research-article

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

Authors:

Prerna Chikersal,

Danielle Belgrave,

Jorge E. Palacios,

Derek Richards,

Anja ThiemeAuthors Info & Claims

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 16

https://doi.org/10.1145/3313831.3376341

Published: 23 April 2020 Publication History

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Abstract

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.

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Cited By

Li MGu DLi RGu YLiu HSu KWang XZhang G(2025)The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text Analysis and Empirical StudyJournal of Medical Internet Research10.2196/6029227(e60292)Online publication date: 14-Jan-2025
https://doi.org/10.2196/60292
Sweeney CEnnis EMulvenna MBond RO'Neill S(2024)Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic ReviewJMIR Mental Health10.2196/5574711(e55747)Online publication date: 27-Jun-2024
https://doi.org/10.2196/55747
Weiner LCrowley RSheeber LKoegler FDavis JWells MFunkhouser CAuerbach RAllen N(2024)Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled TrialJMIR Mental Health10.2196/5136611(e51366)Online publication date: 19-Sep-2024
https://doi.org/10.2196/51366
Show More Cited By

Index Terms

Understanding Client Support Strategies to Improve Clinical Outcomes in an Online Mental Health Intervention
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

10688 pages

ISBN:9781450367080

DOI:10.1145/3313831

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 23 April 2020

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Science Foundation Ireland

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CHI '20

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SIGCHI

CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

Li MGu DLi RGu YLiu HSu KWang XZhang G(2025)The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text Analysis and Empirical StudyJournal of Medical Internet Research10.2196/6029227(e60292)Online publication date: 14-Jan-2025
https://doi.org/10.2196/60292
Sweeney CEnnis EMulvenna MBond RO'Neill S(2024)Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic ReviewJMIR Mental Health10.2196/5574711(e55747)Online publication date: 27-Jun-2024
https://doi.org/10.2196/55747
Weiner LCrowley RSheeber LKoegler FDavis JWells MFunkhouser CAuerbach RAllen N(2024)Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled TrialJMIR Mental Health10.2196/5136611(e51366)Online publication date: 19-Sep-2024
https://doi.org/10.2196/51366
Yang WFang AShah RMathur YYang DZhu HKraut R(2024)What Makes Digital Support Effective? How Therapeutic Skills Affect Clinical Well-BeingProceedings of the ACM on Human-Computer Interaction10.1145/36410298:CSCW1(1-29)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641029
Kang RReynolds T(2024)“This app said I had severe depression, and now I don’t know what to do”: the unintentional harms of mental health applicationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642178(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642178
Saha KGupta PMark GKiciman EDe Choudhury M(2024)Observer Effect in Social Media UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642078(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642078
Sreedevi BBarathi SAshish NVamsi PYadav PRevathy GSelvi S(2024)Uncovering anorexia and depression symptoms in social media using fine-grained emotional representationsFOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN PHYSICAL SCIENCES AND MATERIALS: ICAPSM 202310.1063/5.0215990(080003)Online publication date: 2024
https://doi.org/10.1063/5.0215990
Tamilkodi RSujatha BLeelavathy N(2024)Emotion detection in text: advances in sentiment analysisInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02597-016:2(552-560)Online publication date: 17-Nov-2024
https://doi.org/10.1007/s13198-024-02597-0
Chow KFritz THolsti LBarbic SMcGrenere J(2023)Feeling Stressed and Unproductive? A Field Evaluation of a Therapy-Inspired Digital Intervention for Knowledge WorkersACM Transactions on Computer-Human Interaction10.1145/360933031:1(1-33)Online publication date: 29-Nov-2023
https://dl.acm.org/doi/10.1145/3609330
Yoo DBhatnagar AErnala SAli ABirnbaum MAbowd GDe Choudhury M(2023)Discussing Social Media During Psychotherapy Consultations: Patient Narratives and Privacy ImplicationsProceedings of the ACM on Human-Computer Interaction10.1145/35794797:CSCW1(1-24)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579479
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