Understanding Client Support Strategies to Improve Clinical Outcomes in an Online Mental Health Intervention

要旨

Online mental health interventions are increasingly important in providing access to, and supporting the effectiveness of, mental health treatment. While these technologies are effective, user attrition and early disengagement are key challenges. Evidence suggests that integrating a human supporter into such services mitigates these challenges, however, it remains under-studied how supporter involvement benefits client outcomes, and how to maximize such effects. We present our analysis of 234,735 supporter messages to discover how different support strategies correlate with clinical outcomes. We describe our machine learning methods for: (i) clustering supporters based on client outcomes; (ii) extracting and analyzing linguistic features from supporter messages; and (iii) identifying context-specific patterns of support. Our findings indicate that concrete, positive and supportive feedback from supporters that reference social behaviors are strongly associated with better outcomes; and show how their importance varies dependent on different client situations. We discuss design implications for personalized support and supporter interfaces.

キーワード
Mental health
digital behavioral intervention
CBT
support
machine learning
AI
unsupervised learning
data mining
著者
Prerna Chikersal
Carnegie Mellon University, Pittsburgh, PA, USA
Danielle Belgrave
Microsoft Research Cambridge, Cambridge, United Kingdom
Gavin Doherty
Trinity College Dublin, Dublin, Ireland
Angel Enrique
SilverCloud Health & Trinity College Dublin, Dublin, Ireland
Jorge E. Palacios
SilverCloud Health & Trinity College Dublin, Dublin, Ireland
Derek Richards
SilverCloud Health & Trinity College Dublin, Dublin, Ireland
Anja Thieme
Microsoft Research, Cambridge, United Kingdom
DOI

10.1145/3313831.3376341

論文URL

https://doi.org/10.1145/3313831.3376341

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Mental health & depression

Paper session
314 LANA'I
5 件の発表
2020-04-29 01:00:00
2020-04-29 02:15:00
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