evidence assessment library
Wearable Wellness Devices

Wearable Wellness Devices

There is sufficient evidence that the usage of consumer wearable wellness devices is associated with improved social outcomes.

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Study Characteristics and Contextual Tags

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Impact Assessment

The findings below synthesize the results of the studies on wearable wellness devices across three domains of measurement:

  • Healthcare Cost, Utilization & Value: More evidence is needed. No included studies reported healthcare cost, utilization, or economic outcomes associated with the use of consumer wearable wellness devices.
  • Health: More evidence is needed on the health impact of consumer wearable wellness devices. A randomized controlled trial with a limited sample size showed indicative improvements in cardiovascular fitness. More robust studies are required to examine the impacts of the use of these devices on a broad range of clinical measures.
  • Social: There is sufficient evidence for the impact of wearable device usage on social outcomes. Studies including randomized clinical trials and observational studies suggest potential benefits for physical activity, sleep quality, mental wellbeing, and stress reduction. Additionally, positive impacts were more pronounced when combining wearable use with personalized feedback or behavioral support programming. The current evidence base could be supported by more longitudinal studies to assess the long-term impact of wearable wellness device usage and device impact on psychological wellbeing where results are mixed.
Background of the Need / Need Impact on Health

Physical inactivity and chronic stress are among the most prevalent modifiable health risks facing adults in the United States (U.S.) today. Nearly one in four adults in the U.S. do not meet federal physical activity guidelines, and inadequate physical activity is associated with $192 billion in annual healthcare costs, representing 12.6% of total national health care spending[1]. Physical inactivity is linked to increased risk of cardiovascular disease, type 2 diabetes, cancer, hypertension, stroke, and premature death, contributing to an estimated one in ten premature deaths nationally[2]. Hispanic, non-Hispanic Black, and adults with lower-incomes have significantly higher rates of inactivity than other Americans[3].

Stress, anxiety, and depression place an equally significant burden on population health. In 2022, approximately one in five adults in the U.S. experienced symptoms of anxiety (18.2%) or depression (21.4%) in the previous two weeks, with prevalence significantly higher among younger adults, women, and those with lower incomes and education levels[4]. Depression prevalence has increased steadily from 2013 to 2023 across all age groups, with nearly 88% of adults with depression reporting difficulty with work, home, or social activities as a result[5]. Each year, serious mental illness costs the US economy $193.2 billion in lost earnings, while depression and anxiety disorders together result in a loss of $1 trillion from the global economy due to lost productivity[2].

For payers and providers, the burden is significant. Physical inactivity, chronic stress, anxiety, and depression drive healthcare utilization across primary care, behavioral health, and specialty settings, and disproportionately affect historically underserved populations and people with lower incomes. Scalable, consumer-facing tools that support self-monitoring and behavior change, including wearable wellness devices, represent a growing area of interest for health promotion and chronic disease prevention across insured populations[6]. For vulnerable populations and people with lower incomes, structural factors such as safe and accessible places for physical activity and time and labor demands of work may have more influence over physical activity behaviors than personal attributes like motivation and capability.

Background on the Intervention

Consumer wearable wellness devices, including Oura Ring and smartwatches such as Apple Watch, Fitbit, and Garmin, have become mainstream health technology in the U.S. As of 2023, nearly one in three Americans used a wearable device to monitor health metrics such as heart rate, daily activity, sleep, fall detection, temperature, Vo2 max, noise exposure and blood pressure.  Approximately one in five adults regularly wears a smartwatch or fitness tracker[7]. The U.S. wearables market was valued at $18.6 billion in 2024 and is projected to grow at 11.4% annually through 2032, driven by rising consumer demand for personalized health monitoring and preventive care[8].  Several smartwatch fitness trackers have the ability to send data to electronic health records (EHRs) presenting new opportunities for clinical applications but also new risks[9]. There is an emerging field of wearables designed to leverage real-time tracking with artificial intelligence to support personalized medicine. 

