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Context: This study proposes a Bayesian network model to aid mental health specialists making data-driven decisions on suitable treatments. The aim is to create a probabilistic machine learning model to assist psychologists in selecting the most suitable treatment for individuals for four potential mental disorders: Depression, Panic Disorder, Social Phobia, or Specific Phobia. Methods: This study utilized a dataset from 1,094 individuals in Denmark containing socio-demographic details and mental health information. A Bayesian network was initially employed in a purely data-driven approach and was later refined with expert knowledge, referred to as a hybrid model. The model outputted probabilities for each disorder, with the highest probability indicating the most suitable disorder for treatment. Results: By incorporating expert knowledge, the model demonstrated enhanced performance compared to a strictly data-driven approach. Specifically, it achieved an AUC score of 0.85 vs 0.80 on the test data. Furthermore, we evaluated some cases where the predictions of the model did not match the actual treatment. The symptom questionnaires indicated that these participants likely had comorbid disorders, with the actual treatment being proposed by the model with the second highest probability. Conclusions: In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which treatment to prioritize.

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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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Background: The number of days between treatment sessions is often overlooked as a predictor of attrition in psychotherapy. In text-based Internet interventions, days between sessions may be a simple yet powerful predictor of attrition. Objective: We hypothesized that a larger number of days between sessions increased the likelihood of attrition among participants with Binge Eating Disorder (BED) in a 12-session Internet-based cognitive behavioral therapy (iCBT) program. Participants could work on the sessions whenever convenient for them and received written support from a psychologist. Material and methods: We compared 201 adult participants with mild to moderate BED (85 non-completers and 116 completers) on the number of days between sessions to predict attrition rates. Results: Mixed model binomial logistic regression showed that non-completers spent significantly more days between sessions across the first four treatment sessions (1-4) when controlling for age, gender, and intake measures of BMI, BED, overall health status (EQ VAS), and depression symptoms (MDI) (OR = 1.042, p < .001). Age (OR = 0.976, p < .001) and EQ VAS (OR = 0.984, p < .001) were also significant. The risk of attrition increased by 4.2 % for each additional day participants spent completing a session.A receiver operating characteristic (ROC) curve analysis showed that classification accuracy increased across sessions from 61.1 % in session 1 and 65.7 % in session 2 to 68.8 % in session 3 and 73.2 % in session 4. The optimal cut-off point in session 4 was 17.5 days, which detected 60.4 % of non-completers (sensitivity) and 78.4 % of completers (specificity).An exploratory repeated measures of ANOVA of days between sessions showed a significant within-subjects effect, where both non-completers and completers spent more days between sessions as they progressed from sessions 1 through 4 (F = 20.54, df = 3, p < .001). There was no interaction effect, suggesting that the increase in slope did not differ between non-completers and completers. Conclusions: Participants spending more days between sessions are at increased risk of dropping out of treatment. This may have important implications for identifying measures to reduce attrition, e.g., intensifying interventions through automated reminders or therapist messages. Our findings may have important transdiagnostic implications for text-based Internet interventions. Further studies should investigate the predictive value of days between sessions in other diagnoses.

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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.

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Background: Some evidence suggests that in internet-based cognitive behavioral therapy (iCBT) the likelihood of adherence is increased when patients write longer messages to the therapist in the program. This association has not previously been investigated in iCBT for Binge Eating Disorder (BED). Objective: In this study, we hypothesized that the number of words written by patients with mild to moderate BED was associated with increased likelihood of treatment completion in a text-based iCBT program. Material and methods: We compared 143 BED patients (92 completers and 51 non-completers) on the number of messages and words written to their therapist during the treatment. Results: Completers wrote significantly more words per message (words/message) than non-completers. The results remained significant after controlling for gender, age, educational level, marital status, children, source of income and intake measures of BED, BMI and depression symptoms (Wald = 14.48, p < .001). The odds ratio of completion increased by 1.5% for each additional word patients wrote per message (OR = 1.015). The model showed a 72.4% classification accuracy, and an optimal cut-off point of 68.99 words/message for differentiating completers and non-completers. The model accurately identified 80.9% of completers (sensitivity) and 54.9% of non-completers (specificity). Conclusions: The number of words/message patients write may have important implications for ascertaining likelihood of adherence and improving adherence rates. From a clinical perspective, therapists should encourage patients to use the option of writing messages to the therapist. Words/message may prove to be a transdiagnostic predictor of treatment adherence in text based iCBT.

