronic health records, which is currently very costly [\n \n 3\n \n ], and to deal with cyber risks, for example by highlighting transparency and accountability in healthcare infrastructures [\n \n 46\n \n ,\n \n 47\n \n ]. Ensuring that a wearable device is interoperable is thus an essential way to approach overestimation and the promise of providing more personal and precise healthcare in digital health.\n

\n Access for health equity\n

\n One of the defining features of wearables is their ability to be worn on our bodies. This means that they can often be personal devices, in the sense that they might fulfil a personal need of the user (such as tracking fitness activities and exercise) as well as be used as personal and fashion objects (such as rings and wristwatches). As such, wearables play a crucial role towards an increasingly personalised, precise, and person-centred medicine. In this way, wearable technology is uniquely positioned to move in the direction of one of the goals of digital health: expanding access to health services and thus improving\n \n health equity\n \n . Health equity is about making sure that different users are equally provided with services and care as part of their interactions with the health system, as defined in several policy initiatives such as the Thirteenth General Programme of Work of the World Health Organization (WHO). While we agree the contribution of wearables to these goals is promising, issues connected to access raise significant concerns.\n

\n As we have seen, wearable devices can clearly provide data that are personal to user needs, issues, and concerns [\n \n 48\n \n ]. However, the extent to which individual users can access the benefits of this data collection seems unequally distributed. Users with more digital literacy and socioeconomic resources are disproportionately advantaged to access benefits from the use of wearables as tools to detect and predict states of health and disease [\n \n 49\n \n ,\n \n 50\n \n ]. In addition, the use of wearables and other digital health tools for monitoring in the context of public health efforts might raise concerns about surveillance, in different ways for different social groups. Historically, members of marginalised social groups have been targeted by health surveillance and monitoring with unclear benefits and sometimes harmful results. For example, COVID-19 surveillance and policy restrictions have disproportionately affected structurally disadvantaged social groups [\n \n 51\n \n ]. If wearables as digital health technologies are to be made part of public health policy and campaigns, access to the technology needs to be ensured as much as access to clear benefits from the use of the technology. Currently, the benefits of health monitoring through wearables are disproportionately available to consumer technology companies, rather than individual users. Most wearables available on the market are developed and sold by some of the largest corporations in the world, such as Apple and Google. The increasing collection of health data through wearables by consumer technology creates clear economic and political benefits for these corporations, which can use the data for marketing and advertising. Individual users do not necessarily have access to these benefits of data collection or at least not at the same level [\n \n 52\n \n ].\n

\n In addition, even for those who can and do use wearables, other issues of access raise concerns on health equity. As we have seen, contextual information on the collection, classification, interpretation of wearable data is usually not shared by data providers and device manufacturers. This is an issue for health equity: epistemically, information of the ways in which wearable data are analysed for detection and screening is crucial to interpret data and translate results into significant actions of health promotion for individual users. Without this information, users can struggle to understand why the analysis of wearable data leads to the detection of a condition and how they can act upon this function. This can also create doubt and anxiety, as users do not know the extent to which the data are reliable and are unsure about the actions they can take to counter possibly alarming conclusions [\n \n 28\n \n ]. In other words, this creates a situation of health inequity. For some users, the collection of wearable data can be a source of actions to improve their health, but for others barriers to data access can create new burdens.\n

\n Several approaches have been proposed in recent years to counteract the burdens of health inequity [\n \n 53\n \n ]. A way forward for these challenges in the wearable context is an expansion of both the access to the data and related interpretation tools. More access to data can partially counter the economic and political power of technology corporations [\n \n 54\n \n ]. Access to interpretation, in turn, can empower users, enabling them to make sense of the trustworthiness, quality, and actionability of the functions provided by wearables. We see these as goals that should be part of health campaigns and public health policy involving wearables and other digital health technologies. Crucially for health equity, however, access to technology should be approached critically, in light of considerations of the specific social and political context of use. For example, some members of the general population might not be interested in tracking their health or might find it confusing, alienating, guilt-inducing, stressful. The specific use and position of wearables as digital health technology needs to be openly and critically discussed to ensure that those who choose not to be part of the movement are not unequally treated and loose access to other health services.\n

\n Representativity for fairness\n

\n Wearables are at the centre of several attempts to make health more mobile and digital. As we have seen in our overview, wearables are technologies that can track and collect digital data on various daily activities and provide users with individual monitoring and screening in connection to other digital tools and services. Wearables can also be ways of further developing remote detection and prediction, without the need to interact with other health services. In the digital health context, this use of digital devices and services such as wearables is connected to various benefits. For example, digital health is often framed explicitly as an opportunity to shift the medical knowledge system towards the representation of the majority that is typically excluded from more traditional research methodology [\n \n 55\n \n ]. While wearables are clearly promising tools to achieve these crucial goals, we raise concerns on their\n \n fairness\n \n . In the health context, fairness is close to the notion of equity and related attempts for the equal distribution of services and care. Yet, fairness is also about the just treatment of individuals when they interact with health services and thus about making sure that people are not treated in unjust ways in healthcare because of bias, discrimination, lack of consideration [\n \n 56\n \n ]. We argue that current use and features of wearables disproportionately target some members of the general population and exclude others, thus creating issues of fairness.\n

\n Thanks to wearable and other digital devices, data points such as steps have been tracked for almost a decade, at a scale that is unprecedented when compared to more traditional and preceding data practices. In these ways, more generally, wearables are contributing to the increasing datafication of activities and aspects of our lives. But they are contributing to their medicalisation too, as the possibility of quantifying and measuring these activities and aspects renders them as new areas of research and intervention. In the health context, current processes of datafication and medicalisation are contributing to a re-configuration of health, by expanding the limits and remits of biomedical research, producing new markers of health and disease, redefining what counts as health data, broadening the categories of influential stakeholders, and involving and empowering more individuals [\n \n 32\n \n ]. Datafication and medicalisation through wearables can thus create various benefits by uncovering new health needs and issues of specific communities. Consider, for example, the role that patient groups have played throughout the COVID-19 pandemic in raising concerns on the limitations and diverse impact of public health interventions and raising awareness on the long-lasting effects of COVID-19 infection, which are now known as long COVID. Enabling patients to track their own health individually and actively can provide them with more powerful tools and empowerment in this direction.\n

\n However, current uses and applications of wearable technology for health focus only on some members and groups of the general population, thus rendering the use of the technology unfair. For example, consider the framing of wearable technology as a crucial tool for the remote and constant monitoring of the elderly and patients that need to practice social distancing, avoid hospital visits but require monitoring [\n \n 8\n \n ]. Looking at current figures on the adoption of wearable devices, members of the population that fit into these categories are severely underrepresented and excluded by the application of this technology [\n \n 2\n \n ]. This is highly problematic from the point of view of fairness: Wearable technology seems to exclude the users that arguably would benefit the most from the use of wearables. Children are also an interesting type of users in this sense. Age groups including young adolescents and children have increasing access to digital technologies, including wearables. Yet, the adoption of wearable technology in children can vary substantially, for example depending on whether they use other technologies (e.g., smartphones are normally gateways for wearables), where they live, and the socioeconomic status of their family. The new contribution of these age groups to biomedical research is an exciting opportunity of wearable technology, potentially enabling the retooling of medical knowledge system to represent groups that are currently excluded and underrepresented [\n \n 55\n \n ]. At the same time, the opportunity of further introducing wearable technology in these age groups needs to be balanced against ethical reflections about security, privacy, intrusiveness. More generally, the cases of the elderly and children suggest that, however, large and extended wearable datasets may be, wearables usually target some social, economic, age groups more than others. This is crucial because excluding important and large parts of the general population can lead to biased and underrepresentative datasets, which do not give us a good picture of population health, thus creating a weak and unsound basis for knowledge claims and focusing health policy only on few members of the population.\n

\n Thus, issues of fairness raise concerns on the legitimacy of using and recommending wearable technology for health in the general population. To overcome these challenges, more focus needs to be given on the representativity of various members of the general population in wearable technology and digital health. We see the focus on representativity as one of the steps of the assessment of data quality and fairness [\n \n 36\n \n ], which should be one of the first steps for discussions on using wearables as part of health promotion and public health programmes too. In addition, focusing on representativity can also be a way of taking into account the context around the use and introduction of wearable technology. In communities and parts of the world with limited availability of fast and inexpensive testing, for example, early detection of pre-symptomatic COVID-19 is not as useful or might be useful only for some members of the population, thus creating issues of fairness. Consider one of the prime areas of application of wearables for health: the tracking of physical activity to suggest interventions and behavioural change [\n \n 57\n \n ,\n \n 58\n \n ]. Wearables can be powerful tools in this context\u2014yet alerting a person that they have been sedentary might not be as useful, if they do not have opportunities or services that can make them more active.\n

\n Conclusions\n

\n In this paper, we have discussed various implications of wearable technology for digital health. First, we have identified functions that wearable technology currently serves in biomedical research and clinical care as a way of specifying the epistemic contribution of wearables to the development and application of biomedical knowledge through monitoring, screening, detection, and prediction (\n \n Table 1\n \n ). On this basis, we have discussed a number of challenges that are connected to these functions, particularly at the level of data quality, estimations, equity, and fairness. As a way to overcome these challenges, we have introduced recommendations and possible solutions based on local standards of quality, interoperability, access, and representativity (\n \n Table 2\n \n ). Our analysis has thus been aimed at improving our understanding of the position and relations between wearables and other biomedical technologies and data sources, as well as ways to approach their adoption and regulation.\n

\n Throughout the article, we have applied an integrated approach for the discussion of wearables for health, which we see as a starting point for more work. In recent years, philosophers, sociologists, and ethicists of science and technology have started to work more closely in collaboration with biomedical scientists, engineers, and practitioners. An increasing number of publications is the result of collaborations between science scholars and scientists; philosophical work is increasingly relevant and cited in science journals [\n \n 59\n \n ]. In this context, approaches such as ELSI (Ethical, Legal and Social Issues) and E\n ^{\n 2\n}\n LSI show the need for a systematic integration of epistemic, ethical, legal, and social considerations [\n \n 60\n \n ]. This is a particularly important step to take in the context of new and evolving technologies for digital health, as important decisions are being taken now on their regulation, inclusion in healthcare programmes, and use in research. Our work in this article provides a first step for thinking about these as integrated issues.\n

\n Funding Statement\n

\n This research was funded by the Fondazione Silvio Tronchetti Provera (Project: Etica e tecnologie emergenti) and Regione Lombardia (Project: BASE5G, ID 1155850). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n

