https://orcid.org/0009-0009-5113-9671
Figures
Abstract
Background
The use of wearable devices for remote health monitoring is a rapidly expanding field. These devices might benefit patients and providers; however, they are not yet widely used in healthcare. This scoping review assesses the current state of the literature on wearable devices for remote health monitoring in non-hospital settings.
Methods
CINAHL, Scopus, Embase and MEDLINE were searched until August 5, 2024. We performed citation searching and searched Google Scholar. Studies on wearable devices in an outpatient setting with a clinically relevant, measurable outcome were included and were categorized according to intended use of data: monitoring of existing disease vs. diagnosis of new disease.
Results
Eighty studies met eligibility criteria. Most studies used device data to monitor a chronic disease (68/80, 85%), most often neurodegenerative (22/68, 32%). Twelve studies (12/80, 15%) used device data to diagnose new disease, majority being cardiovascular (9/12, 75%). A range of wearable devices were studied with watches and bracelets being most common (50/80, 63%). Only six studies (8%) were randomized controlled trials, four of which (67%) showed evidence of positive clinical impact. Feasibility determinants were inconsistently reported, including compliance (51/80, 64%), patient-reported useability (13/80, 16%), and participant technology literacy (1/80, 1%).
Conclusions
Evidence for clinical effectiveness of wearable devices remains scant. Heterogeneity across studies in terms of devices, disease targets and monitoring protocols makes data synthesis challenging, especially given the rapid pace of technical innovation. These findings provide direction for future research and implementation of wearable devices in healthcare.
Author summary
Wearable devices, such as smartwatches, have potential to benefit patients and assist healthcare workers; however, these devices are not yet widely used in healthcare. This review looks at existing research around wearable devices used to monitor health markers outside of the hospital. We worked with a research librarian to comprehensively search online databases until August 5, 2024. Studies on use of wearable devices outside of a hospital setting with clinically relevant, measurable outcomes were included and sorted by whether they used data to monitor an existing disease or identify a new disease. We included 80 studies: most focused on monitoring existing chronic diseases (68/80, 85%), while the minority looked at identifying a new disease (12/80, 15%). Many different wearable devices were investigated in these studies. A small proportion (6/80, 8%) of the studies were randomized controlled trials. Four of these six studies (67%) showed a positive clinical impact. We also found that feasibility information was reported inconsistently. There is limited evidence for the clinical effectiveness of wearable devices. Studies varied on device used, disease target, and monitoring protocol, making it difficult to draw conclusions based on this literature, especially since the technology is rapidly evolving.
Citation: Lodewyk K, Wiebe M, Dennett L, Larsson J, Greenshaw A, Hayward J (2025) Wearables research for continuous monitoring of patient outcomes: A scoping review. PLOS Digit Health 4(5): e0000860. https://doi.org/10.1371/journal.pdig.0000860
Editor: Haleh Ayatollahi, Iran University of Medical Sciences, IRAN, ISLAMIC REPUBLIC OF
Received: June 10, 2024; Accepted: April 17, 2025; Published: May 9, 2025
Copyright: © 2025 Lodewyk 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: All data are in the manuscript and/or supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The global market for wearable electronics was estimated at US$32.5 billion in 2022 and is projected to reach US$173.7 billion by 2030, growing at an annual rate of 23.3% over this period [1]. These devices can measure an expanding array of biometrics, including heart rate, blood pressure, and oxygen saturation, and more [2]. They are increasingly integrated with daily life, offering new ways to monitor health unobtrusively for large, distributed populations [2–5]. Remote health monitoring (RHM) is rapidly gaining interest in healthcare research, leveraging wearable technologies, like smartwatches, to track health status in non-hospital environments [3,6]. In some contexts, RHM has been shown to shorten hospital stays [7], reduce hospital readmissions [8], lower healthcare costs [9], and decrease clinician burnout [10].
