literature review on wearable technology

This paper is in the following e-collection/theme issue:

Published on 5.9.2019 in Vol 21 , No 9 (2019) : September

Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers

Authors of this article:

• Tatjana Loncar-Turukalo 1 , PhD   ; 
• Eftim Zdravevski 2 , PhD   ; 
• José Machado da Silva 3 , PhD   ; 
• Ioanna Chouvarda 4 , PhD   ; 
• Vladimir Trajkovik 2 , PhD  

1 Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

2 Faculty of Computer Science and Engineering, Saints Cyril and Methodius University, Skopje, North Macedonia

3 Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Porto, Portugal

4 Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece

Corresponding Author:

Tatjana Loncar-Turukalo, PhD

Faculty of Technical Sciences

University of Novi Sad

Trg Dositeja Obradovica 6

Novi Sad, 21000

Phone: 381 691463297

Email: [email protected]

Background: Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered.

Objective: This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues.

Methods: This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries.

Results: The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations.

Conclusions: This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain. The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed.

Introduction

As the worldwide population grows and the access to health care is increasingly being demanded, real-time monitoring of various physiological signals has driven the research and development of diverse wearable and implantable systems. Connected health (CH) describes the new paradigm of a technology-enabled model of health and lifestyle management [ 1 ]. It is implicitly a multidisciplinary technology domain set up to provide preventive and remote treatments. CH relies on a digital information structure based on the internet, sensing, communications, and intelligent techniques, in support of health-related applications, systems, and engineering.

Wearables, as well as hearables (in-ear devices) and nearables (neighboring devices that interact with wearables) integrated into the wider concept of Internet of Things (IoT), are being considered the most likely technologies to transform future health care and lifestyles [ 2 , 3 ]. This revolution began with the smartphone, which is now becoming a widespread intrusive and ubiquitous technology. Most current wearables and nearables are equipped with different types of sophisticated sensors. Different types of sensors powered by advanced analytics are being explored to develop functionalities of truly portable medical laboratories. Seamless integration of these measurements in smartphone apps permits for targeted information to be delivered on time, enhancing the user experience in typical assisted living scenarios. The general acceptance, ease of use, and reliability of smartphones facilitates user adherence to different added value apps that allow filling a gap in the area of self-physiological sensing and fitness monitoring [ 4 ]. Wearable technology has become mainstream, with the most significant influence on fitness and health care industries [ 2 ].

The importance gained by wearables among consumer devices can be tracked by their increasing share in consumer electronics shows promoting self-care and health management. According to the International Data Corporation, 172.2 million wearable units were shipped in 2018 [ 4 ], and this number is expected to grow, contributing significantly to the revolution of the IoT market [ 5 ]. Advances in wearable technologies and user acceptance of available consumer wearable devices pave the pathway toward seamless physiological monitoring.

The first body area networks (BANs) and wearable units comprised a number of sensors with a processing unit and wireless nodes assembled on printed circuit boards [ 6 , 7 ]. The design was bulky and uncomfortable, accompanied by large batteries, and had numerous issues associated with frequent recharge and loss of data communication. Since then, tremendous progress has been made in sensing technologies. The bulky design is being rescaled to a system on chip. Lowered power consumption, reliable communications, distributed processing, and data analytics improved the potential of wearables and made a significant impact on technology acceptance [ 7 ]. The technology innovations directly responded to user-related concerns (sensor miniaturization, seamless monitoring, secured communications, lower power consumption, energy harvesting, and plug-and-play functionalities) as well as safety and security (reliable sensing and data preprocessing, secured data communication, and reliable analytics).

However, the user feedback reviews report that initial user enthusiasm on wearables is often lost because of unclear use cases (unclear end user need), price, and associated complexities in device pairing with a smartphone [ 8 ]. The translation to long-term commitment to wearables requires clear use scenarios, valuable feedback, and constructive recommendations [ 8 , 9 ]. The inevitable transformation from a traditional, reactive health care model to a proactive and preventive model will bring clear use cases of CH solutions for early diagnostic or chronic condition monitoring [ 1 ]. Innovative CH scenarios are strongly motivated, exact, and economically beneficial [ 3 , 10 ].

The role that sensing, and information and communication technologies have gained as essentials in digital health has been summarized and elaborated in numerous research articles on sensors, data analytics, and secure and reliable communication platforms for CH solutions [ 3 , 10 - 16 ]. To stimulate and facilitate knowledge transfer and dissemination among policymakers and stakeholders, it is equally important to summarize those original findings with respect to specific application scenarios and specific user groups. Systematic review studies deliver such overviews based on an exhaustive manual screening of available digital libraries, providing a qualitative analysis of included studies, and unbiased performance comparison of the corresponding CH solutions [ 17 , 18 ]. The examples of such review studies offering a useful insight into the spectra of the related wearable technologies, target user groups, and application domains are plentiful. Wilde et al [ 19 ] reviewed the usage of apps or wearables for monitoring physical activity and sedentary behavior and emphasized the barriers and facilitators for their adoption. A scoping review [ 20 ] summarized the practices and recommendations for designing, implementing, and evaluating mobile health (mHealth) technologies to support the management of chronic conditions of older adults, considering articles published from 2005 till 2015. Kvedar et al [ 10 ] focused on the concept of CH as an overarching structure for telemedicine and telehealth and provided examples of its value to professionals and patients. In the study by Liu et al [ 21 ], materials, design strategies, and powering systems applied in soft electronics were reviewed. It also summarizes the application of these devices in cardiology, dermatology, electrophysiology, and sweat diagnostics and discusses the possibilities for replacement of the corresponding traditional clinical tools.

The transformation of the wearable landscape in the last decade is thus evidenced in a plethora of original articles and patent documentation and summarized and compared in numerous focused systematic reviews [ 3 , 10 - 16 , 19 - 21 ]. In this paper, we scoped the wearable technology field over the decade, starting from 2010 to February 2019, to identify trends in literature with respect to 4 important pillars: technology, safety and security, prescriptive insight, and user concerns. The collected literature reflects on the achieved progress, open issues, perspectives, and gaps in the development of wearable systems for future CH domain. The covered topics mainly relate to enabling technology: sensing, data aggregation and processing, communication protocols, power supply, data protection, and data analytics. However, the results of numerous pilots and experience gained with consumer wearables provide an insight into different user-related concerns. After exploring the literature published over the last decade, we have summarized state-of-the-art technologies, future research focus, and paper statistics related to the following key issues: enabling technology topics, application of wearable sensors in CH, and different user concerns.

With the more general, high-level perspective on the research on wearable technology, user-related concerns and challenges experienced over broad application area, this scoping review aimed at overlooking research trends unconstrained to a particular user group, health condition, or lifestyle scenario and including both mHealth and smart living environments. The extensive search scope is supported by automated search procedures relying on natural language processing (NLP) algorithms. The trends over the last decade were analyzed using a set of identified articles from 3 large digital libraries.

Purpose of This Review

Many studies elaborating on the use of sensors and wearables in assisted living environments, CH, and wellness and fitness apps were published in the last decade [ 3 , 10 - 16 , 19 - 22 ]. Those studies provide significant input for designing future CH systems, indicating benefits, but also shortcomings, barriers, and user feedback [ 19 , 23 - 29 ]. Nevertheless, there is a lack of studies with a general overview of the nature and extent of published research in that context.

This study aimed to identify and scope the scientific literature related to wearables in health monitoring, as measured by trends in the research evidence available in 3 large digital libraries: Institute of Electrical and Electronics Engineers (IEEE), PubMed, and Springer. The study scoped the field from several perspectives aiming to capture key drivers and major constraints in the deployment of wearable technology for health. The enabling technology relies on advances in sensing, processing, communications, and data protection. Conversely, multiple user perspectives imply privacy, utility, complexity, price, relevance, reliability, and significance of delivered feedback.

The objective of this study was to scope the research on wearable technology for health with regard to the following research questions:

• What are the most significant research trends and milestones on wearables seen as an enabling technology and as a key driver facilitating CH solutions?
• What are the most critical identified barriers and concerns from the technology and user perspectives and what trends are reflected in the research literature relating to these issues?

As an added value, this review can help identify the topics that need more detailed research in terms of elaboration of the obstacles and potential breakthroughs. The list of relevant articles resulting from this study can be filtered with respect to different fields (eg, keywords) to identify articles of interest for a systematic review in a specific subfield. The details in the list facilitate fast manual screening and selection of the subset of articles for further qualitative analysis. This type of preliminary search in planning a systematic review provides valuable answers on the feasibility (ie, does any evidence in literature exist), relevance (ie, has a similar systematic review already been done), and amount of time needed (ie, volume of the found evidence) to conduct a systematic review.

Scoping Review Methodology

This study adopted a scoping review methodology to identify and process the literature on wearables published from January 2010 to February 2019. Using a scoping technique, we aimed to examine the research evidence in the broad field of wearables, analyzing technology trends, including the resolved and emerging issues. The lack of a qualitative analysis of identified papers, the broad topic range, and the number of studies involved defined our approach as a scoping review and differentiated it from a systematic review [ 30 , 31 ]. The purpose of this study fully complies with the aims of a scoping review “to search, select and synthesize the knowledge addressing an exploratory question to map key concepts, types of evidence, and gaps in research,” as defined by Colquhoun et al [ 32 ]. Systematic reviews in the field of wearables, for its breadth and depth, have to focus more narrowly on wearable solutions and user concerns in a prespecified application scenario to facilitate qualitative analysis of included studies.

All emerging review types share their basis in scientific methodology, that is, they rely on formal and explicit methods for search and assessment of published studies and synthesizing of research evidence in conclusions on a well-defined research question [ 17 ]. One of the protocols for systematic reviews in health care, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) [ 18 ], provides a good example of thorough and rigorous checklist guidance. The corresponding PRISMA flow diagram illustrates the information flow reflecting the number of studies in different systematic review stages: study collection, study scanning, eligibility evaluation, thorough qualitative synthesis, and quantitative synthesis in meta-analysis [ 18 ]. The methodological framework for scoping reviews is underpinned by this exact and transparent way systematic reviews are conducted [ 17 ], providing sufficient details to reproduce the results. The workflow for a scoping review proposed by Arksey and O’Malley [ 30 ], and adopted in this study, includes 5 stages:

• Identification of a research question;
• Identification of relevant studies;
• Study selection;
• Charting the data; and
• Collating, summarizing, and reporting the results.

