{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:25:27Z","timestamp":1787851527161,"version":"build-2784847793"},"reference-count":53,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"\n Background<\/jats:title>\n Sustaining self-management is critical for optimizing clinical outcomes in individuals with type 2 diabetes mellitus (T2DM). Although digital health interventions (DHIs) have shown benefits for glycemic control and self-care, much of this evidence has focused on efficacy, and the behavioral mechanisms through which DHIs produce sustained effects remain insufficiently understood. Clarifying these mechanisms could inform the development of theory-driven interventions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study examined the longitudinal behavioral pathways through which Artificial Intelligence-based Health Education Accurately Linking System, a WeChat (Tencent)\u2013based digital health program, influences T2DM self-management, using the Extended Multi-Theory Model (MTM) of health behavior change.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n An explanatory sequential mixed methods prospective longitudinal cohort study was conducted among adults with T2DM (aged \u226518 y and proficient in WeChat use), recruited from 45 primary health care institutions in Beijing, China, between July 2023 and July 2024. Self-management behavior was assessed as the primary outcome using the Summary of Diabetes Self-Care Activities, and psychosocial determinants using the Extended MTM Scale and the Diabetes-related Skills Scale. Exploratory and confirmatory factor analyses assessed the construct validity of the Extended MTM Scale. Structural equation modeling examined longitudinal pathways among Artificial Intelligence-based Health Education Accurately Linking System users across baseline and 3, 6, and 12 months. For the qualitative phase, a purposive subsample was selected through maximum variation sampling based on baseline glycated hemoglobin; interviews were analyzed thematically until thematic saturation, and integrated with quantitative findings using a joint display.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Of the 406 enrolled participants, 391 completed baseline assessments. The Extended MTM Scale demonstrated a 6-factor, 22-item structure with excellent internal consistency (Cronbach \u03b1=0.928) and satisfactory construct validity. The structural equation modeling showed satisfactory fit (CFI=0.984, RMSEA=0.036). Changes in the social environment (\u03b2=0.23, 95% CI 0.07\u20100.38; P=.003) and physical environment (\u03b2=0.25, 95% CI 0.10\u20100.40; P=.001) at baseline, and diabetes-related skills at month 6 (\u03b2=0.16, 95% CI 0.03\u20100.29; P=.01), were directly associated with self-management behavior at month 12, whereas behavioral confidence and emotional transformation showed no significant direct effects. Social environment changes were indirectly associated with behavioral confidence through participatory dialogue at month 3 (\u03b2=0.20, 95% CI 0.04\u20100.36; P=.01; \u03b2=0.66, 95% CI 0.57\u20100.74; P<.001). Thematic analysis of 17 interviews identified 3 domains: environmental context, cognitive processes, and attitudes and skills. Environmental factors converged across both data strands, while qualitative data expanded on the cognitive and attitudinal processes underlying sustained self-management.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study is among the first to apply the Extended MTM framework to DHI-supported T2DM self-management, with environmental factors emerging as key drivers alongside selected cognitive, attitudinal, and skills-related processes. Complementing efficacy-focused research, it illuminates the psychosocial pathways underlying sustained self-management and refines the Extended MTM in digital health contexts. These insights can inform the design of theory-driven DHIs in primary care.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n Chinese Clinical Trial Registry ChiCTR2300068952; https:\/\/www.chictr.org.cn\/showprojEN.html?proj=170361<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/79081","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:20:08Z","timestamp":1783574408000},"page":"e79081","source":"Crossref","is-referenced-by-count":0,"title":["Behavioral Mechanisms of a Digital Health Intervention for Self-Management in Type 2 Diabetes Mellitus: Prospective Longitudinal Cohort 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