Despite immense potential, real-world implementations of RHM with wearable devices remain limited. Most published use-cases are for the most common, chronic diseases like diabetes, hypertension, chronic obstructive pulmonary disorder (COPD) or congestive heart failure (CHF), and evidence for effectiveness is conflicting [6]. Further, available data are derived from a diverse set of devices, many of which are now out of date, in a range of patient populations, using different biometric parameters and disease outcomes [3]. Researchers are increasingly exploring wearable devices to improve health outcomes across a broad range of disease contexts. While recent reviews have examined clinical applications of wearables, they primarily focus on chronic disease [11,12]. Given the rapid evolution of wearable technologies and the growing number of potential use cases, there is a need to update existing reviews to include non-chronic conditions and provide a more comprehensive overview of their potential impact.
We therefore performed a scoping review exploring the current clinical applications for wearable devices and RHM for non-hospital settings, summarizing evidence specifically on the impact on patient outcomes, investigating for actual change in these outcomes due to use of a wearable device.
Methods
The protocol for this scoping review was published on Open Science Framework (https://doi.org/10.17605/OSF.IO/AT7VS). We followed Arksey and O’Malley’s guidelines for scoping review methods [13].
Eligibility criteria
The inclusion and exclusion criteria for this scoping review (Table 1) were developed around the PCC (population, concept, context) framework, as per the Joanna Briggs Institute for scoping reviews [14]. We targeted an adult population, excluding studies including patients under 18 years of age. For inclusion, studies needed to use a wearable device to monitor disease. Therefore, studies including solely healthy subjects were excluded. Wearable devices had to meet the following definition based on Gao et al. 2016: devices that can be worn on human skin to continuously and closely monitor an individual’s activities, without interrupting or limiting the user’s motions” [5]. Examples include smartwatches and wearable textiles/clothing with embedded sensors.
We focused on outpatient or non-hospital RHM, excluding studies with hospitalized populations and long-term care facilities. This was done for two main reasons: 1) our primary interest was in settings where clinical teams are less able to support patients in their use of technology for more accurate data on usability and acceptability; and 2) to increase feasibility of the search given an expansive literature base. We also excluded studies with implantable devices, such as blood glucose monitors because they do not meet our definition for ‘wearable’ [5]. All trial designs were eligible, including randomized controlled trials, longitudinal studies, feasibility studies, and observational studies. We focused on studies that measured a clinical outcome, and therefore validation studies were excluded. Conference proceedings, opinion pieces, reviews, and cross-sectional studies were also excluded. Only studies written in the English language were included as we did not have the resources to support a translation service.
Search strategy and study selection
A research librarian (coauthor LD) assisted in designing our search strategy. We searched the following electronic databases from inception to August 5, 2024: CINAHL (1937-present), Scopus (2004-present), Embase (1974-present) and MEDLINE (1946-present via OVID). Our search strategy for MEDLINE is shown in S1 File. This search strategy was translated for use in the other databases. We searched Google Scholar on January 23, 2024. Covidence Systematic Review software [15] was used for study screening. Two reviewers (KL, MW) independently screened studies at both the title/abstract and full text stages. A third reviewer (JH) was involved to resolve conflicts. Citation searching was performed for included studies.
Data extraction and analysis
A data extraction spreadsheet was developed based on the review question and PCC. Two reviewers (KL, MW) completed data extraction. Extracted data included study characteristics (e.g., study design, mean participant age, country), wearable device information (device name, company, sensor type, body part, length of monitoring, instructions on use, frequency of removal, and technical support, and parameters monitored), control group (where applicable), and study findings (including clinical and economic outcomes, protocol compliance, device useability and feasibility).
Results
Search results and study selection
Fig 1 shows the PRISMA flow diagram summarizing search results and study selection. Our search identified 3980 studies after the removal of duplicates. Title and abstract screening resulted narrowed this to 386 studies for full-text screening; the final sample included 80 studies that met our eligibility criteria.
