literature review on wearable technology

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• v.10(1); 2022 Jan

The Impact of Wearable Technologies in Health Research: Scoping Review

Sophie huhn.

1 Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany

Hanns-Christian Gunga

2 Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany

Martina Anna Maggioni

3 Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy

Stephen Munga

4 Kenya Medical Research Institute, Kisumu, Kenya

Ali Sié

5 Centre de Recherche en Santé Nouna, Nouna, Burkina Faso

Valentin Boudo

Aditi bunker, rainer sauerborn, till bärnighausen.

6 Harvard Center for Population and Development Studies, Cambridge, MA, United States

7 Africa Health Research Institute, KwaZulu-Natal, South Africa

Sandra Barteit

Associated data.

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

Details on search and search strings.

Medical Education Research Study Quality Instrument scores of included studies.

Vital signs measured by studies.

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

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

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

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

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

Introduction

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

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

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

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

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

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

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

Eligibility Criteria

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

Our review scopes the current information available on affordable, noninvasive wearables, which are (1) worn on the wrist, arm, and chest; (2) measure vital signs; and (3) analyze the generated wearable data for outcome assessment. Validation and qualitative studies were excluded. We focused only on devices that cost <€500 (US $570) per device (1) to allow the affordability of larger studies, for example, where wearable devices need to be provided to study participants via the study and (2) to ensure that wearables are available commercially and (3) intended for consumers. As the definition of vital signs is not distinct [ 35 ], we included the following vital signs [ 9 , 36 , 37 ]: HR, HR variability, ECG measurements or heart rhythm analysis (detection of arrhythmias), blood pressure, blood oxygen, respiratory rate, body temperature, sleep, electrodermal activity, electromyogram measurements, and PA ( Textbox 1 ).

Inclusion and exclusion criteria.

Inclusion criteria

• Full text available
• English language
• Peer-reviewed articles
• Published between 2013 and 2020
• Commercially available wearable, price <€500 (US $570) per device (Only hardware prices were considered. Software, subscriptions, or similar, which might be necessary for device use, were not included. All prices were captured in the timeframe of this study and therefore are only considered as approximations)
• Wearables worn on the arm, wrist, chest, and waist
• Measuring and analyzing one or more vital sign
• Range of vital signs as defined in this review, including heart rate, heart rate variability, electrocardiogram measurements or heart rhythm analysis (detection and classification of atrial fibrillation, extrasystoles, and other arrhythmic events), blood pressure, blood oxygen, respiratory rate, body temperature, sleep (time, deepness, etc), electrodermal activity, electromyogram measurements, physical activity (steps, distance covered, intensity, energy expenditure, etc; physical activity included as basic measurements of wearables or very similar or related parameters) [ 9 , 36 , 37 ].

Exclusion criteria

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

Information Sources and Search

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

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

Quality Assessment

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

Data Synthesis

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

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

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

Study Characteristics

Demographics.

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

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

Characteristics of studies.

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

b Studies aimed to observe and characterize the study population and patients.

c Studies aimed to evaluate patient-reported outcomes, health care practices, diagnostics, screenings, and others.

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

Study Types and Fields

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

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

Studies per medical field.

Wearable Characteristics

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

Characteristics of wearable devices.

a Research-grade wearable devices unavailable for consumers or not consumer grade per se.

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

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

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

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

f Analysis—statistical tests sums up to more than the total number of included studies, as some studies applied more than one type of analysis or statistical test.

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

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

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

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

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

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

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

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

Most studies (120/189, 63.5%) used wearables that cost <€200 (US $228). In some studies (15/189, 7.9%), wearable prices were not transparent, as data were provided through a variety of participant-owned wearables [ 87 ] or the wearable hardware was part of a subscription or a membership fee, that is, Whoop strap of Whoop [ 178 ].

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

Categorization of Wearable Application in the Studies

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

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

Correlations—Wearable and Other Physiological Data

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

Population-Based Research

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

Outcome Assessment

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

Prognosis, Forecasting, and Risk Stratification

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

Explorative Analysis of Big Data Sets

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

Method Evaluation

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

Feasibility

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

Diagnostics and Screening

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

Disease Monitoring

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

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

Strengths and Shortcomings of Wearables

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Global Health and Low-Resource Contexts

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

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

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

Large-scale and Big Data Sets for Wearable Research

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

Limitations

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

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

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

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

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

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

Acknowledgments

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

Abbreviations

Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3, multimedia appendix 4, multimedia appendix 5.

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

Conflicts of Interest: None declared.

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.

