ArticleJMIR mental health2026
Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.
Article in JMIR mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Well-being is a cornerstone of public health and social progress; yet, its determinants are multifaceted and dynamic. As behavioral data become increasingly available and artificial intelligence (AI) systems gain prominence, scalable assessments of well-being are becoming more feasible. However, to be useful in practice, such systems must remain understandable to the people they aim to support. Explainable AI is therefore essential to foster trust and enable reflection. Objective: This research aimed to investigate (1) the extent to which modifiable lifestyle and contextual factors can predict subjective well-being, and (2) how different explanation modalities influence users' satisfaction when interpreting AI-generated well-being feedback. Methods: We conducted a 2-stage, application-grounded investigation. First, we developed a parsimonious regularized linear model using a small set of lifestyle-related predictors to estimate individual well-being. Second, we experimentally compared multiple explanation modalities (visual, interactive, textual, quantitative, and population-comparison) against a no-explanation control to evaluate how each format shapes end users' satisfaction with the AI-generated assessment. Results: Across conditions, providing any explanation increased users' satisfaction relative to the no-explanation control in the final sample (n=1252 participants). Visual (B=0.915, SE 0.077; P<.001) and interactive (B=0.914, SE 0.076; P<.001) explanations produced the highest satisfaction scores, while textual (B=0.850, SE 0.076; P<.001) and quantitative (B=0.782, SE 0.077; P<.001) formats also showed strong positive effects. Population-comparison (contextual) feedback yielded a smaller effect (B=0.218, SE 0.077; P=.005) and was consistently the least preferred and least effective at conveying why the model produced a given assessment. Conclusions: The findings suggest that well-being tools should combine simple, interpretable models with visual or interactive explanations that foreground actionable behavioral levers rather than emphasizing population norms. These insights offer design guidance for deploying explainable AI in well-being tools to support user satisfaction.
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