Study characteristics
Table 2 provides a summary of key study characteristics of the included studies, categorized by disease category (i.e., body system) and outcome type (i.e., monitoring of existing disease or diagnosis of new disease). The oldest study was published in 2001, with the majority of included studies published between 2019 and 2024 (51 studies, 64%). The most frequent study location was the United States of America (40 studies, 50%), followed by the UK (10 studies, 13%) and the Netherlands (8 studies, 10%). Only three studies (4%) reported including rural patients. Six of the included studies were randomized controlled trials (RCTs) (8%). Control groups were present in 22 studies (28%).
Most of the included studies monitored changes in an existing disease (68 studies, 85%), while the remainder detected new diseases or risk factors for disease (12 studies, 15%). Of the 12 studies that identified new diseases, the majority were on cardiovascular (9/12, 75%), and the remainder were respiratory (3/12, 25%). Of the 68 studies that monitored an existing disease, the most common disease category was neurodegenerative disorders (22/68, 32%), followed by respiratory diseases (9/68, 13%), psychiatric disorders (9/68, 13%) and cardiac diseases and stroke (9/68, 13%). Other categories for existing disease were diabetes, cancer, musculoskeletal disorders, and renal disease.
Wearable devices
Table 2 lists wearable device names and monitoring lengths for each of the included studies. Fig 2 and Table 3 show device types and brands. S2 File contains detailed information on device name, model, company, and sensor type for each study, organized by wearable device type. Studies used many different wearable device types, models, brands, and sensors. Some studies did not include details on device name (6/80, 8%), model (38/80, 48%), brand (11/80, 14%), sensor type (24/80, 30%), or body part (14/80, 18%). The most common device types were watches and bracelets (50 studies, 63%), and the most frequently used brand was Fitbit (22 studies, 42%). A wide variety of parameters were measured across studies, including movement or activity (37 studies, 46%), step count (33 studies, 41%), heart rate (15 studies, 19%), ECG (10 studies, 13%), sleep-related parameters (10 studies, 13%), temperature (7 studies, 9%), breathing rate (3 studies, 4%), blood oxygen saturation (3 studies, 4%), and blood pressure (2 studies, 3%).
Light blue indicates wrist-worn devices (72.3% of total devices), purple indicates chest band (1.2%), green indicates patch devices (9.6%), light pink indicates ring wearables (2.4%), dark pink indicates attachable devices (6.0%), yellow indicates insole devices (3.6%), red indicates garment devices (3.6%), and dark blue indicates wearable defibrillator (1,2%).
Table 4 contains information on study methods related to wearable devices. Almost half of the included studies monitored participants for up to one month (33 studies, 41%). Seventeen studies (21%) monitored participants for one to three months, and 12 studies (15%) monitored participants for three to six months. Eight studies (10%) monitored participants for six months to one year. One study (1%) monitored patients for over one year. Forty-one of the included studies (51%) reported the onboarding instructions provided to participants, 25 studies (31%) included information on the frequency of device charging or removal from the body, and 13 studies (16%) included information on technical support with maintenance and setup of the device that was provided to study participants. One study (1%) used a device that required a prescription.
Findings of the included studies
A summary of outcome-related findings for each of the 80 included studies can be found in Table 1. Six of the studies (9%) were randomized controlled trials (RCTs), and four of these RCTs (4/6, 67%) showed a positive clinical impact, including evidence of improved patient outcomes resulting from device use. Observational studies used a wide range of wearable devices and health parameters for a diverse set of diseases. All but two observational studies (62/64, 97%) found at least one significant correlation between a device parameter and a clinical outcome of interest. Table 3 shows studies that reported findings related to compliance, useability, and participant technology literacy. Over half of the included studies (51 studies, 64%) reported study protocol compliance, including device wear time or study dropout. Patient-reported usability was reported in 13 studies (16%). Only one study (1%) assessed technology literacy among the participants.
