  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.
