Introduction

Wearable health technology refers to electronic devices that are close to or worn on a body part or embedded in clothing or accessories, which detect and collect data through retrieval methods []. It uses the miniaturization of sensors and the integration of network connectivity and predictive analytics to automatically capture, transmit, and analyze biometric information []. These intelligent devices not only play a role in disease diagnosis [,] and treatment but also promote health improvement []. They actively record physiological parameters and monitor metabolic status, providing valuable information for personal health management and clinical care []. Wearable devices play a crucial role in tracking health conditions, remote health monitoring, and ongoing treatments []. By providing real-time data, these devices enable early detection of health issues, promote independent living, and encourage healthy behaviors [].

With global aging, the share of the global population aged 65 years or older is projected to rise from 10% in 2022 to 16% by 2050 [], older adults are more susceptible to chronic diseases and functional decline and face critical health challenges that necessitate a significant shift in lifestyle and health care needs []. These individuals are at high risk for many symptoms that studies with wearable devices attempt to monitor. On one hand, wearable technologies will play a significant role in advancing precision by enabling the precise measurement of clinically relevant parameters []. For instance, continuous health tracking through wearable devices, such as heart rate, blood pressure, and sleep monitoring, provides the detailed, real-time information needed to manage chronic conditions proactively and enable timely medical interventions [-]. On the other hand, wearable technologies integrate advanced algorithms and machine learning to precisely address users’ diverse needs, offering more personalized and effective solutions []. Applied research to explore applying machine learning techniques using the body signals for elderly care has been growing over the last decade. Among the areas that have garnered researchers’ attention are fall detection [,], vital sign monitoring and prediction [], and activity recognition [,]. In addition, these devices promote independence by assisting with daily activities through reminders for medication, appointments, and hydration [-]. However, the current applications of wearable health technology primarily focus on certain members and groups within the general population, and there are numerous issues for special populations [], especially older adults, such as the lack of physical presence and face-to-face contact, as well as concerns about the ethics and acceptability of new technologies []. This review found that several key factors influence the acceptance and use of wearable devices by older adults, including intrinsic and extrinsic motivation for device use, ease of use, device purpose, and perceived added value to the user’s life []. Addressing the unmet care and technical support needs of an aging population and designing services and solutions around the needs or desires of older adults, is becoming an urgent public health priority [,]. To effectively meet these needs, it is crucial to identify and understand existing technologies. To date, there is limited recent evidence synthesis regarding wearable devices for older adults.

The research on the use of wearable devices is increasing, with studies expanding in areas such as palliative care [], cardiovascular medicine [], mental health [] and chronic disease self-management []. However, evidence is still lacking regarding the wider use of wearable devices among older adults. A scoping review, as a preliminary assessment of the potential size and scope of available research on a topic, aims to identify the nature and extent of research evidence []. Evidence mapping is a useful methodology to provide an overview of available research about broad knowledge areas. We used evidence mapping to represent the volume of work in different content areas, and the maps can provide an organized and understandable presentation of a large body of research []. We conducted a systematic scoping review of existing research and created an evidence map. This systematic review responds to the following research questions:

What are the basic characteristics of the published wearable devices?What are the health targets of wearable devices used by older adults?Which technologies are being used in wearable devices for older adults?How is current research evaluating wearable devices for older adults?What are the challenges and opportunities associated with wearable devices for older adults?
MethodsOverview

This scoping review was guided by the framework and principles reported by Arksey and O’Malley [] and in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline []. A scoping review provides a literature overview by mapping key concepts in the evidence base of the research field, which can be used to inform needs and identify knowledge gaps []. PRISMA-ScR checklist for this review is presented in .

Information Sources

A comprehensive search strategy was developed by information and health specialists using a combination of MeSH (Medical Subject Headings) terms and free-text terms. The databases searched were PubMed, Web of Science, CINAHL, Association for Computing Machinery (ACM), IEEE Xplore, and ScienceDirect from inception to July 28, 2024. In addition, relevant studies were identified through a manual search of the reference lists from all available records and conference proceedings obtained in the initial search. The actual strategies listing all search terms used and how they are combined are available in the .

Inclusion Criteria

Eligibility for our scoping review was determined using the PCC (Population, Concept, and Context) framework, which is listed in .

Textbox 1. Inclusion criteria for this scoping review.

