Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study

TL;DR

This study introduces a personalized, context-aware algorithm for selecting stress-reducing micro-interventions via mHealth, demonstrating improved engagement and efficacy in a real-world four-week experiment with parents, highlighting optimal timing for interventions.

Contribution

The paper presents a novel personalized, context-aware intervention selection algorithm that enhances engagement and effectiveness of mHealth stress interventions in real-world settings.

Findings

PCAR outperforms random selection and control in engagement and efficacy.

One-minute interventions significantly reduce perceived stress levels.

Interventions during transitional periods are most effective.

Abstract

Sustaining long-term user engagement with mobile health (mHealth) interventions while preserving their high efficacy remains an ongoing challenge in real-world well-being applications. To address this issue, we introduce a new algorithm, the Personalized, Context-Aware Recommender (PCAR), for intervention selection and evaluate its performance in a field experiment. In a four-week, in-the-wild experiment involving 29 parents of young children, we delivered personalized stress-reducing micro-interventions through a mobile chatbot. We assessed their impact on stress reduction using momentary stress level ecological momentary assessments (EMAs) before and after each intervention. Our findings demonstrate the superiority of PCAR intervention selection in enhancing the engagement and efficacy of mHealth micro-interventions to stress coping compared to random intervention selection and a control group that did not receive any intervention. Furthermore, we show that even brief, one-minute interventions can significantly reduce perceived stress levels (p=0.001). We observe that individuals are most receptive to one-minute interventions during transitional periods between activities, such as transitioning from afternoon activities to bedtime routines. Our study contributes to the literature by introducing a personalized context-aware intervention selection algorithm that improves engagement and efficacy of mHealth interventions, identifying key timing for stress interventions, and offering insights into mechanisms to improve stress coping.