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\n \n \n\n", "best_field": "cleaned_text", "enhanced": true, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://pmc.ncbi.nlm.nih.gov/static/img/favicons/apple-touch-icon.png", "state": "candidate", "enhanced": true, "answer": null, "valid_answer": false, "source_citation_index": 12, "user_web_page_id": null, "status": "ready", "reader": false, "rank": 2, "reason": "The source reviews challenges and recommendations for wearable devices in digital health, relevant to methods of evaluating accuracy."}, {"id": 540709, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8826148/", "slug": "nih", "title": "The Impact of Wearable Technologies in Health Research: Scoping Review - PMC", "snippet": "Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 540709, "website_id": 34203, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8826148/", "title": "The Impact of Wearable Technologies in Health Research: Scoping Review - PMC", "snippet": "Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used ...", "cleaned_text": "Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >\u20ac500 (US $570), or obtrusive smart clothing. We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <\u20ac200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations\u2014wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.\n\nWearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen considerably in the last few years [1-3]. Noninvasive, consumer-grade wearables (hereafter wearables) may provide manifold advantages for health research; they are generally unobtrusive, less expensive than gold standard research devices [4], comfortable to wear [5], and affordable for consumers [6]. In recent years, the quality and accuracy of wearables have improved [7,8], resulting in more clinically approved certifications [9]. Wearables can measure long-term data in the naturalistic environment of study participants, allowing for ecologic momentary assessments [10,11]. Therefore, wearables are valuable developments, particularly for generating data for health research in large study populations, that is, global health or epidemiological studies, or in low-income contexts [6,9,12]. One example of a large study is the so-called Datenspende study by the Robert Koch Institute, the German research institute for disease control and prevention, which aims to tackle the COVID-19 (corona virus disease) pandemic with anonymous data donations acquired through wearables [13]. On the basis of the study by Radin et al [14], researchers used wearable data to calculate the regional probability of COVID-19 outbreaks incorporating data on pulse, physical activity (PA), and sleep, as well as weather data. Using a large sample size exceeding half a million participants, they forecasted the number of COVID-19 infections for the preceding 4 days. The Apple Heart Study [15] is another example that was a breakthrough for showing that wearable devices may detect atrial fibrillation (AF) and foster a discussion of potentials and limitations with regard to health care providers, researchers, and members of the media and economy [16,17]. Apart from these 2 examples, wearables are applied in diverse fields of health, including acoustic, gastrointestinal sensors for ileus prediction [18]; UV sun exposure [19]; heat-related illness measurements [20]; electrolyte monitoring, for example, for cystic fibrosis or training management [21,22]; early warning of AF with a wearable ring [23]; generation of electrocardiograms (ECGs) [15]; measurement of cardiopulmonary resuscitation quality [24]; measurement of continuous noninvasive blood glucose [25], as well as smart inhalers and activity trackers for asthma monitoring [26]. Numerous reviews and studies have investigated validation and accuracy, particularly for specific affordable wearables, comparing these to the gold standard measurements [21] or comparing evidence in a meta-analysis [8]. Many studies have focused on novel technologies, presenting prototypes, or investigating the feasibility and acceptance of a wearable device in a specific setting [3,27]. Similarly, reviews on the application and potential of wearables have focused on (1) specific wearable devices or specific wearable measurements, for example, only smartwatches [4] or only sleep measurements [28] or (2) applications of specific medical fields and interventions, for example, only for diagnosis and treatment in cardiological conditions [29] or wearables as an intervention to promote PA in patients with oncologic conditions [30]. Among these publications, we identified a lack of aggregated insight for wearable use in health research and its respective strengths and shortcomings. With this scoping review, we aim to overview and categorize the current research conducted on wearable devices.\n\nWe conducted a scoping review to explore the applications of affordable wearables worn on wrists, arms, chests, or waists, which constitute the characteristic locations [31]. We focused on the following aspects: (1) demographics; (2) wearable devices and measured vital signs; (3) wearable data and its analysis; (4) reported shortcomings and strengths of wearables; and (5) study aims, results, and types of wearable use. We present our findings in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting standard and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews; Multimedia Appendix 1) [32] and the methodological framework of Arksey and O\u2019Malley [33] and Peters et al [34]. A scoping review seemed most appropriate given the broad nature of this subject and the range of potential implementations in the setting of health research. We sought to define and characterize the state of affordable wearables for health research. Eligible publications were peer reviewed, published in English, and published after 2013 (after wearables became widely commercially available [1-3]) and had a full-text version available (in instances no full text was available, authors were contacted 3 times with a waiting period of 7 days between each contact before exclusion). Our review scopes the current information available on affordable, noninvasive wearables, which are (1) worn on the wrist, arm, and chest; (2) measure vital signs; and (3) analyze the generated wearable data for outcome assessment. Validation and qualitative studies were excluded. We focused only on devices that cost <\u20ac500 (US $570) per device (1) to allow the affordability of larger studies, for example, where wearable devices need to be provided to study participants via the study and (2) to ensure that wearables are available commercially and (3) intended for consumers. As the definition of vital signs is not distinct [35], we included the following vital signs [9,36,37]: HR, HR variability, ECG measurements or heart rhythm analysis (detection of arrhythmias), blood pressure, blood oxygen, respiratory rate, body temperature, sleep, electrodermal activity, electromyogram measurements, and PA (Textbox 1).\n\u2022 \n\u2022 None Commercially available wearable, price <\u20ac500 (US $570) per device (Only hardware prices were considered. Software, subscriptions, or similar, which might be necessary for device use, were not included. All prices were captured in the timeframe of this study and therefore are only considered as approximations)\n\u2022 None Wearables worn on the arm, wrist, chest, and waist\n\u2022 \n\u2022 None Measuring and analyzing one or more vital sign\n\u2022 None Range of vital signs as defined in this review, including heart rate, heart rate variability, electrocardiogram measurements or heart rhythm analysis (detection and classification of atrial fibrillation, extrasystoles, and other arrhythmic events), blood pressure, blood oxygen, respiratory rate, body temperature, sleep (time, deepness, etc), electrodermal activity, electromyogram measurements, physical activity (steps, distance covered, intensity, energy expenditure, etc; physical activity included as basic measurements of wearables or very similar or related parameters) [9,36,37].\n\u2022 \n\u2022 None Studies not analyzing wearable-generated data for (health) outcome assessment, including studies focusing on (1) accuracy, validation, improvement (algorithms and software); (2) patents; (3) smart clothing; (4) obtrusive wearables (the device comprises obstructive parts or wires, etc); (5) behavior change intervention studies (ie, where the wearable is provided as promotion for more physical activity only and not for health outcome assessment); (6) qualitative studies; or (7) studies with research objectives and outcomes not related to health or a medical condition\n\u2022 \n\u2022 None Wearable not commercially available (eg, prototype and discontinued)\n\u2022 \n\u2022 None Not measuring vital sign, that is, gait, posture, and motion recognition analysis (eg, gesture recognition for sign language)\n\u2022 None Studies with research objectives and outcomes not related to health or a medical condition We used PubMed, Ovid, Web of Science, and CINAHL to search peer-reviewed literature using a search string based on the following three concepts: synonyms and medical subject headings terms, including (1) wearables (synonyms, top 15 vendors with most market shares [38-40], or frequently used in research [2,7]), (2) physical wear location of wearables (torso, arm, and wrist), and (3) measurement of vital signs (for full search string see Multimedia Appendix 2 [41]). We manually searched the reference lists for relevant articles. We imported the identified articles into the literature reference management system Zotero [42] and then into the systematic review management platform Covidence [41]. Literature was screened by 2 independent reviewers. Any disagreements were resolved by discussion between the 2 reviewers (SH and MA) and a third researcher (SB). To assess the quality of the included studies and their various study designs (credibility), we considered the Medical Education Research Study Quality Instrument [43] score as adequate (Multimedia Appendix 3 [14,15,20,44-219]). We conducted data synthesis in accordance with Arksey and O\u2019Malley [33], comprising the analytic framework, analysis of the extent and nature of studies, and thematic analysis. We categorized the findings by title, author, year, country of study, objectives of study, study population, sample size, methods, intervention type, outcomes, and key findings related to the scoping review question [34]. We extracted mutually exclusive groups, including wearable manufacturers, built-in sensors, scope of measurements (vital signs), shortcomings and strengths of wearables mentioned by the authors, the used methods for data analysis, and medical fields.\n\nOverall, we have identified a positive trend in wearable studies, underlining the growing interest in wearables in health research, in line with other reviews [3,224-226]. Our results show a strong interest of researchers and study participants in this technology, but we also identified cautionary behavior toward using wearables. The vast majority of studies were undertaken in North America, about twice as many as in Europe, which is consistent with the previous literature [225]. One study in North America, conducted in 2019 with over 8 million participants [153], dominated the image of the distribution of participants. The reasons for the American-European gap may be multifaceted. One factor may be the differences in political and administrative frameworks, for example, comparing CE and FDA processes, which may result in slower certification processes for wearables and new technologies in general [31]. Another factor may be cultural mentality resulting in faster adoption of new technology in the United States, as the North Americans own proportionally more fitness trackers in comparison to the Europeans [227,228]. Some factors discussed in other research were not or only briefly mentioned in the included studies [6,29,31], but should also be reflected, especially technical and legal aspects, such as data security [224], data synching, and export. For example, the Germany-based study of Koehler et al [114] was one of the few that detailed data security and transfer of home-based telemonitoring data to the clinic. Data security and privacy are severely governed by the European Union General Data Protection Regulation, which is according to their website the \u201ctoughest privacy and security law in the world\u201d [229]. Administrative limitations and challenges presumably obscure the benefits of wearable research in Europe. A possible solution for data security and usability might be data trusts [230] as an alternative to large platforms. Most medical fields represented in the included studies showed similarities with other reviews [224], for example, studies often focusing on cardiology, sports medicine, and neurology. However, we found a multitude of studies from multidisciplinary fields as well as the field of global health, indicating a likely adoption and expansion of wearables in other medical fields. This underlines the potential for wearables in health research beyond a mere trend or hype, as wearables may provide new possibilities for a broad spectrum of health research, such as for infectious disease prediction like COVID-19 or fertility awareness, among many others. Similar to other reviews, most devices were wrist-worn fitness trackers and accelerometers, and most of them are from the company Fitbit, measuring PA, HR, and sleep [3,27,31,224,225]. These vital signs and device types seem to become the standard in wearable research [3,27,31,224,225]. The included studies also emphasized the growing wearable use [147,195,197], which is also reflected in commercially available devices [38-40]. Currently, further wearable devices emerge, measuring, for example, oximetry, blood pressure, skin temperature, or respiratory rate. Categorization of Wearable Application in the Studies In general, the included studies covered a great scope of health applications such as fertility tracking; monitoring of body characteristics such as weight or diseases such as Alzheimer disease, diabetes mellitus, and AF; as well as associations of coffee intake, sleep, and PA, or blood pressure and steps. We have noted an increase in smaller studies that also included rare populations and conditions, such as fibromyalgia or the rare genetic Pompe disease, indicating that wearables may be valuable for insights into patients with rare conditions. Using affordable, consumer-grade wearables for rare disease assessment and monitoring might eventually be less expensive than specifically developed devices and easier to use for patients. Therefore, currently underrepresented populations may be better researched through wearables [231], that is, different ethnic groups, nationalities, individuals with disabilities, or (rare) conditions. Future studies could examine the participation of underrepresented groups in wearable research in greater depth, particularly in studies analyzing wearable user data. Included studies are predominantly from high-income countries, constituting a gap in wearable studies in low-resource contexts. The AliveCor device was shown to be feasible in Kenya to help detect AF [232], as well as for early diagnosis. The literature underlines the potential for wearable-based research in low-resource settings to generate data and improve health care [9], based on their low cost and ease of use (data acquisition, hardware, and software handling) [233]. Xu et al [234] emphasized that physiological monitoring with wearables hold \u201cpromise for substantial improvements in neonatal outcomes\u201d in low- and middle-resource countries. Wearables can generate a solid database for global health research, particularly for morbidity measurements [235], large-scale studies, and modeling and descriptive studies. Topics such as climate change\u2013induced impacts focusing on extreme weather events as an outcome and impact on health [236] may be approached. For example, 1 study [20] measured the physiological response of farm workers to climate conditions with wearables to investigate heat-related illness in a high-income setting. Lam et al [237] investigated the thermal adaptation and comfort of participants originating from various climatic regions. The fitness tracker measured HR data was integrated with other weather and human-based measurements and predicted the thermal sensation of nonlocal participants, among others. Similar studies can be conducted in low-resource regions. A few studies have experienced issues or shortcomings, such as inaccuracies in measurements and technical issues. Nevertheless, most authors were satisfied with wearables, as strengths were mentioned more frequently than shortcomings. Novelty and innovation outweighed the shortcomings for most authors. The most mentioned positive wearable characteristics were validity and accuracy, technical reliability, innovation, and unobtrusiveness. Only a few authors have mentioned data access through APIs or cloud platforms as a strength. However, the practical value of wearables is heavily reliant on the mode and reliability of data access. Depending on the company, there may be different data access policies in place, whereby it may not be possible to access the raw data of the wearable. Most authors have not considered wearable data access. However, data access and availability of wearable devices is an important aspect that researchers need to be aware of before using a potential study device. Another aspect is open access to the wearables\u2019 raw data or source codes, as companies might change the source code and implement algorithms without the obligation to announce or detail changes that might lead to bias and inconsistency of data [224]. For example, Thijs et al [195] mentioned the consequences of nondisclosed algorithms (Fitbit) for data analysis and standardization. Moreover, the lack of standardization and replicability of wearable raw data and analysis [28] hinders comparability among studies. Most studies mentioned and discussed validation, accuracy, and certification of the used wearables as part of good research practice approaches. However, the mention of validation or accuracy did not necessarily imply that the wearables had been certified (FDA or CE) or validated in peer-reviewed research. Nevertheless, the authors reported that the wearable device is sufficiently accurate even with existing inaccuracies [14,143,197]. The authors seemed to tolerate smaller inaccuracies and validation drawbacks\u2014especially of established consumer-grade wearables\u2014if usability was of high importance, such as in large-scale studies. We noted an increase in large-scale wearable studies in recent years, which is consistent with previous literature [225]. During the COVID-19 pandemic, there has been an increase in studies using secondary data. Studies aimed at generating insights with regard to the developing pandemic, focusing on forecasting models and their effects on different populations. Overall, wearable-generated big data sets might decrease biased data because measurements are objectively taken in the natural environment of numerous and diverse individuals. Although data analytic skills are needed for handling big data sets, their analysis might be extremely valuable for health research in generating new evidence [31,225]. First, not all studies using wearables might have been identified by our search. We included only the wearables of companies in the search that had the highest market share. Therefore, the wearables of smaller or new companies may be missing in this review. In addition, we only included studies published in English, which may have excluded evidence from other regions that may not publish in English. Although this review provides a wide scope of wearable research, the list of included studies is by no means exhaustive. In addition, wearable costs are only approximations and could be imprecise: (1) companies follow different sales and distribution models, for example membership, rental, and subscription; (2) we only incorporated wearable (hardware) prices, excluding costs for software, maintenance, and other charges such as subscription fees, which may even exceed wearable hardware costs; and (3) sales prices are subject to fluctuation. We also excluded many studies as wearables were discontinued. The fluctuant and unstable market, therefore, might also be a factor in decisions regarding the use of wearables [28]. Although interesting and promising, some wearables and similar devices were beyond the scope of this study but might also be valuable for health research. We have provided a wide overview of wearable devices; however, the included studies did not show the full range of possible wearables and measured vital signs [9,37]. In addition, we report the opinions of the included studies with regard to the shortcomings and strengths of wearables. Although these insights might be helpful, they are not objective measures. Moreover, our introduced categories for studies and aims to use wearables might overlap, as separation and categorization are artificial. We see a growing uptake of wearables in health research and a trend to use wearables for large-scale, population-based studies. Wearables, which were often piloted in the included studies, were used in diverse health fields including COVID-19 prediction, fertility awareness, geriatrics, AF detection, evaluation of methods, drug effects, psychological interventions, and patient-reported outcomes. Measurement of steps, PA, HR, and sleep may be considered standard wearable measurements. Nevertheless, wearables are becoming more diverse in their measurements and appearance. Therefore, wearable-induced research may include currently underrepresented populations such as the older adults, participants who are disabled, participants with rare chronic or genetic diseases, participants from low socioeconomic backgrounds, and others. For many researchers, novelty and innovation seem to outweigh shortcomings such as measurement inaccuracies. Overall, the included studies shared key characteristics that the wearables should meet: validity, technical reliability (including data access solutions), innovation, and unobtrusiveness. We identified a lack of wearable research in low-resource settings. We assume that the reasons for the gap may be a lack of funding and doubts about the usefulness of the wearables. However, wearable devices may be used to generate data in such settings, which may otherwise be difficult and expensive to obtain. Therefore, wearable devices may be valuable for health research in a global context. During the COVID-19-pandemic in particular, large-sized wearable studies were used to generate insights into the developing pandemic and may potentially lead to novel insights into population health trends and forecasts. Future research is needed to determine the usability of wearable devices for underrepresented populations, as well as the feasibility and usefulness of health research in low-resource contexts.", "cleaned_text_custom_parser": "Official websites use .govA.govwebsite belongs to an official\n government organization in the United States.