Holmberg Trine Theresa, Sainte-Marie Maxime, Jensen Esben Kjems, Linnet Jakob, Runge Eik, Lichtenstein Mia Beck, Tarp Kristine
Research Unit for Digital Psychiatry, Center for Digital Psychiatry, Mental Health Services in the Region of Southern Denmark, Odense, Denmark.
Department of Political Science, Danish Center for Studies in Research and Research Policy, Aarhus University, Aarhus, Denmark.
Front Psychiatry. 2022 Nov 4;13:969115. doi: 10.3389/fpsyt.2022.969115. eCollection 2022.
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.
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.
Contrary to what most current literature suggests, we found that in our sample ( = 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.
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.
与大多数标准治疗程序相比，暴饮暴食症（BED）的在线治疗是一种易于获得的治疗选择。然而，对于动机类型如何表征这一人群以及这些动机如何影响在线环境中的治疗依从性和效果，我们知之甚少。因此，我们旨在调查一组来自BED患者的书面动机陈述样本，以更多地了解治疗，特别是在线治疗在这一人群中的呈现方式。
在混合方法的背景下使用自我决定理论，我们调查了样本中哪种动机类型普遍存在，这与患者情绪有何关联，以及这些结构如何影响治疗和依从性。
与当前大多数文献所表明的情况相反，我们发现在我们的样本（n = 148）中，动机类型与治疗结果无关。我们确实发现情绪得分与动机类型之间存在很强的关联，表明该模型能够检测到效果。我们发现，在比较成年人和年轻人时，他们在动机类型上没有差异，并且该治疗在年轻人和成年人中同样有效。在情绪得分方面，年轻人群体的情绪得分与依从性之间存在差异，因为年轻人越积极，他们完成该项目的可能性就越小。
由于动机类型对在线治疗的影响程度与面对面治疗不同，这表明在在线环境中，典型的治疗障碍可能不那么关键。在接诊时应考虑到这一点；因为积极性较低的患者可能能够更好地坚持在线治疗，因为后者所带来的障碍较少，而这些障碍只有强烈的动机才能克服。书面文本的动机类型和情绪得分密切相关这一事实，表明基于情绪的自动化模型有检测动机的潜力。
