A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

Abstract

Health recommender systems (HRSs) have the potential to effectively personalize well-being related behavior change interventions to the needs of individuals. However, personalization is often conducted with a narrow perspective, and the underlying user features are inconsistent across HRSs. Particularly, theory-based determinants of behavior and the variety of lifestyle domains influencing well-being are poorly addressed. We propose a comprehensive theory-based framework of user features, the virtual individual (VI) model, to support the extensive personalization of digital well-being interventions. We introduce a prototype HRS (With-Me HRS) with knowledge-based filtering, which recommends behavior change objectives and activities from several lifestyle domains. With-Me HRS realizes a minimum set of important VI model features related to well-being, lifestyle, and behavioral intention. We report the preliminary validity and usefulness of the HRS, evaluated in a real-life health-coaching program with 50 participants. The recommendations were used in decision-making for half of the participants and were hidden for others. For 73% of the participants (85% with visible vs. 62% with hidden recommendations), at least one of the recommended activities was included into their coaching plans. The HRS reduced coaches’ perceived effort in identifying appropriate coaching tasks for the participants (effect size: Vargha-Delaney $\hat {A}$ = 0.71, 95% CI 0.59-0.84) but not in identifying behavior change objectives. From the participants’ perspective, the quality of coaching improved (effect size for one of three quality metrics: $\hat {A}$ = 0.71, 95% CI 0.57-0.83). These results provide a baseline for testing the influence of additional user model features on the validity of recommendations generated by knowledge-based multi-domain HRSs.

Keywords

• Behavioral sciences
• digital health behavior change interventions
• disease prevention
• eHealth
• filtering algorithms
• knowledge based systems