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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Jan 22, 2025
Date Accepted: Nov 4, 2025

The final, peer-reviewed published version of this preprint can be found here:

Health Motivation as a Predictor of mHealth Engagement Across BMI: Cross-Sectional Survey

Chang SH, Chang CM, Chen D, Hue KK, Yu KMC, Yeh LK

Health Motivation as a Predictor of mHealth Engagement Across BMI: Cross-Sectional Survey

JMIR Mhealth Uhealth 2025;13:e71625

DOI: 10.2196/71625

PMID: 41325596

PMCID: 12706447

Health Motivation and mHealth Use in Adults With Varying BMI: A Cross-Sectional Survey

  • Shao-Hsuan Chang; 
  • Ching-Mao Chang; 
  • Daishi Chen; 
  • Kae-Kuen Hue; 
  • Karen Mei-Ching Yu; 
  • Lung-Kun Yeh

ABSTRACT

Background:

Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among populations with varying body mass index (BMI). While digital health technologies have the potential to promote healthier behaviors, little is known about how psychological and behavioral factors interact with BMI to influence usage patterns.

Objective:

This study aimed to explore the relationship between BMI and digital health technology use, and to examine how factors such as health awareness, self-efficacy, and health motivation contribute to technology engagement.

Methods:

A cross-sectional online survey was conducted from January 2024 to February 2024. A total of 184 valid questionnaire participants were included in this study. The questionnaire measured on a five-point Likert scale. Descriptive statistics, chi-squared tests, and multiple regression analyses were applied.

Results:

Of the participants, 38.6% (N=71) had a BMI < 24, 42.4% (N=78) had a BMI between 24–29.9, and 19.0% (N=35) had a BMI ≥ 30. Significant BMI differences were observed based on gender (P < .001) and age (P < .001) but not based on prior digital health tool use. Use rates for Bluetooth/Wi-Fi devices, wearables, and mobile apps were 32.1%, 38.6%, and 39.1% , respectively. A negative correlation between BMI and mobile app usage frequency was identified (p = .025. Multiple regression analysis indicated that health motivation significantly predicted digital health usage (p < .001), whereas health awareness, lifestyle, and self-efficacy did not.

Conclusions:

Individuals with higher BMI reported lower frequency of digital health tool usage, potentially due to lower health motivation in the studied population. Health motivation was the strongest predictor of digital health engagement. Integrating personalized medical records into apps may enhance health motivation, thereby improving user engagement and promoting healthier behaviors in individuals with higher BMI. Clinical Trial: Ethical Approval: This study was approved by Institutional Review Board committee of National Taiwan University Hospital (Registration number: 202307210RINC).


 Citation

Please cite as:

Chang SH, Chang CM, Chen D, Hue KK, Yu KMC, Yeh LK

Health Motivation as a Predictor of mHealth Engagement Across BMI: Cross-Sectional Survey

JMIR Mhealth Uhealth 2025;13:e71625

DOI: 10.2196/71625

PMID: 41325596

PMCID: 12706447

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