The identification of relevant studies and study selection stages in the scoping review methodology corresponds to the PRISMA workflow phases: study collection, scanning, and eligibility evaluation. To ensure transparency, we have enclosed the workflow chart to illustrate the number of identified, scanned, and included articles in this scoping review ( Figure 1 ).

The scope of this study was substantial and the collected research evidence on wearables surpassed the potentials of a manual search. Relying on the advances in NLP algorithms, the NLP tool kit [ 33 ] was used to ensure an efficient and exhaustive search of the literature corpus. The NLP tool kit is designed to automate the literature search, scanning, and eligibility assessment in the PRISMA methodological framework for systematic reviews [ 18 ], which are aligned with the scoping review phases: identification of relevant studies and study selection.

In the following sections, we clarify the usage of the NLP tool kit for study identification, selection (ie, scanning procedures and eligibility criteria assessment), and charting the data. It is worth noting that no quality assessment of the selected articles has been conducted, as this review has a scoping character. Instead, in the final step, we collated, summarized, and reported the results by aggregating the included studies to address the objectives of this review.

Setting Up the Natural Language Processing Tool Kit

This stage concerns the development of a plan comprising decisions on which digital libraries will be queried, relevant time span, suitable keywords, and properties that should be satisfied. This scoping review has employed the NLP tool kit [ 33 ] enabling both automated search, scanning, and processing procedures. The NLP tool kit ensures compliance with the terms of use of the digital libraries, with regard to the number of requests per unit time.

The NLP tool kit input parameters are a collection of keywords that are used to identify potentially relevant articles and a set of properties that should be satisfied by the identified articles. The input is further expanded by proposing synonyms to the search keywords and properties. Synonyms can be provided by the user or proposed by the tool kit and fine-tuned if needed.

Keywords are search terms or phrases that are used to query a digital library (eg, “health” and “ambient and assisted living,” “health” and “enhanced living environment”). Eventual duplicates in the results are removed in a later phase. Properties are words or phrases that are being searched in the title, abstract or keywords section of the articles identified with the keywords. Examples of such properties employed in this study are “monitoring,” “recommendation,” and “detection.” Property groups are thematically, semantically, or otherwise grouped properties for a more comprehensive presentation of results. For example, the property group for the set of properties given in the example above can be “information delivery.” Table 1 summarizes the relevant input categories used in this work.

Start year indicates the starting year of publishing (inclusive) for the papers to be included in the study. End year is the last year of publishing (inclusive) to be considered in the study. This review encompasses studies published from January 2010 to February 2019. Minimum of the relevant properties is a number denoting the minimum number of properties that an article has to contain to be considered as relevant. In this study, this value was set to 3, providing a right balance between falsely identifying relevant papers and potentially missing a relevant paper.

When researches perform a scoping review according to the above-mentioned methodology, the actual tasks they perform involve searching digital libraries with different search phrases, often involving complex Boolean conditions. The NLP tool kit counterpart to these phrases are the keywords described above. By screening the title and abstract, a reviewer determines whether the article is indeed relevant for the study. In the NLP tool kit, this process is automated using the properties and their synonyms to define what we are looking for in an article. Articles that contain more properties are considered as more relevant. Undoubtedly, a human reader might better understand the context and better assess the relevance of an article. However, the NLP tool kit is mimicking these tasks, but in an automated and more thorough way, providing incredible efficiency of the scoping review process. For more information about the actual implementation, we refer the reader to the study by Zdravevski et al [ 33 ].

Identification of Relevant Studies

Upon provision of the defined input categories, the literature search was started using only the specified keywords to query the selected digital libraries. The NLP tool kit indexed the following digital libraries (ie, sources): IEEE Xplore, Springer, and PubMed. It is worth noting that the NLP tool kit has used search engines of the corresponding publishers and retrieved the search results. Depending on the digital library in each search, the number of retrieved articles was constrained. In the PubMed library, all articles matching the given search criteria were retrieved for further analysis. The IEEE’s search engine limits the number of articles in each search to 2000, all of which were retrieved. For Springer, the search for each keyword separately is limited to 1000 articles or 50 pages with results, whichever comes first, sorted by relevance determined by Springer.

Study Selection

After articles had been identified based on the specified keywords and retrieved from the publishers, the study selection (screening and eligibility assessment) procedures followed.

Upon merging the results from multiple independent keyword-based searches, some articles could be present multiple times because they could be identified by different keywords or in multiple libraries. Therefore, the collected articles were screened, and duplicates were removed using their digital object identifier (DOI). In addition, the screening process discarded articles that were not published in the required time span (ie, last 10 years) or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or other reasons.

The selection of studies from the remaining subset of articles relied on the advanced functionalities enabled by NLP tools. The NLP tool kit automates analysis of a title and abstract for each study, significantly reducing the number of articles for manual screening. The automated eligibility analysis involved the following processing: tokenization of sentences [ 34 , 35 ] and English stop words removal, stemming, and lemmatization [ 35 ] using the Natural Language Tool kit library [ 36 ]. Stemmed and lemmatized properties were searched in the cleaned abstracts and titles, and each article was tagged with the properties it contained.

The processed articles were selected (ie, labeled as relevant) if they contained at least 3 of the predefined properties in its title or abstract (considering the above NLP-enhanced searching capabilities, thus performing a rough screening). To help in the eligibility analysis, the selected articles were sorted by the number of identified property groups, number of identified properties, number of citations (if available), and year of publication, all in descending order. For the relevant articles, the tool kit automatically generated a bibliographic file (as defined by BibTeX reference management software) for simplified citations.

The information flow diagram illustrating the numbers of identified, screened, processed, and removed studies in the automated NLP procedure is presented in the Results section ( Figure 1 ) to ensure transparency and reproducibility.

The listing of the relevant identified articles extracted from IEEE, PubMed, and Springer is available in Multimedia Appendix 1 as an Excel file with the following fields: DOI, link, title, authors, publication date, publication year, number of citations, abstract, keyword, source, publication title, affiliations, number of different affiliations, countries, number of different countries, number of authors, BibTeX cite key, number of found property groups, and number of found properties. These additional files facilitate refined manual search of the articles with specific filtering criteria. The subset of targeted articles can subsequently be retrieved from their publisher and manually analyzed for potential inclusion in the qualitative and quantitative synthesis. It should be noted that not all the references provided within this study are from the identified set of relevant papers. Some additional papers identified in a manual search were used to illustrate and confirm the findings of this scoping review. However, these referenced papers from other libraries have not been used to identify trends in this scoping review.

To replicate the results manually, the keywords in Table 1 have to be used to inquire the selected digital libraries using their search engines. The properties serve for identification of the relevant articles by scanning titles and abstracts of the identified studies. The results can be compared with the resulting list of included studies, provided in Multimedia Appendix 1 .

Charting the Data

To answer the research questions, we defined indicators to be extracted from the selected studies. The trends in the past decade were analyzed relying on a broad scope of literature. The processed and retained relevant articles were aggregated by several criteria:

• Source (digital library) and relevance selection criteria;
• Publication year;
• Digital library and publication year;
• Search keyword and digital library;
• Search keyword and year;
• Property group and year;
• Property and year, generating separate charts for each property group; and
• Number of countries, number of distinct affiliations and authors, aiming to simplify the identification of collaboration patterns (eg, written by multiple authors with different affiliations).

These aggregated metrics are available in the form of comma-separated values files and charts. The plotting of the aggregate results was integrated and streamlined using the Matplotlib library [ 37 ] and NetworkX [ 38 ]. The NLP tool kit enables graphical visualization of the results, where each node represents one of the properties, each edge connects 2 different properties, and its weight is determined by the number of articles containing both properties connected by that edge. Articles that do not contain at least 2 properties, and properties that were not present in at least 2 articles were excluded. For a clearer visualization, only the top 25% property pairs by the number of occurrences are shown in a graph.

A similar graph for the countries of affiliations was generated. The top 50 countries by the number of collaborations were considered for this graph. Countries and an edge between them are shown if the number of bilateral or multilateral collaborations was in the top 10% (above 90th percentile) within those 50 countries.

Collating, Summarizing, and Reporting Results

Using charted data and extracted evidence, we were able to analyze the trends in data and provide qualitative analysis for each thematic segment (as defined by the property groups). The results were reported with regard to the raised research questions. The meaning of these findings was related to the study purpose, and the potential impact on the future research direction was discussed.

Number and Distribution of Identified Articles

Using the NLP tool kit and searching 3 digital libraries: PubMed, IEEE, and Springer, we identified 21,288 studies with potential relevance ( Figure 1 ). Duplicates that emerged in multiple independent searches were removed, reducing the total number to 15,218 studies. The first screening process further eliminated 5006 studies published before 2010 or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or any other reason. The remaining 10,212 studies underwent an automated eligibility assessment using the advanced NLP tool kit functionalities. After processing, the articles were tagged with identified properties, and all articles containing less than 3 properties were removed. Overall, 2406 articles were deemed eligible for further manual inspection and inclusion in identifying the research trends and summarizing the results. The statistics on the number of the collected articles, duplicates, articles with invalid time span or the articles with incomplete data, and relevant articles are presented in Figure 2 for each digital library.

The distribution of the number of collected and relevant articles per year is presented in Figure 3 . An increasing trend in the number of collected articles can be noticed from January 2010 to February 2019. The same trend is followed by the number of included articles, which rises from 136 in 2010 to 393 in 2018.

Combining the information on the digital library (source) and publication year of the identified relevant articles, the obtained distribution reveals that IEEE, being a more technology-oriented library, has an increasing trend in the number of relevant articles from 2010, peaking in 2017 ( Figure 4 ). PubMed leads in the number of articles dealing with CH and assisted living and covers more of the searched properties related to user concerns. The number of PubMed articles follows an increasing trend from 2010 and saturates in research evidence from 2016 onward. The Springer library shows an oscillating trend from 2010 to February 2019, with an average of around 50 articles per year.

Geographical Distribution and Collaboration Evidence

The authors’ affiliations were used to identify wearables’ research community clusters and eventual hubs at the research forefront. Multiple country associations were discovered, but for the sake of presentation clarity, the graph in Figure 5 shows 25 countries (nodes) and 56 edges with at least 7 joint articles (90th percentile) specified as edge weights. The number of papers per presented node is color coded, where violet corresponds to the higher and yellowish (paler) color to the lower number of articles. The identified hubs, United States, Canada, United Kingdom, Germany, China, and Italy, feature both national and international scientific production, whereas the strongest edges exist between the United States and Canada and between the United States and China. The collaboration patterns largely correspond to the neighboring geographical areas. The European countries demonstrate active collaboration scheme as well. The United States, United Kingdom, and China have significant national scientific production in the analyzed research domains.

