Evidence map›Paper›PMID 41666365›Full record

ArticleJMIR medical informatics2026

Exploring Self-Management-Based Mobile Health User Typologies and Associations Between User Types and Satisfaction With Key Mobile Health Functions: Comparative Study of Various Fitness and Weight Management App User Types.

Tong Wang, Yiwen Fan, Zheng Li, Xiaoyi Jiao, Qichuan Fang, Junhao Ma, Jianbo Lei

Abstract readComparative Study
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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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

7 authors.

Tong WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.ORCID 0000-0002-4422-6197
Yiwen FanDepartment of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.ORCID 0009-0005-3595-3519
Zheng LiDepartment of Rehabilitation Medicine & Physical Therapy, The Affiliated Hospital of Southwest Medical University, Luzhou, China.ORCID 0009-0000-3113-5352
Xiaoyi JiaoCenter for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, China.ORCID 0009-0004-1357-1352
Qichuan FangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.ORCID 0009-0005-9913-095X
Junhao MaSchool of Public Health, Hangzhou Medical College, Hangzhou, China.ORCID 0009-0006-3308-5284
Jianbo LeiInstitute of Advanced Clinical Medicine, Center for Medical Informatics, Peking University, Beijing, China.ORCID 0000-0002-1744-0235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExploring user satisfaction is crucial for enhancing and ensuring the sustainable development of mobile health (mHealth) apps, particularly in the fitness and weight management sectors. Analyzing user types and developing user profiles are valuable for understanding differences in satisfaction. However, prior research lacks a classification of user types based on self-management characteristics and an analysis of satisfaction disparities among these types.

objectiveThis study analyzes user heterogeneity from a self-management perspective among fitness and weight management app users by identifying user types and constructing profiles. It further explores differences in satisfaction with the functional design of these mHealth apps across user types.

methodsFirst, 8 feature indicators were selected based on the Health Belief Model and the Behavior Change Wheel to evaluate users' levels of health knowledge and beliefs, as well as self-regulation related to self-management. Existing research was integrated to categorize mHealth app functional design into 5 categories: health guidance, health education, health monitoring, social features, and gamification. Second, a questionnaire survey was used to collect data on users' 8 health management characteristics and their satisfaction with the 5 functional design categories. A total of 2518 responses were collected, of which 1025 were included in the analysis. Cluster analysis was conducted to classify users into distinct types based on the 8 health management characteristics, and user profiles were constructed according to the distribution of these characteristics within each type. Finally, the Kruskal-Wallis test was used to analyze differences in satisfaction across user types with respect to the 5 functional design categories of mHealth apps.

resultsCluster analysis revealed that users could be categorized into 6 types based on the 8 self-management characteristics: positively proactive energizers, proactive intenders, negatively proactive energizers, low health management demanders, potential health management demanders, and passive attitude holders. Significant differences were observed across all 8 health management characteristics among the 6 user types (all P<.001). The Kruskal-Wallis test indicated significant variations in user satisfaction with the 5 functional designs of mHealth apps: H(4)=445.388, (P<.001). Overall, users reported the highest satisfaction with health guidance and health monitoring (median 4.00, IQR 1.00) and the lowest satisfaction with gamification (median 3.00, IQR 1.00). Positively proactive energizers, proactive intenders, and negatively proactive energizers demonstrated the highest satisfaction with health education and health guidance (median 4.00). Potential health management demanders, proactive intenders, positively proactive energizers, and negatively proactive energizers reported the highest satisfaction with health monitoring (median 4.00). Proactive intenders reported the highest satisfaction with social features and gamification (median 4.00).

conclusionsUsers of mHealth apps exhibit diverse types, with significant differences in health management characteristics and satisfaction with the 5 functional designs of fitness and weight management apps. This study clarifies individual-level differences in user satisfaction with mHealth apps.

Indexed as

Mobile ApplicationsPatient SatisfactionSelf-ManagementAdultDigital HealthFemaleHumansMaleMiddle AgedSurveys and QuestionnairesTelemedicinemHealth appself-management characteristicsunsupervised learninguser profileuser satisfaction

Identifiers

PMID41666365
PMCPMC12933165

What Socratic holds

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LicenceCC BY
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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 Socratic graph.