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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• Systematic Review
• Delivery of Health Care
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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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A., Jacobs, K., & McLoone, H. (2017). The effect of a wearable device prompting high school students aged 17-18 years to break up periods of prolonged sitting in class. Work, 56 (3), 475-482. Fryar, C. D., Ostchega, Y., Hales, C. M., Zhang, G., & Kruszon-Moran, D. (2017). Hypertension Prevalence and Control Among Adults: United States, 2015-2016. NCHS Data Brief (289), 1-8. Fujikawa, T., Tochikubo, O., Kura, N., Kiyokura, T., Shimada, J., & Umemura, S. (2009). Measurement of hemodynamics during postural changes using a new wearable cephalic laser blood flowmeter. Circulation Journal, 73 (10), 1950-1955. Gayat, E., Bodin, A., Sportiello, C., Boisson, M., Dreyfus, J.-F., Mathieu, E., & Fischler, M. (2011). Performance evaluation of a noninvasive hemoglobin monitoring device. Annals of Emergency Medicine, 57 (4), 330-333. Ghafar-Zadeh, E. (2015). Wireless integrated biosensors for point-of-care diagnostic applications. Sensors, 15 (2), 3236-3261. Ghosh, A., Torres, J. M. M., Danieli, M., & Riccardi, G. (2015). Detection of essential hypertension with physiological signals from wearable devices. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. Gibson, R. M., Amira, A., Ramzan, N., Casaseca-de-la-Higuera, P., & Pervez, Z. (2017). Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomedical Signal Processing and Control, 33 , 96-108. Goldberg, E. M., & Levy, P. D. (2016). New approaches to evaluating and monitoring blood pressure. Current Hypertension Reports, 18 (6), 49. González, S., Sedano, J., Villar, J. R., Corchado, E., Herrero, Á., & Baruque, B. (2015). Features and models for human activity recognition. Neurocomputing, 167 , 52-60. Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., . . . Andreescu, S. (2015). Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. Paper presented at the 2015 IEEE international conference on services computing (SCC). Heintzman, N. D. (2016). A digital ecosystem of diabetes data and technology: services, systems, and tools enabled by wearables, sensors, and apps. Journal of Diabetes Science and Technology, 10 (1), 35-41. Hernandez-Silveira, M., Ahmed, K., Ang, S.-S., Zandari, F., Mehta, T., Weir, R., . . . Brett, S. J. (2015). Assessment of the feasibility of an ultra-low power, wireless digital patch for the continuous ambulatory monitoring of vital signs. BMJ Open, 5 (5), e006606. Hetterich, C., Pobiruchin, M., Wiesner, M., & Pfeifer, D. (2014). How Google Glass could support patients with diabetes mellitus in daily life. Paper presented at the MIE. Hiremath, S., Yang, G., & Mankodiya, K. (2014). 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The usefulness and actual use of wearable devices among the elderly population. Computer Methods and Programs in Biomedicine, 153 , 137-159. Kiral-Kornek, I., Roy, S., Nurse, E., Mashford, B., Karoly, P., Carroll, T., . . . O'Brien, T. (2018). Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine, 27 , 103-111. Klein, H. U., Meltendorf, U., Reek, S., Smid, J., Kuss, S., Cygankiewicz, I., . . . Wollbrueck, A. (2010). Bridging a temporary high risk of sudden arrhythmic death. Experience with the wearable cardioverter defibrillator (WCD). Pacing and Clinical Electrophysiology, 33 (3), 353-367. Kroll, R. R., Boyd, J. G., & Maslove, D. M. (2016). Accuracy of a wrist-worn wearable device for monitoring heart rates in hospital inpatients: a prospective observational study. Journal of Medical Internet Research, 18 (9). Lee, H., Song, C., Hong, Y. S., Kim, M. S., Cho, H. R., Kang, T., . . . Kim, D.-H. (2017). Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Science Advances, 3 (3), e1601314. Lin, Z., Dai, H., Xiong, Y., Xia, X., & Horng, S.-J. (2017). Quantification assessment of bradykinesia in Parkinson's disease based on a wearable device. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. Lv, Z., Chirivella, J., & Gagliardo, P. (2016). Bigdata oriented multimedia mobile health applications. Journal of Medical Systems, 40 (5), 120. Nguyen, N. H., Hadgraft, N. T., Moore, M. M., Rosenberg, D. E., Lynch, C., Reeves, M. M., & Lynch, B. M. (2017). A qualitative evaluation of breast cancer survivors’ acceptance of and preferences for consumer wearable technology activity trackers. Supportive Care in Cancer, 25 (11), 3375-3384. Pannurat, N., Thiemjarus, S., & Nantajeewarawat, E. (2017). A hybrid temporal reasoning framework for fall monitoring. 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Open Access

Peer-reviewed

Research Article

Mapping the ethical landscape of digital biomarkers: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft

Affiliation Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

Roles Data curation, Formal analysis, Investigation

Roles Funding acquisition, Supervision, Writing – review & editing

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

* E-mail: [email protected]

• Mattia Andreoletti, 
• Luana Haller, 
• Effy Vayena, 
• Alessandro Blasimme

• Published: May 16, 2024
• https://doi.org/10.1371/journal.pdig.0000519
• Reader Comments

In the evolving landscape of digital medicine, digital biomarkers have emerged as a transformative source of health data, positioning them as an indispensable element for the future of the discipline. This necessitates a comprehensive exploration of the ethical complexities and challenges intrinsic to this cutting-edge technology. To address this imperative, we conducted a scoping review, seeking to distill the scientific literature exploring the ethical dimensions of the use of digital biomarkers. By closely scrutinizing the literature, this review aims to bring to light the underlying ethical issues associated with the development and integration of digital biomarkers into medical practice.