This EA focuses on wearables designed for fitness and/or wellbeing that do not require a prescription to obtain, however, some of these devices do have Food and Drug Administration (FDA) approval and are also able to serve as medical devices. In addition, this EA focuses solely on watches, rings and bracelets and excludes the less common patches, glasses and clothes. 

Consumer wellness wearables are not generally currently covered by traditional Medicare or Medicaid, as the Centers for Medicare and Medicaid Services (CMS) classifies them as general health and fitness devices, rather than medically necessary equipment[10]. While the devices are not covered, some Medicaid agencies do reimburse for clinician review of digital device data and consultation. For example, North Carolina reimburses for Remote Patient Monitoring which “enables providers to collect and analyze information such as vital signs (blood pressure, heart rate, weight, and blood oxygen levels) in order to make treatment recommendations”[11]. Some Medicaid Managed Care Organizations offer lower cost devices like fitbits as value added benefits. Medicare provides limited coverage for some Remote Patient Monitoring clinical activities including patient education and device set up, device supply and treatment management for patients with acute or chronic conditions requiring monitoring with internet-connected devices that have been approved by the FDA for clinical purposes [12]. Between 2019 and 2022 there was a tenfold increase in the number of remote patient monitoring services covered by Medicare (both original Medicare and Medicare Advantage)[13]. In addition, Medicare Advantage plans may offer limited coverage for wearable devices in some cases, and fitness trackers may be eligible for reimbursement through flexible spending accounts (FSAs) or health savings accounts (HSAs), but only with a Letter of Medical Necessity documenting that the device will be used to treat a specific diagnosed condition[14]. The Center for Medicare and Medicaid Innovation also launched the Advancing Chronic Care with Effective Scalable Solutions (ACCESS) Model which combines an outcomes-based payment model linked to condition-specific clinical measures (such as blood pressure, hemoglobin A1c, lipids, weight or Patient Reported Outcome Measures) with “technology supported care services” such as telehealth visits, wearable devices, and apps that coach on lifestyle changes. Applications for this ten year model will be accepted on a rolling basis beginning in spring 2026. The first awardees will launch in January of 2027. Additionally, some employers offer wearables as part of workplace wellness programs, and lifestyle spending accounts (LSAs) increasingly cover devices such as Fitbit, Apple Watch, and Garmin[15].   

Data privacy, device accuracy, and equitable access remain key implementation considerations, with wearable adoption consistently higher among adults who are younger, have a higher income, and have a higher education level. In December 2024, the U.S. Equal Employment Opportunity Commission published new guidance addressing how federal nondiscrimination laws, including the Americans with Disabilities Act, apply to employer use of wearable devices in workplace wellness programs, reflecting growing regulatory attention to privacy[16].

Additional Research and Tools
Evidence Review
Note: The vocabulary used in the table is the same terminology used in the study in order to preserve the integrity of the summary. 
Study
Population
Intervention Summary
Type of Study Design
Outcomes

Adults aged 28-47 years (mean age 35.7-36.3 years). 50% female, residing in the Los Angeles, California area. Participants had little to no exercise in the prior three months and no significant medical diagnoses including cardiovascular or pulmonary disease.

12-month study evaluating the Oura ring (2nd generation), a commercially available multisensory wearable sleep and activity tracker, combined with a personalized behavioral modification program delivered remotely via smartphone application. The intervention comprised 12 weekly 30-minute digital presentations on stress reduction, relaxation, and sleep hygiene, as well as daily guided text message feedback personalized to each participant's Oura ring sleep and activity data. The control group received equal-attention wellness education on general health topics without sleep-specific or activity-specific guidance. All participants wore the Oura ring continuously throughout the study.

Randomized controlled trial. N=56 adults (26 control, 30 intervention: 15 long-term guided text message feedback and 15 short-term guided text message feedback following three-month randomization).

