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Qualitative Analysis of User Experiences of a mHealth Self-Care Intervention for Care Partners of Individuals with
Rongqi Bei1, Christopher M Graves2, Sung Won Choi3
1School of Information, University of Michigan, Ann Arbor, MI.
Objective:
To identify challenges and solutions for conducting mobile health (mHealth) research in a rehabilitation care partner population that involved personalized self-care push notifications and self-monitoring data collection.
Design:
Qualitative semistructured debriefing interviews with participants who completed a 6-month app-based self-care intervention involving self-monitoring alone or self-monitoring plus personalized push notifications. Interviews were guided by a data-prompted approach to aid participants' recall.
Setting:
Two academic medical centers.
Participants:
Thirty-six care partners (N=36) of individuals with complicated mild to severe traumatic brain injury requiring assistance with daily activities.
Interventions:
The CareQOL app is a mHealth intervention that delivers personalized self-care push notifications based on daily self-monitoring data, including self-reported caregiver strain, depression, and anxiety, plus Fitbit-tracked sleep and step data.
Main Outcome Measures:
Qualitative interview data about participants' experiences with, impressions of, and suggestions for the CareQOL app: (1) self-monitoring components (ie, daily questions, health data dashboard); (2) personalized self-care notifications, including overall experience and perceptions of selected messages.
Results:
Deductive thematic analysis was used to uncover participants' experiences with the CareQOL app and identify considerations across intervention and study design. Intervention-wise, push notifications personalized solely based on passive sensing and self-report can be misaligned with participants' needs, reducing support relevance; limited contextual cues in dashboard displays constrained meaningful self-reflection. Study-wise, technical breakdowns disrupted engagement during the extended study duration; data-prompted interviews effectively supported recall, with participant preferences informing methodological considerations.
Conclusions:
Findings identified critical considerations and practical recommendations for designing personalized mHealth interventions for self-care and evaluating them in longitudinal randomized controlled trials.