Author summary

This scoping review focuses on the ethical complexities inherent in the use of digital biomarkers, recognizing the necessity of understanding and addressing these challenges. The review examines literature across various fields to illuminate the ethical issues surrounding the development, validation, and implementation of digital biomarkers. The following ethical concerns are highlighted: privacy and data security, informed consent, validation, equity and sustainability, transparency, impact of stigmatization, regulatory issues, accountability, and data ownership.

Citation: Andreoletti M, Haller L, Vayena E, Blasimme A (2024) Mapping the ethical landscape of digital biomarkers: A scoping review. PLOS Digit Health 3(5): e0000519. https://doi.org/10.1371/journal.pdig.0000519

Editor: Nicole Yee-Key Li-Jessen, McGill University, CANADA

Received: December 1, 2023; Accepted: April 22, 2024; Published: May 16, 2024

Copyright: © 2024 Andreoletti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: This article does not include original research data. Articles included in the scoping review are fully accessible through the online databases mentioned in the article.

Funding: This work was supported by the ERA-NET ELSA_NEURON_026 grant, funded in Switzerland by the Swiss National Science Foundation (SNSF - 10NE17_199434 to AB and MA), and by the NRP77 grant (SNSF - 407740_187356 to EV and AB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Roughly speaking, digital biomarkers are biological traits that can be measured using digital devices (see below). In recent years, the use of novel digital biomarkers in both research and clinical settings has sparked significant scientific interest, due to their potential to revolutionize health monitoring, diagnosis, biomedical discovery, and drug development. Considerable effort is underway to develop and validate digital biomarkers in diverse domains such as neurology, mental health, cardiology, and healthy aging. Alongside the development of such digital tools, there is a growing focus on the ethical considerations associated with these advancements.

The rise of digital biomarkers has been facilitated by the widespread adoption of smartphones, wearable devices, and Internet of Things (IoT) technologies, which enable extensive data collection and real-time analysis. These digital tools have empowered individuals to actively participate in their own healthcare management by providing access to personalized health information and facilitating early detection and prevention of disease. Digital biomarkers extend beyond the direct measurement of pathophysiological variables, to encompass an array of digital signals related to medical conditions or health trajectory. For instance, digital biomarkers such as key-stroke dynamics can correlate to fluctuations in individual cognitive capacity, enabling real-time detection of functional impairment caused by stress, anxiety, fatigue, or an underlying neurological condition [ 1 ].

Unlike molecular biomarkers, digital biomarkers are not primarily intended as tools for patient stratification. Digital biomarkers have capacity to measure proxies of both clinical and pre-clinical conditions, and detect pre-clinical signs of functional deterioration. Thus, they hold promise to greatly expand our ability to monitor individual health across the life course, and are pivotal to enabling preventive, health promotion and public health interventions, including those outside of conventional clinical settings; for instance, in the field of healthy aging. In this respect, digital biomarkers represent a major component to the success of digital health.

Given the disruptive potential of digital biomarkers, it is crucial to undertake a comprehensive exploration of the ethical considerations and challenges associated with their implementation.

An example of a digital biomarker is variability in gait speed, measured by wearables, for early detection of Alzheimer’s Disease (AD). Alzheimer’s disease patients commonly display motor difficulties in the initial phases of the condition, occurring a minimum of ten years before cognitive impairment symptoms become evident [ 2 ]. Motor difficulties can be actively assessed in a gait laboratory using tests such as walking for a predetermined distance or duration, or continuously distant monitored with digital devices. However, the collection of such data raises immediate ethical concerns, including ensuring proper informed consent, determining data access rights, outlining usage protocols, implementing robust security measures, and delineating constraints on data accessibility–just to name a few.

The use of a scoping review methodology in this study enables a thorough examination of the ethical landscape pertaining to digital biomarkers. This approach facilitates a comprehensive exploration of ethical considerations and challenges that arise during their implementation, providing a general understanding of ethical challenges in this space. Overall, our research endeavors to answer the following question: What ethical issues are associated with the development, validation, and use of digital biomarkers? By employing a scoping review methodology, we can map the breadth of available evidence, encompassing studies in different fields. Through this approach, we aim to identify key ethical issues, address gaps in the existing literature, and shed light on potential areas of concern, ultimately contributing to the development of robust ethical frameworks and policies guiding the responsible development and use of digital biomarkers.

2. Background

2.1 definition.

Biomarkers are indicators of biologic processes, pathologic processes, or biological reactions to a therapeutic intervention [ 3 ]. Initially, biomarkers were primarily used for patient stratification in the context of clinical research, as they enable prognosis, and can predict which patients are more likely to respond well to a drug and experience fewer side effects. Biomarkers have been widely adopted in drug development to measure drug response in terms of pharmacodynamics, target engagement, safety, and toxicity. More recently, biomarkers have found application in the clinical context, most notably in oncology, where they have enabled considerable progress in targeted oncological therapy [ 4 ] through the development of companion diagnostics assays [ 5 , 6 ].

The first occurrences of the term digital biomarker in the literature can be found in two studies from the mid 2010s, one describing a patch that continuously monitors oxygen saturation during sleep, to detect sleep respiratory problems [ 7 ]; the other, illustrating the use of thoracic impedance measurements to identify high-risk patients in the setting of developing drugs for cardiovascular conditions [ 8 ]. It is worth noting that the concept of digital biomarkers has since evolved significantly.

