Evidence map›Paper›PMID 42542756›Full record

ArticleDigital health

Trust in healthcare and wearable use among U.S. adults: A cross-sectional analysis of the 2022 health information national trends survey.

Shashank Madhu, Justice Centrello, Cameron Riopelle

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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.

Shashank MadhuDepartment of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA.ORCID https://orcid.org/0000-0001-9070-0892
Justice CentrelloDepartment of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA.ORCID https://orcid.org/0009-0002-3092-4717
Cameron RiopelleResearch Data & Open Scholarship, University of Miami Libraries, University of Miami, Coral Gables, FL, USA.ORCID https://orcid.org/0000-0002-0668-0516

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Wearable devices are increasingly used to monitor and track health, yet the factors shaping adoption remain incompletely understood. Amid longstanding declines in public trust in the U.S. healthcare system, this study examines how trust in healthcare relates to wearable device use. Methods: Data from the 2022 Health Information National Trends Survey (HINTS 6) were analyzed in a sample of 6,118 respondents with complete data on trust, wearable use, and key sociodemographic and psychosocial characteristics. Structural equation modeling (SEM) evaluated relationships among trust, wearable use, and four latent constructs: technological aptitude, health literacy, social media use, and perceived competence. Results: Among respondents, 84.8% reported moderate to high trust in the U.S. healthcare system and 33.2% reported using a wearable device in the prior year. Technological aptitude was the strongest correlate of wearable use (β = .88, p < .001), while perceived competence (β = -.68, p < .001) and social media use (β = -.46, p < .001) were inversely associated. Trust was positively associated with technological aptitude (β = .29, p = .005) and health literacy (β = .36, p < .001), and negatively associated with social media use (β = -.19, p < .001). Trust and wearable use demonstrated a negative association (β = -.14, p = .015). Conclusions: Digital readiness and lower healthcare trust together shape wearable adoption, with implications for how clinicians might leverage self-tracking behaviors to rebuild patient-provider relationships.

Indexed as

behaviormHealthself efficacysocial mediatelehealthtrustwearables

Identifiers

PMID42542756
PMCPMC13428102

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.