Literature DB >> 24709016

Web-based depression treatment: associations of clients' word use with adherence and outcome.

Rianne Van der Zanden1, Keshia Curie2, Monique Van Londen3, Jeannet Kramer4, Gerard Steen5, Pim Cuijpers6.   

Abstract

BACKGROUND: The growing number of web-based psychological treatments, based on textual communication, generates a wealth of data that can contribute to knowledge of online and face-to-face treatments. We investigated whether clients' language use predicted treatment outcomes and adherence in Master Your Mood (MYM), an online group course for young adults with depressive symptoms.
METHODS: Among 234 participants from a randomised controlled trial of MYM, we tested whether their word use on course application forms predicted baseline levels of depression, anxiety and mastery, or subsequent treatment adherence. We then analysed chat session transcripts of course completers (n=67) to investigate whether word use changes predicted changes in treatment outcomes.
RESULTS: Depression improvement was predicted by increasing use of 'discrepancy words' during treatment (e.g. should). At baseline, more discrepancy words predicted higher mastery level. Adherence was predicted by more words used at application, more social words and fewer discrepancy words. LIMITATIONS: Many variables were included, increasing the chance of coincidental results. This risk was constrained by examining only those word categories that have been investigated in relation to depression or adherence.
CONCLUSIONS: This is the first study to link word use during treatment to outcomes of treatment that has proven to be effective in an RCT. The results suggest that paying attention to the length of problem articulation at application and to 'discrepancy words' may be wise, as these seem to be psychological markers. To expand knowledge of word use as psychological marker, research on web-based treatment should include text analysis.
Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Depression; Linguistics; Text analysis; Web-based treatment; Word use; e-Mental health

Mesh:

Year:  2014        PMID: 24709016     DOI: 10.1016/j.jad.2014.01.005

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  14 in total

1.  Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention.

Authors:  Jason E Owen; Michaela Curran; Erin O'Carroll Bantum; Robert Hanneman
Journal:  Cyberpsychol Behav Soc Netw       Date:  2016-06

2.  Can linguistic analysis be used to identify whether adolescents with a chronic illness are depressed?

Authors:  Lauren Stephanie Jones; Emma Anderson; Maria Loades; Rebecca Barnes; Esther Crawley
Journal:  Clin Psychol Psychother       Date:  2020-01-09

3.  Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

Authors:  Mark Hoogendoorn; Thomas Berger; Ava Schulz; Timo Stolz; Peter Szolovits
Journal:  IEEE J Biomed Health Inform       Date:  2016-08-17       Impact factor: 5.772

4.  Life Satisfaction and the Pursuit of Happiness on Twitter.

Authors:  Chao Yang; Padmini Srinivasan
Journal:  PLoS One       Date:  2016-03-16       Impact factor: 3.240

Review 5.  How do eHealth Programs for Adolescents With Depression Work? A Realist Review of Persuasive System Design Components in Internet-Based Psychological Therapies.

Authors:  Lori Wozney; Anna Huguet; Kathryn Bennett; Ashley D Radomski; Lisa Hartling; Michele Dyson; Amanda S Newton; Patrick J McGrath
Journal:  J Med Internet Res       Date:  2017-08-09       Impact factor: 5.428

6.  Content of client emails in internet-delivered cognitive behaviour therapy: A comparison between two trials and relationship to client outcome.

Authors:  Joelle N Soucy; Heather D Hadjistavropoulos; Catherine A Couture; Victoria A M Owens; Blake F Dear; Nickolai Titov
Journal:  Internet Interv       Date:  2018-02-01

7.  Evaluation of clustering and topic modeling methods over health-related tweets and emails.

Authors:  Juan Antonio Lossio-Ventura; Sergio Gonzales; Juandiego Morzan; Hugo Alatrista-Salas; Tina Hernandez-Boussard; Jiang Bian
Journal:  Artif Intell Med       Date:  2021-05-07       Impact factor: 7.011

Review 8.  Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence.

Authors:  Floor Sieverink; Saskia M Kelders; Julia Ewc van Gemert-Pijnen
Journal:  J Med Internet Res       Date:  2017-12-06       Impact factor: 5.428

9.  Language Patterns Discriminate Mild Depression From Normal Sadness and Euthymic State.

Authors:  Daria Smirnova; Paul Cumming; Elena Sloeva; Natalia Kuvshinova; Dmitry Romanov; Gennadii Nosachev
Journal:  Front Psychiatry       Date:  2018-04-10       Impact factor: 4.157

10.  Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial.

Authors:  John Wallert; Emelie Gustafson; Claes Held; Guy Madison; Fredrika Norlund; Louise von Essen; Erik Martin Gustaf Olsson
Journal:  J Med Internet Res       Date:  2018-10-10       Impact factor: 5.428

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