The term "digital" refers to the use of numerous types of hardware, sensors, and software to collect, analyze and present data. Measurements frequently take place in home environments and can involve both commercial products such as smartphone and fitness bands, and medical devices such as wearables, and implantable or ingestible sensors [ 9 ]. The prospects of using large arrays of digitally collected data for health-related predictions and to inform clinical decision making, are also linked to the use of advanced data analytics techniques such as machine learning, deep neural networks, natural language processing and predictive analytics [ 10 ].

The notion of digital biomarker can also be used to refer more generally to the collection of any health-relevant data enabled by digital devices (see e.g., [ 11 ]). Here we distinguish digital biomarkers from digital phenotyping, defined as the collection and analysis of contextual digital data through personal devices and digital platforms (smartphone usage patterns, social media activity, GPS location) to infer an individual’s behavioral or psychological characteristics, either in isolation [ 12 ] or in conjunction with clinical measurements, such as health records and molecular or imaging data [ 13 ]. Digital phenotyping tends to emphasize behavioral and lifestyle aspects, but can also inform diagnosis of specific disorders [ 14 ].

On the other hand, digital biomarkers most often refer to objective, quantifiable measures obtained through digital devices or sensors beyond those that are commercially available, providing information about an individual’s pathological state. Digital biomarkers tend to focus on physiological signals and pathophysiological processes, enabling more direct inference of health outcomes. There is considerable semantic overlap between the two approaches, with both offering great promise for early detection, monitoring, and personalized interventions in healthcare.

With discussion of definitions ongoing, some authors have advocated for a more stringent and consistent usage of the term digital biomarker (see e.g., [ 15 ]), in contrast to its current varied application. One recent attempt at clarification was made by the FDA, defining a digital biomarker as “a characteristic or set of characteristics, collected from digital health technologies, that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions” [ 16 ]. This definition has the merit of clearly establishing the scope of digital biomarkers and highlighting their potential application in diagnostics, pharmacodynamics/response, and monitoring. It can serve as a crucial entry point for discussion of this topic, including ethical considerations.

3. Methodology

3.1 information sources and eligibility criteria.

The methodology for this scoping review study was developed based on the Arksey and O’Malley framework [ 17 ]. To ensure comprehensive coverage of the literature, we conducted our search across three major electronic databases: PubMed, Scopus, and Web of Science. This multi-platform approach aimed to minimize the risk of missing relevant papers. Throughout the screening process, we independently reviewed the papers to ensure an unbiased approach, applying eligibility and exclusion criteria, and discussing each point of disagreement.

Considering the relatively recent emergence of the field, we set the timeframe for the literature search from 2007 (the year of the first iPhone release) to 2022. This timeframe was chosen to ensure replicability and establish a library that can be easily updated in the future. The final search string used was: (digital OR sensor* OR mobile OR smartphone OR biometr* OR wearable) AND (biomarker*) AND ethic* in title, abstract, and keywords. We used the most common synonyms and related keywords for digital, in order to capture a larger sample of the literature. We considered papers published in peer-reviewed journals and book chapters. We also considered review articles, given the limited availability of original research. Articles had to be written in English for inclusion in our sample ( Fig 1 ).

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https://doi.org/10.1371/journal.pdig.0000519.g001

Our literature search was conducted between March and April 2023. Search results from all databases yielded a total count of 687 articles, which were then exported into EndNote software for sorting and removal of duplicates. After eliminating duplicates (197), retractions (1), conference proceedings (5), and non-English literature (2), our library consisted of 482 records.

These records were initially screened for the presence of the keyword "ethic(s)" in the title, abstract, or keywords. Abstracts mentioning ethics solely in the context of ethics dissemination or approval by an Ethics Committee were excluded. We revisited 106 papers and removed those that were not relevant to ethical issues of digital biomarkers, based on a more thoughtful analysis of the abstracts. Any inconsistencies were resolved through joint examination of the full text. This further screening process resulted in 33 records eligible for inclusion. Additionally, snowballing research led to the identification of one more relevant publication. After this selection process, a total of 13 papers were included in this review ( Table 1 ).

https://doi.org/10.1371/journal.pdig.0000519.t001

The full text of each selected paper was then thoroughly analyzed to identify all ethical issues related to digital biomarkers. Articles that did not explicitly discuss ethical issues associated with digital biomarkers, even in a limited manner, were excluded. To enhance comprehension of the wide-ranging areas explored in the chosen papers, we systematically categorized the papers based on their respective topics. This approach enabled us to construct a comprehensive overview of the diverse domains and subtopics addressed in the literature. Significant findings from each paper were then succinctly summarized in an overview table, streamlining the subsequent analysis of the results.