Keywords Statistics

The selected keywords used to map the literature corpus on wearables with respect to the set research questions appear in the relevant articles with different distributions. Figure 6 presents the annual number of research papers identified by the search engines of 3 libraries with the defined keywords and additionally filtered manually based on their relevance to the defined properties. Please note that the internals of their search engines are not known, and the libraries might differ in the way they look for these keywords: only in a title, keywords section, abstract, or a whole article. Depending on the digital library, the ratio of the relevant papers containing specific keywords changes ( Figure 7 ). The IEEE digital library has a focus on enabling technology for CH, in terms of novelties in wearable sensing, data processing analytics, computing, and communication protocols. PubMed publications are also oriented toward CH technologies from an assistive and supportive perspective. Springer publications cover slightly different topics, focusing mainly on ambient assisted living (AAL) and ambient intelligence and generally contain more technical articles that address assistive technologies.

Statistics of Properties

As the number of research articles increases within the observed time frame, the number of articles dealing with associated topics summarized in property groups increases accordingly ( Figure 8 ). The increasing trend is accompanied by the stable ratio of papers, with technology-related publications being the leading in number, followed by research related to information delivery, safety and security, and user concerns. When the view is zoomed from property groups to properties, the graph reveals the centrality of monitoring as the essential function of a wearable system tightly connected with the key technology: sensing ( Figure 9 ). The 2 properties interrelate with communication, detection, reliability, safety, security, transmission, data analytics, and privacy as technologically empowered concepts. Acceptance is the key user-related property in the graph core, with privacy and protection to follow.

Principal Findings

Wearable medical devices play a critical role as an enabling technology and as a key driver that has facilitated the emergence of CH solutions. This paper presents an overview of the most important milestones and trends that have driven research and development initiatives on wearable technology domains in the last decade. Simultaneously, it aimed to identify the most critical barriers or concerns, as far as technology and user aspects are concerned, that hinder the generalized adoption of wearables and still require further research.

The adopted methodology used the NLP tool kit for searching in 3 digital libraries, PubMed, IEEE, and Springer, for papers that address research on wearable technologies for medical applications. In the following, we address the findings related to the research trends in technology, information delivery, user concerns, safety, and security.

Technology as a Key Driver

The literature ( Figure 10 ) reflects the intense research and development in sensor design, communication protocols, and data processing and analytics. The emergence and evolution of concepts of edge computing, cloud, and fog could be easily tracked. As technology is a key enabler of future CH systems, we briefly review significant technological advances in the comprising components of a wearable system.

Evolution of Sensing Technology

Available sensors and their characteristics largely influence the design of CH systems. The direct sensor’s contact with the body implies their stiffness and size, as the most important features concerning comfort and measurement accuracy. The placement of wearable sensors influences their characteristics, user acceptance, and engineering requirements. As sensors evolve from wearable and implantable to ingestible sensors, barriers arise on multiple pathways: regulatory, technical, and translational [ 39 ].

The marked progress in wearable sensors is linked to advances in material science and embedded systems. Smart garments or electronic textiles, featuring sensor flexibility, made the first promise toward seamless and pervasive monitoring. The sensor integration into fabrics varies from garment level, assuming sensor integration at a later stage, to fabric level implying sensor integration by application of coatings to the fabrics [ 40 ]. The striving level is a fiber level [ 40 ] implying integration of conductive threads and fibers in the knitting process to result in a smart fabric (a concept first proposed about 20 years ago [ 41 ]).

Microcontroller-based systems can as well be included within different textile fabric for health applications [ 42 ]. Some products have already been approved and introduced to the market, but most of them are at a prototyping stage. The limitations arise at the electronic and textile integration step, slowing down technology transfer. In addition, there are multiple regulatory concerns, such as safety, reliability, and recycling [ 43 ]. Another promising technology for wearable CH solutions is microfluidics. Both sensing and drug delivery can be realized by combining microfabrication and liquid manipulation techniques with conductive elements on stretchable and flexible materials [ 44 , 45 ].

Low-power microelectronics, biocompatible materials, micro- and nano-fabrication, advances in data transmission, and management of sensor drift have driven the development of implantable biosensors [ 46 ]. Recent advances report the use of polyamide, flexible material for sensor platforms [ 47 , 48 ]. Research on flexible mechanical and electrical sensing has demonstrated great potential in in vitro diagnostics [ 49 ] and advanced therapy delivery [ 50 ]. Polymer-based switching matrices used for electronic skin to enable pressure sensing (robots, displays, and prosthetics), evolved into skin-attachable wearable electronic devices [ 48 ]. Another use-case involves surgical procedures, where these matrices are used in surgical procedures as part of mapping systems attached to the surface of the organs [ 50 ]. Active research directions in polymer sensors are focused on transparency [ 51 ], self-powering [ 52 ], and self-healing [ 53 ] capabilities.

The new generation of implantable sensing solutions for tissue and organ monitoring is enabled by advances in epidermal electronics based on soft lithography and thin-film sensors [ 46 , 54 ]. For example, electrocardiogram, blood glucose, and blood pressure sensors integrated with microstructures provide optical, thermal, and electrical stimulation [ 55 ].

Hearables are one of the latest wearable devices aiming to integrate sensing of multiple physiological signals into a single device [ 56 ]. The in-ear placement of such a device requires a flexible and comfortable fit and provides stable position regardless of the subject’s gross movements. The viscoelastic foam used as a substrate additionally ensures artefacts absorption, as the ear channel is affected by small movements, when speaking, swallowing, or chewing. The solution proposed by Goverdovsky et al [ 56 ] offers continuous measurements of cardiac, brain, and respiratory functions.

Implantable pacemakers, pressure sensors, cochlear implants, drug infusion pumps, and stimulators are all examples of implantable devices delivering therapy or providing physiological monitoring [ 39 ]. The majority of implantable devices currently operate in an open loop. New research challenges are focused on combining monitoring and therapy delivery for the optimized closed-loop personalized therapy [ 39 ]. The neural signal recording is ultimately the most demanding task, as it requires precise, low-power, and low-noise electronics and miniaturized and light weight implantable designs [ 57 ]. Neural implants face the hardest challenges in the translational pathway of the research-grade solutions into clinically approved products.

Ingestible sensors for image and data recording in gastrointestinal endoscopy have already proven their benefits in early detection of gastrointestinal cancers [ 58 ]. Ingestible, similarly to implantable devices, face challenges that shape the ongoing research: operation frequency selection, amplifiers, antenna design and performance, wireless channel modeling, increasing data rates, and power considerations.

Besides tracking basic physiological parameters (electrocardiogram, blood pressure, blood oxygen saturation, temperature, etc) sensing functions in wearable medical devices have also moved off the body toward contactless or seamless ambient embedded physiological sensing in, for example, keyboards, joysticks, steering wheels, bicycle handles, doors [ 59 ], mattresses [ 60 ], beds [ 61 ], and toilet seats [ 62 ]. The combination of such monitoring products with the data-driven services has promoted the development of the AAL concept. The AAL is a new ambient intelligence paradigm where new technologies are associated with the social environment, to transparently improve and assist the daily quality of peoples’ lives. Despite the high number of research and industry organizations already active in the AAL field, significant efforts are still needed to bring these technologies into a real-world usage [ 15 ].

Powering Wearables: Constraining Consumption and Energy Harvesting

One of the limitations for a widespread adherence to wearable electronic products concerns the power supply needs [ 7 , 9 , 63 ]. Active wearable systems need to be comfortable, light, user-friendly, and power efficient. The identified research trends reveal that research on battery technology lags compared with research on other wearable system components ( Figure 10 ). This implies that energy efficacy and efficiency remain an important design concern, both for wearable systems and in the design of networks to serve future landscape of wearables (notably fifth generation [5G] architectures).

Energy harvesting technologies have been explored as an alternative energy source to recharge power batteries or super capacitors. The ongoing research in this domain has investigated technologies to explore motion [ 64 , 65 ], thermal [ 66 , 67 ], optical, electromagnetic [ 68 ], solar [ 69 ], and chemical forms of energy [ 70 ]. However, miniature devices that can harvest proper levels of energy are still in their infancy.

Complementary efforts are being invested in the integration of power-efficient technologies and design techniques in wearable systems. Among those are energy-efficient and low-power wireless communication, voltage scaling, low-leakage and low-voltage complementary metal oxide semiconductors [ 71 ], and power-performance management.

Communication Protocols for Wearable Systems

The medical data are low in volume, but with strict requirements in terms of latency, link reliability, and security [ 7 ]. Wearable body sensor networks or BANs refer to sensor networks applied for acquisition or monitoring of vital physiological body parameters unobtrusively. These systems can be used in clinical settings or at home by patients or even healthy people who want to improve or monitor their health conditions.

BANs enable wireless communication in and around a human body in 3 different tiers: intra-BAN, inter-BAN, and the beyond-BAN. Intra-BAN communications refer to communications between on-body sensors, within the surrounding body area, enabling wireless data transmission to a personal server. According to the application and design parameters, the intranetwork can be wired or wireless, or even use the human body as a communication medium. Wired networks, as a second type of communication infrastructure for BAN applications, provide high-speed, reliable, and low-power solutions [ 72 ].

The international IEEE 802.15.6 standard enables delivering of low power, short range (in the vicinity or inside, within the human body) reliable wireless communications, with data rates from 75.9 kbps to 15.6 Mbps, making use of industrial, scientific, and medical bands, as well as frequency bands approved by national medical and regulatory authorities [ 73 ].

The inter-BAN communications include communicating data from personal devices such as smartphones to the access points, either in an infrastructure-based manner or in an ad hoc manner. Wireless BANs can interact with other existing wireless technologies such as ZigBee, wireless local area networks (WLAN), Bluetooth, wireless personal area network, video surveillance systems, and cellular networks [ 73 ].

Finally, the beyond-BAN tier connects the access points to the internet and other networks. Beyond-BAN architectures can be implemented in cloud or fog network infrastructures [ 74 ] implying protocols , cloud-based systems, and fog systems as research topics in the wearable CH domain. The major challenges in BAN are associated with media (path loss because of the body absorption), physical layer (minimization of power consumption with uncompromised reliability and interference), medium-access control layer (supporting multiple BANs in parallel application), security, and transmission (loss and delay sensitive real-time transmission) [ 75 ].