Health: Over the first three months, the intervention group demonstrated significant improvements compared to the control group in cardiovascular fitness (VO2max), which increased from 36.3 to 40.6 ml/kg/min (p<0.001; Hedges' g=3.04), body fat percentage, which decreased from 26.8% to 23.0% (p<0.001; g=1.57), and heart rate variability (HRV rMSSD), which increased from 30.2 to 35.5 ms (p<0.001; g=2.89). The long-term  guided text message feedback group continued to show significant improvements across all outcomes through 12 months, with VO2max reaching 48.0 ml/kg/min (p<0.001) and rMSSD reaching 42.3 ms (p<0.001). The short-term guided text message feedback group, which stopped receiving  guided text message feedback after three months, largely maintained but did not further improve most outcomes.

Social: Over the first three months, the intervention group demonstrated significant improvements compared to the control group in sleep onset latency (SOL), the time required to fall asleep, which decreased from 0.42 to 0.23 hours (p<0.001; g=18.00), daily step count, which increased from 7,446 to 9,626 steps (p<0.001; g=2.26), percentage of time jogging, which increased from 2.1% to 7.8% (p<0.001; g=11.29) . The long-term guided text message feedback group continued to show significant improvements across all outcomes through 12 months. The short-term guided text message feedback group, largely maintained but did not further improve most outcomes, with sleep onset latency remaining below the 20-minute insomnia threshold at 12 months.

Female breast cancer survivors aged 21-85 years (mean age 57 years, Standard Deviation [SD] 10.4 years), predominantly non-Hispanic White (81.3%), with college education or greater (73.4%), residing in the San Diego, California area. All participants had completed chemotherapy or radiation treatment, were sedentary at baseline (defined as fewer than 60 minutes of moderate-to-vigorous physical activity [MVPA] per week), and were on average 2.6 years from diagnosis at study enrollment.

The full intervention arm received a Fitbit One activity tracker, an in-person meeting with a health coach trained in motivational interviewing to set personalized physical activity goals, two scheduled phone calls, and emails every three days throughout the 12-week intervention. Health coaches reviewed participants' Fitbit data weekly and provided personalized feedback. The light intervention arm received a Fitbit One, a brief 15-20-minute goal-setting meeting with a research assistant, optional phone calls, and the same automated emails, but no health coach review of Fitbit data or personalized feedback.

Secondary data analysis of a 12-week randomized controlled trial with two-year follow-up, using linear mixed effects models and generalized additive mixed effects models (GAMM) to compare patterns of Fitbit adherence and MVPA between groups over time. N=75 female breast cancer survivors (37 full intervention, 38 light intervention).

Social: During the 12-week intervention period, the full intervention arm achieved significantly higher mean MVPA than the light intervention arm (27.89 minutes per week, SD 16.38 vs 18.35 minutes per week, SD 12.64; p<0.001). Both groups showed significant declines in average MVPA during the two-year follow-up period compared to the intervention period (full intervention arm: 21.74 minutes per week, SD 24.65; p=0.002; light intervention arm: 15.03 minutes per week, SD 13.27; p=0.004), with no significant difference in average MVPA between groups during follow-up (p=0.33). However, the temporal pattern of daily MVPA during follow-up was significantly different between groups (p<0.001), with the full intervention arm demonstrating a more gradual and stable decline compared to the irregular fluctuating pattern observed in the light intervention arm.

General adult population aged 21 years and older across three countries. The United States sample comprised 1,004 respondents with a mean age of 49.91 years (SD 17.36), 53.8% female, and 64.1% White. Income and education distributions were quota-sampled to align with national census profiles. Black (13.9%), Hispanic (13.3%), and Asian (6.5%) respondents were also represented.

Participants self-reported current or prior use of a list of top health apps and wearables including Apple Watch, Fitbit, and others. Use was categorized as health apps only, wearables only, or combined health apps and wearables use.

Observational study with a comparison group. This was a cross-sectional online survey using quota sampling conducted between October 2021 and January 2022. Outcomes were analyzed using ordinary least squares regression for wellbeing outcomes and negative binomial regression for exercise frequency, adjusting for social determinants including gender, age, income, education, ethnicity, and urbanicity. Results reported here are from the United States sample only (N=1,004).

