ArticleFrontiers in psychiatry2022
An analysis of patient motivation for seeking online treatment for binge eating disorder-A mixed methods study combining systematic text condensation with sentiment analysis.
Article in Frontiers in psychiatry, 2022. 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, 4 citations in OpenAlex.
- Patient Perspectives on Blended Internet-Based and Face-to-Face Cognitive Behavioral Therapy for Alcohol Use Disorder: Qualitative Study.Journal of medical Internet research · 2024Trial
- Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder.Internet interventions · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
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Abstract
Objective: Online treatment for binge eating disorder (BED) is an easily available option for treatment compared to most standard treatment procedures. However, little is known about how motivation types characterize this population and how these impact treatment adherence and effect in an online setting. Therefore, we aimed to investigate a sample of written motivation statements from BED patients, to learn more about how treatment and online treatment in particular, presents in this population. Methods: Using self-determination theory in a mixed methods context, we investigated which types of motivation were prevalent in our sample, how this was connected with patient sentiment, and how these constructs influence treatment and adherence. Results: Contrary to what most current literature suggests, we found that in our sample ( Discussion: Because motivation type does not influence online treatment to the same degree as it would in face-to-face treatment it indicates that the typical barriers to treatment may be less crucial in an online setting. This should be considered during intake; as less motivated patients may be able to adhere better to online treatment, because the latter imposes fewer barriers of the kind that only strong motivation can overcome. The fact that motivation type and sentiment score of the written texts are strongly associated, indicate a potential for automated models to detect motivation based on sentiment.
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