Limited spectra and the need for higher data rates drive the communication community toward the new generation of cellular networks such as 5G [ 22 , 63 ]. The high-speed data and low-latency features of 5G networks will allow wearable devices to communicate faster (in less than 1 millisecond) and perform real-time control. 5G will be a platform for various services and applications, with support to different communication requirements. The transition to millimeter wave (mmW) frequencies will require new communication architectures to be designed for specific mmW propagation. For protection and regulation of exposures to such frequencies, more appropriate metrics are needed, such as temperature elevation of the contact area [ 7 ].

The design of wearable antennas, with safety concerns, device-centric architectures, and smart device communication are some of the changes 5G will require. The development of 5G brought the promises supporting the wearables market, such as radio-frequency sensor charging [ 63 ], reduction in latency, high data rates and capacity, and network densification, enabling the massive number of deployed wearables per micro- or picocell [ 22 ].

The 5G architectures proposed to serve wearables include microbase stations for blanket coverage, whereas local coverage and data throughput should be ensured with small base stations and remote radio headers (RRHs) [ 7 ]. RRHs can also support different wireless technologies to ensure backward compatibility (Bluetooth, visible light communication (VLC), etc). The connection to cloud data servers via base stations enables storage, retrieval, and analytics of user-specific data. Realization of communications between wearables and network edge nodes can be done using licensed or unlicensed communication bands. Licensed communication bands provide quality of service at an increased cost at several levels: a service provider cost for more expensive licensed chips and more power consumed on licensed communication protocols. Unlicensed communication (eg, Bluetooth, WLAN, and VLC) is a cheaper, power-preserving option but limited in range [ 7 ].

Data Processing and Analytics

The large volume and heterogeneous data types collected using wearable technology have grown beyond the abilities of commonly used data processing techniques [ 76 ]. The necessity for reducing the volumes of captured data at the source, to reduce the power consumption and latency, brought processing closer to the sensor nodes, mapping the data algorithms to ultralow-power microcontrollers [ 46 ]. Preprocessing approaches, such as noise filters, peak detection, and feature extraction, allow for significant data reduction at the source [ 77 ]. Conversely, advanced data analytics imply sensor data integration, thus relying on the powerful devices located in the cloud. 5G should offer mobile edge computing to reduce latency and traffic demands to the central node. In the wearable scenario, communication between various user devices is fostered by 5G machine-to-machine communications, enabling local processing, low latency, and power saving [ 7 ].

High-performance computing permits efficient processing of large data volumes through a map-reduce framework [ 78 ]. Advanced data caching and in-memory processing coupled with GPU accelerators and coprocessors support intensive parallel operations. The availability of higher computational power enabled the rebirth of computationally intensive deep neural networks, resulting in superhuman performance and cutting-edge research in multiple domains. These are enabling technologies that will bring to reality the third generation of pervasive sensing platforms [ 46 ] that will integrate and extract information from a variety of sources: sensed data, clinical records, genomics, proteomics, and social networks, leading to a system-level approach to human health [ 79 ].

Information Delivery and Valuable Feedback

The research in the user-associated information delivery is primarily concerned with recommendations, provision of feedback, and real-time user insight ( Figure 11 ). Current commercial wearable technologies, tracking vital signs and patterns of activity, lack the relevance for many potential consumers, presenting an additional burden [ 7 ]. The motivation to buy and use wearable systems has to be justified in a functional CH application context. The clear user benefit comes from a validated system that would transform collected data into manageable and useful information for medical action, safety instructions, or self-performance estimation and improvement.

To gain wider consumer preference, the information generated by wearables has to be fitted into specific contexts, offering the needed insight and recommendation on actions that should be taken. The second generation of wearable systems, which aims to enable context sensing, needs to integrate many different types of context information, such as sensor information, user profiles and preferences, activity patterns, medical history, and spatial information (location and environment conditions). If not strictly depending on medical condition, the timing, content, and frequency of prompting have to be adjusted to user preferences [ 80 ]. As a basic example, the time of day or night implies different content and presentation of the prompting messages because of the different level of user’s readiness and wakefulness [ 81 ]. The fusion of physiological and context sensing data will rely on sophisticated data analytics for extraction of relevant information and decision making on an action to be prescribed or advised to the user. The feedback to prompting messages generated in day-to-day system’s interactions with a user would ensure the adaptation to user preferences in time, relying on reinforcement learning.

The transformation of wearables from measurement devices into resources of reliable real-time information, history mining, and smart and personalized decisions would qualify them for health and performance monitoring solutions.

Wearables will reshape individuals and society, promoting self-care and health management, moving care outside hospitals, affecting enterprises, and revolutionizing health care [ 8 , 82 , 83 ]. Their seamless integration into consumers’ electronics is well witnessed throughout the Consumer Intelligence Series on wearables from 2014 and 2016 [ 8 , 83 ]. According to these sources, numerous user concerns such as design, accuracy, reliability, security, privacy, and dampened human interaction are becoming less worrying to the users. Research on sensor materials and communication solutions can provide advances in human-centered design and enhance the user experience.

Another big hurdle for deploying wearable systems in the real-world concerns technology acceptance [ 84 , 85 ]. Even though wearables are adopted by the millennials, the older population is still uncomfortable with using and relying on technology. As opposed to the smartphone, the use of wearables in fitness and well-being scenarios does not have clear usage need and benefits. Consumers complain about uncomfortable and unattractive design, short battery life, and frequent connectivity challenges [ 8 ]. With the first wearable devices, we have witnessed a wearables fatigue attitude, which is noticeable in a significant percentage of wearables being discarded within the first 6 months of use [ 86 ].

Our findings are aligned with the outcomes of the user feedback reviews on wearable technologies [ 8 , 83 ], as the identified articles confirm the steady increase in research addressing user-related issues such as technology acceptance, technology adoption, and privacy ( Figure 12 ). The primary design requirements are that a wearable device must be fit for the purpose and seamlessly adapted to the user’s lifestyle to be accepted.

Preserving privacy and confidentiality is a priority to be considered in design specifications. Communications should be encrypted and secured, and the involved parties should ensure confidentiality. This is particularly important in the case of wireless data communications that are easier to intercept [ 87 ]. Personal monitoring devices should unobtrusively authenticate the user identity using biometrics or key physiological signs (owner-aware devices).

Different user concerns, such as quality of experience, security, privacy, technology acceptance, and human-centered design, are relevant research topics in the wearable CH domain and can be used to identify future challenges and research trends. Although some of them (eg, quality of experience and human-centered design) might be decreased as the end user pool gains digital competence and technology matures with time, some of them (security, privacy, and technology acceptance) will probably evolve and mix with other, more societal research topics such as environmental impact, circular economy, and digitalization of society. These can raise a new set of concerns related to the socioeconomic impact of wearable technologies in combination with IoT and 5G technologies used for health care and lifestyle.

It is worth mentioning that another spectrum of concerns and barriers relates to the stakeholders involved in the provision and management of health care. Health professionals need scientific evidence on the reliability of collected data, the performance of analytical models mapping the collected data to disease progression, and eventually positive patient outcome in using wearable-based CH solutions [ 1 ]. Reshaping the health care critically depends on research work devoted to the design and evaluation of care pathways, provision of optimized feedback, and eventually providing evidence on long- and short-term cost-effectiveness of CH solutions [ 1 , 88 ].

Safety and Security

Safety and security are primary considerations for medical devices, tightly coupled with reliability at all system levels. Our findings confirm the increasing importance and research efforts related to these major user concerns ( Figure 13 ). If the wearable device is required to perform safety-critical functions, the tolerance for error is zero. A failure in such a device can cost a life and that requires more effort and time (ultimately cost) to be invested in thoroughly testing and validating the device before it is deemed safe to use.

Along the life cycle of a wearable device, efficient mechanisms are required to detect and diagnose deviations occurring in the captured data. Correct differentiation of errors due to system-related faults from those due to a change in health status is a necessity. Increased level of false alarms (false positive) would prevent user reliance, reduce user alertness, and hamper user adherence to the provided feedback.

Both features, safety and security, are technology conditioned and should be ensured by the system design. Wearable medical devices are required to comply with IEC 62366-1:2015 standards [ 89 ] that regulate the application of usability engineering to medical devices to achieve approval. Two new EU regulations on medical devices issued in 2017 that will come into force in May 2020 are Regulation (EU) 2017/745 on medical devices [ 90 ] and Regulation (EU) 2017/746 on in vitro diagnostic medical devices [ 91 ]. These regulations will have a significant impact on the sector of medical devices that incorporates wearable technology. More stringent procedures for evaluation of medical devices and conformity should improve patient safety.

The CH paradigm involves more connectivity and communications into health care and medical devices. Any device connected to the internet is prone to be targeted for malicious purposes, putting it at a constant threat of damage, theft, and financial cost.

The number of detected data breaches in health care organizations has increased significantly in the last several years [ 92 ]. The reasons are not primarily technical but in part caused by the negligence and lack of knowledge of employees in treating this sensitive data and implementing the information security practices [ 92 ]. According to the 2017 Fourth Annual Data Breach Industry Forecast, health care organizations will be the most targeted sector with new, sophisticated attacks emerging. New security frameworks for mHealth are being proposed to ensure security and reliability of medical devices and personal health data [ 93 - 96 ].

After the General Data Protection Regulation was put in place on May 25, 2018, the requirements for data protection and privacy assurance have been raised and unified across Europe. The health care and monitoring systems have to adhere to the privacy by design principle, which requires the incorporation of privacy protection in systems design and not as an afterthought add-on solution.

Limitations of the Study

This study considered only 3 digital libraries, and some relevant articles from nonindexed publishers were not considered. However, keeping in mind the size of the considered digital libraries, we believe that the obtained results are indicative for the purpose of the study.

All digital libraries that were used in this work have different internal search engines with different rules for the maximum number of papers that can be retrieved and different formatting of search results. The papers obtained for this study are the results of the same search query sent to those different search engines. However, keeping in mind the number of papers that were analyzed within this scoping review, we believe that specificities of the publishers’ search engines have limited impact and have not influenced the findings of this work.