Social: In the United States sample, combined use of health apps and wearables was positively associated with psychological wellbeing (β=0.23, p<0.001). Use of health apps or wearables alone was not significantly associated with exercise frequency or self-reported general health in the US.

African American undergraduate and graduate university students at Concordia University, St. Paul, Minnesota. The majority identified as female (78.7%), with 20.0% male and 1.3% non-binary. Most participants were over 22 years old (70.7%). Over half (54.7%) received financial aid and 29.3% were employed part-time while attending school. Of the sample, 65.3% reported current wearable technology use, 42.7% had used wearable technology for one year or more, and 33.3% reported daily use.

Wearable technology (WT) use, categorized as general use, duration of use (less than one year versus one year or more), and daily versus less frequent use.

Observational study with a comparison group. This was a cross-sectional correlational study using independent samples t-tests and three-way factorial analyses of variance (ANOVA) to examine main and interaction effects of gender, year in school, and wearable technology use on perceived stress, physical activity frequency, and sedentary behavior. N=75 African American university students.

Social: Students who reported using wearable technology for one year or more had significantly lower perceived stress scores than those with shorter use histories (t(73)=-1.317, p=0.044; Cohen's d=-0.323), representing a small-to-moderate effect. Both gender (F(1,66)=8.203, p=0.006) and wearable technology use (F(1,66)=11.405, p=0.001) were significantly associated with  perceived stress. The relationship between wearable technology use and stress varied significantly by gender(F(1,66)=12.276, p<0.001). Male students using wearables reported notably lower stress than male non-users. In contrast, female students showed similar stress levels regardless of wearable technology use. No significant main or interaction effects of gender, year in school, or wearable technology use were found for sedentary leisure time (all p>0.05).

No significant differences were observed between wearable technology users and non-users in physical activity frequency, physical activity duration, or strength training frequency (all p>0.05). The relationship between wearable technology use and physical activity frequency was significantly moderated by academic year (F(1,66)=4.010, p=0.049) and by the combination of gender and academic year (F(1,66)=5.835, p=0.018). Graduate-level students and female undergraduates showed modest increases in physical activity frequency with wearable use. However, male undergraduates who used wearables reported notably lower physical activity frequency than male undergraduates who did not use wearables.(F(1,66)=4.010, p=0.049).

Nationally representative sample of 1,273 self-identified informal caregivers aged 18 years and older in the United States, representing an estimated 73.1 million informal caregivers (individuals caring for an aging or chronically ill person) nationally. The sample was 60% female, 63.6% non-Hispanic White, 75.7% had some college education or more, and 67.3% had one or more chronic medical conditions. Approximately 56.3% were aged 50 years or older and 44.5% had an annual household income greater than $75,000. Data were drawn from the National Cancer Institute's Health Information National Trends Survey (HINTS) 5, cycles 3 (2019) and 4 (2020).

Wearable health and activity tracker use, defined as self-reported use of an electronic wearable device to monitor or track health or activity in the past 12 months, including devices such as Fitbit, Apple Watch, or Garmin Vivofit. Intervention costs not applicable.

Observational study with a comparison group. This was a cross-sectional secondary analysis of nationally representative survey data using multivariable logistic regression with jackknife replicate weights to assess the independent association between wearable use and meeting World Health Organization (WHO) physical activity recommendations. N=1,273 informal caregivers.

Social: Informal caregivers who reported wearable use in the past 12 months had significantly higher odds of meeting the WHO recommendation of 150 or more minutes of at least moderate-intensity physical activity per week compared with non-users (Adjusted Odds Ratio [OR] 1.1; 95% Confidence Interval [CI] 1.04-1.77; p=0.04), after adjusting for sociodemographic characteristics, health status, and caregiving-related factors. Among caregivers meeting physical activity recommendations, 43.1% reported wearable use, compared with 26.4% among those not meeting recommendations (p=0.03).