Population

• Older people (all participants aged 50 y or older)

• Individuals of any health status (healthy, at risk of disease, or with existing conditions)

Concept

• Wearable technologies

Context

• Health Promotion and Disease Prevention: described or implemented wearable technologies whose primary focus was to improve health

• Any health target in the context of healthy aging (eg, mobility, mental health, cognition, or disease diagnosis)

• Any nonclinical or community-based settings

Study type

• Primary studies with any designs

• Reviews with systematic methodology (eg, systematic reviews)

Study Selection

We imported the retrieved records into EndNote X9 (Thomson Reuters Scientific LLC) for management. The full texts of the included articles were independently evaluated by 2 reviewers. Disagreements regarding selection and inclusion were resolved through consensus-based decision-making.

Data Extraction

The data extraction form was based on a previous publication and adapted to the needs of this review on the same topic, and we randomly selected 10 studies to test and refine it. The included studies obtained information on target population, sample size, age, wearable strategies, measurements, main results, and conclusions. The data extraction process was performed by two authors, and inconsistent data were resolved by the third author.

Data Analysis (Mapping the Evidence)

Data extracted from primary studies were mapped to visually summarize outcome measures identified and coded. We summarized the results using a narrative descriptive synthesizing approach and presented them in tables and figures. The evidence map presents outcome measures by tabulating the type of wearable devices against the domain of the outcome measure. The data extraction form used in this review was piloted and is shown in .

ResultsIncluded Studies

The schematic flow for the selection of the studies included is shown in . In total, 3798 studies were identified. After removing duplicate citations, 3076 studies remained for the title and abstract screening, of which 222 were considered potentially eligible studies for full-text review. Among them, 109 papers met the inclusion criteria after full-text review [-].

‎Figure 1. The schematic flow for the selection of the included studies. ACM: Association for Computing Machinery. Bibliographic Characteristics of Included Studies

The number of studies generally increased over time, except for a decline in 2018 (). They were conducted across 26 countries, with more than half of the studies originating from European countries and the United States ( and ). Most of the included participants mostly did not report any health conditions (51/109, 46.79%), The remaining studies primarily focused on populations with frailty, Parkinson disease, atrial fibrillation, chronic obstructive pulmonary disease (COPD), stroke, and other conditions. The study designs were diverse, the application design studies (38/109, 34.86%) and observational studies (37/109, 33.94%) formed the largest proportion, accounting for nearly two-third of all studies. The characteristics of the included studies are presented in .

Table 1. Detailed information on all included studies.Study characteristicsNumber of publications, nPublication year
20112
20133
20146
20156
20167
20179
201814
201912
202010
202111
202211
202313
20245Study designs
Application design study38
Case-control study2
Diagnostic trial4
Mixed methods5
Observational study37
Pilot study2
Qualitative study3
RCTa11
Retrospective study2
Systematic review5Health status
Health51
Frailty15
Parkinsons8
Atrial fibrillation6
Chronic obstructive pulmonary disease4
Stroke4
Mild cognitive impairment3
Obesity1
Cancer2
Dementia2
Depression2
Heart disease2
Neurological disorders2
Cognitively impaired1
Diabetes1
Diseased gait1
Knee osteoarthritis1
Other3

aRCT: randomized controlled trial.

‎Figure 2. Distribution of the included articles in geographical map. Health Targets of Wearable Devices Used by Older Adults

The most commonly reported health targets of wearable devices were (1) mobility (n=23/109), (2) mental health (6/109), (3)fall-related (18/109), (4) arrhythmia detection (10/109), (5)activity recognition (22/109), and (6) disease diagnosis (6/109),and (7) sleep monitoring (8/109) and other conditions. The results are shown in .

‎Figure 3. Health targets of wearable devices technologies. Techniques Used in Wearable Devices Used by Older AdultsOverview of Wearable Sensor

Wearable techniques comprise devices and sensors that can be attached to the human body for data collection. Wearable sensors consist of accelerometers, photoplethysmography, thermometers, gyroscopes, magnetometers, etc. An overview of wearable sensors is given in .