All retrieved papers in our study were published from 2019 onwards, underscoring the novelty of the topic. The publications included in our scoping review encompassed an array of domains, shedding light on the utilization of digital biomarkers in various fields. The largest number of papers concentrated on mental health/psychiatry, investigating potential applications of digital biomarkers for diagnosing and treating mental health conditions. Four articles were published in the personalized and digital medicine realm, while one article fell under each neurology and general medicine ( Fig 2 .)

https://doi.org/10.1371/journal.pdig.0000519.g002

It is not surprising that a significant portion of the discussion on digital biomarkers has emerged from the area of psychiatry and mental health, as the field has consistently prioritized the exploration of alternative biomarkers, due to the limitations of traditional biomarkers, which are often invasive or financially burdensome.

Most of the articles included in our review are review articles, suggesting that the specific ethical issues related to digital biomarkers so far have remained largely unexplored. Only two papers are labelled as original research, while two are perspective or commentary articles. One record is a book chapter within a specialized publication ( Fig 3 ).

https://doi.org/10.1371/journal.pdig.0000519.g003

Our review reveals that, within the literature, privacy and data security stand out as central ethical concerns (totaling 13 sources). Closely linked with these concerns is a significant emphasis on the appropriate use of informed consent for acquiring health data through digital technologies (found in six sources). Additionally, the validation of algorithms enabling data analysis, such as risk prediction, is a frequently discussed issue (mentioned in six sources). Furthermore, there are notable considerations regarding equity and sustainability, as reflected in five sources. Transparency, specifically pertaining to data handling and analysis, emerges as a less common yet still significant concern in four articles. Stigmatization and the predictive use of digital biomarkers for incurable diseases also attract ethical attention (discussed in three articles). Ethical worries surrounding data ownership, and regulatory aspects for development and market access, are represented in two sources. Accountability, with regard to the responsible management of data and decision-making processes, is discussed in two sources ( Table 2 ). The following sections further elaborate on our findings, offering concise context and descriptions of the major themes and implications extracted from the retrieved literature.

https://doi.org/10.1371/journal.pdig.0000519.t002

4.1 Privacy, data security, and informed consent

Privacy and data security emerge as paramount concerns in the implementation of digital biomarkers, giving rise to ethical dilemmas regarding the protection of sensitive patient data against unauthorized access and misuse [ 18 – 30 ]. The collection and sharing of sensitive personal health information raises questions about who should have access to the data and how it should be used. Patients may hesitate to share data with their healthcare providers, families, and employers due to privacy concerns. They may worry about consequences of disclosing biomarker results, such as stigmatization or potential discrimination by insurance companies or employers, based on health risks revealed by the data.

The use of such data for research purposes and by private companies raises additional ethical concerns. This data is often valuable for commercial purposes, such as targeted advertising, product development, or sale to third parties. Users may be unaware of how their digital biomarker data is being used or may not have sufficient control over its dissemination.

However, in practice, privacy is often difficult to achieve. For instance, Dagum and Montag [ 21 ] (drawing on [ 31 ]) highlight the challenges of protecting privacy when mobile devices automatically record user whereabouts. Common methods for privacy protection include collecting anonymized data, using third-party storage for anonymized data, and de-identifying identifiable data. However, such strategies might not be effective when dealing with location and accelerometer data obtained through mobile sensors. Many studies have demonstrated that it is in fact possible to identify individuals and their behaviors with a high degree of accuracy based on such anonymized data. Dagum and Montag [ 21 ] also emphasize privacy concerns related to data linkage, as combining multiple datasets increases the risk of identification. The simultaneous collection of various types of data from mobile sensors further enhances the probability of linking data to identifying information.

Given the numerous privacy concerns associated with digital biomarkers, ensuring informed consent is essential prior to data collection. The process of informed consent for digital biomarkers requires a setting conducive to good decision-making and should go beyond merely signing a form. Further challenges arise when obtaining consent from individuals with cognitive impairment, such as in cases of neurodegenerative or psychiatric diseases that require special considerations and engagement [ 23 ]. This seems to be a particularly urgent issue in digital biomarkers as, so far, they have been employed mostly in such contexts.

4.2 Equity and sustainability

Digital biomarkers, if not implemented carefully, can exacerbate existing health disparities, and create further inequities in access and outcomes [ 20 , 25 , 27 , 30 ]. One key aspect is the digital divide, with disparities in access to technology and digital literacy preventing marginalized populations from benefiting from digital biomarkers. The economic burden associated with the cost of technologies can further exacerbate the situation. This issue is worsened by the limited availability of mobile health technologies in languages other than English [ 27 ], widening the gap between those who have access to the latest healthcare technologies and those who do not, and perpetuating health inequalities. Moreover, acceptance of digital technologies can vary among cultures. As [ 20 ] illustrates, in a study promoting physical activity among refugee women, the researcher opted to employ wrist-worn accelerometers to assess daily mobility. These devices were distributed to participants, who were instructed on their usage. Upon returning a week later to collect the data, the researcher discovered that no measurements had been recorded. This outcome was attributed to the cultural and social taboo surrounding the use of wrist-worn mobile technology, as it would have attracted unwanted attention [ 32 ].