Community-dwelling adults recruited via flyers and web-based platforms in the San Francisco Bay Area, California. Participants were required to use walking as their primary source of physical activity, have limited prior experience with activity-tracking technology, and possess an iPhone 5s or newer. Participants who were pregnant were excluded.

Participants were provided with an Apple Watch equipped with a custom step-counting app (AccuSteps) displaying step count feedback on the watch face, worn for five weeks. Following a one-week no-feedback baseline, participants were randomized to receive accurate step count, step count deflated by 40%, step count inflated by 40%, or accurate step count plus a web-based meta-mindset intervention. The meta-mindset intervention consisted of three short videos and reflection activities teaching participants about activity adequacy mindsets and encouraging them to reframe their physical activity as adequate and beneficial, with brief follow-up reflection activities embedded in subsequent daily check-ins and weekly surveys. Participants received US$175 for satisfactory participation.

Randomized controlled trial. This was a five-week parallel trial with four experimental arms, using multilevel longitudinal models with Huber-White robust standard errors, adjusted for actual step count and self-reported physical activity. N=162 community-dwelling adults (accurate step count n=41, deflated step count n=40, inflated step count n=40, meta-mindset intervention n=41).

Health: Participants receiving accurate step count feedback reported significant improvements in mental health including reduced anxiety, depression, as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS)-29 (b=0.15, SE 0.07; p=0.03) Participants receiving deflated step count feedback report significant declines in mental health status (b=-0.20, SE 0.09; p=0.03) increases in resting heart rate (b=3.54, SE 1.79; p=0.049) and mean arterial pressure (b=3.64, SE 1.83; p=0.048) compared to the accurate step count condition.  

Social: Participants receiving accurate step count feedback reported significant improvements in sleep disturbance, and fatigue, as measured by PROMIS-29 (b=0.15, SE 0.07; p=0.03) as well as a marginal increase in self-esteem (b=0.11, SE 0.06; p=0.06) compared to baseline. Participants also significantly reduced high-fat food intake (b=-0.38, SE 0.16; p=0.02) and increased healthy produce consumption (b=0.36, SE 0.15; p=0.02). Participants receiving deflated step count feedback experienced significant declines in self-esteem (b=-0.24, SE 0.11; p=0.03), more frequent negative affect (b=0.12, SE 0.07; p=0.07), and significantly unhealthier dietary choices compared to the accurate step count condition. Participants in the meta-mindset intervention condition experienced improvements in functional health (b=0.18, SE 0.07; p=0.008), reductions in negative affect (b=-0.13, SE 0.07; p=0.07), and maintenance of positive affect significantly more favorable than the decline in the accurate step count condition (b=0.23, SE 0.10; p=0.02). Actual step count did not change in any condition.

The authors conclude that activity adequacy mindsets (AAMs) induced by trackers or adopted deliberately can influence affect, behavior, and health independently of actual physical activity.

Systematic Reviews
Note: The vocabulary used in the table is the same terminology used in the study in order to preserve the integrity of the summary. 
Study
Population
Intervention Summary
Type of Study Design
Outcomes
Scudds and Lasikiewicz (2025)

Healthy adults across nine included studies, predominantly White males and females with average ages ranging from 21.5 to 49 years, either employed or students, with body mass index (BMI) mostly within the normal range. Sample sizes and demographic details varied across included studies. Only two of nine studies reported ethnicity data.

The use of wrist-based wearable activity trackers (WATs). Included devices comprised Fitbit Flex, Fitbit Charge HR, Fitbit Charge, Apple Watch, Jawbone, Garmin Vivofit 3, Polar M400, and Oregon Scientific Smart Dynamo across nine studies. Intervention durations ranged from three weeks to nine months. Most studies incorporated WATs as part of broader physical activity interventions; some included additional components such as counselling, goal setting, motivational text messaging, or social connectivity features.

Systematic review. Five databases were searched in December 2022 with a follow-up search in October 2023 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered on the Open Science Framework. Of 904 citations identified after deduplication, nine studies met the inclusion criteria. Quality was assessed using an 18-item tool adapted from prior literature; quality scores ranged from 8 to 16 out of 18, with 89% of studies achieving above 50%. Only two of nine studies were randomized controlled trials (RCTs), and only four included a suitable control or comparison group without WAT access. Nine studies were included.