Table 2. Types of wearable sensors and their applications.Name of sensorPurposeApplicationAccelerometerMeasures the acceleration forces acting on it, including gravity. It can detect changes in speed and directionUsed in applications like step counting, motion detection, and orientation sensingPPGaDetects blood volume changes in the microvascular bed of living tissueMonitor heart rate, blood oxygen saturation (SpO2), and even estimate blood pressureThermometerMeasures body temperatureUsed in medical settings, homes, and increasingly in wearable devices to monitor body temperatureGyroscopeMeasures the angular velocity or rotation rate of an objectUsed in combination with accelerometers in many devices to improve the accuracy of motion trackingEEGbMeasures the electrical activity of the brainUsed in medical diagnostics, neuroscience research, brain-computer interfaces (BCIs), sleep analysis, and mental state monitoringECGcMeasures the electrical activity of the heartUsed for diagnosing heart conditions, monitoring heart rate, and detecting arrhythmias. Commonly found in clinical settings and wearable health devicesPressure sensorMeasures the force or pressure applied to itUsed in blood pressure monitoring, industrial processes, and environmental monitoringMagnetometerMeasures the strength and direction of magnetic fieldsUsed in navigation systems, compasses, and for detecting magnetic fields in the environmentEMGdMeasures the electrical activity produced by skeletal musclesUsed in medical diagnostics, rehabilitation, and in the development of prostheticsIMUeCombines multiple sensors (typically an accelerometer, gyroscope, and sometimes a magnetometer) to measure and report on a device’s specific force, angular rate, and sometimes the magnetic field surrounding the deviceUsed in motion tracking, navigation, and stabilization

aPPG: photoplethysmography.

bEEG: electroencephalography.

cECG: electrocardiography.

dEMG: electromyography sensor.

eIMU: inertial measurement unit.

Placement of Wearable Sensor

Wearable devices are typically worn on four main areas of the human body: head-mounted, wrist-worn, portable (depending on the specific context), and body-worn. Depending on the specific application requirements, these devices can be positioned at various locations on the body to measure different parameters. illustrated the various positions where sensors are placed for data collection. The analysis showed that the most commonly used placement is the wrist, waist, chest, back, and ankle.

‎Figure 4. Placement of wearable sensors. Statistical or Algorithms

presents the key characteristics of machine learning techniques in research studies, including tree-based models (decision tree, random forest, and gradient boosting), linear models, deep learning neural networks, multilayer perceptrons, long short-term memory networks, and convolutional neural networks). K-nearest neighbors, support vector machines, and others.

Table 3. Machine learning techniques research work for wearable sensors.Category and MLa techniquesReason for useDecision Trees and Ensemble methods
Decision treeHandles high-dimensional data.
Robust to noisy data.

Random forestRobustness through ensemble learning.
Handles imbalanced datasets, Suitable for large datasets.

Gradient boostingImproves model performance iteratively.
Flexible parameter tuning.

XGBoostbEfficient through parallel computation.
Optimizes to prevent overfitting.
Linear models
Logistic regressionSimple and efficient.
Easy to train and optimize.

Elastic net regressionHandles multicollinearity.
Sparse solution.

Ridge regressionHandles overfitting by adding bias.

Lasso regressionPerforms feature selection by shrinking unimportant features’ coefficients to zero.

Linear regressionBasic model for simple linear relationships.
Deep learning models
Convolutional neural networkAutomatic feature extraction.
Suitable for image and signal processing tasks.

Multilayer perceptronLearns nonlinear mappings.

Long short-term memoryHandles sequence data effectively.

Deep neural networkHandles data from different domains.
Customizable for personalized health monitoring.
Nearest neighbor algorithms
K-nearest neighborSimple and intuitive, suitable for real-time classification tasks.
Support vector machines
Support vector machineHandles high-dimensional data effectively.
Robust to nonlinear relationships.
Bayesian methods
Naïve BayesianHandles uncertainty well.
Mixture models
Gaussian mixture modelsCaptures complex distribution patterns.

aML: machine learning.

bXGBoost: extreme gradient boosting.

Evaluation Approach of Wearable Devices

In this study, we found two types of wearable devices evaluation approaches: (1) metrics for performance evaluation in a controlled laboratory and clinical setting and (2) evaluations involving end users in real-world settings. Analysis revealed that different studies use a variety of metrics for performance evaluation. Despite variations in machine learning algorithms, the figure showed the evaluation metrics used across 45 studies. In our study, 39.45% (43/109) of included studies used the metrics to assess performance. Sensitivity (29/109, 26.61%), accuracy (23/109, 21.10%), specificity (22/109, 20.18%), precision recall (11/109, 10.09%), and F1-score (11/109, 10.09%) were the most used metrics among offline evaluation metrics. Other popular offline evaluation metrics are accuracy-related measurements, such as root-mean-square error (3/109, 2.75%), and the mean absolute error (1/109, 0.92%).

