Engagement with mHealth alcohol interventions: User perspectives on an app or chatbot-delivered program to reduce drinking (Preprint)

BACKGROUND Research suggests participant engagement is a key mediator of mHealth alcohol interventions’ effectiveness. Understanding the features that promote user engagement is critical to maximizing the effectiveness of mHealth-delivered interventions. OBJECTIVE The purpose of this study was to identify factors related to mHealth alcohol intervention utilization and engagement amongst hazardous-drinking participants who were randomized to use either an app or Artificial Intelligence (AI) chatbot-based intervention for reducing drinking. METHODS We conducted semi-structured interviews with 20 participants who used the app or chatbot for three months, utilizing thematic analysis to identify common facilitators of their continued use as well as factors that diminished engagement. RESULTS Participants of both interventions reported that tracking their drinking, receiving feedback about their drinking, feeling accountable; and daily notifications about high-risk drinking times and reminders to track their drinking promoted continued engagement. Positivity, personalization, gaining insight into their drinking, and daily tips were stronger facilitator themes among bot users, indicating these may be strengths of the AI chatbot-based intervention when compared to a user-directed app. While tracking drinking was a theme among both groups, it was more salient among app users, potentially due to the option to quickly track drinks in the app that was not present with the conversational chatbot. Notification glitches, technology glitches, and difficulty with tracking drinking data were barriers for both users. Lengthy setup processes was a stronger barrier for app users. Repetitiveness of the bot conversation, receipt of non-tailored daily tips, and inability to self-navigate to desired content were reported as barriers by bot users. Participants in both conditions reported that their engagement with a behaviorally focused mHealth intervention was encouraged by tailored feedback about their alcohol use and timely notifications. CONCLUSIONS To maximize engagement with AI interventions, future developers should include tracking to reinforce behavior change self-monitoring, and be mindful of repetitive conversations, lengthy setup, and pathways that limit self-directed navigation. CLINICALTRIAL ClinicalTrials.gov NCT04447794 INTERNATIONAL REGISTERED REPORT RR2-10.2196/33037