\nSecure .gov websites use HTTPSAlock(LockLocked padlock icon) orhttps://means you've safely\n connected to the .gov website. Share sensitive\n information only on official, secure websites.\nPERMALINK\nThe Impact of Wearable Technologies in Health Research: Scoping Review\nSophie Huhn\nMiriam Axt,BA\nHanns-Christian Gunga,Prof Dr\nMartina Anna Maggioni,PD, PhD\nStephen Munga,PhD\nDavid Obor,MSc\nAli Si\u00e9,PhD\nValentin Boudo,MSc\nAditi Bunker,Dr sc hum\nRainer Sauerborn,Prof Dr\nTill B\u00e4rnighausen,Prof Dr\nSandra Barteit,Dr sc hum\nReceived 2021 Oct 20; Revision requested 2021 Nov 12; Revised 2021 Nov 23; Accepted 2021 Dec 17; Collection date 2022 Jan.\nThis is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication onhttps://mhealth.jmir.org/, as well as this copyright and license information must be included.\nAbstract\nBackground\nWearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.\nObjective\nIn this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.\nMethods\nWe performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >\u20ac500 (US $570), or obtrusive smart clothing.\nResults\nWe included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <\u20ac200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations\u2014wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).\nConclusions\nWearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.\nKeywords:wearable, consumer-grade wearables, commercially available wearables, public health, global health, population health, fitness trackers, big data, low-resource setting, tracker, review, mHealth, research, mobile phone\nIntroduction\nBackground\nWearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen considerably in the last few years [1-3].\nNoninvasive, consumer-grade wearables (hereafterwearables) may provide manifold advantages for health research; they are generally unobtrusive, less expensive than gold standard research devices [4], comfortable to wear [5], and affordable for consumers [6]. In recent years, the quality and accuracy of wearables have improved [7,8], resulting in more clinically approved certifications [9]. Wearables can measure long-term data in the naturalistic environment of study participants, allowing for ecologic momentary assessments [10,11]. Therefore, wearables are valuable developments, particularly for generating data for health research in large study populations, that is, global health or epidemiological studies, or in low-income contexts [6,9,12].\nOne example of a large study is the so-calledDatenspendestudy by the Robert Koch Institute, the German research institute for disease control and prevention, which aims to tackle the COVID-19 (corona virus disease) pandemic with anonymous data donations acquired through wearables [13]. On the basis of the study by Radin et al [14], researchers used wearable data to calculate the regional probability of COVID-19 outbreaks incorporating data on pulse, physical activity (PA), and sleep, as well as weather data. Using a large sample size exceeding half a million participants, they forecasted the number of COVID-19 infections for the preceding 4 days. The Apple Heart Study [15] is another example that was a breakthrough for showing that wearable devices may detect atrial fibrillation (AF) and foster a discussion of potentials and limitations with regard to health care providers, researchers, and members of the media and economy [16,17].\nApart from these 2 examples, wearables are applied in diverse fields of health, including acoustic, gastrointestinal sensors for ileus prediction [18]; UV sun exposure [19]; heat-related illness measurements [20];electrolyte monitoring, for example, for cystic fibrosis or training management [21,22]; early warning of AF with a wearable ring [23]; generation of electrocardiograms (ECGs) [15]; measurement of cardiopulmonary resuscitation quality [24]; measurement of continuous noninvasive blood glucose [25], as well as smart inhalers and activity trackers for asthma monitoring [26].\nNumerous reviews and studies have investigated validation and accuracy, particularly for specific affordable wearables, comparing these to the gold standard measurements [21] or comparing evidence in a meta-analysis [8]. Many studies have focused on novel technologies, presenting prototypes, or investigating the feasibility and acceptance of a wearable device in a specific setting [3,27]. Similarly, reviews on the application and potential of wearables have focused on (1) specific wearable devices or specific wearable measurements, for example, only smartwatches [4] or only sleep measurements [28] or (2) applications of specific medical fields and interventions, for example, only for diagnosis and treatment in cardiological conditions [29] or wearables as an intervention to promote PA in patients with oncologic conditions [30]. Among these publications, we identified a lack of aggregated insight for wearable use in health research and its respective strengths and shortcomings.\nObjectives\nWith this scoping review, we aim to overview and categorize the current research conducted on wearable devices.\nMethods\nOverview\nWe conducted a scoping review to explore the applications of affordable wearables worn on wrists, arms, chests, or waists, which constitute the characteristic locations [31]. We focused on the following aspects: (1) demographics; (2) wearable devices and measured vital signs; (3) wearable data and its analysis; (4) reported shortcomings and strengths of wearables; and (5) study aims, results, and types of wearable use. We present our findings in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting standard and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews;Multimedia Appendix 1) [32] and the methodological framework of Arksey and O\u2019Malley [33] and Peters et al [34]. A scoping review seemed most appropriate given the broad nature of this subject and the range of potential implementations in the setting of health research.\nEligibility Criteria\nWe sought to define and characterize the state of affordable wearables for health research. Eligible publications were peer reviewed, published in English, and published after 2013 (after wearables became widely commercially available [1-3]) and had a full-text version available (in instances no full text was available, authors were contacted 3 times with a waiting period of 7 days between each contact before exclusion).\nOur review scopes the current information available on affordable, noninvasive wearables, which are (1) worn on the wrist, arm, and chest; (2) measure vital signs; and (3) analyze the generated wearable data for outcome assessment. Validation and qualitative studies were excluded. We focused only on devices that cost <\u20ac500 (US $570) per device (1) to allow the affordability of larger studies, for example, where wearable devices need to be provided to study participants via the study and (2) to ensure that wearables are available commercially and (3) intended for consumers. As the definition of vital signs is not distinct [35], we included the following vital signs [9,36,37]: HR, HR variability, ECG measurements or heart rhythm analysis (detection of arrhythmias), blood pressure, blood oxygen, respiratory rate, body temperature, sleep, electrodermal activity, electromyogram measurements, and PA (Textbox 1).\nInclusion and exclusion criteria.\nCommercially available wearable, price <\u20ac500 (US $570) per device (Only hardware prices were considered. Software, subscriptions, or similar, which might be necessary for device use, were not included. All prices were captured in the timeframe of this study and therefore are only considered as approximations)\nRange of vital signs as defined in this review, including heart rate, heart rate variability, electrocardiogram measurements or heart rhythm analysis (detection and classification of atrial fibrillation, extrasystoles, and other arrhythmic events), blood pressure, blood oxygen, respiratory rate, body temperature, sleep (time, deepness, etc), electrodermal activity, electromyogram measurements, physical activity (steps, distance covered, intensity, energy expenditure, etc; physical activity included as basic measurements of wearables or very similar or related parameters) [9,36,37].\nStudies not analyzing wearable-generated data for (health) outcome assessment, including studies focusing on (1) accuracy, validation, improvement (algorithms and software); (2) patents; (3) smart clothing; (4) obtrusive wearables (the device comprises obstructive parts or wires, etc); (5) behavior change intervention studies (ie, where the wearable is provided as promotion for more physical activity only and not for health outcome assessment); (6) qualitative studies; or (7) studies with research objectives and outcomes not related to health or a medical condition\nNot measuring vital sign, that is, gait, posture, and motion recognition analysis (eg, gesture recognition for sign language)\nStudies with research objectives and outcomes not related to health or a medical condition\nInformation Sources and Search\nWe used PubMed, Ovid, Web of Science, and CINAHL to search peer-reviewed literature using a search string based on the following three concepts: synonyms and medical subject headings terms, including (1) wearables (synonyms, top 15 vendors with most market shares [38-40], or frequently used in research [2,7]), (2) physical wear location of wearables (torso, arm, and wrist), and (3) measurement of vital signs (for full search string seeMultimedia Appendix 2[41]). We manually searched the reference lists for relevant articles.\nWe imported the identified articles into the literature reference management system Zotero [42] and then into the systematic review management platform Covidence [41]. Literature was screened by 2 independent reviewers. Any disagreements were resolved by discussion between the 2 reviewers (SH and MA) and a third researcher (SB).\nQuality Assessment\nTo assess the quality of the included studies and their various study designs (credibility), we considered the Medical Education Research Study Quality Instrument [43] score as adequate (Multimedia Appendix 3[14,15,20,44-219]).\nData Synthesis\nWe conducted data synthesis in accordance with Arksey and O\u2019Malley [33], comprising the analytic framework, analysis of the extent and nature of studies, and thematic analysis. We categorized the findings by title, author, year, country of study, objectives of study, study population, sample size, methods, intervention type, outcomes, and key findings related to the scoping review question [34]. We extracted mutually exclusive groups, including wearable manufacturers, built-in sensors, scope of measurements (vital signs), shortcomings and strengths of wearables mentioned by the authors, the used methods for data analysis, and medical fields.\nResults\nOverview\nOur initial search yielded 7499 hits (PubMed: 2514; Ovid: 1905; Web of Science: 1440; CINAHL: 1640) and we identified 121 publications by manual search. Of 7620 total publications, we screened 4525 (59.38%) nonduplicates for title and abstract, leading to the assessment of 660 full-texts. After full-text screening of the 660 articles, we included 179 (27.1%) studies in our review [14,15,20,44-219] (Figure 1).\nFigure 1.\nPRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [220].\nStudy Characteristics\nDemographics\nBetween 2013 and 2020, we observed an increase in the number of studies and study participants (Figure 2andTable 1). The year 2019 featured the largest sample size, and studies were predominantly conducted in North America (Figure 3[221]). The largest study we identified was conducted in 2019 in North America and included over 8 million participants (75.71%) [153]; the second largest was a European study comprising 742,000 participants (6.85%) [162]. Without the aforementioned, largest study, Europe and Asia would lead in participant numbers and we would see a continuous increase in participant numbers from 2013 to 2020.\nFigure 2.\nNumber of studies and study participants (logarithmic scale) per year of study publication. The sizes of the circles visualize the overlapping and number of studies within the year.\nTable 1.\naStudies aimed to find associations, correlations, or influencing factors within their study population, study outcomes, and generated data.\ncStudies aimed to evaluate patient-reported outcomes, health care practices, diagnostics, screenings, and others.\nFigure 3.\nIncluded studies per continent. The colors of the continents visualize the number of included studies published on the respective continent (created with Mapchart [221]).\nStudy Types and Fields\nMost studies (128/179, 71.5%) used observational study designs such as cross-sectional (66/179, 36.9%) and cohort studies (62/179, 34.6%), comprising 9,780,808 (90.26%) participants and 628,641 (5.8%) participants, out of 10,835,733 participants, respectively. Most frequently, studies (70/179, 39.1%) aimed to find associations, correlations, or influencing factors within their study population, study outcomes, and generated data. Slightly less than one-third of the studies (54/179, 30.2%) aimed to characterize and observe their study population.\nMost studies were conducted in the fields of multidisciplinary and general medicine (43/179, 24%); cardiology, fitness, and sports medicine (29/179, 16.2%); and neurology, psychology, and psychiatry (28/179, 15.6%;Figure 4). The fields of global health, prevention, and epidemiology featured the largest sample size with, with 10,104,217 (93.25%) out of 10,835,733 participants.\nFigure 4.\nWearable Characteristics\nA total of 189 wearable devices were extracted. The company with the most wearable devices in the included studies was Fitbit (97/189, 51.3%), covering 8,361,035 (74.35%) out of 11,224,872 participants. Fitbit is followed by ActiGraph (research-grade wearable devices unavailable for consumers or not consumer grade per se; 19/189, 10.1%), Polar Electro (9/189, 4.8%), and Withings (8/189, 4.2%). In number of study participants, Huawei and Withings comprised 832,036 (7.4%) participants and 794,174 (7.06%) participants out of 11,224,872 participants, respectively (Table 2).\nTable 2.\naResearch-grade wearable devices unavailable for consumers or not consumer grade per se.\nbStudies collected data with multiple wearable devices (that belonged to the study participants) or studies that used secondary data provided by web-based wearable platforms, mobile applications, or wearable companies.\ncDistinct vital sign trackers are specialized on a specific vital sign, for example, oximetry ring, temperature wristband tracker, and blood pressure armband. They differ in measured vital signs and worn locations compared with other wearable device types.\ndUtilized in-built sensors in wearables sums up to more than the total of wearables, as sometimes more than one built-in sensor was used.\neProviding wearable hardware pricing was not transparent, as some studies used data provided by diverse participant-owned wearables or wearable hardware costs were part of a subscription or a membership fee, that is, Whoop strap of Whoop.\nfAnalysis\u2014statistical tests sums up to more than the total number of included studies, as some studies applied more than one type of analysis or statistical test.\nMost studies (156/179, 87.2%) used 1 wearable model. However, most of the study participants (9,090,776/10,835,733, 83.9%) were part of large-scale population-based studies in which data were mostly collected with multiple wearable devices that belonged to the study participants.\nSome large-scale population-based studies (11/179, 6.1%) relied on secondary data collected with mobile apps [87] or web-based wearable platforms [153] or provided through a wearable company [189]. Thus, the device type could not be specified (assigned to categorydiverse wearable devices\u2014secondary data via wearable data platform). A total of 15 (63%) out of 24 studies that used secondary data were conducted in 2020, and 5 (21%) studies in 2019.\nFitness trackers (86/189, 45.5%) and accelerometers (measuring body movement acceleration [37]) worn on the wrist, torso, and hip (49/189, 25.9%) were the most frequent. Other wearable device types included ECG chest straps and patches (21/189, 11.1%), smartwatches (12/189, 6.3%), and distinct vital sign trackers (10/189, 5.3%) such as oximetry rings or blood pressure armbands (Table 2).\nMost wearables were worn on the wrist (138/189, 73%), followed by the hip (25/189, 13.2%) and chest (21/189, 11.1%). Only a few wearables were worn on the arm (3/189, 1.6%) and finger (2/189, 1.1%;Figure 5).\nFigure 5.\nWear locations of wearables and their frequencies. The color and size of the circles assigned to the body location visualize the frequency of wearables worn on the respective location.\nMost of the studies used wearable built-in sensors of (1) accelerometers (146/179, 81.6%) that measure acceleration on a 3- or 1-axis [37] and (2) photoplethysmography (59/179, 33%) defined as an \u201coptical technique that [...] detects blood volume changes in the microvascular bed of tissue\u201d [222]. Other built-in sensors were electrodes for ECG measurements (21/179, 11.7%); gyroscopes (6/179, 3.4%), which determine how different portions of the body rotate [37]; thermometers (4/179, 2.2%) measuring skin temperature; and blood pressure sensors (3/179, 1.7%).\nMost studies investigated steps (95/179, 53.1%), HR (55/179, 30.7%), and sleep time (51/179, 28.5%). We classified measured vital signs into three categories, whereby PA measures were most frequent (228/179, 127.4%;Multimedia Appendix 4[14,15,20,44-219]):\nPA measures included steps, intensity (eg, time spent in moderate to vigorous PA), energy expenditure (eg, kilocalories and metabolic equivalent), axial or raw movement data, distance (covered), and others (such as stairs taken, elevation, and sedentary time).\nCardiac measures included HR, HR variability, and ECG (or other direct heart rhythm analyses, such as AF detection).\nOther measures that included blood or pulse pressure, body temperature, blood oxygen, and respiratory rate.\nMost studies (120/189, 63.5%) used wearables that cost <\u20ac200 (US $228). In some studies (15/189, 7.9%), wearable prices were not transparent, as data were provided through a variety of participant-owned wearables [87] or the wearable hardware was part of a subscription or a membership fee, that is, Whoop strap of Whoop [178].\nRegression analysis (62/179, 34.6%) andttests (42/179, 22.9%) were the most commonly used statistical methods to analyze wearable data. Other methods comprised nonparametric tests, such as correlations, Wilcoxon U test, Kaplan-Meier survival analysis, and chi-square tests. Variance analysis (analysis of variance) and significance tests such as permutations were also used. Further data analyses were conducted in a data-driven manner [223] with artificial intelligence, such as k-means [176] or unsupervised cluster analysis [172], recursive feature elimination technique [170], rotation random forest classifier [130], and supervised machine learning algorithms using logistic regression, decision tree, and random forest [215].\nCategorization of Wearable Application in the Studies\nWe categorized the included studies based on their study objective, the role of the wearable and the collected wearable data within the study in the following 6 categories (overlaps are possible as separation is artificial). In the following, categories are presented in order of their frequency (seeFigure 6andMultimedia Appendix 5[14,15,20,44-219] for article references and examples).