Evidence map›Paper›PMID 34457181›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

The Effects and Patterns among Mobile Health, Social Determinants, and Physical Activity: A Nationally Representative Cross-Sectional Study.

Jiancheng Ye, Qianheng Ma

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 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

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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Digital health technology burden and frustration among patients with multimorbidity.Journal of the American Medical Informatics Association : JAMIA · 2026
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  5. Review
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  15. The impact of electronic health record-integrated patient-generated health data on clinician burnout.Journal of the American Medical Informatics Association : JAMIA · 2021
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  16. Observational
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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

2 authors.

Jiancheng YeFeinberg School of Medicine, Northwestern University, Chicago, USA.
Qianheng MaDepartment of Public Health Sciences, University of Chicago, Chicago, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobile health (mHealth) technologies and applications are becoming more and more accessible. The increased prevalence of wearable and embeddable sensors has opened up new opportunities to collect health data continuously outside of the clinical environment. Meanwhile, wearable devices and smartphone health apps are useful to address the issues of health disparities and inequities. This study aims to identify different characteristics of individuals who use different mHealth technologies (wearable devices and smartphone apps) and explore the effectiveness and patterns of mHealth for impacting physical activities. We found that social determinants are significantly associated with the use of mHealth; mHealth is helping people to exercise more regularly and for a longer time. Smartphone app users are older while wearable device users are younger. Health disparities exist in mHealth use and physical activity level. Social determinants like education and income are associated with mHealth use and physical activity. The integration of passively-tracked patient-generated health data (PGHD) holds promise in increasing physical activities. Physical activity interventions that comprise wearable devices and smartphone apps may be more beneficial, since health goals, data visualization, real-time support and feedback, results interpretation, and group education could be embedded in the integrated "smart system". These findings may be useful for stakeholders like wearable device and smartphone app companies, researchers, health care workers, and public health practitioners, who should work together to design and develop "precision mobile health" products with higher personalized and participatory levels, thus improving the population health.

Indexed as

Mobile ApplicationsTelemedicineCross-Sectional StudiesExerciseHumansSocial Determinants of Health

Identifiers

PMID34457181
PMCPMC8378627

What OpenQuestion holds

Textmetadata
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.