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Introduction: The ItFits implementation toolkit was developed as part of the ImpleMentAll EU Project, to help guide implementation processes. The ItFits toolkit was tested in the online clinic, Internetpsykiatrien, in the Region of Southern Denmark, where it was employed to optimize screening and intake procedures. We hypothesized that a larger proportion of assessed patients would be referred to treatment. Further, we hypothesized the completion rate and effectiveness would increase, as a result of including a more relevant sample. Method: Using the ItFits-toolkit, Internetpsykiatrien developed a revised online screening tool. Data on patient flow and symptom questionnaires was extracted from Internetpsykiatrien six months prior to- and six months after implementation of the revised online screening tool. Results: A total of 1,830 applicants self-referred for treatment during the study period. A significantly lower proportion of patients were referred to treatment after implementation of the revised screening tool (pre-implementation, n = 1,009; post-implementation, n = 821; odds ratio 0.67, 95% CI: 0.51; 0.87). The number of patients that completed treatment increased significantly (pre-implementation: 136/275 [49.45%], post-implementation, n = 102/162 [62.96%]; odds ratio 1.79, 95% CI 1.20; 2.70). The treatment effect was unchanged (B = 0.01, p = .996). Worth noting, the number of patients that canceled their appointment for the video assessment interview decreased drastically. Conclusion: By using the ItFits toolkit for a focused and structured implementation effort, the clinic was able to improve the completion rate, which is an important effect in iCBT. However, contrary to our hypotheses, we did not find an increase in clinical effect, nor a larger ratio being referred to treatment after assessment. The decreased number of referrals for treatment could be a result of increased awareness of inclusion criteria among the clinicians.
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Introduction: This study investigates the implementation of a new, more automated screening procedure using the ItFits-toolkit in the online clinic, Internet Psychiatry (iPsych) (www.internetpsykiatrien.dk), delivering guided iCBT for mild to moderate anxiety and depressive disorders. The study focuses on how the therapists experienced the process. Methods: Qualitative data were collected from semi-structured individual interviews with seven therapists from iPsych. The interviews were conducted using an interview guide with questions based on the Consolidated Framework for Implementation Research (CFIR). Quantitative data on the perceived level of normalization were collected from iPsych therapists, administrative staff, and off-site professionals in contact with the target demographic at 10-time points throughout the implementation. Results: The therapists experienced an improvement in the intake procedure. They reported having more relevant information about the patients to be used during the assessment and the treatment; they liked the new design better; there was a better alignment of expectations between patients and therapists; the patient group was generally a better fit for treatment after implementation; and more of the assessed patients were included in the program. The quantitative data support the interview data and describe a process of normalization that increases over time. Discussion: The ItFits-toolkit appears to have been an effective mediator of the implementation process. The therapists were aided in the process of change, resulting in an enhanced ability to target the patients who can benefit from the treatment program, less expenditure of time on the wrong population, and more satisfied therapists.
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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.
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Objective: Lack of motivation is widely acknowledged as a significant factor in treatment discontinuity and poor treatment outcomes in eating disorders. Treatment adherence is lower in internet-based treatment. The current study aimed to assess the relationship between treatment motivation and treatment outcomes in an internet-based therapist-guided intervention for Binge Eating Disorder (BED). Method: Adults (N = 153) with mild to moderate symptoms of BED participated in a 10-session internet-based treatment program. Baseline and between-session scores of "Readiness to change" and "Belief in change" were used to predict treatment completion and eating disorder symptom reduction (EDE-Q Global, BED-Q, and weekly number of binge eating episodes) at post-treatment. Results: Baseline treatment motivation could not predict treatment completion or symptom reduction. Early measures of treatment motivation (regression slope from sessions 1-5) significantly predicted both treatment completion and post-treatment symptom reduction. "Belief in change" was the strongest predictor for completing treatment (OR = 2.18, 95%-CI: 1.06, 4.46) and reducing symptoms (EDE-Q Global: B = -0.53, p = 0.001; number of weekly binge eating episodes: B = 0.81, p < 0.01). Discussion: The results indicated that patients entering online treatment for BED feel highly motivated. However, baseline treatment motivation could not significantly predict treatment completion, which contradicts previous research. The significant predictive ability of early measures of treatment motivation supports the clinical relevance of monitoring the development of early changes to tailor and optimize individual patient care. Further research is needed to examine treatment motivation in regard to internet-based treatment for BED with more validated measures.
