JMIR mHealth and uHealth · 2026
Real-World Long-Term Engagement With a Mobile App Intervention to Improve Self-Management of Type 2 Diabetes Mellitus in China (SMARTDiabetes): Mixed Methods Study.
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Tao Xuanchen, Peiris David, Ma Yuxia, Zhang Yaoshen, Song Hongyi, Mao Limin, Zhang Puhong
Аннотация
BACKGROUND: Despite the high prevalence of type 2 diabetes mellitus in China, few patients are achieving adequate management targets. While digital behavior change interventions (DBCIs) are promising solutions, sustained long-term user engagement with these new technologies remains challenging.
OBJECTIVE: The objectives of this study were to (1) describe the long-term user engagement with the SMARTDiabetes app, (2) explore the associations between user engagement and trial effectiveness, and (3) identify main influencing factors of user engagement.
METHODS: A mixed methods process evaluation was conducted alongside the 2-year SMARTDiabetes trial to assess long-term user engagement with the DBCI, investigate its association with trial effectiveness, and explore its influencing factors. Quantitative data included clinical outcomes and app usage data; qualitative data included an expert review of the DBCI and semistructured interviews with key stakeholders. Findings were synthesized using Michie's conceptual framework for engagement with DBCIs.
RESULTS: A total of 1038 participants were included in the quantitative analyses, and 12 in-depth interviews were conducted in June 2020. The mean proportion of monthly active users was 46.4% (SD 7.8%), with a gradual decline from 56.3% at the start (April 2018) to 42.2% at the end of trial (July 2019). The proportion of sustained active users was 32.9% (342/1038), with rural users showing a significantly higher rate than urban users (38.3% vs 27.3%; P<.001). We found a strong link between user engagement and the effectiveness in achieving primary health outcome (risk ratio 1.37, 95% CI 1.21-1,55; P<.001) and a clear dose-response relationship between the level of app interactions and degree of improvement in the health outcome. Key drivers of user engagement included 6 behavior change techniques (ie, reward, feedback, reminders, goal setting, self-monitoring, and social support features), 4 implementation facilitators (credibility features, professional support, guidance, and ease of use), and 5 contextual contributors (younger age, better education, greater smartphone literacy, closer patient-doctor relationship, and stronger primary health care facilities commitment).
CONCLUSIONS: Long-term, sustained engagement with a mobile app intervention for type 2 diabetes mellitus self-management is achievable in the primary health care setting in China. Higher engagement is associated with better health outcomes. The influencing factors identified in the SMARTDiabetes study provide valuable insights for developing targeted strategies to enhance user engagement with DBCIs and improve overall program effectiveness.
OBJECTIVE: The objectives of this study were to (1) describe the long-term user engagement with the SMARTDiabetes app, (2) explore the associations between user engagement and trial effectiveness, and (3) identify main influencing factors of user engagement.
METHODS: A mixed methods process evaluation was conducted alongside the 2-year SMARTDiabetes trial to assess long-term user engagement with the DBCI, investigate its association with trial effectiveness, and explore its influencing factors. Quantitative data included clinical outcomes and app usage data; qualitative data included an expert review of the DBCI and semistructured interviews with key stakeholders. Findings were synthesized using Michie's conceptual framework for engagement with DBCIs.
RESULTS: A total of 1038 participants were included in the quantitative analyses, and 12 in-depth interviews were conducted in June 2020. The mean proportion of monthly active users was 46.4% (SD 7.8%), with a gradual decline from 56.3% at the start (April 2018) to 42.2% at the end of trial (July 2019). The proportion of sustained active users was 32.9% (342/1038), with rural users showing a significantly higher rate than urban users (38.3% vs 27.3%; P<.001). We found a strong link between user engagement and the effectiveness in achieving primary health outcome (risk ratio 1.37, 95% CI 1.21-1,55; P<.001) and a clear dose-response relationship between the level of app interactions and degree of improvement in the health outcome. Key drivers of user engagement included 6 behavior change techniques (ie, reward, feedback, reminders, goal setting, self-monitoring, and social support features), 4 implementation facilitators (credibility features, professional support, guidance, and ease of use), and 5 contextual contributors (younger age, better education, greater smartphone literacy, closer patient-doctor relationship, and stronger primary health care facilities commitment).
CONCLUSIONS: Long-term, sustained engagement with a mobile app intervention for type 2 diabetes mellitus self-management is achievable in the primary health care setting in China. Higher engagement is associated with better health outcomes. The influencing factors identified in the SMARTDiabetes study provide valuable insights for developing targeted strategies to enhance user engagement with DBCIs and improve overall program effectiveness.