Literature DB >> 32063574

Understanding perceived barriers to treatment from web browsing behavior.

Stephen M Schueller1, Diana M Steakley-Freeman2, David C Mohr2, Elad Yom-Tov3.   

Abstract

BACKGROUND: The expanding amount of information available from our use of technologies has led researchers to explore how this information can aid in the detection of mental health issues. We expand on past work in this area by exploring how browsing histories might be able to predict perceived barriers to psychological treatment.
METHODS: We obtained 10 days of browsing history data for 255 respondents as well as assessments of Perceived Barriers to Psychological Treatments and depression, the Patient Health Questionnaire.
RESULTS: We found that browsing histories enabled high performance classification of people with high levels of perceived barriers to psychological treatments (AUC average of 0.86). LIMITATIONS: Our high classification accuracy does not help understand why different features within the browsing histories are useful to classify people according to browsing history. We also look at people who decided to contribute their browsing history but the use of this data more generally presents additional ethical questions.
CONCLUSIONS: Browsing histories might be useful to classify people's barriers to seeking psychological treatment. It is clinically relevant to find those who perceive barriers to seeking treatment to better design ways to address those concerns and help them find treatment.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Classification; Depression; Internet; Technology; Treatment

Mesh:

Year:  2020        PMID: 32063574     DOI: 10.1016/j.jad.2020.01.131

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


  4 in total

1.  Mental Health Information Seeking Online: A Google Trends Analysis of ADHD.

Authors:  Xin Zhao; Stefany J Coxe; Adela C Timmons; Stacy L Frazier
Journal:  Adm Policy Ment Health       Date:  2021-09-22

2.  Predicting eating disorders from Internet activity.

Authors:  Shiri Sadeh-Sharvit; Ellen E Fitzsimmons-Craft; C Barr Taylor; Elad Yom-Tov
Journal:  Int J Eat Disord       Date:  2020-07-24       Impact factor: 4.861

3.  Web-Based Single Session Intervention for Perceived Control Over Anxiety During COVID-19: Randomized Controlled Trial.

Authors:  Michael Mullarkey; Mallory Dobias; Jenna Sung; Isaac Ahuvia; Jason Shumake; Christopher Beevers; Jessica Schleider
Journal:  JMIR Ment Health       Date:  2022-04-12

4.  Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines: Viewpoint.

Authors:  Elad Yom-Tov; Yuval Cherlow
Journal:  J Med Internet Res       Date:  2020-09-16       Impact factor: 5.428

  4 in total

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