Evidence map›Paper›PMID 42234931›Full record

ArticleJMIR mHealth and uHealth2026

Managing BMI and Emotional Distress Using mHealth: Nationally Representative Survey Study.

Ranran Z Mi, Fei Shen, Julia Strugala

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ranran Z MiDepartment of Communication, Media and Journalism, Kean University, 1000 Morris Ave, Union, NJ, 07083, United States, 1 908-737-0456, 1 908-737-0465.ORCID 0000-0003-2113-3876
Fei ShenSchool of Psychology, Kean University, Union, NJ, United States.ORCID 0000-0002-1632-8540
Julia StrugalaSchool of Psychology, Kean University, Union, NJ, United States.ORCID 0009-0009-1210-6156

Funding

Wave IV Data CollectionP01HD031921 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HARRIS, KATHLEEN MULLAN · 1994 to 2020
$86.1M
NICHD NIH HHS P01 HD031921
6 · The paper itself

Abstract

Background: Mobile health (mHealth) technologies, including smartphone health apps and wearable trackers, are increasingly used to promote health behaviors. However, their impact on physical and mental well-being remains complex, with both benefits and potential unintended negative consequences. Objective: This study aimed to examine the relationship between mHealth use (ie, health app and wearable tracker) and 2 health outcomes (BMI and emotional distress), as well as the mediating roles of healthy eating, sleep, and physical activity based on a representative sample. Methods: We analyzed data from a nationally representative sample of US adults aged 33 to 43 years (N=1931). Chi-square tests and 1-way ANOVA were used to compare demographic differences between mHealth users and nonusers. A path model examined the relationship between mHealth use (ie, smartphone health apps and wearable trackers) and health outcomes (ie, BMI and emotional distress), with lifestyle factors (ie, healthy eating, physical activity, and sleep) as mediators. Mediation analyses tested indirect effects through these lifestyle factors. Results: mHealth users were more likely to be female, married, have higher levels of education and income, and have health insurance. The primary use of mHealth was the management of physical activity. Smartphone health app use positively correlated with wearable tracker use (β=.394; P<.001). Smartphone health app use predicted greater BMI (β=.068; P=.006), whereas wearable tracker use did not significantly predict BMI. Smartphone health app use was unrelated to emotional distress, while wearable tracker use was associated with lower emotional distress (β=-.074; P=.003). Mediation analyses showed that physical activity negatively mediated the relationships between both types of mHealth use and health outcomes, indicating that mHealth users were more physically active, which was linked to lower BMI and emotional distress. Sleep hours mediated only the association between wearable tracker use and health outcomes, such that greater tracker use was related to fewer sleep hours, predicting higher BMI and emotional distress. Finally, healthy eating mediated only the associations between mHealth use and emotional distress, suggesting that healthier dietary behaviors among mHealth users contributed to lower emotional distress. Conclusions: mHealth technologies can potentially promote healthier behaviors, but their effectiveness depends on users taking the initiative to sustain lifestyle changes. While wearable trackers may aid in mental well-being, their association with reduced sleep warrants further investigation.

Indexed as

Body Mass IndexPsychological DistressAdultDigital HealthFemaleHumansMaleSurveys and QuestionnairesTelemedicinebody mass indexdietaryeatingemotional distressmobile healthphysical activitysleepwearable trackers

Identifiers

PMID42234931
PMCPMC13232912

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.