Evidence map›Paper›PMID 39982763›Full record

ArticleJournal of medical Internet research2025

Usage Trends and Data Sharing Practices of Healthcare Wearable Devices Among US Adults: Cross-Sectional Study.

Ranganathan Chandrasekaran, Muhammed Sadiq T, Evangelos Moustakas

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Digital hypertension in 2024-2025: emerging evidence and future directions.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ranganathan ChandrasekaranDepartment of Information & Decision Sciences / Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0003-2001-578X
Muhammed Sadiq TDepartment of Management Studies, Indian Institute of Technology Madras, Chennai, India.ORCID 0000-0002-4614-2333
Evangelos MoustakasDepartment of Communication and Media, Faculty of Communication, Arts and Sciences, Canadian University of Dubai, Dubai, United Arab Emirates.ORCID 0000-0002-2671-9035

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth care wearable devices can transform health care delivery by enabling real-time, continuous monitoring that facilitates early disease detection, personalized treatments, and improved patient engagement. The COVID-19 pandemic has heightened awareness of the importance of health technology, accelerating interest in wearables as tools for monitoring health and managing chronic conditions. As we navigate the postpandemic era, understanding the adoption and data-sharing behaviors associated with wearable devices has become increasingly critical. Despite their potential, challenges and low adoption rates persist, with significant gaps in understanding the impact of sociodemographic factors, health conditions, and digital literacy on the use and data-sharing behaviors of these devices.

objectiveThis study aimed to explore the usage and data-sharing practices (willingness to share wearable data and actual data-sharing behavior) of wearable devices among US adults specifically during the later phases of the COVID-19 pandemic.

methodsUsing cross-sectional data from the National Cancer Institute's Health Information National Trends Survey 6, conducted from March to November 2022, this study uses responses from 5591 US adults to examine wearable use, willingness to share wearable data with providers, family, and friends, and the wearable data-sharing behavior.

resultsThe results indicate an increase in wearable device adoption to 36.36% (2033/5591) in 2022, up from 28%-30% in 2019. We also find a significant discrepancy between the willingness to share data, with 78.4% (1584/2020) of users open to sharing with health care providers, and the actual sharing behavior, where only 26.5% (535/ 2020) have done so. Higher odds of using wearables were associated with female gender (odds ratio [OR] 1.49, 95% CI 1.17-1.90, P<.01) and higher income levels (OR 2.65, 95% CI 1.42-4.93, P<.01 for incomes between US $50,000 and US $75,000, and OR 3.2, 95% CI 1.71-5.97, P<.01 for incomes above US $75,000). However, the likelihood of usage and data sharing declines significantly with age. Compared with African American respondents, Hispanic respondents were more willing to share wearable data with providers (OR 1.92, 95% CI 1.02-3.62, P<.05), though the odds of their actual sharing of wearable data with providers was relatively less (OR 0.44, 95% CI 0.20-0.97, P<.05). Frequency of provider visits (OR 1.23, 95% CI 1.08-1.39, P<.01), and total medical conditions (OR 1.35, 95% CI 1.05-1.73, P<.01) were significant predictors of data-sharing behavior. The study also identified weight, frequency of provider visits, technological self-efficacy and frequent physical activity as predictors for higher wearable use.

conclusionsInsights from this study are crucial for health care providers and policy makers aiming to leverage wearable technology to enhance health outcomes. Addressing the disparities and barriers identified can lead to more effective integration of these technologies in health care systems, thereby maximizing the potential of digital health tools to improve public health outcomes.

Indexed as

COVID-19Information DisseminationWearable Electronic DevicesAdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedSARS-CoV-2United StatesYoung Adultactivity trackersadultscross-sectional surveydata sharingdata-sharing behaviordigital literacydisease detectionhealthcare deliveryhealthcare wearable devicespatient engagementpost-pandemicsurveyUnited Stateswearableswearable usewillingness to share wearable data

Identifiers

PMID39982763
PMCPMC11890132

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.