\nFigure 6.\nCategorization of wearable applications, showing proportions of the 6 categories (with 4 subcategories). The size of depicted categories (in different colors) corresponds to the number of studies.\nCorrelations\u2014Wearable and Other Physiological Data\nStudies (40/179, 22.3%) have examined the correlation of a wearable derived measure with clinical- and patient-reported and other health-related outcomes to find new associations and correlations. The data generated by the wearable device were correlated with data from mostly physiological or patient-reported outcomes.\nPopulation-Based Research\nIn 17.3% (31/179) of studies, wearables produced insights into a specific population through monitoring (observational and cross-sectional) of vital signs, such as steps and HR. Often, these were cross-sectional studies (17/31, 55%) where the wearable measurement was the sole outcome. The resulting data provide novel insights and characteristics of populations.\nOutcome Assessment\nIn these studies (30/179, 17.3%), wearables generated the outcome measurement and monitored the dependent variable in an (quasi-) experimental setting or intervention, in mostly randomized controlled trials and quasi-experimental designs.\nPrognosis, Forecasting, and Risk Stratification\nIn further studies (28/179, 15.6%), data generated with wearables were integrated into risk calculations (risk for a certain event or outcome), prognostic models, or cut-points. Wearable data constituted inputs for models to estimate risks.\nExplorative Analysis of Big Data Sets\nThese studies (10/179, 5.6%) exploratively analyzed big data [223], generated by wearables and accessible via applications, commercial platforms, eCohorts, or companies themselves, to find trends and generate new hypotheses.\nMethod Evaluation\nStudies (40/179, 22.3%) have evaluated and compared methods and tools (such as screenings for diseases, general practices, questionnaires, or other patient-reported outcomes) with the help of wearables. The wearable device might be the gold standard device or probed itself.\nFeasibility\nIn these studies (12/179, 6.7%), the feasibility of using wearables for screening diseases and to improve on existing methods and practices is focused, mostly accompanied by a qualitative component.\nDiagnostics and Screening\nStudies (6/179, 3.4%) in this category evaluated details of diagnostics and disease screening outcomes, (cost-) effectiveness, utility, and screening length or were compared with standard measurement methods.\nDisease Monitoring\nHere (8/179, 4.5%), wearables supported the monitoring of an already diagnosed condition or a patient at risk (of deterioration).\nOthers\nStudies (14/179, 7.8%) evaluated methods, with no other particular subgroup being appropriate.\nStrengths and Shortcomings of Wearables\nOverall, the studies mentioned more strengths than shortcomings. A few studies (16/179, 8.9%) mentioned no strengths of wearables, whereas 55.3% (99/179) of the studies mentioned no shortcomings.\nMost often, authors (104/179, 58.1%) emphasized the accuracy and reliability, positive results of peer-reviewed validation studies (own and of others), or clinically approved certifications (eg, the Food and Drug Administration [FDA] clearance in the United States or Communaut\u00e9 Europ\u00e9enne [CE] mark of the European Union;Figure 7).\nFigure 7.\nChart of reported strengths and weaknesses of wearables as mentioned by authors. PA: physical activity.\nOften, studies (59/179, 33%) identified the wearable as innovative, that is, as a cutting-edge tool and method [103] with a wearable device potentially closing a gap in or improving health care and research. For example, 1 study described how wireless wearables and data synching could improve the quality of care [69], \u201cThe data can be sent from the wearable to the physician\u2019s office, avoiding the need for office visits, ultimately making possible preventive medicine and improving quality of care.\u201d Low et al [129] concluded that \u201cFitbit devices may provide opportunities to improve postoperative clinical care with minimal burden to patients or clinical providers.\u201d Tomitani et al [199] reflected how wrist-worn blood pressure wearables could \u201csignificantly improve blood pressure control.\u201d As per Shilaih et al [184], wrist-worn wearables might ameliorate fertility awareness research and care.\nSeveral studies (55/179, 30.7%) acknowledged the ability of wearables to measure in the naturalistic environment of the participants, calledecological momentary assessment[10,11,224].\nMultiple studies (51/179, 28.5%) described wearables as objective and superior to self-reported outcomes as they were more accurate, reliable, and easier to generate. Often, the authors valued the relatively low costs of wearables (50/179, 27.9%). Others appreciated wearables as being unobtrusive or noninvasive (48/179, 26.8%) and enabling continuous, long-term measurements (38/179, 21.2%). Furthermore, the handling (37/179, 20.7%) of hardware and software was often found to be user-friendly, as well as the prevalence of wearables in the population (27/179, 15.1%), decreasing stigma and easing participant recruitment. Some studies (26/179, 14.5%) reported that participants accepted and liked the wearables, resulting in high participant compliance (wearing and using the wearable). Some authors (18/179, 10.1%) perceived technical wearable characteristics as positive, for example, good sampling rate of measurements, long battery life, large memory space, raw data availability, data security, compatibility with other devices such as smartphones, and availability of application programing interfaces (APIs).\nFew studies (11/179, 6.1%) described wearables as robust and not easy to break. Authors (10/179, 5.6%) valued the wearable-induced behavior change as a cobenefit, that is, motivating study participants to more PA and increasing health awareness.\nA few studies (8/179, 4.5%) mentioned data accessibility via APIs, apps, and web-based platforms and a few other studies (7/179, 3.9%) potential of large-scale wearable studies, or the ease of data handling. A few (6/179, 3.4%) studies underlined the variety of functionalities and vital sign measurements as positive aspects, and 2.2% (4/179) of studies perceived wearables as fast or time-efficient in data generation.\nMost shortcomings (39/179, 21.8%) were related to the inaccuracy of the wearables or the absence of validation or clinically approved certification. Studies (16/179, 8.9%) also mentioned technical issues, such as a low sampling rate of measurements, no wear time recognition, or missing data. Other technical issues comprised, for example, synchronization, charging and device setup [91] or data cleaning [137]. Rare experienced shortcomings were participants\u2019 noncompliance or dislike toward the wearable (11/179, 6.1%), no access to raw data or company\u2019s algorithms (4/179, 2.2%), difficulties in handling the wearable (3/179, 1.7%), and wearables perceived as obtrusive in daily life (2/179, 1.1%).\nDiscussion\nStudy Characteristics\nOverall, we have identified a positive trend in wearable studies, underlining the growing interest in wearables in health research, in line with other reviews [3,224-226]. Our results show a strong interest of researchers and study participants in this technology, but we also identified cautionary behavior toward using wearables. The vast majority of studies were undertaken in North America, about twice as many as in Europe, which is consistent with the previous literature [225]. One study in North America, conducted in 2019 with over 8 million participants [153], dominated the image of the distribution of participants. The reasons for the American-European gap may be multifaceted. One factor may be the differences in political and administrative frameworks, for example, comparing CE and FDA processes, which may result in slower certification processes for wearables and new technologies in general [31]. Another factor may be cultural mentality resulting in faster adoption of new technology in the United States, as the North Americans own proportionally more fitness trackers in comparison to the Europeans [227,228].\nSome factors discussed in other research were not or only briefly mentioned in the included studies [6,29,31], but should also be reflected, especially technical and legal aspects, such as data security [224], data synching, and export. For example, the Germany-based study of Koehler et al [114] was one of the few that detailed data security and transfer of home-based telemonitoring data to the clinic. Data security and privacy are severely governed by the European Union General Data Protection Regulation, which is according to their website the \u201ctoughest privacy and security law in the world\u201d [229]. Administrative limitations and challenges presumably obscure the benefits of wearable research in Europe. A possible solution for data security and usability might be data trusts [230] as an alternative to large platforms.\nMost medical fields represented in the included studies showed similarities with other reviews [224], for example, studies often focusing on cardiology, sports medicine, and neurology. However, we found a multitude of studies from multidisciplinary fields as well as the field of global health, indicating a likely adoption and expansion of wearables in other medical fields. This underlines the potential for wearables in health research beyond a mere trend or hype, as wearables may provide new possibilities for a broad spectrum of health research, such as for infectious disease prediction like COVID-19 or fertility awareness, among many others.\nWearable Characteristics\nSimilar to other reviews, most devices were wrist-worn fitness trackers and accelerometers, and most of them are from the company Fitbit, measuring PA, HR, and sleep [3,27,31,224,225]. These vital signs and device types seem to become the standard in wearable research [3,27,31,224,225]. The included studies also emphasized the growing wearable use [147,195,197], which is also reflected in commercially available devices [38-40]. Currently, further wearable devices emerge, measuring, for example, oximetry, blood pressure, skin temperature, or respiratory rate.\nCategorization of Wearable Application in the Studies\nIn general, the included studies covered a great scope of health applications such as fertility tracking; monitoring of body characteristics such as weight or diseases such as Alzheimer disease, diabetes mellitus, and AF; as well as associations of coffee intake, sleep, and PA, or blood pressure and steps. We have noted an increase in smaller studies that also included rare populations and conditions, such as fibromyalgia or the rare genetic Pompe disease, indicating that wearables may be valuable for insights into patients with rare conditions. Using affordable, consumer-grade wearables for rare disease assessment and monitoring might eventually be less expensive than specifically developed devices and easier to use for patients. Therefore, currently underrepresented populations may be better researched through wearables [231], that is, different ethnic groups, nationalities, individuals with disabilities, or (rare) conditions. Future studies could examine the participation of underrepresented groups in wearable research in greater depth, particularly in studies analyzing wearable user data.\nGlobal Health and Low-Resource Contexts\nIncluded studies are predominantly from high-income countries, constituting a gap in wearable studies in low-resource contexts. The AliveCor device was shown to be feasible in Kenya to help detect AF [232], as well as for early diagnosis. The literature underlines the potential for wearable-based research in low-resource settings to generate data and improve health care [9], based on their low cost and ease of use (data acquisition, hardware, and software handling) [233]. Xu et al [234] emphasized that physiological monitoring with wearables hold \u201cpromise for substantial improvements in neonatal outcomes\u201d in low- and middle-resource countries. Wearables can generate a solid database for global health research, particularly for morbidity measurements [235], large-scale studies, and modeling and descriptive studies. Topics such as climate change\u2013induced impacts focusing on extreme weather events as an outcome and impact on health [236] may be approached. For example, 1 study [20] measured the physiological response of farm workers to climate conditions with wearables to investigate heat-related illness in a high-income setting. Lam et al [237] investigated the thermal adaptation and comfort of participants originating from various climatic regions. The fitness tracker measured HR data was integrated with other weather and human-based measurements and predicted the thermal sensation of nonlocal participants, among others. Similar studies can be conducted in low-resource regions.\nStrengths and Shortcomings of Wearables\nA few studies have experienced issues or shortcomings, such as inaccuracies in measurements and technical issues. Nevertheless, most authors were satisfied with wearables, as strengths were mentioned more frequently than shortcomings. Novelty and innovation outweighed the shortcomings for most authors. The most mentioned positive wearable characteristics were validity and accuracy, technical reliability, innovation, and unobtrusiveness. Only a few authors have mentioned data access through APIs or cloud platforms as a strength. However, the practical value of wearables is heavily reliant on the mode and reliability of data access. Depending on the company, there may be different data access policies in place, whereby it may not be possible to access the raw data of the wearable. Most authors have not considered wearable data access. However, data access and availability of wearable devices is an important aspect that researchers need to be aware of before using a potential study device. Another aspect is open access to the wearables\u2019 raw data or source codes, as companies might change the source code and implement algorithms without the obligation to announce or detail changes that might lead to bias and inconsistency of data [224]. For example, Thijs et al [195] mentioned the consequences of nondisclosed algorithms (Fitbit) for data analysis and standardization. Moreover, the lack of standardization and replicability of wearable raw data and analysis [28] hinders comparability among studies.\nMost studies mentioned and discussed validation, accuracy, and certification of the used wearables as part of good research practice approaches. However, the mention of validation or accuracy did not necessarily imply that the wearables had been certified (FDA or CE) or validated in peer-reviewed research. Nevertheless, the authors reported that the wearable device is sufficiently accurate even with existing inaccuracies [14,143,197]. The authors seemed to tolerate smaller inaccuracies and validation drawbacks\u2014especially of established consumer-grade wearables\u2014if usability was of high importance, such as in large-scale studies.\nLarge-scale and Big Data Sets for Wearable Research\nWe noted an increase in large-scale wearable studies in recent years, which is consistent with previous literature [225]. During the COVID-19 pandemic, there has been an increase in studies using secondary data. Studies aimed at generating insights with regard to the developing pandemic, focusing on forecasting models and their effects on different populations. Overall, wearable-generated big data sets might decrease biased data because measurements are objectively taken in the natural environment of numerous and diverse individuals. Although data analytic skills are needed for handling big data sets, their analysis might be extremely valuable for health research in generating new evidence [31,225].\nLimitations\nFirst, not all studies using wearables might have been identified by our search. We included only the wearables of companies in the search that had the highest market share. Therefore, the wearables of smaller or new companies may be missing in this review. In addition, we only included studies published in English, which may have excluded evidence from other regions that may not publish in English. Although this review provides a wide scope of wearable research, the list of included studies is by no means exhaustive.\nIn addition, wearable costs are only approximations and could be imprecise: (1) companies follow different sales and distribution models, for example membership, rental, and subscription; (2) we only incorporated wearable (hardware) prices, excluding costs for software, maintenance, and other charges such as subscription fees, which may even exceed wearable hardware costs;and(3) sales prices are subject to fluctuation. We also excluded many studies as wearables were discontinued. The fluctuant and unstable market, therefore, might also be a factor in decisions regarding the use of wearables [28]. Although interesting and promising, some wearables and similar devices were beyond the scope of this study but might also be valuable for health research. We have provided a wide overview of wearable devices; however, the included studies did not show the full range of possible wearables and measured vital signs [9,37].\nIn addition, we report the opinions of the included studies with regard to the shortcomings and strengths of wearables. Although these insights might be helpful, they are not objective measures. Moreover, our introduced categories for studies and aims to use wearables might overlap, as separation and categorization are artificial.\nConclusions\nWe see a growing uptake of wearables in health research and a trend to use wearables for large-scale, population-based studies. Wearables, which were often piloted in the included studies, were used in diverse health fields including COVID-19 prediction, fertility awareness, geriatrics, AF detection, evaluation of methods, drug effects, psychological interventions, and patient-reported outcomes. Measurement of steps, PA, HR, and sleep may be considered standard wearable measurements. Nevertheless, wearables are becoming more diverse in their measurements and appearance. Therefore, wearable-induced research may include currently underrepresented populations such as the older adults, participants who are disabled, participants with rare chronic or genetic diseases, participants from low socioeconomic backgrounds, and others.\nFor many researchers, novelty and innovation seem to outweigh shortcomings such as measurement inaccuracies. Overall, the included studies shared key characteristics that the wearables should meet: validity, technical reliability (including data access solutions), innovation, and unobtrusiveness.\nWe identified a lack of wearable research in low-resource settings. We assume that the reasons for the gap may be a lack of funding and doubts about the usefulness of the wearables. However, wearable devices may be used to generate data in such settings, which may otherwise be difficult and expensive to obtain. Therefore, wearable devices may be valuable for health research in a global context. During the COVID-19-pandemic in particular, large-sized wearable studies were used to generate insights into the developing pandemic and may potentially lead to novel insights into population health trends and forecasts. Future research is needed to determine the usability of wearable devices for underrepresented populations, as well as the feasibility and usefulness of health research in low-resource contexts.\nAcknowledgments\nWe wish to thank the German Research Foundation (Deutsche Forschungsgemeinschaft) for supporting this study as part of a Deutsche Forschungsgemeinschaft\u2013funded research unit (Forschungsgruppe). We acknowledge the support of Else Kr\u00f6ner-Fresenius-Stiftung from the Heidelberg Graduate School of Global Health. Funders did not have a role in the design, data collection and analysis, decision to publish, or preparation of the manuscript.\nAbbreviations\nPreferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews\nPRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.\nCategorization of wearable applications in the studies: article references and examples.\nFootnotes\nAuthors' Contributions: SH, SB, and MA conceived and designed the study. SH drafted the manuscript with the help of SB and MA. All authors contributed to the critical revision of the draft and approved the final version of the manuscript.\nReferences\nAssociated Data\nThis section collects any data citations, data availability statements, or supplementary materials included in this article.\nSupplementary Materials\nPRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.\nCategorization of wearable applications in the studies: article references and examples.\nArticles from JMIR mHealth and uHealth are provided here courtesy ofJMIR Publications Inc.\nACTIONS\nPERMALINK\nRESOURCES\nSimilar articles\nCited by other articles\nLinks to NCBI Databases\nCite\nAdd to Collections", "cleaned_text_custom_parser_2": "\n\n \n \n Skip to main content\n \n

