An analysis of patient motivation for seeking online treatment for binge eating disorder—A mixed methods study combining systematic text condensation with sentiment analysis

Trine Theresa Holmberg*, Maxime Sainte-Marie, Esben Kjems Jensen, Jakob Linnet, Eik Runge, Mia Beck Lichtenstein, Kristine Tarp

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Abstrakt

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 (n = 148), motivation type was not connected with treatment outcome. We did find a strong association between sentiment scores and motivation types, indicating the model is apt at detecting effects. We found that when comparing an adult and young adult population, they did not differ in motivation type and the treatment was equally effective in young adults and adults. In the sentiment scores there was a difference between sentiment score and adherence in the young adult group, as the more positive the young adults were, the less likely they were to complete the program. 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.

OriginalsprogEngelsk
Artikelnummer969115
TidsskriftFrontiers in Psychiatry
Vol/bind13
Antal sider16
ISSN1664-0640
DOI
StatusUdgivet - 4. nov. 2022

Bibliografisk note

Funding Information:
The authors wish to thank Jacob Bøilerehauge Bindzus for his assistance during his internship at the research unit.

Publisher Copyright:
Copyright © 2022 Holmberg, Sainte-Marie, Jensen, Linnet, Runge, Lichtenstein and Tarp.

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