Discussion
Remote health monitoring has the potential to improve healthcare significantly by enabeling continuous monitoring of fidrsdr pstsmryrtd in non-hospital settings, however, real-world impacts remain unproven. (8)(9)(10) In this review, we examined the current evidence for wearables in the medical realm, focusing on measurable effects on clinical outcomes. Building on prior work, we included a wide variety of disease contexts in non-hospital settings and a broadly inclusive set of devices.
The most commonly used devices in this review were watches and bracelets (50/80, 63%), with Fitbit Inc. being the most common brand (22/50, 42%). Wrist-worn devices offer key advantages in clinical settings due to their unobtrusive, comfortable design – similar to traditional wristwatches – which facilitates continuous wear during sleep and physical activity (3). This enables uninterrupted data collection and the potential for early detection of health anomalies [3]. Interestingly, some studies (6/80, 8%) also neglected to report the specific device model/brand using generic terms like “smartwatch”, “inclinometer”, or “wearable activity tracker”. Additional missing details included device model (38/80, 48%), manufacturer (11/80, 14%), sensor type (24/80, 30%), and body placement (14/80, 18%) (S2). Insufficient reporting of technical specifications limits cross-study comparisons and synthesis of findings.
Most included studies monitored an existing disease (68/80, 85%), while a smaller proportion aimed to diagnose a new disease (12/80, 15%). A wide variety of health conditions were monitored, including neurodegenerative disorders (22/68, 32%), respiratory disorders (9/68, 13%), psychiatric disorders (9/68, 13%), cardiac diseases and stroke (9/68, 13%), cancer (7/68, 10%) musculoskeletal disorders (6/68, 9%), diabetes (4/68, 6%), and renal disease (2/68, 3%). Among the studies targeting new disease diagnosis (12/80, 15%), two main categories emerged: cardiac diseases (9/12, 75%) and respiratory diseases (3/12, 25%).
Of the 80 studies included in this review, only six (6/80, 8%) were RCTs, focusing on diabetes (1/6, 17%) respiratory disorders (3/6, 50%), and neurodegenerative disorders (2/6, 33%). Four of these trials (4/6, 67%) showed a positive clinical impact, including evidence of improved patient outcomes associated with device use. Abbott et al (2019) demonstrated a 71% reduction in diabetic ulcer incidence with the use of SurroSense Rx, a smart insole system was used [25]. In COPD, Pedone et al (2013) reported a lower incidence of respiratory events in an experimental group monitored using a wristband tracking heart rate, physical activity, temperature and galvanic skin response, in combination with a commercial pulse-oximeter [40]. In multiple sclerosis (MS), Block et al (2021) found improved adherence to therapy recommendations in patients using a Fitbit Flex 2 but no improvements in disease symptoms [55]. Orme et al (2018) reported mixed effects for COPD using an inclinometer: no change in respiratory symptoms, but reduced fatigue levels [39]. Finally, Tabak et al (2014) found no difference in health status between COPD patients who used a belt-worn Mtx-W sensor and those who did not [42].
As this is a scoping review, we did not perform a systematic quality assessment of studies in this review, however, it is clear that the overall quality of evidence in included studies was low as there were few RCTs. Our findings on minimally published RCTs on this topic do however align with a 2018 meta-analysis reporting a lack of high-quality data on effectiveness for RHM and wearables [95]. In part, the paucity of high-quality data in this area can possibly be explained factors such as high cost associated with complex study protocols, significant implementation challenges, the need for industry partnerships, and complex ethical and legal barriers. There may also be a lack of buy-in from key stakeholders, including patients and doctors [96–98]. Given the paucity of evidence, the use of wearables and real-time data feedback for medical interventions, particularly in acute care, represents an emerging and largely uncharted frontier.