Additionally, issues arise related to data bias and representation, as development and validation of digital biomarkers often relies on data that does not adequately capture the diverse characteristics of different populations. This can lead to biased algorithms and diagnostic tools that are not equally effective for everyone. Ensuring equity in these tools regarding sex, gender, race, ethnicity, and culture is a significant challenge. As explained in [ 29 ], for instance, women are at a higher risk of Alzheimer’s disease (AD), stroke, depression, anxiety disorders, and migraines, factors which increase the risk of dementia. Investigating how gender impacts disease presentation, given the higher prevalence of depression and AD in women, may lead to discovery of distinct digital biomarkers and machine learning techniques for both genders.

Algorithmic fairness in emotional computing should further extend beyond sex and gender disparities. For example, facial analysis algorithms may struggle with faces of diverse racial and cultural backgrounds, due to training on datasets primarily comprising lighter-skinned males [ 29 ]. To improve fairness, transparency, and accountability in emotional computing development, both algorithmic and human bias must be addressed.

4.3 Validation

The validation of digital biomarkers raises not only epistemological considerations, but also ethical implications that cannot be ignored [ 19 , 20 , 24 , 27 , 28 , 30 ]. While the primary concern is ensuring the accuracy and reliability of these biomarkers, the moral responsibility to avoid harm is inherently tied to the validation process. Therefore, it is crucial to only make use of digital biomarkers that have undergone thorough validation procedures. Among the different levels of validation of a digital biomarker (verification, validation, analytic validation, clinical validation), the most relevant for end-users is clinical validation [ 20 ].

Clinical validation involves assessing whether the measurement of interest accurately reflects the intended concept, such as the patient’s subjective experience, functional ability, or overall well-being. It requires evaluating whether a specific measure, such as gait speed, effectively captures relevant aspects of how a particular patient population feels, functions, or survives, a common task in the validation of biomarkers. A further key aspect of validation is consideration of potential risks and harms associated with false positives, false negatives, or misinterpretation of biomarker results. However, determining how to effectively test and validate digital biomarkers to prevent unnecessary harm resulting from errors is a complex and challenging endeavor.

4.4 Regulatory issues

The absence of clear and ad hoc regulatory guidelines is widely recognized as a significant ethical concern within the realm of digital biomarkers. This gap in regulation poses a risk of unproven digital biomarkers being brought to market, facilitated by the low barriers to entry [ 20 , 22 , 23 ]. Currently, the FDA’s oversight of digital biomarkers is embedded within its broader regulation of medical devices and digital health technologies. Typically, digital biomarkers get regulatory approval under the classification of "software as a medical device" (SaMD). SaMDs can fulfill a medical function autonomously, without requiring integration into hardware. However, as digital biomarkers increasingly rely on AI/ML for data interpretation, the regulatory landscape becomes more intricate. There is currently a substantial "regulatory gap" concerning the clinical application of AI/ML, a matter under active discussion with the situation evolving rapidly [ 16 ]. This regulatory ambiguity raises ethical questions about patient safety, the reliability of diagnoses, and the potential for exploitation by companies seeking to capitalize on the burgeoning digital biomarker market.

4.5 Impact of stigmatization and identification of incurable diseases

Particularly in the field of mental health, concern that the use of digital biomarkers could do more harm than good is a significant ethical dilemma. In practice, these tools can lead to heightened anxiety in patients, or unnecessary treatment due to overdiagnosis. The identification of cases with mild or no symptoms may not only cause distress to individuals, but also burden healthcare systems, including when treatment options are limited or non-existent. Disclosure of "sensitive" data concerning medical conditions or symptoms can adversely affect patients’ lives, giving rise to concerns about social stigma and discrimination.

The acceptability of delegating health monitoring tasks to a machine is also seen as ethically questionable, as patients may associate the use of digital devices (e.g., wristbands) with intrusive forms of monitoring that impact individual autonomy and freedom, similar to electronic ankle tagging of prisoners [ 18 ].

4.6 Transparency, accountability, and data ownership

Further significant concerns relate to lack of transparency [ 19 , 22 , 27 ] in the algorithms and methodologies employed to derive digital biomarkers, as well as the allocation of data protection duties and responsibilities among the actors involved in the process [ 26 ].

These issues are closely related to the way digital health tools work, and their accountability, including responsibility and liability. While AI and ML are increasingly utilized in tools such as digital biomarkers, there remains a lack of mechanisms for accountability. AI/ML systems are frequently described as operating inside a "black box," meaning that their inner logic and specific decisions are challenging–if not impossible–for humans to elucidate or understand.

Additionally, the introduction of AI into healthcare may have an impact on the liability of doctors. If doctors are directly involved in patient care, but their treatment decisions incorporate recommendations from AI that are not easily explainable, doctors may face challenges concerning their moral and legal responsibilities. This shift in responsibility could eventually impact the trust patients have in healthcare providers and institutions [ 22 ].

Lastly, the issue of data ownership and control is ethically sensitive, particularly in research environments [ 26 ], where it is often unclear whether data primarily belong to research participants, to researchers, or to study sponsors [ 23 ].

5. Discussion

5.1 theoretical contributions.

Digital biomarkers are expected to transform the way we monitor and manage health, to drive the development of novel paradigms such as precision medicine, and to contribute to preventive medicine through active monitoring and helping people adopt and maintain healthier lifestyles, reducing disease burden at a population level. They also hold great promise to boost clinical research and drug discovery, including the use of digitally measured endpoints in decentralized clinical trials [ 33 ].