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\n \n . 2022 Jan 25;10(1):e34384. doi:\n \n 10.2196/34384\n \n

\n \n

\n The Impact of Wearable Technologies in Health Research: Scoping Review\n

\n \n

\n \n Sophie Huhn\n \n

\n Sophie Huhn\n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

\n Find articles by\n \n Sophie Huhn\n \n

\n ^{\n 1,\n}\n ^{\n \u2709\n}\n ,\n \n Miriam Axt\n \n

\n Miriam Axt\n ,\n BA\n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

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\n ^{\n 1\n}\n ,\n \n Hanns-Christian Gunga\n \n

\n Hanns-Christian Gunga\n ,\n Prof Dr\n

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\n ^{\n 2\n}\n ,\n \n Martina Anna Maggioni\n \n

\n Martina Anna Maggioni\n ,\n PD, PhD\n

\n ^{\n 2\n}\n Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany\n

\n ^{\n 3\n}\n Department of Biomedical Sciences for Health, Universit\u00e0 degli Studi di Milano, Milano, Italy\n

\n Find articles by\n \n Martina Anna Maggioni\n \n

\n ^{\n 2,\n}\n ^{\n 3\n}\n ,\n \n Stephen Munga\n \n

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\n ^{\n 4\n}\n Kenya Medical Research Institute, Kisumu, Kenya\n

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\n Ali Si\u00e9\n ,\n PhD\n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

\n ^{\n 5\n}\n Centre de Recherche en Sant\u00e9 Nouna, Nouna, Burkina Faso\n

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\n ^{\n 1,\n}\n ^{\n 5\n}\n ,\n \n Valentin Boudo\n \n

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\n ^{\n 5\n}\n ,\n \n Aditi Bunker\n \n

\n Aditi Bunker\n ,\n Dr sc hum\n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

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\n ^{\n 1\n}\n ,\n \n Rainer Sauerborn\n \n

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\n ^{\n 1\n}\n ,\n \n Till B\u00e4rnighausen\n \n

\n Till B\u00e4rnighausen\n ,\n Prof Dr\n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

\n ^{\n 6\n}\n Harvard Center for Population and Development Studies, Cambridge, MA, United States\n

\n ^{\n 7\n}\n Africa Health Research Institute, KwaZulu-Natal, South Africa\n

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\n ^{\n 1,\n}\n ^{\n 6,\n}\n ^{\n 7\n}\n ,\n \n Sandra Barteit\n \n

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\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

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\n Editor:\n Amaryllis Mavragani\n

\n Reviewed by:\n Hui Zhang\n ,\n Pierre Escourrou\n ,\n Andr\u00e9 Henriksen\n

\n \n
\n \n
\n \n

\n ^{\n 1\n}\n Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany\n

\n ^{\n 2\n}\n Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany\n

\n ^{\n 3\n}\n Department of Biomedical Sciences for Health, Universit\u00e0 degli Studi di Milano, Milano, Italy\n

\n ^{\n 4\n}\n Kenya Medical Research Institute, Kisumu, Kenya\n

\n ^{\n 5\n}\n Centre de Recherche en Sant\u00e9 Nouna, Nouna, Burkina Faso\n

\n ^{\n 6\n}\n Harvard Center for Population and Development Studies, Cambridge, MA, United States\n

\n ^{\n 7\n}\n Africa Health Research Institute, KwaZulu-Natal, South Africa\n

\n ^{\n \u2709\n}\n

\n Corresponding Author: Sophie Huhn\n sophie.huhn@uni-heidelberg.de\n

\n ^{\n \u2709\n}\n

\n Corresponding author.\n

\n Received 2021 Oct 20; Revision requested 2021 Nov 12; Revised 2021 Nov 23; Accepted 2021 Dec 17; Collection date 2022 Jan.\n

\n \u00a9Sophie Huhn, Miriam Axt, Hanns-Christian Gunga, Martina Anna Maggioni, Stephen Munga, David Obor, Ali Si\u00e9, Valentin Boudo, Aditi Bunker, Rainer Sauerborn, Till B\u00e4rnighausen, Sandra Barteit. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 25.01.2022.\n

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\n \n PMC Copyright notice\n \n

\n PMCID: PMC8826148\u00a0\u00a0PMID:\n \n 35076409\n \n

\n Abstract\n

\n Background\n

\n Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.\n

\n Objective\n

\n In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.\n

\n Methods\n

\n We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >\u20ac500 (US $570), or obtrusive smart clothing.\n

\n Results\n

\n We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <\u20ac200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations\u2014wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).\n

\n Conclusions\n

\n Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.\n

\n \n Keywords:\n \n wearable, consumer-grade wearables, commercially available wearables, public health, global health, population health, fitness trackers, big data, low-resource setting, tracker, review, mHealth, research, mobile phone\n

\n Introduction\n

\n Background\n

\n Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen considerably in the last few years [\n \n 1\n \n -\n \n 3\n \n ].\n

\n Noninvasive, consumer-grade wearables (hereafter\n \n wearables\n \n ) may provide manifold advantages for health research; they are generally unobtrusive, less expensive than gold standard research devices [\n \n 4\n \n ], comfortable to wear [\n \n 5\n \n ], and affordable for consumers [\n \n 6\n \n ]. In recent years, the quality and accuracy of wearables have improved [\n \n 7\n \n ,\n \n 8\n \n ], resulting in more clinically approved certifications [\n \n 9\n \n ]. Wearables can measure long-term data in the naturalistic environment of study participants, allowing for ecologic momentary assessments [\n \n 10\n \n ,\n \n 11\n \n ]. Therefore, wearables are valuable developments, particularly for generating data for health research in large study populations, that is, global health or epidemiological studies, or in low-income contexts [\n \n 6\n \n ,\n \n 9\n \n ,\n \n 12\n \n ].\n

\n One example of a large study is the so-called\n \n Datenspende\n \n study by the Robert Koch Institute, the German research institute for disease control and prevention, which aims to tackle the COVID-19 (corona virus disease) pandemic with anonymous data donations acquired through wearables [\n \n 13\n \n ]. On the basis of the study by Radin et al [\n \n 14\n \n ], researchers used wearable data to calculate the regional probability of COVID-19 outbreaks incorporating data on pulse, physical activity (PA), and sleep, as well as weather data. Using a large sample size exceeding half a million participants, they forecasted the number of COVID-19 infections for the preceding 4 days. The Apple Heart Study [\n \n 15\n \n ] is another example that was a breakthrough for showing that wearable devices may detect atrial fibrillation (AF) and foster a discussion of potentials and limitations with regard to health care providers, researchers, and members of the media and economy [\n \n 16\n \n ,\n \n 17\n \n ].\n

\n Apart from these 2 examples, wearables are applied in diverse fields of health, including acoustic, gastrointestinal sensors for ileus prediction [\n \n 18\n \n ]; UV sun exposure [\n \n 19\n \n ]; heat-related illness measurements [\n \n 20\n \n ]\n \n ;\n \n electrolyte monitoring, for example, for cystic fibrosis or training management [\n \n 21\n \n ,\n \n 22\n \n ]; early warning of AF with a wearable ring [\n \n 23\n \n ]; generation of electrocardiograms (ECGs) [\n \n 15\n \n ]; measurement of cardiopulmonary resuscitation quality [\n \n 24\n \n ]; measurement of continuous noninvasive blood glucose [\n \n 25\n \n ], as well as smart inhalers and activity trackers for asthma monitoring [\n \n 26\n \n ].\n

\n Numerous reviews and studies have investigated validation and accuracy, particularly for specific affordable wearables, comparing these to the gold standard measurements [\n \n 21\n \n ] or comparing evidence in a meta-analysis [\n \n 8\n \n ]. Many studies have focused on novel technologies, presenting prototypes, or investigating the feasibility and acceptance of a wearable device in a specific setting [\n \n 3\n \n ,\n \n 27\n \n ]. Similarly, reviews on the application and potential of wearables have focused on (1) specific wearable devices or specific wearable measurements, for example, only smartwatches [\n \n 4\n \n ] or only sleep measurements [\n \n 28\n \n ] or (2) applications of specific medical fields and interventions, for example, only for diagnosis and treatment in cardiological conditions [\n \n 29\n \n ] or wearables as an intervention to promote PA in patients with oncologic conditions [\n \n 30\n \n ]. Among these publications, we identified a lack of aggregated insight for wearable use in health research and its respective strengths and shortcomings.\n

\n Objectives\n

\n With this scoping review, we aim to overview and categorize the current research conducted on wearable devices.\n

\n Methods\n

\n Overview\n

\n We conducted a scoping review to explore the applications of affordable wearables worn on wrists, arms, chests, or waists, which constitute the characteristic locations [\n \n 31\n \n ]. We focused on the following aspects: (1) demographics; (2) wearable devices and measured vital signs; (3) wearable data and its analysis; (4) reported shortcomings and strengths of wearables; and (5) study aims, results, and types of wearable use. We present our findings in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting standard and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews;\n \n Multimedia Appendix 1\n \n ) [\n \n 32\n \n ] and the methodological framework of Arksey and O\u2019Malley [\n \n 33\n \n ] and Peters et al [\n \n 34\n \n ]. A scoping review seemed most appropriate given the broad nature of this subject and the range of potential implementations in the setting of health research.\n

\n Eligibility Criteria\n

\n We sought to define and characterize the state of affordable wearables for health research. Eligible publications were peer reviewed, published in English, and published after 2013 (after wearables became widely commercially available [\n \n 1\n \n -\n \n 3\n \n ]) and had a full-text version available (in instances no full text was available, authors were contacted 3 times with a waiting period of 7 days between each contact before exclusion).\n

\n Our review scopes the current information available on affordable, noninvasive wearables, which are (1) worn on the wrist, arm, and chest; (2) measure vital signs; and (3) analyze the generated wearable data for outcome assessment. Validation and qualitative studies were excluded. We focused only on devices that cost <\u20ac500 (US $570) per device (1) to allow the affordability of larger studies, for example, where wearable devices need to be provided to study participants via the study and (2) to ensure that wearables are available commercially and (3) intended for consumers. As the definition of vital signs is not distinct [\n \n 35\n \n ], we included the following vital signs [\n \n 9\n \n ,\n \n 36\n \n ,\n \n 37\n \n ]: HR, HR variability, ECG measurements or heart rhythm analysis (detection of arrhythmias), blood pressure, blood oxygen, respiratory rate, body temperature, sleep, electrodermal activity, electromyogram measurements, and PA (\n \n Textbox 1\n \n ).\n

\n Inclusion and exclusion criteria.\n

\n \n Inclusion criteria\n \n

\n
\n Publications\n
\n
- \n
  \n Full text available\n
  \n
- \n
  \n English language\n
  \n
- \n
  \n Peer-reviewed articles\n
  \n
- \n
  \n Published between 2013 and 2020\n
  \n
\n
\n
\n Wearable device\n
\n
- \n
  \n Commercially available wearable, price <\u20ac500 (US $570) per device (Only hardware prices were considered. Software, subscriptions, or similar, which might be necessary for device use, were not included. All prices were captured in the timeframe of this study and therefore are only considered as approximations)\n
  \n
- \n
  \n Wearables worn on the arm, wrist, chest, and waist\n
  \n
\n
\n
\n Outcomes\n
\n
- \n
  \n Measuring and analyzing one or more vital sign\n
  \n
- \n
  \n Range of vital signs as defined in this review, including heart rate, heart rate variability, electrocardiogram measurements or heart rhythm analysis (detection and classification of atrial fibrillation, extrasystoles, and other arrhythmic events), blood pressure, blood oxygen, respiratory rate, body temperature, sleep (time, deepness, etc), electrodermal activity, electromyogram measurements, physical activity (steps, distance covered, intensity, energy expenditure, etc; physical activity included as basic measurements of wearables or very similar or related parameters) [\n \n 9\n \n ,\n \n 36\n \n ,\n \n 37\n \n ].\n
  \n
\n

\n \n Exclusion criteria\n \n

\n
\n Publications\n
\n
- \n
  \n Studies not analyzing wearable-generated data for (health) outcome assessment, including studies focusing on (1) accuracy, validation, improvement (algorithms and software); (2) patents; (3) smart clothing; (4) obtrusive wearables (the device comprises obstructive parts or wires, etc); (5) behavior change intervention studies (ie, where the wearable is provided as promotion for more physical activity only and not for health outcome assessment); (6) qualitative studies; or (7) studies with research objectives and outcomes not related to health or a medical condition\n
  \n
\n
\n
\n Wearable device\n
\n
- \n
  \n Wearable not commercially available (eg, prototype and discontinued)\n
  \n
- \n
  \n Invasive, obtrusive device (comprising obstructive parts or wires, etc)\n
  \n
- \n
  \n Prosthesis, smart clothing (sensors in clothing)\n
  \n
\n
\n
\n Outcomes\n
\n
- \n
  \n Not measuring vital sign, that is, gait, posture, and motion recognition analysis (eg, gesture recognition for sign language)\n
  \n
- \n
  \n Studies with research objectives and outcomes not related to health or a medical condition\n
  \n
\n

\n Information Sources and Search\n

\n We used PubMed, Ovid, Web of Science, and CINAHL to search peer-reviewed literature using a search string based on the following three concepts: synonyms and medical subject headings terms, including (1) wearables (synonyms, top 15 vendors with most market shares [\n \n 38\n \n -\n \n 40\n \n ], or frequently used in research [\n \n 2\n \n ,\n \n 7\n \n ]), (2) physical wear location of wearables (torso, arm, and wrist), and (3) measurement of vital signs (for full search string see\n \n Multimedia Appendix 2\n \n [\n \n 41\n \n ]). We manually searched the reference lists for relevant articles.\n

