ArticleDigital health
How digital therapeutic alliances influence the perceived helpfulness of online mental health Q&A: An explainable machine learning approach.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predictors of Professional Responses in Nonprofit Mental Health Forums: Interpretable Machine Learning Analysis.Journal of medical Internet research · 2026Article
- Deciphering patient selection of physicians in online health communities: insights from a dual-path perspective.BMC psychology · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Objective: This study investigates the role of digital therapeutic alliance (DTA) in predicting and explaining the perceived helpfulness of responses on online mental health Q&A platforms. Methods: This study constructs a large dataset of 19,682 Q&A interactions from online mental health Q&A platforms, employs natural language processing, explainable machine learning, and causal inference methods to identify and understand the factors, particularly DTA, that influence the perceived helpfulness of human counselors' responses to mental health questions. Results: The machine learning-based model for predicting perceived helpfulness demonstrated strong performance, achieving an root mean square error of 0.8234 and a mean absolute percentage error of 22.7288%. The explanatory analysis revealed that peripheral path-related language cues, such as counselor engagement (e.g., word count and response time), had the highest predictive power. Additionally, central path-related language cues, such as those linked to the DTA-specifically emotional bonds and therapeutic tasks-significantly influenced perceived helpfulness and were positively impacted by counselor engagement. Conclusion: This study integrates DTA and elaboration likelihood model theories to propose a computational framework for understanding and predicting the perceived helpfulness of responses in online mental health Q&A platforms. Findings offer theoretical insights into the mechanisms of perceived helpfulness and practical guidance for optimizing platform design, training counselors, and improving user satisfaction through targeted language strategies.
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Registered trials
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