As mentioned, a large proportion of the reviewed publications used common, consumer-grade, wrist-worn devices, for example, fitness trackers or smartwatches. Despite concerns about their accuracy, commercial-grade devices have gained widespread popularity in the general population and therefore offer great potential for wide-spread health impacts. Save for a few select applications (ex. atrial fibrillation detection), these tools are marketed for non-medical use and lifestyle enhancement; however, the integration of their data in medical decision-making seems inevitable. Future research should attempt to test the medical utility of commercial-grade devices in the real-world.
Finally, a key factor in the successful implementation of RHM is patient interest and satisfaction [98]. Involving end users is essential to support patient-centered design principles that promote long-term adoption and usability [99,100]. However, most studies (41/80, 51%) did not report whether participants received device usage instructions. Proper education is critical, as improper use and motion artifacts can compromise data quality and analysis [101]. Additionally, only 13 studies (16%) described providing technical support for device setup or maintenance. This lack of support may contribute to the low compliance rates frequently observed in RHM studies [102,103]. Despite the importance of adherence, only 64% of studies in our review reported on participant compliance. Furthermore, just 16% included patient-reported data on device usability, and only 1% assessed baseline technology literacy. The absence of such data limits the ability to refine digital health interventions and adapt them to diverse populations. Future research should address not only the selection of wearable technologies, but also the critical role of patient education, ongoing technical support, and user engagement to enable successful and scalable implementation [104].
Another issue not well-addressed by the reviewed studies were concerns with data privacy and security. As artificial intelligence advances and risks related to data breaches and cyberattacks grow, the continuous transmission of data via wearables may raise significant privacy issues for patients. Future research must obtain appropriate ethical clearances and adhere to institutional and federal regulations, which may require the development of new policies governing continuous health monitoring technologies. In parallel, researchers and clinical innovators must prioritize the development of secure algorithms and communication channels to safeguard patient data. Compromised security could present a critical barrier to adoption and trust, particularly within research contexts. As wearable technologies become more prevalent in healthcare, careful consideration of regulatory frameworks and ethical requirements is essential to ensure patient safety and data protection.
Limitations
There are several limitations inherent to this review. Since the field of RHM continues to develop and expand, there is a general lack of consensus around terminology, for example, definitions of ‘remote monitoring’ and ‘wearable device’ [105]. This posed a significant challenge when devising a search strategy and we likely did not capture all relevant studies. However, our eligibility criteria were broad compared to prior work [3,106,107], allowing us to capture studies on a wide range of devices and disorders. We also performed citation searching to identify studies that were missed by our search strategy. Importantly, we did not eliminate studies based on methodological quality and we included both consumer-grade and medical-grade technologies. We focused on wearable (not implantable) devices and our findings may not generalize to more invasive forms of RHM, including blood glucose monitors and pacemakers. We also did not include smartphones in our definition of wearable devices, although these can in theory be ‘worn’ if strapped to the body. Lastly, the fast pace of innovation complicates the generalizability of data on older devices included in our review.
Conclusions
To our knowledge, our review is the most comprehensive to date for evidence of wearables influencing clinical outcomes in non-hospital settings. Our findings underscore the challenges of conducting timely and comprehensive evidence synthesis in this rapidly evolving field. The adoption of standardized terminology and consistent reporting on usability, acceptability, adherence, patient education, and technical support would enhance data quality and facilitate comparability across studies. High-quality RCT data is critically needed to establish the clinical utility of wearable technologies. Future research should be strategically prioritized to focus on technologies and applications most relevant to clinical practice and regulatory decision-making. Additionally, continued attention to patient privacy and data security will be essential to prevent unintended harms as these technologies are integrated into clinical care.
Supporting information
S1 File. Detailed version of the MEDLINE search strategy used in this scoping review.
This search strategy was translated for use in other databases.
https://doi.org/10.1371/journal.pdig.0000860.s001
(DOCX)
S2 File. Detailed information on the wearable devices used in each of the 59 included studies.
https://doi.org/10.1371/journal.pdig.0000860.s002
(DOCX)
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