Considerable attention is being devoted to the ethical and societal aspects of the ongoing digital transformation of medicine. In the space of digital biomarkers, challenges fall into two broad categories: issues related to the digital infrastructure needed to develop, test, and use digital biomarkers; and issues related to specific uses of digital biomarkers. Our findings indicate that infrastructure issues are not fundamentally distinct from the ethical concerns commonly associated with digital health in general [ 34 , 35 ].

In a broad literature review, Tomicic et al. explored the ethical, legal, and social issues (ELSI) associated with monitoring, quantification, and tracking practices mediated by digital devices, including (but not limited to) digital biomarkers [ 36 ]. This study identifies several key ethical challenges, including privacy issues, insufficient research, concerns regarding consent, potential impact on human psychology, surveillance, and data security. To a considerable extent, these concerns are related to the level of trust that users place in access and platform providers to ensure secure service environments [ 37 ]. Additionally, trust in government bodies and regulatory authorities to safeguard the integrity of the digital environment and mitigate negative impacts for individuals and society has emerged as a crucial factor influencing the adoption and future potential of digital phenotyping in healthcare.

Against this backdrop, our findings illuminate the use-specific and context-dependent nature of the ethical considerations of digital biomarkers. As evidenced by the literature, the applications and uses of digital biomarkers are diverse, varying according to the setting in which they are employed. Consequently, the ethical issues associated with digital biomarkers differ widely depending on the intended use (e.g., early detection of a neurodegenerative disease vs. pneumonia). The deployment of digital biomarkers in clinical practice may present different ethical challenges compared with their utilization in clinical trials to streamline the drug testing process.

5.2 Potential solutions

In addition to the ethical challenges we have identified, our sample highlights both infrastructure solutions and other specific solutions, the former resonating with current digital health literature. For example, with regard to privacy and data security, since digital biomarker data contains sensitive and personal information about an individual’s health, behavior, and lifestyle, effective security measures must be implemented to safeguard this data and prevent unauthorized parties from accessing or misusing it. Therefore, the implementation of a robust data governance framework with transparent distribution of data security responsibilities among stakeholders, including financial backers, researchers, digital tool operators, healthcare service providers, and biobanks and databanks, emerges as a fundamental prerequisite to safeguard privacy [ 26 ].

With regard to informed consent, technological literacy plays a pivotal role in addressing data collection and privacy concerns in the realm of digital medicine. For instance, as highlighted in [ 20 , 23 , 27 ] the non-profit organization Sage Bionetworks has pioneered accessible informed consent templates for mobile devices, which can serve as a valuable blueprint.

To promote inclusivity, diversity, and fairness in the development and implementation of digital biomarkers, bias mitigation is crucial, both in training and validating datasets, and in ensuring that digital biomarkers perform evenly across patient groups. This necessitates the involvement of diverse populations in research and development, the mitigation of algorithmic biases, and consideration of the unique needs and contexts of different communities to prevent disparities in healthcare access and outcomes [ 20 , 27 ].

To increase transparency and allow accountability, the establishment of well-defined regulatory guidelines and standards is essential, with regulatory bodies and professional organizations playing a pivotal role. This involves publication of validation studies, disclosure of performance metrics, and sharing of algorithm details, such as the testing properties of digital biomarkers (sensitivity, specificity, and thresholds for action). Increased transparency not only would enhance the scientific credibility and clinical utility of digital biomarkers, but also would foster trust and empowers informed decision making for both healthcare providers and patients.

5.3 Practical challenges

Viewing digital biomarkers through the lens of population screening offers a perspective on the interconnected ethical and practical considerations. For example, when considering digital biomarkers as a screening tool for detecting dementia in the general population, specific ethical issues have been noted [ 38 ]. Here, the ethical principles of screenings outlined by Wilson and Junger, in their influential 1968 report for the World Health Organization [ 39 ], can provide guidance for navigating the complex ethical terrain inherent in the use of these innovative tools. Wilson and Junger’s principles incorporate factors such as the severity of a health issue targeted by screening, accessibility of diagnostic technologies, and availability of accepted treatments [ 38 ]. In the context of digital biomarkers, these principles underscore the need to critically assess the potential benefits, risks, and societal implications of digital biomarker-based screening programs to ensure their alignment with the overarching goal of promoting well-being and achieving meaningful healthcare outcomes.

5.4 Future research

Considering the early stage of the field of digital biomarkers, it is crucial to conduct further research and engage in ethical discussion to address the issues identified in the literature. Our review serves as a starting point for such discussion and highlights the numerous gaps in existing research. Additionally, it emphasizes the need for a domain- and context-specific ethical analysis of digital biomarkers, taking into consideration factors such as privacy, consent, algorithm bias, equitable access, and validation. Recognition of these issues in the scientific literature underscores the value of ongoing exploration and deliberation, to promote responsible and ethical use of digital biomarkers in healthcare.