Social: Evidence of the impact of WATs on psychological wellbeing and physical activity was limited and mixed. Of nine included studies, four reported increases in physical activity following WAT-based interventions, though in one case, improvements were only observed when the WAT was combined with social connectivity features. Three studies reported no improvement in physical activity. Of three studies assessing mental health and general wellbeing, two reported improvements though one study only recorded improvements post intervention, and the increases were only in people who also received counselling. 

Neither of two studies assessing quality of life reported improvement. One study found no improvement in depressive symptoms following a nine-week intervention. 

One study found a reduction in burnout one month post-intervention, but this was not maintained at three months or one year. No pooled statistics or meta-analysis were performed due to heterogeneity across included studies. The review concludes that WATs alone appear insufficient to produce consistent improvements in physical activity or psychological wellbeing, and that additional behavioral support components may be necessary for meaningful effects.

Assessment Synthesis Criteria
Strong Evidence
There is strong evidence that the intervention will produce the intended outcomes.
  • At least one well-conducted systematic review or meta-analysis (including two or more large, randomized trials) showing a significant and clinically meaningful health effect; and  
  • Consistent findings of health effects from other studies (cohort, case-control, and other designs).
Sufficient Evidence
There is sufficient evidence that the intervention will produce the intended outcomes.
  • At least one well-conducted systematic review or meta-analysis (including two or more large, randomized trials) showing a significant and clinically meaningful health effect, but inconsistent findings in other studies; or
  • Consistent findings from at least three non-randomized control trial studies (cohorts, practical trials, analysis of secondary data); or
  • A single, sufficiently large well-conducted randomized controlled trial demonstrating clinically meaningful health effect and consistent evidence from other studies; or 
  • Multiple expert opinions/government agencies supporting the intervention.
More Evidence Needed or Mixed Evidence
There is insufficient evidence that the intervention will produce the intended outcomes, however the results may indicate potential impact.
  • Lack of demonstration of improved health outcomes based on any of the following: (a) a systematic review or meta-analysis; (b) a large randomized controlled trial; (c) consistent positive results from multiple studies in high-quality journals; or (d) multiple expert opinions or government agencies supporting the intervention. 
  • An insufficient evidence rating does not mean there is no evidence, or that the intervention is unsafe or ineffective. 
  • In many cases, there is a need for more research or longer-term follow-up.
There is strong evidence that the intervention will produce the intended outcomes.
There is sufficient evidence that the intervention will produce the intended outcomes.
There is insufficient evidence that the intervention will produce the intended outcomes, however the results may indicate potential impact.
  • At least one well-conducted systematic review or meta-analysis (including two or more large, randomized trials) showing a significant and clinically meaningful health effect; and  
  • Consistent findings of health effects from other studies (cohort, case-control, and other designs).
  • At least one well-conducted systematic review or meta-analysis (including two or more large, randomized trials) showing a significant and clinically meaningful health effect, but inconsistent findings in other studies; or
  • Consistent findings from at least three non-randomized control trial studies (cohorts, practical trials, analysis of secondary data); or
  • A single, sufficiently large well-conducted randomized controlled trial demonstrating clinically meaningful health effect and consistent evidence from other studies; or 
  • Multiple expert opinions/government agencies supporting the intervention.
  • Lack of demonstration of improved health outcomes based on any of the following: (a) a systematic review or meta-analysis; (b) a large randomized controlled trial; (c) consistent positive results from multiple studies in high-quality journals; or (d) multiple expert opinions or government agencies supporting the intervention. 
  • An insufficient evidence rating does not mean there is no evidence, or that the intervention is unsafe or ineffective. 
  • In many cases, there is a need for more research or longer-term follow-up.
Sources

