Introduction: Relatively little research has assessed predictors of both treatment outcomes and attrition in binge eating disorder (BED) treatment. Even fewer studies did so for digital forms of...Show moreIntroduction: Relatively little research has assessed predictors of both treatment outcomes and attrition in binge eating disorder (BED) treatment. Even fewer studies did so for digital forms of therapy. This study thus aims to contribute to the current pool of knowledge by examining the predictive value of various variables in a recently developed digital BED-treatment: BED-online. Methods: This study was part of a RCT into the effects of BED-online therapy. Participants were over the age of 18, Dutch-speaking and diagnosed with the DSM-V BED. A total of 180 participants were found to be eligible, of whom 40 (22.2%) dropped-out before the last session. Post-treatment measurements from an interview (EDE) and a self-report questionnaire (EDE-Q) were used to determine the immediate treatment effects. A follow-up (24 weeks post-treatment) EDE-Q measurement determined the long-term effects. These variables served as the dependent variables in three different hierarchical linear regression analyses. A fourth logistical regression analysis was conducted, where treatment-related drop-out formed the dependent variable. The following eight predictor variables were chosen based on literature research: ethnicity, age, gender, educational level, comorbidity, frequency of binge eating episodes, levels of BED pathology and treatment condition. The predictor variables served as the independent variables. Results: BED pathology at baseline was found to be the only significant predictor of treatment outcomes as measured by the EDE (β=.41, t=3.71, p<.001), EDE-Q (β=.57, t=8.23, p<.001) and at follow-up (β=.47, t= 6.24, p< .001). Attrition could be predicted by both ethnicity and gender, where males were 5.63 times more likely to discontinue treatment prematurely (OR=5.63, 95%CI [1.81, 17.53]) and participants born abroad were 3.91 times more likely to discontinue treatment (OR=3.91, 95%CI [1.43, 8.76]). Other independent variables did not significantly contribute to the final regression models. Discussion and conclusion: All predictor variables are discussed in detail, reflecting on the results found and comparing them to findings of previous studies. Limitations are elaborated in depth. Due to these limitations, and the modest number of previous studies, further research is encouraged, exploring both the current and other dependent variablesShow less
Psychological tests are often used to make important decisions about a patient’s trajectory. A high-quality test is characterized by norms that allow meaningful interpretation of the raw scores....Show morePsychological tests are often used to make important decisions about a patient’s trajectory. A high-quality test is characterized by norms that allow meaningful interpretation of the raw scores. With normed scores an individual’s performance can be evaluated properly by taking into account the influence of relevant individual characteristics, such as age, gender, education level or socio-economic status. From several norming techniques, regression-based norming has proven to be most efficient one and especially useful when including more than one demographical predictor variable. Recently, the R package GAMLSS has been proposed as a framework for this technique. It is an exhaustive family of flexible models that may fit a wide variety of empirical data. This research investigated the performance of GAMLSS compared to traditionally used approaches of multiple regression-based norming. Both techniques were applied to data of the Brief-Symptom Inventory, which was collected in a sample representative of the Dutch population (N = 1662). Age and gender were taken into account as normpredictors. The approaches showed very high correspondence, expect for a deviation of GAMLSS-based scores in the lower score ranges. None of the two approaches was able to properly capture the relationship between raw scores and age. GAMLSS-based norms did not outperform the regression-based approach. They can be considered equally suitable but in practice the complexity of the GAMLSS approach makes it less convenient. However, its flexibility could be advantageous when applied to tests that are heavily dependent on continuous norm predictors.Show less