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BACKGROUND: Sleep disturbance symptoms are common in major depressive disorder (MDD) and have been found to hamper the treatment effect of conventional face-to-face psychological treatments such as cognitive behavioral therapy. To increase the dissemination of evidence-based treatment, blended cognitive behavioral therapy (bCBT) consisting of web-based and face-to-face treatment is on the rise for patients with MDD. To date, no study has examined whether sleep disturbance symptoms have an impact on bCBT treatment outcomes and whether it affects bCBT and treatment-as-usual (TAU) equally. OBJECTIVE: The objectives of this study are to investigate whether baseline sleep disturbance symptoms have an impact on treatment outcomes independent of treatment modality and whether sleep disturbance symptoms impact bCBT and TAU in routine care equally. METHODS: The study was based on data from the E-COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment Versus Treatment-as-Usual) study, a 2-arm, multisite, parallel randomized controlled, noninferiority trial. A total of 943 outpatients with MDD were randomized to either bCBT (476/943, 50.5%) or TAU consisting of routine clinical MDD treatment (467/943, 49.5%). The primary outcome of this study was the change in depression symptom severity at the 12-month follow-up. The secondary outcomes were the change in depression symptom severity at the 3- and 6-month follow-up and MDD diagnoses at the 12-month follow-up, assessed using the Patient Health Questionnaire-9 and Mini-International Neuropsychiatric Interview, respectively. Mixed effects models were used to examine the association of sleep disturbance symptoms with treatment outcome and treatment modality over time. RESULTS: Of the 943 patients recruited for the study, 558 (59.2%) completed the 12-month follow-up assessment. In the total sample, baseline sleep disturbance symptoms did not significantly affect change in depressive symptom severity at the 12-month follow-up (ß=.16, 95% CI -0.04 to 0.36). However, baseline sleep disturbance symptoms were negatively associated with treatment outcome for bCBT (ß=.49, 95% CI 0.22-0.76) but not for TAU (ß=-.23, 95% CI -0.50 to 0.05) at the 12-month follow-up, even when adjusting for baseline depression symptom severity. The same result was seen for the effect of sleep disturbance symptoms on the presence of depression measured with Mini-International Neuropsychiatric Interview at the 12-month follow-up. However, for both treatment formats, baseline sleep disturbance symptoms were not associated with depression symptom severity at either the 3- (ß=.06, 95% CI -0.11 to 0.23) or 6-month (ß=.09, 95% CI -0.10 to 0.28) follow-up. CONCLUSIONS: Baseline sleep disturbance symptoms may have a negative impact on long-term treatment outcomes in bCBT for MDD. This effect was not observed for TAU. These findings suggest that special attention to sleep disturbance symptoms might be warranted when MDD is treated with bCBT. Future studies should investigate the effect of implementing modules specifically targeting sleep disturbance symptoms in bCBT for MDD to improve long-term prognosis.