In the future, the NLP tool kit needs to be extended to process more digital libraries. In addition, there is an apparent need of a Web app that will make it available to a wider audience. Until then, readers are encouraged to contact the authors if they are interested in using the tool kit.

Conclusions

Wearable medical solutions, integrated into the wider concept of IoT, provide for pervasive data acquisition from a body and beyond, and rely on powerful data analytics, smart networking, and machine-to-machine communications to facilitate patient-centric, personalized, and holistic care. Although technological innovations and availability support the emergence of CH solutions, the widespread adoption of wearables is still hindered by numerous concerns related to reliability, security, and cost-effectiveness.

This scoping review maps the scientific literature related to wearable technology in health care starting from January 2010 to February 2019, identifying the research trends related to enabling technology, and the trends in addressing the concerns from both user and technology perspectives. The NLP tool kit supported search procedures applied over 3 large digital libraries, IEEE, PubMed, and Springer, which provided for a representative subset of 2406 articles on wearable technologies for medical applications.

On the basis of the investigated sample, the main findings reflect key drivers in the field, some research gaps and relevant topics that would benefit from more systematic qualitative knowledge synthesis:

• User concerns were the least addressed topic, whereas the enabling technology research was the main focus in the literature within the observed time period;
• Major breakthroughs were made in sensor technology, data analytics, communications, and computing architectures (edge and cloud);
• Research on battery technology and efficient solutions for energy harvesting has lagged, implying energy efficiency as one of the major constraints in designing wearable solutions for pervasive monitoring;
• Research on communication technologies focuses on 5G featuring low-latency, massive connectivity, and high capacity to mitigate the current challenges with respect to real-time feedback, energy, and computing constraints;
• The research related to the user-associated information delivery was mainly focused on monitoring and measurement information and much less on the provision of feedback recommendation and prescriptive insight; and
• The most addressed concerns from the user perspective were technology acceptance and issues related to safety and security, implying privacy and reliability as the most central topics.

This study confirms that applications of the wearable technology in the CH domain are becoming mature and established as a scientific domain. However, further research and development are required to improve their reliability, comfortability, and dependability levels. The research focus shifts from sensors and data analytics toward the sustainable delivery of valuable recommendations, reliable, energy-efficient, and low-latency communications and computation offloading. Sensor data integration goes beyond body-level integration to include context sensing, location and environment metrics, medical history, pattern of activities, and user preferences. This is essential for making wearables a robust patients’ representation interface and reliable node of the IoT infrastructure that makes CH a reality.

There is a further need to explore and provide the literature evidence supporting the positive experiences, improved patient outcomes, and cost-effectiveness of CH solutions. Practical adoption in the field still demands design and validation of new care pathways, optimization of interventional strategies, and a sound business model.

Acknowledgments

This article/publication is based upon work from COST Action ENJECT TD 1405, supported by COST (European Cooperation in Science and Technology; www.cost.eu).

Authors' Contributions

TLT and VT conceived of the idea of scoping review, contributed to the scoping review, and drafted and edited the manuscript. EZ contributed to coding for the platform for scoping reviews and visualization of results. JMS contributed to the scoping review and editing of the manuscript. IC contributed to the scoping review methodology and editing of the manuscript.

Conflicts of Interest

None declared.

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Abbreviations

Edited by B Caulfield; submitted 14.03.19; peer-reviewed by V Curcin, PA Silva, E Guisado-Fernandez; comments to author 18.05.19; revised version received 09.06.19; accepted 19.06.19; published 05.09.19

©Tatjana Loncar-Turukalo, Eftim Zdravevski, José Machado da Silva, Ioanna Chouvarda, Vladimir Trajkovik. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.09.2019.

This 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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Wearable Health Devices in Health Care: Narrative Systematic Review

Affiliation.

• 1 Department of Orthopaedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
• PMID: 33164904
• PMCID: PMC7683248
• DOI: 10.2196/18907

Background: With the rise of mobile medicine, the development of new technologies such as smart sensing, and the popularization of personalized health concepts, the field of smart wearable devices has developed rapidly in recent years. Among them, medical wearable devices have become one of the most promising fields. These intelligent devices not only assist people in pursuing a healthier lifestyle but also provide a constant stream of health care data for disease diagnosis and treatment by actively recording physiological parameters and tracking metabolic status. Therefore, wearable medical devices have the potential to become a mainstay of the future mobile medical market.

Objective: Although previous reviews have discussed consumer trends in wearable electronics and the application of wearable technology in recreational and sporting activities, data on broad clinical usefulness are lacking. We aimed to review the current application of wearable devices in health care while highlighting shortcomings for further research. In addition to daily health and safety monitoring, the focus of our work was mainly on the use of wearable devices in clinical practice.

Methods: We conducted a narrative review of the use of wearable devices in health care settings by searching papers in PubMed, EMBASE, Scopus, and the Cochrane Library published since October 2015. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion.

Results: A total of 82 relevant papers drawn from 960 papers on the subject of wearable devices in health care settings were qualitatively analyzed, and the information was synthesized. Our review shows that the wearable medical devices developed so far have been designed for use on all parts of the human body, including the head, limbs, and torso. These devices can be classified into 4 application areas: (1) health and safety monitoring, (2) chronic disease management, (3) disease diagnosis and treatment, and (4) rehabilitation. However, the wearable medical device industry currently faces several important limitations that prevent further use of wearable technology in medical practice, such as difficulties in achieving user-friendly solutions, security and privacy concerns, the lack of industry standards, and various technical bottlenecks.

Conclusions: We predict that with the development of science and technology and the popularization of personalized health concepts, wearable devices will play a greater role in the field of health care and become better integrated into people's daily lives. However, more research is needed to explore further applications of wearable devices in the medical field. We hope that this review can provide a useful reference for the development of wearable medical devices.

Keywords: chronic disease management; health monitoring; medical field; public health; rehabilitation.; wearable.

©Lin Lu, Jiayao Zhang, Yi Xie, Fei Gao, Song Xu, Xinghuo Wu, Zhewei Ye. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 09.11.2020.

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Conflict of interest statement

Conflicts of Interest: None declared.

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The Use of Wearable Devices in the Workplace - A Systematic Literature Review

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• Jayden Khakurel 20 ,
• Simo Pöysä 20 &
• Jari Porras 20  

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The aim of this Systematic Literature Review is to provide a heuristic overview on the recent trends of wearable technology and to assess their potential in workplaces. The search procedure resulted a total of 34 studies. In more details, 29 different types of wearable devices were obtained from the studies. Categorization revealed that obtained wearable devices were used for monitoring: 18 types (e.g. for mental stress, progress, etc.), augmenting: 3 types (e.g. for data, images), assisting: 3 types (e.g. to uplift their work), delivering: 2 types (e.g. for vital information contents) and tracking: 8 types (e.g. sedentary behaviour). To sum up, though wearable technology has already gained momentum for personal use to monitor daily activities, our studies shows that it also has potential to increase work efficiency among employees, improve worker’s physical well-being and reduce work related injuries. Further work in terms of privacy, usability, security, policies, cost of devices and its integration to the existing system is required in order to increase the adoption rate of wearable devices in workplaces.

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Khakurel, J., Pöysä, S., Porras, J. (2017). The Use of Wearable Devices in the Workplace - A Systematic Literature Review. In: Gaggi, O., Manzoni, P., Palazzi, C., Bujari, A., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 195. Springer, Cham. https://doi.org/10.1007/978-3-319-61949-1_30

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Wearable Technology Applications in Healthcare: A Literature Review

Wearable technologies can be innovative solutions for healthcare problems. In this study, we conducted a literature review of wearable technology applications in healthcare. Some wearable technology applications are designed for prevention of diseases and maintenance of health, such as weight control and physical activity monitoring. Wearable devices are also used for patient management and disease management. The wearable applications can directly impact clinical decision making. Some believe that wearable technologies could improve the quality of patient care while reducing the cost of care, such as patient rehabilitation outside of hospitals. The big data generated by wearable devices is both a challenge and opportunity for researchers who can apply more artificial intelligence (AI) techniques on these data in the future. Most wearable technologies are still in their prototype stages. Issues such as user acceptance, security, ethics and big data concerns in wearable technology still need to be addressed to enhance the usability and functions of these devices for practical use.

Introduction

Wearable technologies enable the continuous monitoring of human physical activities and behaviors, as well as physiological and biochemical parameters during daily life. The most commonly measured data include vital signs such as heart rate, blood pressure, and body temperature, as well as blood oxygen saturation, posture, and physical activities through the use of electrocardiogram (ECG), ballistocardiogram (BCG) and other devices. Potentially, wearable photo or video devices could provide additional clinical information. Wearable devices can be attached to shoes, eyeglasses, earrings, clothing, gloves and watches. Wearable devices also may evolve to be skin-attachable devices. Sensors can be embedded into the environment, such as chairs, car seats and mattresses.  A smartphone is typically used to collect information and transmit it to a remote server for storage and analysis. There are two major types of wearable devices that are used for studying gait patterns. Some devices have been developed for healthcare professionals to monitor walking patterns, including the accelerometer, multi-angle video recorders, and gyroscopes. Other devices have been developed for health consumers, including on-wrist activity trackers (such as Fitbit) and mobile phone apps and add-ons. Wearable devices and data analysis algorithms are often used together to perform gait assessment tasks in different scenarios.

Wearable technologies can be innovative solutions for healthcare problems. In this study, we conducted a literature review of wearable technology applications in healthcare. Some wearable technology applications are designed for the prevention of diseases and maintenance of health, such as weight control and physical activity monitoring. Wearable devices are also used for patient management and disease management. The wearable applications can directly impact clinical decision-making.  Some believe that wearable technologies could improve the quality of patient care while reducing the cost of care, such as patient rehabilitation outside of hospitals. The big data generated by wearable devices is both a challenge and opportunity for researchers who can apply more AI techniques on that data in the future.

A search in the PUBMED databases was performed in September 2018. All papers containing the terms “wearable technologies” or “wearable devices” in the title or abstract were identified. In addition, the search was limited to articles whose publication dates were within 10 years (from 2008 to 2018). The abstracts of these studies (n=1126) were then inspected to ascertain whether they contained information about the “wearable technology applications in healthcare.” The authors then reviewed those studies for information regarding wearable device applications and identified 67 relevant papers. 

To summarize the results of the literature review, the wearable technology applications are grouped into three categories based on their roles. For example, wearable devices designed for weight control and physical activity monitoring are listed in the section of prevention of diseases and maintenance of health. In addition, there are sections of patient management and disease management.

