ArticleJournal of medical Internet research2020
Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05966857 (FAPO-X), which is not on this map. Cited by 130 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
FAPO-X: Assisted Digital Telemonitoring With Wearables in Patients After Cardiovascular Surgery
Who cites it
130 citing papers in PubMed, 4 syntheses or guidelines pooled it, 257 citations in OpenAlex.
- Inequalities in Exclusively Mobile Interventions Targeting Weight-Related Behaviors: Systematic Review of Observational Studies.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Enhancing Enrollment and Adherence in Long-Term Wearable Research on Dementia: Qualitative Systematic Review and Meta-Synthesis.JMIR aging · 2025Pooled it
- Digital health, technology-driven or technology-assisted interventions for the management of obesity in children and adolescents.The Cochrane database of systematic reviews · 2025Pooled it
- Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices.International journal of environmental research and public health · 2023Pooled it
- Smartphone App-Based Survey Deployment Patterns and Longitudinal Response Rate: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Isolating the role of researcher observation on reactivity to the measurement of physical activity.Applied psychology. Health and well-being · 2025Trial
- Comparing sleep measures in cancer survivors: self-reported sleep diary versus objective wearable sleep tracker.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Trial
- Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.JMIR mHealth and uHealth · 2024Trial
- Effects of Wearable Fitness Trackers and Activity Adequacy Mindsets on Affect, Behavior, and Health: Longitudinal Randomized Controlled Trial.Journal of medical Internet research · 2023Trial
- Overdiagnosis in atrial fibrillation screening with wearables.Scandinavian journal of primary health care · 2026Article
- Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.Muscle & nerve · 2026Observational
- Device and Data Access Considerations for Digital Health Studies Using Consumer-Grade Wearables: An Experience-Based Tutorial.Clinical and translational science · 2026Article
- Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIHInternational journal of environmental research and public health · 2026Article
- A Two-Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort.Annals of clinical and translational neurology · 2026Article
- The effects of thoracic surgery techniques on postoperative pain and comparison of intercostal interventions.Medicine · 2026Observational
- Mobile Health Technology Ownership and Use Among Cancer Survivors in a Health System.Cancer reports (Hoboken, N.J.) · 2026Article
- Use of and Experience With Wearable Biosensors in Congenital Heart Disease: A Survey Study.CJC pediatric and congenital heart disease · 2026Article
- Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis.Communications medicine · 2026Article
- Population differences in wearable device wear time: Rescuing data to address biases and advance health equity.medRxiv : the preprint server for health sciences · 2026Article
- Intention to use smartphone medical applications among patients with chronic disease in southwest Ethiopia: an extended UTAUT2 model approach.BMC medical informatics and decision making · 2026Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults.
objectiveThe main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults.
methodsUsing a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis.
resultsAbout 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health.
conclusionsThe potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.