Waiting time for treatment in Dutch mental healthcare is increasing. General practitioners substantially deviate from the allocation guideline which seems to contribute to the increase of waiting...Show moreWaiting time for treatment in Dutch mental healthcare is increasing. General practitioners substantially deviate from the allocation guideline which seems to contribute to the increase of waiting time. This study aimed to investigate whether undertreatment can be beneficial for patients and the MHC system. In addition, this study aimed to identify characteristics of undertreated patients that suffered unfavorable undertreatment based on clinical data and clinical judgement by general practitioners. Data was obtained in a naturalistic environment in a mental healthcare institution in the Netherlands and patients received treatment as usual. A total of 6193 electronic patient records were included. Exclusion criteria and missing data left 1057 cases eligible for analyses. A one-way ANOVA and a forward hierarchical binary logistic regression were conducted to investigate both aims. Results showed that undertreatment relates to worse treatment outcomes in comparison to patients in S-MHC. Unfavorable undertreatment was best predicted by waiting time and level of pre-treatment functioning. Longer waiting time is related to the need for more specialized care. Also, lower levels of pre-treatment functioning is related to the need for more specialized care. B-MHC care for undertreated patients must be improved. GPs seem to have valid reason to deviate from the allocation guideline. The allocation guideline could benefit from the addition of variables based on clinical judgement. To provide the care that undertreated patients need, treatment in B-MHC could be prolonged to match outcomes in S-MHC. Another solution could be to limit the negative effects during waiting time for patients.Show less
Research master thesis | Psychology (research) (MSc)
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Using a wide variety of clinical questionnaires might hamper the communication between clinicians and researchers in Routine outcome monitoring (ROM). Linking test scores to T scores as a common...Show moreUsing a wide variety of clinical questionnaires might hamper the communication between clinicians and researchers in Routine outcome monitoring (ROM). Linking test scores to T scores as a common metric might be a solution. Item response theory (IRT) is the preferred way to estimate T-scores. However, required software and a large dataset is needed for that. In this thesis, two methods were used to arrive at a common metric for often used questionnaires and their subversions: the BSI, BSI-18, the DASS-42 and the DASS-21. As one method, we used an IRT approach to estimate theta-based T-scores, leading to crosswalk tables. For the second approach, conversion formulas were estimated for raw scores to calculated T-scores based on the best-fitting regression equation between the raw scores and the previously estimated theta-based T-scores. Crosswalk tables and conversion formulas are both alternative ways to link individual scores to a common metric. It was investigated if conversion formulas are valid by comparing calculated T-scores with theta-based T-scores. The agreement between calculated T-scores and theta-based T-scores were high for all questionnaires except the DASS-42. The agreement was also lower at the extreme ends of the questionnaires (T= < 50 and T= >75). All in all, conversion formulas seem to be a good alternative for estimating a common metric. Offering different options to calculate common metrics can help in improving the communication between professionals in the clinical field. Increased involvement and better communication in ROM is able to increase the quality of mental health care.Show less
Background: The societal lockdown due to COVID-19 forced a change from in-person therapy to videoconferencing, which was introduced on March 16, 2020. Swift implementation of telehealth care was...Show moreBackground: The societal lockdown due to COVID-19 forced a change from in-person therapy to videoconferencing, which was introduced on March 16, 2020. Swift implementation of telehealth care was asked of therapists all over the country. Aim: To investigate whether the effects of treatment for personality disorders through videoconferencing were lower than through in-person therapy. Method: Patients who received treatment between January 2018 and May 2021 were divided into three groups, before (1), partially (2) and entirely during COVID-19 (3). With the ROM score OQ-45 SD as pre- and posttest measurements, a general linear model repeated ANOVA was conducted. We controlled for other variables that were potentially of influence on the achieved outcomes. Results: The hypothesis was not confirmed by the results. The results rather suggested that treatment through videoconferencing was more effective than treatment (predominantly) conducted through in-person. Conclusion: No evidence was found supporting the hypothesized diminished effects of treatment for personality disorder through videoconferencing. Videoconference therapy appeared non-inferior to face-to-face therapy for the treatment of personality disorders. The groups did not differ at the onset. However, treatments were shorter and the dropout rate was 15% higher in the videoconferencing group, which could have led to selection bias. Keywords: Personality disorder; COVID-19; treatment outcome; videoconference therapy; face-to-face therapy; observational design.Show less