[1] U.S. Centers for Disease Control and Prevention. (2026, March). Active People, Healthy Nationsm At A Glance. https://www.cdc.gov/active-people-healthy-nation/php/at-a-glance/index.html

[2] U.S. Centers for Disease Control and Prevention. (2026, May). Fast Facts: Health And Economic Costs of Chronic Conditions. CDC Chronic Disease. https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

[3] U.S. Centers for Disease Control and Prevention. (2025, January). Adult Physical Inactivity Outside of Work. https://www.cdc.gov/physical-activity/php/data/inactivity-maps.html

[4] Terlizzi, E. P., Zablotsky, B. (2024, November). Symptoms of Anxiety and Depression among Adults: United States, 2019 and 2022. National Health Statistics Reports, 213. https://www.cdc.gov/nchs/data/nhsr/nhsr213.pdf

[5] Brody, D. J., Hughes, J. P. (2025, April). Depression Prevalence in Adolescents and Adults: United States, August 2021–August 2023. NCHS Data Brief, 527. https://www.cdc.gov/nchs/data/databriefs/db527.pdf

[6] Matjasko, J. L., Chen, Z., Whitfield, G. P., et al. (2025, September). Inadequate Aerobic Physical Activity and Healthcare Expenditures in the United States: An Updated Cost Estimate. American Journal of Health Promotion, 39(7):1085-1087. doi: 10.1177/08901171251357128

[7] Mali S. (2025, August). Wearable Electronics Market Trends, Size, Share, and Growth Forecast 2025 - 2032. Persistence Market Research. https://www.persistencemarketresearch.com/market-research/wearable-electronics-market.asp

[8] Prescient & Strategic Intelligence. U.S. Wearables Device Market Size & Share Analysis - Emerging Trends, Growth Opportunities, Competitive Landscape, and Forecasts (2025 - 2032). https://www.psmarketresearch.com/market-analysis/us-wearables-device-market

[9] Jena, N., Singh, P., Chandramohan, D., Garapati, H. N., Gummadi, J., Mylavarapu, M., Shaik, B. F., Nanjundappa, A., Apala, D. R., Toquica, C., Lapsiwala, B., & Simhadri, P. K. (2025). Wearable Technology in Cardiology: Advancements, Applications, and Future Prospects. Reviews in Cardiovascular Medicine, 26(6), 39025. https://doi.org/10.31083/RCM39025

[10] Myerson, M. (2023, October). What Physicians Need to Know about Consumer Wearable Health Technology. Medical Economics Journal, 100(11). https://www.medicaleconomics.com/view/what-physicians-need-to-know-about-consumer-wearable-health-technology

[11] North Carolina Department of Health and Human Services, Division of Health Benefits. (2025, February). Telehealth, Virtual Communications and Remote Patient Monitoring. Clinical Coverage Policy No: 1H. https://medicaid.ncdhhs.gov/1h-telehealth-virtual-communications-and-remote-patient-monitoring/open

[12] Emmanuel, M. (2025). Insurance Coverage and Reimbursement for Wearables. https://www.researchgate.net/publication/393385768_Insurance_Coverage_and_Reimbursement_for_Wearables

[13] Grimm, C. A. (2024, September). Additional Oversight of Remote Patient Monitoring in Medicare Is Needed. U.S. Department of Health and Human Services, Office of Inspector General. https://oig.hhs.gov/documents/evaluation/10001/OEI-02-23-00260.pdf

[14] FSA Store. Fitness Tracker: FSA Eligibility. Accessed May 25, 2026. https://fsastore.com/fsa-eligibility-list/f/fitness-tracker

[15] Forma. (2025). Can I Use a Lifestyle Spending Account (LSA) for Health Monitoring Devices?  https://www.joinforma.com/lsa-eligible-expenses/health-monitoring-devices

[16] Groom Law Group, Chartered. (2025, January). Wellness Programs Under Scrutiny in EEOC’s New Wearable Devices Guidance. JDSUPRA. https://www.jdsupra.com/legalnews/wellness-programs-under-scrutiny-in-4084361/‌

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