\n We imported the identified articles into the literature reference management system Zotero [\n \n 42\n \n ] and then into the systematic review management platform Covidence [\n \n 41\n \n ]. Literature was screened by 2 independent reviewers. Any disagreements were resolved by discussion between the 2 reviewers (SH and MA) and a third researcher (SB).\n

\n Quality Assessment\n

\n To assess the quality of the included studies and their various study designs (credibility), we considered the Medical Education Research Study Quality Instrument [\n \n 43\n \n ] score as adequate (\n \n Multimedia Appendix 3\n \n [\n \n 14\n \n ,\n \n 15\n \n ,\n \n 20\n \n ,\n \n 44\n \n -\n \n 219\n \n ]).\n

\n Data Synthesis\n

\n We conducted data synthesis in accordance with Arksey and O\u2019Malley [\n \n 33\n \n ], comprising the analytic framework, analysis of the extent and nature of studies, and thematic analysis. We categorized the findings by title, author, year, country of study, objectives of study, study population, sample size, methods, intervention type, outcomes, and key findings related to the scoping review question [\n \n 34\n \n ]. We extracted mutually exclusive groups, including wearable manufacturers, built-in sensors, scope of measurements (vital signs), shortcomings and strengths of wearables mentioned by the authors, the used methods for data analysis, and medical fields.\n

\n Results\n

\n Overview\n

\n Our initial search yielded 7499 hits (PubMed: 2514; Ovid: 1905; Web of Science: 1440; CINAHL: 1640) and we identified 121 publications by manual search. Of 7620 total publications, we screened 4525 (59.38%) nonduplicates for title and abstract, leading to the assessment of 660 full-texts. After full-text screening of the 660 articles, we included 179 (27.1%) studies in our review [\n \n 14\n \n ,\n \n 15\n \n ,\n \n 20\n \n ,\n \n 44\n \n -\n \n 219\n \n ] (\n \n Figure 1\n \n ).\n

\n Figure 1.\n

\n \n Open in a new tab\n \n

\n PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [\n \n 220\n \n ].\n

\n \n

\n Study Characteristics\n

\n Demographics\n

\n Between 2013 and 2020, we observed an increase in the number of studies and study participants (\n \n Figure 2\n \n and\n \n Table 1\n \n ). The year 2019 featured the largest sample size, and studies were predominantly conducted in North America (\n \n Figure 3\n \n [\n \n 221\n \n ]). The largest study we identified was conducted in 2019 in North America and included over 8 million participants (75.71%) [\n \n 153\n \n ]; the second largest was a European study comprising 742,000 participants (6.85%) [\n \n 162\n \n ]. Without the aforementioned, largest study, Europe and Asia would lead in participant numbers and we would see a continuous increase in participant numbers from 2013 to 2020.\n

\n Figure 2.\n

\n \n Open in a new tab\n \n

\n Number of studies and study participants (logarithmic scale) per year of study publication. The sizes of the circles visualize the overlapping and number of studies within the year.\n

\n \n

\n Table 1.\n

\n Characteristics of studies.\n

\n Study characteristics\n	\n Studies (N=179), n (%)\n	\n Participants (N=10,835,733), n, (%)\n
\n \n Year of publication\n \n
\n 2013\n	\n 1 (0.56)\n	\n 146 (<0.01)\n
\n 2014\n	\n 3 (1.68)\n	\n 165 (<0.01)\n
\n 2015\n	\n 2 (1.12)\n	\n 3284 (0.03)\n
\n 2016\n	\n 14 (7.82)\n	\n 124,060 (1.14)\n
\n 2017\n	\n 21 (11.73)\n	\n 27,377 (0.25)\n
\n 2018\n	\n 34 (18.99)\n	\n 16,700 (0.15)\n
\n 2019\n	\n 48 (26.82)\n	\n 9,016,909 (83.21)\n
\n 2020\n	\n 56 (31.28)\n	\n 1,647,092 (15.2)\n
\n \n Continents\n \n
\n North America\n	\n 94 (52.51)\n	\n 8,916,888 (82.29)\n
\n Europe\n	\n 50 (27.93)\n	\n 991,357 (9.15)\n
\n Asia\n	\n 24 (13.41)\n	\n 925,768 (8.54)\n
\n Australia\n	\n 8 (4.47)\n	\n 1198 (0.01)\n
\n South America\n	\n 3 (1.68)\n	\n 522 (<0.01)\n
\n \n Study objectives\n \n
\n Correlations and influencing factors of study population and outcome data\n ^{\n a\n}\n	\n 70 (39.11)\n	\n 394,296 (3.64)\n
\n Population and patient characterization\n ^{\n b\n}\n	\n 54 (30.17)\n	\n 8,315,559 (76.74)\n
\n Evaluation of method or intervention\n	\n 47 (26.26)\n	\n 2,124,328 (19.6)\n
\n Prognostic evaluation\n ^{\n c\n}\n	\n 8 (4.5)\n	\n 1550 (0.01)\n
\n \n Study design\n \n
\n Cross-sectional study\n	\n 66 (36.87)\n	\n 9,780,808 (90.26)\n
\n Cohort study\n	\n 62 (34.64)\n	\n 628,641 (5.8)\n
\n Nonrandomized experimental study\n	\n 14 (7.82)\n	\n 724 (0.01)\n
\n Randomized controlled trial\n	\n 11 (6.15)\n	\n 2332 (0.02)\n
\n Method evaluation\n	\n 8 (4.47)\n	\n 314,247 (2.9)\n
\n Other\n	\n 7 (3.91)\n	\n 108,462 (1)\n
\n Case control study\n	\n 7 (3.91)\n	\n 348 (<0.01)\n
\n Mixed methods, feasibility study\n	\n 4 (2.23)\n	\n 171 (<0.01)\n
\n \n Medical field of study\n \n
\n Multidisciplinary and general medicine\n	\n 43 (24.02)\n	\n 107,148 (0.99)\n
\n Neurology and psychiatry\n	\n 29 (16.2)\n	\n 2630 (0.02)\n
\n Cardiology, fitness, and sports medicine\n	\n 28 (15.64)\n	\n 557,120 (5.14)\n
\n Global health, epidemiology, and prevention\n	\n 19 (10.61)\n	\n 10,104,217 (93.25)\n
\n Gynecology and pediatrics\n	\n 18 (10.06)\n	\n 5575 (0.05)\n
\n Orthopedics and surgery\n	\n 16 (8.94)\n	\n 2749 (0.03)\n
\n Pulmonology\n	\n 13 (7.26)\n	\n 1326 (0.01)\n
\n Other\n	\n 13 (7.26)\n	\n 54,968 (0.51)\n

\n \n Open in a new tab\n \n

\n ^{\n a\n}\n Studies aimed to find associations, correlations, or influencing factors within their study population, study outcomes, and generated data.\n

\n ^{\n b\n}\n Studies aimed to observe and characterize the study population and patients.\n

\n ^{\n c\n}\n Studies aimed to evaluate patient-reported outcomes, health care practices, diagnostics, screenings, and others.\n

\n Figure 3.\n

\n \n Open in a new tab\n \n

\n Included studies per continent. The colors of the continents visualize the number of included studies published on the respective continent (created with Mapchart [\n \n 221\n \n ]).\n

\n \n

\n Study Types and Fields\n

\n Most studies (128/179, 71.5%) used observational study designs such as cross-sectional (66/179, 36.9%) and cohort studies (62/179, 34.6%), comprising 9,780,808 (90.26%) participants and 628,641 (5.8%) participants, out of 10,835,733 participants, respectively. Most frequently, studies (70/179, 39.1%) aimed to find associations, correlations, or influencing factors within their study population, study outcomes, and generated data. Slightly less than one-third of the studies (54/179, 30.2%) aimed to characterize and observe their study population.\n

\n Most studies were conducted in the fields of multidisciplinary and general medicine (43/179, 24%); cardiology, fitness, and sports medicine (29/179, 16.2%); and neurology, psychology, and psychiatry (28/179, 15.6%;\n \n Figure 4\n \n ). The fields of global health, prevention, and epidemiology featured the largest sample size with, with 10,104,217 (93.25%) out of 10,835,733 participants.\n

\n Figure 4.\n

\n \n Open in a new tab\n \n

\n Studies per medical field.\n

\n \n

\n Wearable Characteristics\n

\n A total of 189 wearable devices were extracted. The company with the most wearable devices in the included studies was Fitbit (97/189, 51.3%), covering 8,361,035 (74.35%) out of 11,224,872 participants. Fitbit is followed by ActiGraph (research-grade wearable devices unavailable for consumers or not consumer grade per se; 19/189, 10.1%), Polar Electro (9/189, 4.8%), and Withings (8/189, 4.2%). In number of study participants, Huawei and Withings comprised 832,036 (7.4%) participants and 794,174 (7.06%) participants out of 11,224,872 participants, respectively (\n \n Table 2\n \n ).\n

\n Table 2.\n

\n Characteristics of wearable devices.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

\n Wearable characteristics\n	\n Studies (N=189), n (%)\n	\n Participants (N=11,244,872), n (%)\n
\n \n Wearable companies used in studies\n \n
\n Fitbit\n	\n 97 (51.32)\n	\n 8,361,035 (74.35)\n
\n ActiGraph\n ^{\n a\n}\n	\n 19 (10.05)\n	\n 2571 (0.02)\n
\n Polar electro\n	\n 9 (4.76)\n	\n 6970 (0.06)\n
\n Withings\n	\n 8 (4.23)\n	\n 794,174 (7.06)\n
\n iRhythm\n	\n 6 (3.17)\n	\n 128,641 (1.14)\n
\n Xiaomi\n	\n 5 (2.65)\n	\n 176 (<0.01)\n
\n Axivity\n ^{\n a\n}\n	\n 4 (2.12)\n	\n 291,871 (2.6)\n
\n Garmin\n	\n 4 (2.12)\n	\n 308 (<0.01)\n
\n Apple\n	\n 4 (2.12)\n	\n 420,826 (3.74)\n
\n Activinsights\n ^{\n a\n}\n	\n 3 (1.59)\n	\n 1971 (0.02)\n
\n Samsung\n	\n 2 (1.06)\n	\n 120 (<0.01)\n
\n Ava AG\n	\n 2 (1.06)\n	\n 285 (<0.01)\n
\n Huawei\n	\n 2 (1.06)\n	\n 832,036 (7.40)\n
\n Whoop\n	\n 2 (1.06)\n	\n 305 (<0.01)\n
\n Omron\n	\n 2 (1.06)\n	\n 159 (<0.01)\n
\n Other companies (wearable only included in 1 study)\n	\n 20 (10.58)\n	\n 423,424 (3.77)\n
\n \n Number of wearable device models per study (n=179)\n \n
\n 1\n	\n 156 (87.15)\n	\n 486,684 (4.49)\n
\n 2\n	\n 11 (6.15)\n	\n 420,007 (3.88)\n
\n 3\n	\n 3 (1.68)\n	\n 838,266 (7.74)\n
\n >3 or not applicable\n ^{\n b\n}\n	\n 9 (5.03)\n	\n 9,090,776 (83.9)\n
\n \n Wearable device types\n \n
\n Fitness tracker\n	\n 86 (45.5)\n	\n 22,823 (0.2)\n
\n Accelerometer (worn on wrist, torso, and hip)\n	\n 49 (25.93)\n	\n 299,251 (2.66)\n
\n Electrocardiogram chest patch or strap\n	\n 21 (11.11)\n	\n 530,332 (4.72)\n
\n Smartwatch\n	\n 12 (6.35)\n	\n 1,259,605 (11.2)\n
\n Diverse wearable devices\u2014secondary data via wearable data platform\n	\n 11 (5.82)\n	\n 9,122,758 (81.13)\n
\n Distinct vital sign trackers (eg, oximetry ring, temperature wristband tracker, and blood pressure armband)\n ^{\n c\n}\n	\n 10 (5.29)\n	\n 10,103 (0.09)\n
\n \n Physical location of wearable\n \n
\n Wrist\n	\n 138 (73.02)\n	\n 10,702,843 (95.18)\n
\n Hip\n	\n 25 (13.23)\n	\n 2257 (0.02)\n
\n Chest\n	\n 21 (11.11)\n	\n 550,332 (4.89)\n
\n Arm\n	\n 3 (1.59)\n	\n 9392 (0.08)\n
\n Finger\n	\n 2 (1.06)\n	\n 48 (<0.01)\n
\n \n In studies used in-built sensor in wearables\n ^{\n d\n}\n \n \n (n=179)\n \n
\n Accelerometer\n	\n 146 (81.56)\n	\n 1,157,069 (10.68)\n
\n Photoplethysmography\n	\n 59 (32.96)\n	\n 9,622,147 (88.8)\n
\n Electrodes (ie, electrocardiogram)\n	\n 21 (11.73)\n	\n 550,500 (5.08)\n
\n Gyroscope\n	\n 6 (3.35)\n	\n 1585 (0.01)\n
\n Thermometer\n	\n 4 (2.23)\n	\n 842 (0.01)\n
\n Blood pressure sensor\n	\n 3 (1.68)\n	\n 9397 (0.09)\n
\n \n Wearable costs (\u20ac; US $)\n \n
\n <200 (228)\n	\n 120 (63.49)\n	\n 340,460 (3.03)\n
\n 200-350 (228-399)\n	\n 41 (21.69)\n	\n 18,256 (0.16)\n
\n >350 (399)\n	\n 13 (6.88)\n	\n 551,128 (4.9)\n
\n Not applicable\n ^{\n e\n}\n	\n 15 (7.94)\n	\n 10,355,028 (92.09)\n
\n \n Analysis\u2014statistical tests\n ^{\n f\n}\n \n \n in studies (n=179)\n \n
\n Regression\n	\n 62 (34.64)\n	\n 1,021,032 (9.42)\n
\n \n t\n \n test\n	\n 41 (22.91)\n	\n 8,309,202 (76.68)\n
\n Correlation (Pearson, Spearman, etc)\n	\n 40 (22.35)\n	\n 11,044 (0.1)\n
\n Wilcoxon\n \n U\n \n , Mann\u2013Whitney\n \n U\n \n , and other nonparametric tests\n	\n 23 (12.85)\n	\n 7180 (0.07)\n
\n Chi-square and Fisher\u2013Yates tests\n	\n 15 (8.38)\n	\n 433,785 (4)\n
\n Mixed methods model and other statistical models\n	\n 14 (7.82)\n	\n 57,938 (0.53)\n
\n Artificial Intelligence (data mining, cluster, machine learning, etc)\n	\n 11 (6.15)\n	\n 835,967 (7.71)\n
\n Analysis of variance\n	\n 11 (6.15)\n	\n 810 (0.01)\n
\n Descriptive\n	\n 8 (4.47)\n	\n 423,093 (3.9)\n
\n Prognostic analysis (Kaplan\u2013Meier, permutation test, etc)\n	\n 3 (1.67)\n	\n 420,928 (3.88)\n