Prevention of Diseases and Maintenance of Health

Fall identification and prevention.

In many countries, providing care to an aging population has become a significant challenge. For example, the number of Americans 65 and older will grow from about 49 million in 2018 to approximately 100 million in 2060 ( Vespa, Armstrong, & Medina, 2018 ). The World Health Organization expects that the global elderly population 60 or older will rise to 2 billion by 2050 (World Health Organization (WHO), 2015 ). The aging population has increased risks for chronic conditions, falls, disabilities and other adverse health outcomes ( Ambrose, Paul, & Hausdorff, 2013 ). Providing preventive interventions to the aged population to improve health outcomes has become an important research and development topic. Wearable devices could be used to address some of the challenges related to detecting and managing adverse health conditions in aging populations. Wearable devices have great potential to be used in fall prevention among older adults. Falls occur in 30% to 60% of older adults each year, and 10% to 20% result in injury, hospitalization or death ( Rubenstein, 2006 ). For the elderly people in the USA, falls lead to four to 12 days of hospital stay per fall ( Bouldin et al., 2013 ). Recent studies have focused on developing wearable devices and associated algorithms to collect and analyze gait (manner of walking) data for fall prevention ( Awais et al., 2016 ).

In research settings, the performance of fall detection using wearable devices has already achieved considerable good results. For example, one study developed a solution to recognize walking and activities ( González et al., 2015 ). The study used a genetic algorithm and two triaxle accelerometer bracelets to detect walking patterns that could lead to disruptive events, such as falling and seizure onset. Pannurat, Thiemjarus, & Nantajeewarawat (2017 ) presented a method to detect a fall at different phases using a wireless accelerometer and classification algorithms. Their evaluation results showed an 86% and 91% accuracy for fall pre-impact and post-impact detection. Hsieh, Liu, Huang, Chu, & Chan (2017 ) developed a novel hierarchical fall detection system using accelerometer devices on the waist. The results showed that the system achieved a high accuracy at 99% in identifying fall events. Similarly, Gibson, Amira, Ramzan, Casaseca-de-la-Higuera, & Pervez (2017 ) presented a fall detection system using a database of fall and daily activities. Their method used the Shimmer biomedical device on the chest to collect data. The detection signals were extracted using compress sensing and principal component analysis techniques. The obtained binary tree classifiers achieved 99% precision in identifying fall events. These studies were performed in research laboratory settings. A recent study ( Awais et al., 2016 ) compared and evaluated the performance of wearable sensors in classifying physical activities for older adults in real-life and in-lab scenarios. This study found that systems developed in a controlled lab setting might not be able to perform well in real-life conditions. Therefore, new systems should be tested in real-life conditions.

Physical Activity and Interaction Monitoring

Prolonged sedentary behavior is associated with many adverse health outcomes. To investigate whether reminders could change student posture and positively influence their wellbeing, Frank, Jacobs, & McLoone (2017 ) designed wearable device-based system to monitor student activities. Vibration reminders were sent through the wearable devices after 20 minutes of sitting. The results show that the strategy was effective in changing student behavior, although the health effects of this change were inconclusive.

Choo, Dettman, Dowell, & Cowan (2017 ) evaluated the effectiveness of using wearable devices and smartphones for tracking language patterns. The study conducted a Language Environment Analysis (LENA) using a language-tracking wearable device to collect mother-child communication data. The collected data were used to provide feedback to mothers about the communication pattern. The after-study evaluation showed that mothers had a positive response to the device and felt that the communication data collected by the wearable device provided useful information to improve mother-child communication.

Mental Status Monitoring

Developing wearable devices and algorithms to monitor mental conditions is a relatively new domain. Some wearable devices are equipped with sensors that can detect human physiology status, such as heartbeat, blood pressure, body temperature, or other complex vital signs (e.g. electrocardiograms). Using these signals, new systems can be developed to monitor mental conditions. Stress detection is the most common application of such systems.

To detect stress patterns of children, Choi, Jeon, Wang, & Kim (2017 ) proposed a framework using wearable devices and machine learning-based techniques. The wearable devices collected both audio and heart rate signals for stress detection. The framework has a potential to be used to remotely monitor child safety through stress patterns. The study results showed that by combining audio and heart rate signals, the system had a better performance in fighting noise signals when compared with audio-only methods. Support Vector Machine (SVM) is one machine learning method. The accuracy of the best algorithm (SVM+Wrapper) is 93.47%.  A study by Setz and colleagues (2010 ) showed that even simple electrodermal activity (EDA) sensors have the capacity to identify stress level. An EDA sensor can measure skin conductance, which usually is correlated with the stress level of a person. They described how a Swiss team developed an EDA-based system called Emotion Board. The system can collect and measure skin conductance signals. The collected signals were processed using linear discriminant analysis (LDA) and an SVM-based classifier was used to detect stress. The evaluation on 33 subjects showed that the maximum accuracy was 82.8%.

Sports Medicine

Wearable devices can help athletes or coaches to systematically manage athletic training and matches. For example, Skazalski, Whiteley, Hansen, & Bahr (2018 ) used commercially available wearable devices as a valid and reliable method to monitor the jump load of elite volleyball players and to measure jump-specific training and competition load in the players’ jumps. The results of this study also indicate that the devices showed excellent jump height detection capacities. The wearable devices can monitor functional movements, workloads, heart rate, etc., so they may be more widely used in sport medicine to maximize performance and minimize injury.

Chen, Lin, Lan, & Hsu (2018 ) developed a method to monitor and detect heat stroke. Heat stroke can harm people when they are doing exercises in hot temperatures. The team proposed a fuzzy logic-based method for inferencing signals collected from multiple wearable devices, environmental temperatures and humidity sensors. The experimental results showed that the system can be used to monitor heat stroke risk and alert users.

Weight Control and Monitoring

Tracking physical activities using wearable devices has become a popular method to help people assess activity intensity and calories expended. There is a growing interest among health consumers to use wearable devices, especially consumer wearable devices, to track weight control activities and outcomes. A study by Dooley, Golaszewski, & Bartholomew (2017 ) compared and validated three major consumer devices for measuring exercise intensities. The study devices included Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. The project enrolled 62 participants aged 18-38 and measured their heart rates and energy expenditures using all three devices. A hypothetical ideal "gold standard" test had a sensitivity of 100% and a specificity of 100%. The study showed a high magnitude of errors across all devices when compared to the gold standard. This study indicated that these devices might be useful as a stimulus to increase activity, but they have limitations as a tracking and outcome measurement method.

Although there are studies that show that wearable devices can be used as a stimulus mechanism to increase user activities, there is still a lack of evidence-based studies to validate the use of wearable device for the outcome of weight loss. A recent randomized clinical trial was conducted in Korea to examine the effectiveness of using wearable devices and smartphones to reduce childhood obesity ( Yang et al., 2017 ).  The project aimed to enroll a thousand 5th- and 6th-grade students to assess a wearable device-based intervention system called “Happy Me.” The outcome measures of the trial were behavioral changes (e.g. physical activity, healthy eating) and anthropometric changes (e.g. body weight, body mass index, waist circumference). The results of the study attempted to provide scientific evidence for the effectiveness of using a wearable device system for weight control.

Public Education

Medical and healthcare education is rapidly changing and is influenced by many factors including the changing healthcare environment, the changing role of health professionals, altered societal expectations, rapidly changing medical science, and the diversity of pedagogical techniques. Technologies such as podcasts and videos with flipped classrooms, mobile devices with apps, video games, simulations (part-time trainers, integrated simulators, virtual reality), and wearable devices (google glass) are some of the techniques available to address the changing educational environment. These technologies should also be used to educate the public about health-related topics.

Patient Management

Wearable technology can also improve patient management efficiency in hospitals. Researchers hope to use wearable technology for the early detection of health imbalances. Wireless communication in wearable techniques enable researchers to design a new breed of point-of-care (POC) diagnostic devices ( Ghafar-Zadeh, 2015 ). For example, garments integrated with wearable solutions, such as commercial portable sensors and devices in the emergency medical services (EMS), emergency room (ER) or intensive care unit (ICU) environments, have facilitated the continuous monitoring of risks that endanger patient lives. The system enables detection of patient health-state parameters (heart rate, breathing rate, body temperature, blood oxygen saturation, position, activity and posture) and environmental variables (external temperature, presence of toxic gases, and heat flux passing through the garments) to process data and remotely transmit useful information to healthcare providers ( Curone et al., 2010 ).

Wireless wearable devices have supported mobility in patients. Activity monitoring is used to manage chronic conditions of patients ( Chiauzzi, Rodarte, & DasMahapatra, 2015 ). Wearable device activity tracking abilities provide a mechanism to allow health consumers to enhance their self-management capacities. Many health consumers are already tracking their weight, diet, or health routines in some way. Wearable devices further improve the self-tracking ability by providing sensor data as objective evidence.

Cancer Survivors

Endometrial cancer survivors are the least physically active of all cancer survivor groups and exhibit up to 70% obesity ( Basen-Engquist et al., 2009 ) , but lifestyle interventions can result in improved health outcomes. A study was conducted to evaluate the acceptability and validity of the Fitbit Alta™ physical activity monitor for sociocultural diverse endometrial cancer survivors ( Rossi et al., 2018 ). The study found that the Fitbits were well accepted by 25 participants and the physical activity data indicated an insufficiently active population. Physical inactivity and sedentary behavior are common amongst breast cancer survivors. Another study used wearable activity trackers (WATs) as behavioral interventions to increase physical activity and reduce sedentary behavior within this population ( Nguyen et al., 2017 ). They found that wearable technique programs have the potential to provide effective, intensive, home-based rehabilitation.

Patients with Stroke

Stroke, predominantly a condition of advanced age, is a major cause of acquired disability in the global population. Conventional treatment paradigms in intensive therapy are expensive and sometimes not feasible because of social and environmental factors. Researchers used wearable sensors to monitor activity and provide feedback to patients and therapists. In a study by Burridge and colleagues (2017), the researchers developed a wearable device with embedded inertial and mechanomyographic sensors, algorithms to classify functional movement, and a graphical user interface to present meaningful data to patients to support a home exercise program.

