ArticleNPJ digital medicine2024
Assessment of ownership of smart devices and the acceptability of digital health data sharing.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 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
- Technology-Supported Physical Activity and Its Potential as a Tool to Promote Young Women's Physical Activity and Physical Literacy: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Using App Store Reviews to Explore User Experiences and Perceptions of Mental Health Mobile Apps: Qualitative Study.JMIR formative research · 2026Article
- Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.Muscle & nerve · 2026Observational
- DOSE: An open-source, iOS watch-based tool for experience sampling.PLOS digital health · 2026Article
- Device and Data Access Considerations for Digital Health Studies Using Consumer-Grade Wearables: An Experience-Based Tutorial.Clinical and translational science · 2026Article
- Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.PLOS digital health · 2026Article
- Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIHInternational journal of environmental research and public health · 2026Article
- Smartphone-based digital phenotyping for detection of high-risk depression and anxiety in Korean community settings.Internet interventions · 2026Article
- Wearable Devices and Data Sharing in the US.JAMA network open · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Lived Experiences of Older Adults Using Wearables With Real-Time Feedback: Phenomenological Study.JMIR mHealth and uHealth · 2026Article
- Exergame enjoyment using various devices and awareness of their use for physical activity and health improvement among young adults.BMC sports science, medicine & rehabilitation · 2026Article
- Use of Wearable Devices to Augment Traditional Measurements of Postoperative Outcomes Following Total Joint Arthroplasty: Systematic Review.JMIR rehabilitation and assistive technologies · 2026Review
- Willingness to Share Internet Use Data for Research on Early Disease Detection: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- Case-Based Lessons on Remote Patient Monitoring in Neurology Using Consumer-Grade Wearables.Journal of central nervous system disease · 2026Article
- Aligning femtech innovation With equity: a strategic framework for real world impact.Frontiers in global women's health · 2026Article
- Patient engagement with consumer wearable devices in the electronic health record.Frontiers in digital health · 2026Article
- Governing Evidence in Contentious Times: A GUARD Framework for Public Health Leadership.Public health reviews · 2026Article
- Public perceptions of health data sharing for artificial intelligence research: a qualitative focus group study in the UK.BMJ digital health & AI · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18-25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.
Identifiers
What OpenQuestion holds
Registered trials
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.