Predictors and Perceptions of mHealth App Engagement in Older Adults: A Mixed-Methods Study on Nurse-Supported
1Department of Nursing, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China; School of Nursing, Jinzhou Medical University, Jinzhou, China.
Objectives:
To examine nurse-supported interventions on sustained mobile health (mHealth) engagement and identify predictors of long-term use in community-dwelling older adults.
Design:
Explanatory sequential mixed-methods.
Setting And Participants:
Twelve clinical sites in Liaoning, China (March 2023 to February 2024). Included 1532 participants (quantitative); 32 for qualitative interviews.
Methods:
Quantitative: XGBoost machine learning on longitudinal behavioral data. Qualitative: Thematic analysis of in-depth interviews. Integration via joint display.
Results:
Morning (8-10 am) notification responsiveness was the strongest retention predictor (mean SHapley Additive exPlanations = 0.34). "Digital companionship" emerged qualitatively (58% anthropomorphized features; χ2 = 9.32, P = .002). Nurse mediation increased data-sharing willingness by 16% vs commercial platforms (P < .001); 89% of high-adherence users valued clinical integration (P < .001).
Conclusions And Implications:
Nurse support enhances engagement via optimized timing, trust, and integration. Implement nurse-led coaching, electronic health record integration, and supportive reimbursement. Future studies should assess long-term outcomes and cross-cultural applicability.
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