In addition to performance testing, there were also 34 studies involving end users that evaluated the effectiveness, safety, user acceptability, usability, adherence, satisfaction, user experience, cost, and satisfaction. Clinical effectiveness (n=22, 20.18%) and acceptability (n=17, 15.60%) were the most commonly evaluated domains. Specifically, only one study (n=1, 0.92%) evaluated the user experience and satisfaction. The details are illustrated in .

Challenges and Opportunities With Wearable Devices

There were several challenges and opportunities associated with wearable devices that were reported in studies or inductively emerged based on study descriptions ( and ). The opportunities presented by wearable devices are countered by multiple challenges, including data availability and reliability, technical limitations, utility and user acceptance, cost, security and privacy, performance gaps, and challenges.

Table 4. Categorization of challenges and opportunities with wearable devices.ChallengesOpportunitiesData availability and reliability
Low quality of data acquired by sensors
Data validation and feedback：
Ensure the quality and reliability of sensor data in wearable devices through data validation and user feedback.
Improve data processing efficiency with more efficient algorithms.
Enhance data quality through machine learning and data cleaning.
Introduce higher precision sensors to improve data accuracy.
Expand functionality with additional sensors for more physiological parameters.
Common and local standards of data quality.

Existing data samples lack representativeness and are relatively small
Expanding sample diversity and size:
Use multicenter study designs to cover more geographic locations.
Collaborate with multiple institutions to share datasets and expand sample size.
In compliance with international data protection laws, data sources should be made accessible to researchers as far as possible.
Technical limitation
Interoperability of devices, short battery life, large device size, lack of personalized settings, and inadequate support
Engaging with older adults and seeking their input on the design and operation of these digital devices, understanding user needs, and achieving this goal.
Utility and user acceptance
Devices may not be available for independent use at home
Users may have difficulty in learning new skills and high dependence on health care providers for health management
Technology dissemination and training：
Develop effective training methods to master the use of technology, such as through simplified guides and increased use of visual tutorials.

Devices may not cater to individual preferences, leading to a less satisfactory user experience
Involve users in the design and evaluation of technology products to obtain more direct and effective feedback.
Improve the design of wearable devices and develop easy-to-understand and operational applications or interfaces.
Cost
High technological costs and ongoing support may bring higher costs
Rigorous validation from evidence-based practice, providing support, and lowering the cost for better financial sustainability.
Assessing the cost-effectiveness of technology, facilitating broader adoption and application.
Security and privacy
Security risks include but are not limited to malicious hardware or software injections, denial-of-service attacks, and different routing and physical attacks
Homomorphic Encryption: Use homomorphic encryption to allow computations on encrypted data.
Software Updates: Regularly update the firmware and software on wearable devices to patch known security vulnerabilities and improve overall security.
Regulatory Compliance: Ensure compliance with relevant data protection regulations such as GDPRa, HIPAAb, and CCPAc.
Performance gaps and challenges
Lack of evaluations in real-world settings to adopt wearable devices for health care tasks
Large-scale clinical trials involving older adults to assess and validate their accuracy

aGDPR: General Data Protection Regulation.

bHIPAA: Health Insurance Portability and Accountability Act.

cCCPA: California Consumer Privacy Act.

‎Figure 5. Categorization of challenges to wearable applications, showing proportions of the 6 categories.
DiscussionPrincipal Findings

Our study is the first to conduct a systematic scoping review and create an evidence map to provide an overview of the state of evidence regarding the development and evaluation of wearable devices. A total of 109 studies on wearable devices were included in our systematic review. This study provided a detailed mapping of existing wearable devices, outlining their basic characteristics, health targets, and underlying technologies, while also showing the evaluating results and identifying the challenges and opportunities associated with their use by older adults.