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AIMS: To examine combined and sex-specific temporal changes in risks of adverse cardiovascular events and coronary revascularization in patients with chronic coronary syndrome undergoing coronary angiography. METHODS AND RESULTS: We included all patients with stable angina pectoris and coronary artery disease examined by coronary angiography in Western Denmark from 2004 to 2016. Patients were stratified by examination year interval: 2004-2006, 2007-2009, 2010-2012, and 2013-2016. Outcomes were 2-year risk of myocardial infarction, ischaemic stroke, cardiac death, and all-cause death estimated by adjusted incidence rate ratios using patients examined in 2004-2006 as reference. A total of 29 471 patients were included, of whom 70% were men. The 2-year risk of myocardial infarction [2.8% vs. 1.9%, adjusted incidence rate ratio 0.65, 95% confidence interval (CI) 0.53-0.81], ischaemic stroke (1.8% vs. 1.1%, adjusted incidence rate ratio 0.48, 95% CI 0.37-0.64), cardiac death (2.1% vs. 0.9%, adjusted incidence rate ratio 0.38, 95% CI 0.29-0.51), and all-cause death (5.0% vs. 3.6%, adjusted incidence rate ratio 0.65, 95% CI 0.55-0.76) decreased from the first examination interval (2004-2006) to the last examination interval (2013-2016). Coronary revascularizations also decreased (percutaneous coronary intervention: 51.6% vs. 42.5%; coronary artery bypass grafting: 24.6% vs. 17.5%). Risk reductions were observed in both men and women, however, women had a lower absolute risk. CONCLUSION: The risk for adverse cardiovascular events decreased substantially in both men and women with chronic coronary syndrome from 2004 to 2016. These results most likely reflect the cumulative effect of improvements in the management of chronic coronary artery disease.

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BACKGROUND: It was recently shown that new-onset diabetes patients without previous cardiovascular disease have experienced a markedly reduced risk of adverse cardiovascular events from 1996 to 2011. However, it remains unknown if similar improvements are present following the diagnosis of chronic coronary syndrome. The purpose of this study was to examine the change in cardiovascular risk among diabetes patients with chronic coronary syndrome from 2004 to 2016. METHODS: We included patients with documentation of coronary artery disease by coronary angiography between 2004 and 2016 in Western Denmark. Patients were stratified by year of index coronary angiography (2004-2006, 2007-2009, 2010-2012, and 2013-2016) and followed for two years. The main outcome was major adverse cardiovascular events (MACE) defined as myocardial infarction, ischemic stroke, or death. Analyses were performed separately in patients with and without diabetes. We estimated two-year risk of each outcome and adjusted incidence rate ratios (aIRR) using patients examined in 2004-2006 as reference. RESULTS: Among 5931 patients with diabetes, two-year MACE risks were 8.4% in 2004-2006, 8.5% in 2007-2009, and then decreased to 6.2% in 2010-2012 and 6.7% in 2013-2016 (2013-2016 vs 2004-2006: aIRR 0.70, 95% CI 0.53-0.93). In comparison, 23,540 patients without diabetes had event rates of 6.3%, 5.2%, 4.2%, and 3.9% for the study intervals (2013-2016 vs 2004-2006: aIRR 0.57, 95% CI 0.48-0.68). CONCLUSIONS: Between 2004 and 2016, the two-year relative risk of MACE decreased by 30% in patients with diabetes and chronic coronary syndrome, but slightly larger absolute and relative reductions were observed in patients without diabetes.

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OBJECTIVE: Binge-eating disorder (BED) is characterized by recurrent episodes of binge eating, accompanied by a lack of control and feelings of shame. Online intervention is a promising, accessible treatment approach for BED. In the current study, we compared completers with noncompleters in a 10-session guided internet-based treatment program (iBED) based on cognitive behavioral therapy. METHODS: Adults (N = 75) with mild to moderate BED participated in iBED with weekly written support from psychologists. Participants were compared on the Eating Disorder Examination Questionnaire (EDE-Q), diagnostic criteria for BED (BED-Q), major depression inventory (MDI), quality of life (EQ-5D-5L), body mass index (BMI) and sociodemographic variables. RESULTS: Minor differences were observed between completers and noncompleters on depression. No differences were found in BED-symptoms, BMI, and sociodemographic variables. Participants who completed treatment showed large reductions in eating disorder pathology. DISCUSSION: More research is needed to determine risk factors for attrition or treatment outcome in internet-based interventions for BED. It is suggested that iBED is an efficient intervention for BED. However, more studies of internet-interventions are needed.

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