Patients with Brain and Spinal Cord Injuries

Patients with brain and spinal cord injuries need exercises to improve motor recovery. Often, these patients are not qualified to monitor or assess their own conditions and they need healthcare provider guidance. Therefore, there is a need to transmit physiological data to clinicians from patients in their home environment. Researchers like Burns and Adeli (2017) are doing just that, by reviewing wearable technology for in-home health monitoring, assessment and rehabilitation of patients with brain and spinal cord injuries.

Chronic Pulmonary Patients

As a chronic illness, chronic obstructive pulmonary disease typically worsens over time, so extensive, long-term pulmonary rehabilitation exercises and patient management are required.  A group of researchers designed a remote rehabilitation system for a multimodal sensors-based application for patients who have chronic breathing difficulties ( Tey, An, & Chung, 2017 ). The system included a set of rehabilitation exercises specific for pulmonary patients, and provided exercise tracking progress, patient performance, exercise assignments, and exercise guidance.  Patients in the study could receive accurate pulmonary exercises guidance from the sensory data. Further evaluation studies are needed to verify if the proposed remote system can provide a comfortable and cost-effective option in the healthcare rehabilitation system.

Disease Management

Significant progress in the development of wearable device systems for healthcare applications has been made in the past decade. Wearable technology can make disease management more effective as outlined below.

Heart Disorders

Wearable devices have been developed to do cardiovascular monitoring and enable mHealth applications in cardiac patients. Low-power wearable ECG monitoring systems have been developed ( Winokur, Delano, & Sodini, 2013 ). Some wearable devices can monitor heart rate variability (HRV). In a study, a wearable patch-style heart activity monitoring system (HAMS) was developed for recording the ECG signal ( Yang et al., 2008 ). The wearable devices can be used efficiently as health monitoring system during daily routines in many places and situations.

Wearable technology can assess patient heart activity outside of a laboratory or clinical environment. It is possible to perform heart assessments during a wide range of everyday conditions without interfering with a patient's activity tasks. For example, researchers designed a textile-based wearable device for the unobtrusive recording of ECG, respiration and accelerometric data and to assess the 3D sternal seismocardiogram (SCG) in daily life. Researchers also designed a portable and continuous ballistocardiogram (BCG) monitor that is wearable in the ear ( Da He, Winokur, & Sodini, 2012 ). The ear devices can reveal important information about cardiac contractility and its regulation.

The wearable cardioverter defibrillator (WCD) was introduced into clinical practice in 2001, and indications for its use are currently expanding. The WCD represents an alternative approach to prevent sudden arrhythmic death until either Implantable Cardioverter Defibrillator (ICD) implantation is clearly indicated, or the arrhythmic risk is considered significantly lower or even absent ( Klein et al., 2010 ).

Hernandez-Silveira and colleagues (2015 ) studied the feasibility of using a wireless digital watch as a wearable surveillance system for monitoring the vital signs of patients. The researchers compared the wearable system with traditional clinical monitors. The results showed that the tested wearable device provided reliable heart rate value for about 80% of the patients and the overall agreement between the new device and clinical monitor was satisfactory because the comparison was statistically significant. A similar study by Kroll, Boyd, & Maslove (2016 ) showed that a wrist-worn personal fitness tracker device can be used to monitor the heart rate of patients even though the collected heart rates were slightly lower than the standard of continuous electrocardiographic (cECG) monitoring.

As well, heat stroke can be potentially damaging for people while exercising in hot environments. To prevent this dangerous situation, a researcher designed a wearable heat-stroke-detection device (WHDD) with early notification ability. If a dangerous situation was detected, the device activated the alert function to remind the user to avoid heat stroke  ( Chen et al., 2018 ).

Blood Disorders

Wearable trackers have drawn interest from health professionals studying blood disorders. Overall, the U.S. prevalence of hypertension among adults was 29.0% during 2015–2016 ( Fryar, Ostchega, Hales, Zhang, & Kruszon-Moran, 2017 ). Wearable devices can detect hypertension with physiological signals ( Ghosh, Torres, Danieli, & Riccardi, 2015 ). Some of the most widely used wearable devices are applications for evaluating and monitoring blood pressure, including cuff-less blood pressure sensors, wireless smartphone-enabled upper arm blood pressure monitors, mobile applications, and remote monitoring technologies. They have the potential to improve hypertension control and medication adherence through easier logging of repeated blood pressure measurements, better connectivity with health-care providers, and medication reminder alerts ( Goldberg & Levy, 2016 ).

The study of blood flow is called hemodynamics. Patients with orthostatic hypotension have pathologic hemodynamics related to changes in body posture. Researchers designed a new cephalic laser blood flowmeter that can be worn on the tragus to investigate hemodynamics upon rising from a sitting or squatting posture. This new wearable cerebral blood flow ( CBF ) meter is potentially useful for estimating cephalic hemodynamics and objectively diagnosing cerebral ischemic symptoms of patients in a standing posture ( Fujikawa et al., 2009 ). In another study, researchers detected site-specific blood flow variations in people while running, using a wearable laser doppler flowmeter ( Iwasaki et al., 2015 ).

Diabetes Care Management

Patients and healthcare providers need to track many factors that influence blood glucose dynamics (e.g., medication, activity, diet, stress, sleep quality, hormones, and environment) to effectively manage diabetes. Recent consumer technologies are helping the diabetic community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease ( Heintzman, 2016 ). These consumer technologies include smartphone apps, wearable devices and sensors. One well-known example is the wearable artificial endocrine pancreas for diabetes management, which is a closed-loop system formed by a wearable glucose monitor and an implanted insulin pump ( Dudde, Vering, Piechotta, & Hintsche, 2006 ). Closed-loop control (CLC) for the management of type 1 diabetes (T1D) is a novel method for optimizing glucose control. More studies of CLC were conducted recently. For example, overnight CLC improved glycemic control in a multicenter study of adults with type 1 diabetes ( Brown et al., 2017 ). Researchers also explored the possibilities of using Google Glass to simplify the daily life of people with diabetes mellitus ( Hetterich, Pobiruchin, Wiesner, & Pfeifer, 2014 ).

With the increasing cost of healthcare, wearable devices and systems could have potential to facilitate self-care through monitoring and prevention. For instance, a wearable bioelectronic technology was developed to provide non-invasive monitoring of sweat-based glucose level ( Lee et al., 2017 ). 

Parkinson’s Disease

To manage Parkinson’s disease, wearable devices offer huge potential to collect rich sources of data that provide insights into the diagnosis and the effects of treatment interventions. Ten-second whole-hand-grasp action is widely used to assess bradykinesia severity, since bradykinesia is one of the primary symptoms of Parkinson's disease. Researchers developed a wearable device to assess the severity of the Parkinsonian bradykinesia  ( Lin, Dai, Xiong, Xia, & Horng, 2017 ). Many assessments of dyskinesia severity in Parkinson's disease patients are subjective and do not provide long-term monitoring. In another study an objective dyskinesia score was developed using a motion capture system to collect patient kinematic data ( Delrobaei, Baktash, Gilmore, McIsaac, & Jog, 2017 ). The portable wearable technology can be used remotely to monitor the full-body severity of dyskinesia, necessary for therapeutic optimization, especially in the patients’ home environment. The Parkinson@home study ( de Lima et al., 2017 ) showed the feasibility of collecting objective data using multiple wearable sensors during daily life in a large cohort.

It is important for autistic children to recognize and classify their emotions, such as anger, disgust, fear, happiness, sadness and surprise. Daniels and colleagues (2018) conducted a project that used Google Glass to study the feasibility of a prototype therapeutic tool for children with autism spectrum disorder (ASD) to see if the children would wear such a device. The feasibility study supported the utility of a wearable device for social affective learning in ASD children and demonstrated subtle differences in how ASD affected neurotypical controls children perform on an emotion recognition task.

Wearable technology can also assist with the screening, diagnosis and monitoring of psychiatric disorders, such as depression. The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. Researchers explored a system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment to characterize the autonomic status of bipolar patients ( Valenza et al., 2015 ). In another study, a wearable depression monitoring system was proposed with an application-specific system-on-chip (SoC) solution. The system accelerated the filtering and feature extraction of heart-rate variability (HRV) from an electrocardiogram (ECG) ( Roh, Hong, & Yoo, 2014 ) to improve the accuracy of successfully recognizing depression.

Most wearable technologies are still in their prototype stages. Issues such as user acceptance, security, ethics, and big data concerns in wearable technology still need to be addressed to enhance the usability and functions of these devices for practical use.

User Acceptance

User preferences need to be considered to design devices that will gain acceptance both in a clinical and home setting. Sensor systems become redundant if patients or clinicians do not want to work with them. A body-worn sensor system should be compact, embedded and simple to operate and maintain. It also should not affect daily behavior, nor seek to directly replace a healthcare professional. It became apparent that despite the importance of user preferences, there is a lack of high-quality studies in this area. Researchers should be encouraged to focus on the implications of user preferences when designing wearable sensor systems. These issues become increasingly important if they seek to obtain measurements over longer time periods, for example, in monitoring chronic diseases, or during activity levels where the data collection is essential but not necessarily lifesaving ( Bergmann & McGregor, 2011 ).

One concern about older adult use of wearable device applications is their acceptance and interest in using consumer-wearable devices for personal health purposes. A recent review by Kekade and colleagues (2018 ) of 31 studies shows that more than 60% of elderly people were interested in the future use of a wearable device for improving physical and mental health. However, not many elderly people were currently using wearable devices because generally there is a lack of awareness among the older generations. The study showed that wearable devices should be tested to determine if they meet the needs of elderly people, especially sick and female participant groups (Kekade et al., 2018). The study also indicated that older populations could benefit from using wearable devices; however, more work should be done to increase the awareness of the technology use.

Patient confidentiality and data security are major concerns when using wearable devices since it can be challenging to ensure compliance with HIPAA regulations. The communication security of the collected data in Wireless Body Area Networks (WBAN) is a major concern ( Ali & Khan, 2015 ). Encryption is a key element of comprehensive data-centric security. Encrypted data and the use of encryption as an authentication mechanism within an organization's network is generally trusted, but direct access to keys and certificates allows anyone to gain elevated privileges. Key management is vital to security strength. The dependability of cryptographic schemes for key management has become an important aspect of this security. However, the extremely constrained nature of biosensors has made designing key management schemes a challenging task. For this reason, many lightweight key management schemes have been proposed to overcome these constraints. Because the physiological data are transmitted over the WiFi, there is a need for secure WBAN communications to prevent eavesdropping and the interrupting of personal information. This security can be achieved by using a cryptographic scheme to ensure basic security services like confidentiality, integrity and authenticity. However, most cryptographic schemes require secret keys. Because the security of these cryptographic schemes depends upon the keys, there is a need for secure key agreement and distribution among the nodes in the network. Security must be evaluated based on the stringent HIPAA principles for information privacy and security.