Mobility, fall-related incidents, and mental health monitoring are the primary focuses of wearable devices designed for older adults. Mobility-focused devices often include features such as step counting, gait analysis, and balance tracking to help users maintain their physical activity levels, promote healthy behaviors like exercise, and offer a wide range of applications in managing various diseases, such as high-risk older patients in the preoperative stage [], hospitalized older people [], patients with cancer [], those with Parkinson disease [], and those with COPD []. Faller classification in older adults can facilitate preventative care before a fall occurs. While mobility is prominent, mental health and cognition also remain important considerations for wearable technology targeting older adults. Early identification and monitoring of mental illnesses may be quite challenging. Contrary to the results of traditional patient self-reports, wearable devices could assess psychological states by continuously monitoring other indicators. Chan et al [] used wrist-worn accelerometers to investigate whether gait biomarkers could predict the incidence of depressive episodes in older individuals and indeed found that these biomarkers can serve as predictive indicators. Toosizadeh et al [] confirmed significant associations between upper-extremity speed, range of motion, and speed variability with both the Montreal Cognitive Assessment (MoCA) score and the gait performance within the dual-task condition. Vital signs monitoring is a major component of wearable devices, primarily including heart rate. The detection and diagnosis of atrial fibrillation (AF) are often challenging due to the asymptomatic and intermittent nature of AF. For patients who may be at risk of cardiac arrest, wearable devices can help monitor arrhythmias. The novel Necklace-ECG device is able to detect AF obtained with novel Necklace-ECG device [], mobile device [,]. Santala et al [] have demonstrated that an all-time wearable Necklace-ECG can detect and diagnose AF with high accuracy. In addition, there were measurements of physiological indicators, such as sleep calmness. Abbas et al [] use devices that monitor daily steps, sleep duration, and quality, as well as activity levels, to distinguish physical frailty from daily routine. In summary, existing evidence on the broad implementation of wearable devices in clinical care highlights their benefits, which include enabling patient-centered care, enhancing the monitoring of physical activity, facilitating timely treatment adjustments, reducing the occurrence of unexpected events, and minimizing the need for unnecessary clinic visits.

Our systematic review explores how wearable devices are evaluated. Comparing sensor technologies with clinical scales and performance measures in a controlled laboratory and clinical setting is crucial for evaluating device effectiveness in specific tasks. The performance of the algorithm depends on several factors, including the dataset used, the type of classifier, the data acquisition device, and the ratio of training to testing data []. Performance testing of wearable devices typically evaluates key metrics such as accuracy (data and classification), reliability (stability and failure rates), precision and recall, sensitivity and specificity, and F1-score. Researchers have noticed this lack of user participation in single wearable real-world studies, and it has been identified as the main challenge in this field []. Although there has been an increasing trend in studies focusing on using wearable devices for predicting or quantifying the response to an intervention. In addition to effectiveness, only a very small number of studies have investigated the acceptability of the technology, user adherence, user experience, device cost, and safety. For example, Ward et al [] reported on findings from a single-arm trial that an activity monitor technology could be able to support effective remote walking exercise prescription and participation during pulmonary rehabilitation in COPD. Preliminary findings from 3 studies [-], which assessed safety by reporting on related adverse events, indicated that the intervention was safe and well-accepted by the participants. Conducting a cost-benefit comparison between programs implemented in health care systems is challenging, and only one study explored the costs of home pedometer-assisted physical activity in patients with COPD []. The Model for Assessment of Telemedicine Applications (MATA) offers a structured framework for the multidisciplinary evaluation of telemedicine applications. This model assists decision makers in selecting the most efficient and cost-effective technologies, taking into account the specific characteristics of the application, safety, clinical efficacy, patient perspectives, and economic, organizational, sociocultural, ethical, and legal considerations []. Due to the limitations of laboratory studies in terms of their observation window (min to h) and the environment (generally safe, no obstacles) [,]. Researchers and developers can gain a deeper understanding of the real-world impact and effectiveness of wearable devices in health monitoring [], ultimately leading to better-designed and more impactful solutions for end users. Wearable technology may be able to support effective remote walking.

There are many barriers to the full clinical adoption of digital monitoring, including technical limitations that hinder usability and satisfaction, as well as the lack of clear guidelines for wearable applications and data collection. First, the technical limitations of wearable devices, including interoperability issues, short battery life, large device size, lack of personalized settings, and inadequate support, restrict their application scenarios and may lead to a less satisfactory user experience by not catering to individual preferences [,]. Given the limitations observed in the structural design and practical applications of wearable technology, we can undertake several optimizations to improve their effectiveness in clinical practice. Second, our review demonstrated that there is substantial heterogeneity among the wearable sensor technologies used for the assessment of older adults. A large proportion of the devices, their potential benefits, and utility are still far from implementation in clinical routine []. Besides, future research should also focus on identifying factors that could enhance the effectiveness of wearable technologies and promote their acceptance and adherence in the longer term. Such factors include the digital technology types (eg, user-friendly design and personalized settings) and the nondigital elements in digital health interventions (eg, motivation and engagement) []. Third, wearable devices face major challenges including inadequate data availability and reliability, as well as privacy protection concerns []. Food and Drug Administration [] and the European Medicines Agency [] have developed guidelines for the use, validation, and data reporting of wearable sensor technology in clinical trials. However, these guidelines are not yet widely known, and journals should recommend and even require their adoption to disseminate these best practices [,]. Last, given the complexity of human responses and the variability among individuals, data collection can be quite challenging []. However, the data collection process is often both costly and time-consuming, leading most academic studies to rely on relatively limited datasets. To address this, it is advisable to gather as much data as possible from participants in order to develop robust algorithms for data cleansing and to construct more accurate and broadly applicable models. Adopting a transparent and reproducible approach to data collection, training, and model evaluation is highly recommended to bolster confidence in research outcomes and to facilitate the effective accumulation of research efforts [].