\n \n Open in a new tab\n \n

\n ^{\n a\n}\n Research-grade wearable devices unavailable for consumers or not consumer grade per se.\n

\n ^{\n b\n}\n Studies collected data with multiple wearable devices (that belonged to the study participants) or studies that used secondary data provided by web-based wearable platforms, mobile applications, or wearable companies.\n

\n ^{\n c\n}\n Distinct vital sign trackers are specialized on a specific vital sign, for example, oximetry ring, temperature wristband tracker, and blood pressure armband. They differ in measured vital signs and worn locations compared with other wearable device types.\n

\n ^{\n d\n}\n Utilized in-built sensors in wearables sums up to more than the total of wearables, as sometimes more than one built-in sensor was used.\n

\n ^{\n e\n}\n Providing wearable hardware pricing was not transparent, as some studies used data provided by diverse participant-owned wearables or wearable hardware costs were part of a subscription or a membership fee, that is, Whoop strap of Whoop.\n

\n ^{\n f\n}\n Analysis\u2014statistical tests sums up to more than the total number of included studies, as some studies applied more than one type of analysis or statistical test.\n

\n Most studies (156/179, 87.2%) used 1 wearable model. However, most of the study participants (9,090,776/10,835,733, 83.9%) were part of large-scale population-based studies in which data were mostly collected with multiple wearable devices that belonged to the study participants.\n

\n Some large-scale population-based studies (11/179, 6.1%) relied on secondary data collected with mobile apps [\n \n 87\n \n ] or web-based wearable platforms [\n \n 153\n \n ] or provided through a wearable company [\n \n 189\n \n ]. Thus, the device type could not be specified (assigned to category\n \n diverse wearable devices\u2014secondary data via wearable data platform\n \n ). A total of 15 (63%) out of 24 studies that used secondary data were conducted in 2020, and 5 (21%) studies in 2019.\n

\n Fitness trackers (86/189, 45.5%) and accelerometers (measuring body movement acceleration [\n \n 37\n \n ]) worn on the wrist, torso, and hip (49/189, 25.9%) were the most frequent. Other wearable device types included ECG chest straps and patches (21/189, 11.1%), smartwatches (12/189, 6.3%), and distinct vital sign trackers (10/189, 5.3%) such as oximetry rings or blood pressure armbands (\n \n Table 2\n \n ).\n

\n Most wearables were worn on the wrist (138/189, 73%), followed by the hip (25/189, 13.2%) and chest (21/189, 11.1%). Only a few wearables were worn on the arm (3/189, 1.6%) and finger (2/189, 1.1%;\n \n Figure 5\n \n ).\n

\n Figure 5.\n

\n \n Open in a new tab\n \n

\n Wear locations of wearables and their frequencies. The color and size of the circles assigned to the body location visualize the frequency of wearables worn on the respective location.\n

\n \n

\n Most of the studies used wearable built-in sensors of (1) accelerometers (146/179, 81.6%) that measure acceleration on a 3- or 1-axis [\n \n 37\n \n ] and (2) photoplethysmography (59/179, 33%) defined as an \u201coptical technique that [...] detects blood volume changes in the microvascular bed of tissue\u201d [\n \n 222\n \n ]. Other built-in sensors were electrodes for ECG measurements (21/179, 11.7%); gyroscopes (6/179, 3.4%), which determine how different portions of the body rotate [\n \n 37\n \n ]; thermometers (4/179, 2.2%) measuring skin temperature; and blood pressure sensors (3/179, 1.7%).\n

\n Most studies investigated steps (95/179, 53.1%), HR (55/179, 30.7%), and sleep time (51/179, 28.5%). We classified measured vital signs into three categories, whereby PA measures were most frequent (228/179, 127.4%;\n \n Multimedia Appendix 4\n \n [\n \n 14\n \n ,\n \n 15\n \n ,\n \n 20\n \n ,\n \n 44\n \n -\n \n 219\n \n ]):\n

\n
\n PA measures included steps, intensity (eg, time spent in moderate to vigorous PA), energy expenditure (eg, kilocalories and metabolic equivalent), axial or raw movement data, distance (covered), and others (such as stairs taken, elevation, and sedentary time).\n
\n
\n
\n Cardiac measures included HR, HR variability, and ECG (or other direct heart rhythm analyses, such as AF detection).\n
\n
\n
\n Other measures that included blood or pulse pressure, body temperature, blood oxygen, and respiratory rate.\n
\n

\n Most studies (120/189, 63.5%) used wearables that cost <\u20ac200 (US $228). In some studies (15/189, 7.9%), wearable prices were not transparent, as data were provided through a variety of participant-owned wearables [\n \n 87\n \n ] or the wearable hardware was part of a subscription or a membership fee, that is, Whoop strap of Whoop [\n \n 178\n \n ].\n

\n Regression analysis (62/179, 34.6%) and\n \n t\n \n tests (42/179, 22.9%) were the most commonly used statistical methods to analyze wearable data. Other methods comprised nonparametric tests, such as correlations, Wilcoxon U test, Kaplan-Meier survival analysis, and chi-square tests. Variance analysis (analysis of variance) and significance tests such as permutations were also used. Further data analyses were conducted in a data-driven manner [\n \n 223\n \n ] with artificial intelligence, such as k-means [\n \n 176\n \n ] or unsupervised cluster analysis [\n \n 172\n \n ], recursive feature elimination technique [\n \n 170\n \n ], rotation random forest classifier [\n \n 130\n \n ], and supervised machine learning algorithms using logistic regression, decision tree, and random forest [\n \n 215\n \n ].\n

\n Categorization of Wearable Application in the Studies\n

\n We categorized the included studies based on their study objective, the role of the wearable and the collected wearable data within the study in the following 6 categories (overlaps are possible as separation is artificial). In the following, categories are presented in order of their frequency (see\n \n Figure 6\n \n and\n \n Multimedia Appendix 5\n \n [\n \n 14\n \n ,\n \n 15\n \n ,\n \n 20\n \n ,\n \n 44\n \n -\n \n 219\n \n ] for article references and examples).\n

\n Figure 6.\n

\n \n Open in a new tab\n \n

\n Categorization of wearable applications, showing proportions of the 6 categories (with 4 subcategories). The size of depicted categories (in different colors) corresponds to the number of studies.\n

\n \n

\n Correlations\u2014Wearable and Other Physiological Data\n

\n Studies (40/179, 22.3%) have examined the correlation of a wearable derived measure with clinical- and patient-reported and other health-related outcomes to find new associations and correlations. The data generated by the wearable device were correlated with data from mostly physiological or patient-reported outcomes.\n

\n Population-Based Research\n

\n In 17.3% (31/179) of studies, wearables produced insights into a specific population through monitoring (observational and cross-sectional) of vital signs, such as steps and HR. Often, these were cross-sectional studies (17/31, 55%) where the wearable measurement was the sole outcome. The resulting data provide novel insights and characteristics of populations.\n

\n Outcome Assessment\n

\n In these studies (30/179, 17.3%), wearables generated the outcome measurement and monitored the dependent variable in an (quasi-) experimental setting or intervention, in mostly randomized controlled trials and quasi-experimental designs.\n

\n Prognosis, Forecasting, and Risk Stratification\n

\n In further studies (28/179, 15.6%), data generated with wearables were integrated into risk calculations (risk for a certain event or outcome), prognostic models, or cut-points. Wearable data constituted inputs for models to estimate risks.\n

\n Explorative Analysis of Big Data Sets\n

\n These studies (10/179, 5.6%) exploratively analyzed big data [\n \n 223\n \n ], generated by wearables and accessible via applications, commercial platforms, eCohorts, or companies themselves, to find trends and generate new hypotheses.\n

\n Method Evaluation\n

\n Studies (40/179, 22.3%) have evaluated and compared methods and tools (such as screenings for diseases, general practices, questionnaires, or other patient-reported outcomes) with the help of wearables. The wearable device might be the gold standard device or probed itself.\n

\n Feasibility\n

\n In these studies (12/179, 6.7%), the feasibility of using wearables for screening diseases and to improve on existing methods and practices is focused, mostly accompanied by a qualitative component.\n

\n Diagnostics and Screening\n

\n Studies (6/179, 3.4%) in this category evaluated details of diagnostics and disease screening outcomes, (cost-) effectiveness, utility, and screening length or were compared with standard measurement methods.\n

\n Disease Monitoring\n

\n Here (8/179, 4.5%), wearables supported the monitoring of an already diagnosed condition or a patient at risk (of deterioration).\n

\n Others\n

\n Studies (14/179, 7.8%) evaluated methods, with no other particular subgroup being appropriate.\n

\n Strengths and Shortcomings of Wearables\n

\n Overall, the studies mentioned more strengths than shortcomings. A few studies (16/179, 8.9%) mentioned no strengths of wearables, whereas 55.3% (99/179) of the studies mentioned no shortcomings.\n

\n Most often, authors (104/179, 58.1%) emphasized the accuracy and reliability, positive results of peer-reviewed validation studies (own and of others), or clinically approved certifications (eg, the Food and Drug Administration [FDA] clearance in the United States or Communaut\u00e9 Europ\u00e9enne [CE] mark of the European Union;\n \n Figure 7\n \n ).\n

\n Figure 7.\n

\n \n Open in a new tab\n \n

\n Chart of reported strengths and weaknesses of wearables as mentioned by authors. PA: physical activity.\n

\n \n

\n Often, studies (59/179, 33%) identified the wearable as innovative, that is, as a cutting-edge tool and method [\n \n 103\n \n ] with a wearable device potentially closing a gap in or improving health care and research. For example, 1 study described how wireless wearables and data synching could improve the quality of care [\n \n 69\n \n ], \u201cThe data can be sent from the wearable to the physician\u2019s office, avoiding the need for office visits, ultimately making possible preventive medicine and improving quality of care.\u201d Low et al [\n \n 129\n \n ] concluded that \u201cFitbit devices may provide opportunities to improve postoperative clinical care with minimal burden to patients or clinical providers.\u201d Tomitani et al [\n \n 199\n \n ] reflected how wrist-worn blood pressure wearables could \u201csignificantly improve blood pressure control.\u201d As per Shilaih et al [\n \n 184\n \n ], wrist-worn wearables might ameliorate fertility awareness research and care.\n

\n Several studies (55/179, 30.7%) acknowledged the ability of wearables to measure in the naturalistic environment of the participants, called\n \n ecological momentary assessment\n \n [\n \n 10\n \n ,\n \n 11\n \n ,\n \n 224\n \n ].\n

\n Multiple studies (51/179, 28.5%) described wearables as objective and superior to self-reported outcomes as they were more accurate, reliable, and easier to generate. Often, the authors valued the relatively low costs of wearables (50/179, 27.9%). Others appreciated wearables as being unobtrusive or noninvasive (48/179, 26.8%) and enabling continuous, long-term measurements (38/179, 21.2%). Furthermore, the handling (37/179, 20.7%) of hardware and software was often found to be user-friendly, as well as the prevalence of wearables in the population (27/179, 15.1%), decreasing stigma and easing participant recruitment. Some studies (26/179, 14.5%) reported that participants accepted and liked the wearables, resulting in high participant compliance (wearing and using the wearable). Some authors (18/179, 10.1%) perceived technical wearable characteristics as positive, for example, good sampling rate of measurements, long battery life, large memory space, raw data availability, data security, compatibility with other devices such as smartphones, and availability of application programing interfaces (APIs).\n

\n Few studies (11/179, 6.1%) described wearables as robust and not easy to break. Authors (10/179, 5.6%) valued the wearable-induced behavior change as a cobenefit, that is, motivating study participants to more PA and increasing health awareness.\n

\n A few studies (8/179, 4.5%) mentioned data accessibility via APIs, apps, and web-based platforms and a few other studies (7/179, 3.9%) potential of large-scale wearable studies, or the ease of data handling. A few (6/179, 3.4%) studies underlined the variety of functionalities and vital sign measurements as positive aspects, and 2.2% (4/179) of studies perceived wearables as fast or time-efficient in data generation.\n

\n Most shortcomings (39/179, 21.8%) were related to the inaccuracy of the wearables or the absence of validation or clinically approved certification. Studies (16/179, 8.9%) also mentioned technical issues, such as a low sampling rate of measurements, no wear time recognition, or missing data. Other technical issues comprised, for example, synchronization, charging and device setup [\n \n 91\n \n ] or data cleaning [\n \n 137\n \n ]. Rare experienced shortcomings were participants\u2019 noncompliance or dislike toward the wearable (11/179, 6.1%), no access to raw data or company\u2019s algorithms (4/179, 2.2%), difficulties in handling the wearable (3/179, 1.7%), and wearables perceived as obtrusive in daily life (2/179, 1.1%).\n

\n Discussion\n

\n Study Characteristics\n

\n Overall, we have identified a positive trend in wearable studies, underlining the growing interest in wearables in health research, in line with other reviews [\n \n 3\n \n ,\n \n 224\n \n -\n \n 226\n \n ]. Our results show a strong interest of researchers and study participants in this technology, but we also identified cautionary behavior toward using wearables. The vast majority of studies were undertaken in North America, about twice as many as in Europe, which is consistent with the previous literature [\n \n 225\n \n ]. One study in North America, conducted in 2019 with over 8 million participants [\n \n 153\n \n ], dominated the image of the distribution of participants. The reasons for the American-European gap may be multifaceted. One factor may be the differences in political and administrative frameworks, for example, comparing CE and FDA processes, which may result in slower certification processes for wearables and new technologies in general [\n \n 31\n \n ]. Another factor may be cultural mentality resulting in faster adoption of new technology in the United States, as the North Americans own proportionally more fitness trackers in comparison to the Europeans [\n \n 227\n \n ,\n \n 228\n \n ].\n

\n Some factors discussed in other research were not or only briefly mentioned in the included studies [\n \n 6\n \n ,\n \n 29\n \n ,\n \n 31\n \n ], but should also be reflected, especially technical and legal aspects, such as data security [\n \n 224\n \n ], data synching, and export. For example, the Germany-based study of Koehler et al [\n \n 114\n \n ] was one of the few that detailed data security and transfer of home-based telemonitoring data to the clinic. Data security and privacy are severely governed by the European Union General Data Protection Regulation, which is according to their website the \u201ctoughest privacy and security law in the world\u201d [\n \n 229\n \n ]. Administrative limitations and challenges presumably obscure the benefits of wearable research in Europe. A possible solution for data security and usability might be data trusts [\n \n 230\n \n ] as an alternative to large platforms.\n