Ethical Issues

Mobile technology is increasingly being used to measure individuals' moods, thoughts and behaviors in real time. Current examples include the use of smartphones to collect ecological momentary assessments (EMA); wearable technology to passively collect objective measures of participants' movement, physical activity, sleep, and physiological response; and smartphones and wearable devices with global positioning system (GPS) capabilities to collect precise information about where participants spend their time. Although advances in mobile technology offer exciting opportunities for measuring and modeling individuals' experiences in their natural environments, they also introduce new ethical issues. A study by Roy (2017) in Chicago discussed ethical challenges specific to the methodology (e.g., unanticipated access to personal information) and broader concerns related to data conceptualization and interpretation (e.g., the ethics of "monitoring" low-income youth of color). Lessons can be learned from the collection of GPS coordinates and EMAs done in this study to measure mood, companionship and health-risk behavior with a sample of low-income, predominantly racial/ethnic minority youth living in Chicago area. While Roy (2017) encouraged researchers to embrace innovations offered by mobile technology, the discussion highlighted some of the many ethical issues that also need to be considered in the process.

Wearable devices may collect very large amounts of personal data due to their capacity for continuous data recording at high frequencies coupled with potential large population use. The collected data fits into the big data domain by meeting the four “V” characteristics (volume, variety, veracity, velocity) of big data. Because wearable devices can collect highly personalized data among large populations, the collected information not only could be used to improve personalized intervention, but also used for population pattern discovery. Researchers in nursing science explored new ways of symptom science research in the era of big data ( Corwin, Jones, & Dunlop, 2019 ) . They reviewed the concepts of an interdisciplinary approach and team science, as well as their benefits and challenges.

With significant growth of the internet, mobile devices and cloud computing, the Wearable Internet of Things (Wearable IoT) has become an emergent topic of research and applications ( Hiremath, Yang, & Mankodiya, 2014 ). A network of sensors will generate even more complex and larger data sets. Such data also creates new opportunities, such as the development of IoT sensing-based health monitoring and management ( Hassanalieragh et al., 2015 ), generating new models to define human behavior ( Paul, Ahmad, Rathore, & Jabbar, 2016 ), analyzing connection communities ( Sun, Song, Jara, & Bie, 2016 ), and developing new mobile health applications ( Lv, Chirivella, & Gagliardo, 2016 ).

For example, in blood transfusions, big data have been used for benchmarking, detecting transfusion-related complications, determining patterns of blood use, and defining blood order schedules for surgery. More generally, rapidly available information can monitor compliance with key performance indicators for patient blood management and inventory management leading to better patient care and reduced use of blood ( Pendry, 2015 ).

Integrating multimodal and multiscale big health data from wearable sensors is a great challenge since heterogeneous data need to be processed to generate unified and meaningful conclusions for clinical diagnosis and treatment. Health data accompanied with a large amount of noisy, irrelevant and redundant information also give spurious signals in clinical decision support systems ( Zheng et al., 2014 ).

Future Trends

Interoperability.

There is further work required regarding interoperability challenges. For example, the fifth generation of wireless networking technology (5G) enables us to connect many times more hospital devices to the network at once and to gain remote access at home. Australia’s Commonwealth Scientific and Industrial Research Organization (CSIRO) developed a project called the Hospital Without Walls, which aimed to provide continuous monitoring of patients in certain diagnostic categories ( Wilson et al., 2000 ). The key technology used was a miniature, wearable, low-power radio that could transmit vital signs and activity information to a home computer, and data was sent by telephone line and the Internet to appropriate medical professionals. The initial clinical scenario for this work was monitoring elderly patients who had presented to hospitals following repeated falls. Accelerometers built into the radio sets monitored activity and detected and characterized falls. Simultaneous measurement of heart rate also provided information about abnormalities of cardiovascular physiology at the time of a fall. It is believed that with these future developments, unobtrusive and wearable devices could advance health informatics, lead to fundamental changes of how healthcare is provided, and help to reform underfunded and overstretched healthcare systems.

New Devices

Hemoglobin is a red protein responsible for transporting oxygen in the blood. Wearable technologies provide portable, noninvasive point-of-care ways to measure hemoglobin concentration. The wearable devices have the potential to increase the quality of care. Unfortunately, a study showed that widely available noninvasive point-of-care hemoglobin monitoring devices were systematically biased and too unreliable to guide transfusion decisions  ( Gayat et al., 2011 ). Wearable devices with better accuracy are needed.  For future development, wearable devices should also play a role in disease intervention through integration with actuators that are implanted inside/on the body. New wearable drug delivery systems for blood pressure management are likely to be developed in the future.

The advancement of wearable technology and the possibilities of using AI in healthcare is a concept that has been investigated by many studies. The availability of the smartphone and wearable sensor technology are leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. 

For instance, seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. A study by Kiral-Kornek and colleagues (2018 ) presented a proof-of-concept for a seizure prediction system that would be accurate, fully automated, patient-specific, and tunable to an individual's needs. A deep learning classifier was trained to distinguish between preictal and interictal signals. This study demonstrated that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.

Another study aimed to automatically score Parkinsonian tremors by proposing machine-learning algorithms to predict the Unified Parkinson's Disease Rating Scale (UPDRS) ( Jeon et al., 2017 ). In this study, the tremor signals of 85 patients with Parkinson's disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall and precision and was compared to findings in similar studies.

As machine-learning algorithms are increasingly used to support clinical decision-making, reliably quantifying their prediction accuracy is vital.  Inaccurate results can mislead both clinicians and data scientists. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on a part of the data the algorithm has not seen during training. A study  compared two popular CV methods: record-wise and subject-wise approaches ( Saeb, Lonini, Jayaraman, Mohr, & Kording, 2017 ). Using both a publicly available dataset and a simulation, researchers found that record-wise CV often massively overestimates the prediction accuracy of the algorithms.

In summary, various designs of wearable technology applications in healthcare are discussed in this literature review. Further evaluation studies for those applications are needed to confirm the benefits of wearable technologies for the future.

Citation: Wu, M. & Luo, J. (Fall, 2019). Wearable technology applications in healthcare: A literature review. Online Journal of Nursing Informatics (OJNI), 23(3)

The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

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Min Wu holds a PhD in biomedical engineering from the University of North Carolina at Chapel Hill. He is an Associate Professor and department chair of health informatics and administration in the College of Health Sciences at the University of Wisconsin, Milwaukee.

Dr. Wu’s research focuses on identifying unmet medical needs and implementing technological solutions to meet them. For example, he has developed a national mammogram image archive, and a web-based training method for dentists to interpret dental images. Dr. Wu was the first researcher who proposed to use HIPAA messages for public health data collection and sharing in 2005. He designed a new user interface, a fisheye viewer, to view gene expression data for bioinformatics researchers.  His writing has appeared in the Journal of Medical Informatics , the Journal of Medical Systems , BMC Bioinformatics and Academic Radiology . Dr. Wu is a past recipient of the Best Article Award for his work in the Journal of Digital Imaging .

Having 15 years of teaching experiences in health informatics, Dr. Wu published a textbook about electronic health records, “Information Technology in Healthcare.” This book describes key concepts in the discipline of health informatics, particularly electronic medical records, which are now widely used in healthcare. In addition, he is a certified Oracle Database Administrator (DBA) and has taught database courses for more than 10 years. 

Jake Luo completed his PhD degree in machine learning computer science at the Queen’s University, Belfast, U.K. He is an Associate Professor in the Department of Health Informatics and Administration at the University of Wisconsin-Milwaukee. His research interest lies in data-driven predictive analysis using machine learning-based algorithms and technologies, such as data mining, natural language processing, and knowledge representation and modeling. He is interested in investigating how these computing technologies can be used to improve healthcare by providing intelligent decision support for clinicians, medical researchers, patients and policymakers. Dr. Luo’s active research programs involve developing innovative heath data science technologies for knowledge discovery, adapting machine learning algorithms to enhance clinical data processing, implementing collaborative team science initiatives to improve health services and research, and creating intelligent clinical informatics tools to support evidence-based decision making. 

Dr. Luo has developed tools and methods that have been shared with researchers at multiple institutions, including Vanderbilt, Mayo Clinic, UC-San Francisco, and Pfizer, etc. He co-authored a paper that won a Distinguished Paper Award at the AMIA Clinical Research Informatics Summit. One of his papers, “Dynamic Categorization of Clinical Research Eligibility Criteria,” was also one of the top 25 hottest papers in the Journal of Biomedical Informatics . To improve biomedical research collaboration, he leads several projects that aim to integrate services and expert resources located at disparate institutional silos. His team designed and implemented scalable infrastructures for system functionality enhancement, data management, and computational analysis. These systems provided secure and policy-compliant access to enhance translational and comparative effectiveness research. For example, the Request Management System provides a single-entry point for more than 1,500 clinical investigators to consult domain experts and establish collaboration across multiple institutions. He led crucial research programming and development efforts for the informatics infrastructures used in major centers. His lab analyzed clinical trial data collected from over 250,000 studies for new knowledge discovery, such as predicting severe adverse events using advanced computational models. His currently funded projects include developing data-driven methods to analyze and predict drug adverse events and systematically integrating medical image-text to bridge the gaps between textual and imaging information representations.

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

Stefano canali.

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

Viola Schiaffonati

Andrea aliverti.

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.

Introduction

Devices that can be worn on our bodies and track several activities and parameters— wearable devices —are 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 – 3 ]. 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 – 7 ]. 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—yet 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.

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 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.

An 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—as 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.

Local standards for data quality

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 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—high-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—access 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 ( F indability, A ccessibility, I nteroperability, and R euse) can be used as a basis to discuss future developments in this direction [ 34 – 36 ]. 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.

Interoperability for balanced estimations

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 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 – 40 ]. 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—these 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—elevated 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.

Access for health equity

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 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.

Representativity for fairness

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 [ 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—yet 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.

Conclusions

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 ( 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.

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 [ 59 ]. In this context, approaches such as ELSI (Ethical, Legal and Social Issues) and E 2 LSI 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.

Funding Statement

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.