It is worth noting that the literature included in our scoping review generally provides limited in-depth discussion on ethical issues related to digital biomarkers. In most of the retrieved papers, ethical concerns were mentioned briefly or listed without further exploration. This observation aligns with the fact that many of these publications primarily focus on scientific aspects rather than ethical considerations. As scientific journals prioritize scientific findings and methodologies, the limited emphasis on ethical discussion is therefore not surprising. However, the recognition of ethical aspects within these papers underscores the need for interdisciplinary collaboration between the fields of bioethics and digital health. By bridging these disciplines, we can foster a more comprehensive understanding of the ethical challenges and promote the integration of ethical frameworks for the development, implementation, and regulation of digital biomarkers.

Further specialized research in the domain of bioethics is arguably needed not only to identify ethical challenges, but to offer guidance linked to specific uses of digital biomarkers. More specifically, guidelines regarding the ethical aspects of digital biomarkers should take into account the specific ethical issues presented by use of digital biomarkers in different medical specialties and clinical settings. Instead of relying solely on overarching principles and generic ethical claims about, for instance, privacy, data security, or informed consent, it may be useful to develop domain- and context-specific guidelines to serve as intermediate-level maxims. Therefore, we argue that to offer support to the clinical development and implementation of digital biomarkers, general frameworks of norms and ethical principles in digital health should be specified into more concrete guidelines for both researchers and healthcare practitioners [ 40 ].

5.5 Limitations

The main obstacle we encountered in our research was the absence of a widely shared definition of digital biomarkers. Often, the term is used in a broad sense, encompassing the collection of digital data that may not necessarily serve as (bio)markers. While it is true that many ethical issues discussed in relation to digital data collection (e.g., digital phenotyping) may apply to digital biomarkers, it is possible that digital biomarkers have their own specific ethical concerns. However, our findings suggest that these concerns may remain largely concealed, probably in part due to this terminological confusion.

Because of the limited availability of specialized literature on the subject, we were required to adopt a more inclusive approach when selecting papers for our review. This choice allowed us to identify relevant papers across different journals and disciplines, whose quality and scope varies widely. However, it also limited the level of automated screening, and required subjective judgment in determining which papers to include in the final analysis. As a result, the exact replicability of our review may be compromised, as different researchers might make different judgments. Nonetheless, we believe that this issue does not impact on our general findings.

Conversely, we deliberately chose not to include publications on the ethics of digital health or digital phenotyping. Since these were not the primary focus of our review, we made the decision to narrow our scope and concentrate on the specific domain of digital biomarkers, to capture their specific ethical issues.

6. Conclusion

So far, there have been few attempts to investigate the ethical issues surrounding digital biomarkers. This is likely because it is an emerging and rapidly evolving field. Additionally, the literature appears to be flawed due to a terminological confusion between digital phenotyping and digital biomarkers. A lack of clear and widely adopted definitions has probably hindered streamlined investigation. Furthermore, the discussion of ethical issues in the broader realm of digital health has likely overshadowed a more detailed analysis of specific technologies such as digital biomarkers. Considering the potential impact of digital biomarkers on healthcare, a more focused and thoughtful ethical reflection is necessary. This scoping review can serve as a starting point for future analyses and investigation.

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RSC Advances

A liquid metal-embedded 3d interconnected-porous tpu/mxene composite with improved capacitive sensitivity and pressure detection range †.

* Corresponding authors

a Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, Hubei, China E-mail: [email protected]

b Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA

Flexible capacitive sensors are widely deployed in wearable smart electronics. Substantial studies have been devoted to constructing characteristic material architectures to improve their electromechanical sensing performance by facilitating the change of the electrode layer spacing. However, the air gaps introduced by the designed material architectures are easily squeezed when subjected to high-pressure loads, resulting in a limited increase in sensitivity over a wide range. To overcome this limitation, in this work, we embed the liquid metal (LM) in the internally interconnected porous structure of a flexible composite foam to fabricate a flexible and high-performance capacitive sensor. Different from the conventional conductive elements filled composite, the incompressible feature of the embedded fluidic LM leads to significantly improved mechanical stability of the composite foam to withstand high pressure loadings, resulting in a wider pressure sensing range from 10 Pa to 260 kPa for our capacitive composite sensor. Simultaneously, the metal conductivity and liquid ductility of the embedded LM endow the as-fabricated capacitive sensor with outstanding mechanical flexibility and pressure sensitivity (up to 1.91 kPa −1 ). Meanwhile, the LM-embedded interconnected-porous thermoplastic polyurethane/MXene composite sensor also shows excellent reliability over 4000 long-period load cycles, and the response times are merely 60 ms and 110 ms for the loading and unloading processes, respectively. To highlight their advantages in various applications, the as-proposed capacitive sensors are demonstrated to detect human movements and monitor biophysical heart-rate signals. It is believed that our finding could extend the material framework of flexible capacitive sensors and offer new possibilities and solutions in the development of the next-generation wearable electronics.

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A liquid metal-embedded 3D interconnected-porous TPU/MXene composite with improved capacitive sensitivity and pressure detection range

Z. Zheng, X. Fang, Y. Pan, S. Song, H. Xue, J. Li, Y. Li and J. Li, RSC Adv. , 2024,  14 , 15730 DOI: 10.1039/D4RA01215A

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