\n Most medical fields represented in the included studies showed similarities with other reviews [\n \n 224\n \n ], for example, studies often focusing on cardiology, sports medicine, and neurology. However, we found a multitude of studies from multidisciplinary fields as well as the field of global health, indicating a likely adoption and expansion of wearables in other medical fields. This underlines the potential for wearables in health research beyond a mere trend or hype, as wearables may provide new possibilities for a broad spectrum of health research, such as for infectious disease prediction like COVID-19 or fertility awareness, among many others.\n

\n Wearable Characteristics\n

\n Similar to other reviews, most devices were wrist-worn fitness trackers and accelerometers, and most of them are from the company Fitbit, measuring PA, HR, and sleep [\n \n 3\n \n ,\n \n 27\n \n ,\n \n 31\n \n ,\n \n 224\n \n ,\n \n 225\n \n ]. These vital signs and device types seem to become the standard in wearable research [\n \n 3\n \n ,\n \n 27\n \n ,\n \n 31\n \n ,\n \n 224\n \n ,\n \n 225\n \n ]. The included studies also emphasized the growing wearable use [\n \n 147\n \n ,\n \n 195\n \n ,\n \n 197\n \n ], which is also reflected in commercially available devices [\n \n 38\n \n -\n \n 40\n \n ]. Currently, further wearable devices emerge, measuring, for example, oximetry, blood pressure, skin temperature, or respiratory rate.\n

\n Categorization of Wearable Application in the Studies\n

\n In general, the included studies covered a great scope of health applications such as fertility tracking; monitoring of body characteristics such as weight or diseases such as Alzheimer disease, diabetes mellitus, and AF; as well as associations of coffee intake, sleep, and PA, or blood pressure and steps. We have noted an increase in smaller studies that also included rare populations and conditions, such as fibromyalgia or the rare genetic Pompe disease, indicating that wearables may be valuable for insights into patients with rare conditions. Using affordable, consumer-grade wearables for rare disease assessment and monitoring might eventually be less expensive than specifically developed devices and easier to use for patients. Therefore, currently underrepresented populations may be better researched through wearables [\n \n 231\n \n ], that is, different ethnic groups, nationalities, individuals with disabilities, or (rare) conditions. Future studies could examine the participation of underrepresented groups in wearable research in greater depth, particularly in studies analyzing wearable user data.\n

\n Global Health and Low-Resource Contexts\n

\n Included studies are predominantly from high-income countries, constituting a gap in wearable studies in low-resource contexts. The AliveCor device was shown to be feasible in Kenya to help detect AF [\n \n 232\n \n ], as well as for early diagnosis. The literature underlines the potential for wearable-based research in low-resource settings to generate data and improve health care [\n \n 9\n \n ], based on their low cost and ease of use (data acquisition, hardware, and software handling) [\n \n 233\n \n ]. Xu et al [\n \n 234\n \n ] emphasized that physiological monitoring with wearables hold \u201cpromise for substantial improvements in neonatal outcomes\u201d in low- and middle-resource countries. Wearables can generate a solid database for global health research, particularly for morbidity measurements [\n \n 235\n \n ], large-scale studies, and modeling and descriptive studies. Topics such as climate change\u2013induced impacts focusing on extreme weather events as an outcome and impact on health [\n \n 236\n \n ] may be approached. For example, 1 study [\n \n 20\n \n ] measured the physiological response of farm workers to climate conditions with wearables to investigate heat-related illness in a high-income setting. Lam et al [\n \n 237\n \n ] investigated the thermal adaptation and comfort of participants originating from various climatic regions. The fitness tracker measured HR data was integrated with other weather and human-based measurements and predicted the thermal sensation of nonlocal participants, among others. Similar studies can be conducted in low-resource regions.\n

\n Strengths and Shortcomings of Wearables\n

\n A few studies have experienced issues or shortcomings, such as inaccuracies in measurements and technical issues. Nevertheless, most authors were satisfied with wearables, as strengths were mentioned more frequently than shortcomings. Novelty and innovation outweighed the shortcomings for most authors. The most mentioned positive wearable characteristics were validity and accuracy, technical reliability, innovation, and unobtrusiveness. Only a few authors have mentioned data access through APIs or cloud platforms as a strength. However, the practical value of wearables is heavily reliant on the mode and reliability of data access. Depending on the company, there may be different data access policies in place, whereby it may not be possible to access the raw data of the wearable. Most authors have not considered wearable data access. However, data access and availability of wearable devices is an important aspect that researchers need to be aware of before using a potential study device. Another aspect is open access to the wearables\u2019 raw data or source codes, as companies might change the source code and implement algorithms without the obligation to announce or detail changes that might lead to bias and inconsistency of data [\n \n 224\n \n ]. For example, Thijs et al [\n \n 195\n \n ] mentioned the consequences of nondisclosed algorithms (Fitbit) for data analysis and standardization. Moreover, the lack of standardization and replicability of wearable raw data and analysis [\n \n 28\n \n ] hinders comparability among studies.\n

\n Most studies mentioned and discussed validation, accuracy, and certification of the used wearables as part of good research practice approaches. However, the mention of validation or accuracy did not necessarily imply that the wearables had been certified (FDA or CE) or validated in peer-reviewed research. Nevertheless, the authors reported that the wearable device is sufficiently accurate even with existing inaccuracies [\n \n 14\n \n ,\n \n 143\n \n ,\n \n 197\n \n ]. The authors seemed to tolerate smaller inaccuracies and validation drawbacks\u2014especially of established consumer-grade wearables\u2014if usability was of high importance, such as in large-scale studies.\n

\n Large-scale and Big Data Sets for Wearable Research\n

\n We noted an increase in large-scale wearable studies in recent years, which is consistent with previous literature [\n \n 225\n \n ]. During the COVID-19 pandemic, there has been an increase in studies using secondary data. Studies aimed at generating insights with regard to the developing pandemic, focusing on forecasting models and their effects on different populations. Overall, wearable-generated big data sets might decrease biased data because measurements are objectively taken in the natural environment of numerous and diverse individuals. Although data analytic skills are needed for handling big data sets, their analysis might be extremely valuable for health research in generating new evidence [\n \n 31\n \n ,\n \n 225\n \n ].\n

\n Limitations\n

\n First, not all studies using wearables might have been identified by our search. We included only the wearables of companies in the search that had the highest market share. Therefore, the wearables of smaller or new companies may be missing in this review. In addition, we only included studies published in English, which may have excluded evidence from other regions that may not publish in English. Although this review provides a wide scope of wearable research, the list of included studies is by no means exhaustive.\n

\n In addition, wearable costs are only approximations and could be imprecise: (1) companies follow different sales and distribution models, for example membership, rental, and subscription; (2) we only incorporated wearable (hardware) prices, excluding costs for software, maintenance, and other charges such as subscription fees, which may even exceed wearable hardware costs;\n \n and\n \n (3) sales prices are subject to fluctuation. We also excluded many studies as wearables were discontinued. The fluctuant and unstable market, therefore, might also be a factor in decisions regarding the use of wearables [\n \n 28\n \n ]. Although interesting and promising, some wearables and similar devices were beyond the scope of this study but might also be valuable for health research. We have provided a wide overview of wearable devices; however, the included studies did not show the full range of possible wearables and measured vital signs [\n \n 9\n \n ,\n \n 37\n \n ].\n

\n In addition, we report the opinions of the included studies with regard to the shortcomings and strengths of wearables. Although these insights might be helpful, they are not objective measures. Moreover, our introduced categories for studies and aims to use wearables might overlap, as separation and categorization are artificial.\n

\n Conclusions\n

\n We see a growing uptake of wearables in health research and a trend to use wearables for large-scale, population-based studies. Wearables, which were often piloted in the included studies, were used in diverse health fields including COVID-19 prediction, fertility awareness, geriatrics, AF detection, evaluation of methods, drug effects, psychological interventions, and patient-reported outcomes. Measurement of steps, PA, HR, and sleep may be considered standard wearable measurements. Nevertheless, wearables are becoming more diverse in their measurements and appearance. Therefore, wearable-induced research may include currently underrepresented populations such as the older adults, participants who are disabled, participants with rare chronic or genetic diseases, participants from low socioeconomic backgrounds, and others.\n

\n For many researchers, novelty and innovation seem to outweigh shortcomings such as measurement inaccuracies. Overall, the included studies shared key characteristics that the wearables should meet: validity, technical reliability (including data access solutions), innovation, and unobtrusiveness.\n

\n We identified a lack of wearable research in low-resource settings. We assume that the reasons for the gap may be a lack of funding and doubts about the usefulness of the wearables. However, wearable devices may be used to generate data in such settings, which may otherwise be difficult and expensive to obtain. Therefore, wearable devices may be valuable for health research in a global context. During the COVID-19-pandemic in particular, large-sized wearable studies were used to generate insights into the developing pandemic and may potentially lead to novel insights into population health trends and forecasts. Future research is needed to determine the usability of wearable devices for underrepresented populations, as well as the feasibility and usefulness of health research in low-resource contexts.\n

\n Acknowledgments\n

\n We wish to thank the German Research Foundation (Deutsche Forschungsgemeinschaft) for supporting this study as part of a Deutsche Forschungsgemeinschaft\u2013funded research unit (Forschungsgruppe). We acknowledge the support of Else Kr\u00f6ner-Fresenius-Stiftung from the Heidelberg Graduate School of Global Health. Funders did not have a role in the design, data collection and analysis, decision to publish, or preparation of the manuscript.\n

\n Abbreviations\n

\n AF\n: \n
\n atrial fibrillation\n
\n
\n API\n: \n
\n application programing interface\n
\n
\n CE\n: \n
\n Communaut\u00e9 Europ\u00e9enne\n
\n
\n ECG\n: \n
\n electrocardiogram\n
\n
\n FDA\n: \n
\n Food and Drug Administration\n
\n
\n HR\n: \n
\n heart rate\n
\n
\n PA\n: \n
\n physical activity\n
\n
\n PRISMA\n: \n
\n Preferred Reporting Items for Systematic Reviews and Meta-Analyses\n
\n
\n PRISMA-ScR\n: \n
\n Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews\n
\n

\n Multimedia Appendix 1\n

\n PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.\n

\n \n mhealth_v10i1e34384_app1.docx\n \n ^{\n (116.3KB, docx)\n}\n

\n Multimedia Appendix 2\n

\n Details on search and search strings.\n

\n \n mhealth_v10i1e34384_app2.docx\n \n ^{\n (15.9KB, docx)\n}\n

\n Multimedia Appendix 3\n

\n Medical Education Research Study Quality Instrument scores of included studies.\n

\n \n mhealth_v10i1e34384_app3.docx\n \n ^{\n (61.5KB, docx)\n}\n

\n Multimedia Appendix 4\n

\n Vital signs measured by studies.\n

\n \n mhealth_v10i1e34384_app4.docx\n \n ^{\n (254.4KB, docx)\n}\n

\n Multimedia Appendix 5\n

\n Categorization of wearable applications in the studies: article references and examples.\n

\n \n mhealth_v10i1e34384_app5.docx\n \n ^{\n (64.8KB, docx)\n}\n

\n Footnotes\n

\n Authors' Contributions: SH, SB, and MA conceived and designed the study. SH drafted the manuscript with the help of SB and MA. All authors contributed to the critical revision of the draft and approved the final version of the manuscript.\n

\n Conflicts of Interest: None declared.\n

\n References\n

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\n Associated Data\n

\n \n This section collects any data citations, data availability statements, or supplementary materials included in this article.\n \n

\n Supplementary Materials\n

\n Multimedia Appendix 1\n

\n PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.\n

\n \n mhealth_v10i1e34384_app1.docx\n \n ^{\n (116.3KB, docx)\n}\n

\n Multimedia Appendix 2\n

\n Details on search and search strings.\n

\n \n mhealth_v10i1e34384_app2.docx\n \n ^{\n (15.9KB, docx)\n}\n

\n Multimedia Appendix 3\n

\n Medical Education Research Study Quality Instrument scores of included studies.\n

\n \n mhealth_v10i1e34384_app3.docx\n \n ^{\n (61.5KB, docx)\n}\n

\n Multimedia Appendix 4\n

\n Vital signs measured by studies.\n

\n \n mhealth_v10i1e34384_app4.docx\n \n ^{\n (254.4KB, docx)\n}\n

\n Multimedia Appendix 5\n

\n Categorization of wearable applications in the studies: article references and examples.\n

\n \n mhealth_v10i1e34384_app5.docx\n \n ^{\n (64.8KB, docx)\n}\n

\n ACTIONS\n

\n \n View on publisher site\n \n
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\n Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness\n

\n Stefano Canali\n

\n Viola Schiaffonati\n

\n Andrea Aliverti\n

\n Roles\n

\n Abstract\n

\n Introduction\n

\n Table 1. The main functions served by wearables for health, with examples.\n

\n Table 2. Summary of the identified areas of concern and key issues and proposed recommendations.\n

\n An overview of wearable technology for digital health\n

\n Local standards for data quality\n

\n Interoperability for balanced estimations\n

\n Access for health equity\n

\n Representativity for fairness\n

\n Conclusions\n

\n Funding Statement\n

\n References\n

\n ACTIONS\n

\n PERMALINK\n

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\n The Impact of Wearable Technologies in Health Research: Scoping Review\n

\n Sophie Huhn\n

\n Miriam Axt\n ,\n BA\n

\n Hanns-Christian Gunga\n ,\n Prof Dr\n

\n Martina Anna Maggioni\n ,\n PD, PhD\n

\n Stephen Munga\n ,\n PhD\n

\n David Obor\n ,\n MSc\n

\n Ali Si\u00e9\n ,\n PhD\n

\n Valentin Boudo\n ,\n MSc\n

\n Aditi Bunker\n ,\n Dr sc hum\n

\n Rainer Sauerborn\n ,\n Prof Dr\n

\n Till B\u00e4rnighausen\n ,\n Prof Dr\n

\n Sandra Barteit\n ,\n Dr sc hum\n

\n Abstract\n

\n Background\n

\n Objective\n

\n Methods\n

\n Results\n

\n Conclusions\n

\n Introduction\n

\n Background\n

\n Objectives\n

\n Methods\n

\n Overview\n

\n Eligibility Criteria\n

\n Inclusion and exclusion criteria.\n

\n Information Sources and Search\n

\n Quality Assessment\n

\n Data Synthesis\n

\n Results\n

\n Overview\n

\n Figure 1.\n

\n Study Characteristics\n

\n Demographics\n

\n Figure 2.\n

\n Table 1.\n

\n Figure 3.\n

\n Study Types and Fields\n

\n Figure 4.\n

\n Wearable Characteristics\n

\n Table 2.\n

\n Figure 5.\n

\n Categorization of Wearable Application in the Studies\n

\n Figure 6.\n

\n Correlations\u2014Wearable and Other Physiological Data\n

\n Population-Based Research\n

\n Outcome Assessment\n

\n Prognosis, Forecasting, and Risk Stratification\n

\n Explorative Analysis of Big Data Sets\n

\n Method Evaluation\n

\n Feasibility\n

\n Diagnostics and Screening\n

\n Disease Monitoring\n

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