Despite many barriers to their application, wearable devices have brought huge benefits to the aging global population. In addition, their impact on health care is profound as the younger, technologically literate generation ages into adulthood. On the one hand, the potential to significantly boost the technological literacy of future older populations becomes more apparent. Currently, older adults may have faced a double exclusion due to a lack of digital literacy and social interaction who rely on health monitoring technologies for personalized medical guidance []. In contrast, future cohorts of older adults, being more adept with data and platforms, may actively better understand the data and recommendations provided by the devices, leading to more active participation in remote health education programs. On the other hand, the dynamic and continuous accumulation of health data contrasts sharply with traditional, static, and episodic medical history records. This shift has the potential to revolutionize the entire health care system, moving health care from a “diagnosis-treatment model” toward a “prediction-prevention model.” For instance, existing research indicates that early signs of cognitive function may be identified through sleep data []. Consequently, future medical services may place greater emphasis on the prediction and prevention of disease, potentially reducing the need for treatment approaches.

Evidence Gaps and Ideas for Future Research

Furthermore, improvements are needed for (1) research should include a broader and more diverse range of older adults to ensure that the findings are applicable to various demographic groups, which includes considering differences in age, gender, socioeconomic status, and cultural backgrounds, (2) understanding the underlying physiological processes that these technologies monitor, and influence can lead to more effective and targeted interventions. This includes studying how sensors and algorithms interact with the body’s biological signals to provide accurate and reliable data, (3) research should extend beyond laboratory settings to include various clinical and care environments. This will help identify the practical challenges and benefits of using wearable technologies in real-world scenarios, such as hospitals, nursing homes, and home care settings, (4) outcome evaluations are necessary to identify the factors that can enhance the health benefits of digital technologies. This includes assessing both short- and long-term outcomes, with a focus on health technology assessments, including the effectiveness, patient safety, and impact on health care costs, (5) Technologically literate older adults may be more proactive in managing their health, leveraging wearables for continuous monitoring of vital signs, chronic conditions, and mental health. Adherence could be further enhanced with the provision of additional cognitive aids, such as detailed documentation of study procedures and comprehensive user manuals, as well as by allowing sufficient time for users to establish wearing routines [], (6) by integrating quantitative and qualitative insights from participants, we can further refine wearable device technology. The research about how to simplify interfaces and operations to make them more intuitive for older adults, while exploring ways to tailor devices based on individual health conditions, lifestyle, and preferences to provide personalized health management and reminder services.

Limitations

First, not all research involving wearable devices was identified by our search. For example, some company products may not appear in databases. Due to practical constraints, we included only studies published in the English language. Consequently, it is likely that we missed some studies published in other languages, potentially leading to an incomplete and potentially biased set of results. Second, we reported on the challenges and opportunities of wearable devices from the included studies. While these insights may be helpful, they are not objective measures. Last, the categories of research and the objectives for using wearable devices introduced in our study may overlap, as separation and classification are artificial.

Conclusion

We see a growing uptake of wearable technology in health research with a notable trend toward its use in older adults. In the future, a broader and more diverse range of studies should be designed to investigate which types of wearable devices are most beneficial for older adults. In addition, it is important to explore the potential of emerging innovations in enhancing health outcomes. Focusing on optimizing wearable device technology to improve usability, and effectiveness is also crucial. Outcome evaluations are needed to identify factors that could enhance the health benefits of these technologies and to measure their real-world impact. This review can help nonmedical professionals and policy makers visualize and better understand wearable technology in future studies.

This research was supported by Shaanxi Province Postdoctoral Fund (number 2024BSHEDZZ005).

None declared.

Edited by A Mavragani; submitted 22.11.24; peer-reviewed by D Chen, Y Gao, C Sakal; comments to author 12.12.24; revised version received 30.03.25; accepted 13.04.25; published 24.06.25.

©Yue Sun, Ji Chen, Mengmeng Ji, Xiaomei Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.06.2025.

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 (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.