A Non-Randomized Direct Comparison of Cognitive-Behavioral Short- and Long-Term Treatment for Binge Eating Disorder

BACKGROUND: To compare treatment outcomes of a cognitive-behavioral long-term (CBT-L) and short-term (CBT-S) treatment for binge eating disorder (BED) in a non-randomized comparison and to identify moderators of treatment outcome. METHODS: 76 female patients with BED participated in the study: 40 in CBT-L and 36 in CBT-S. Outcome values were compared at the end of the active treatment phase (16 sessions for CBT-L, 8 sessions for CBT-S) and at 12-month follow-up. RESULTS: Both treatments produced significant reductions in binge eating. At the end of active treatment, but not at the end of follow-up, effects of primary outcomes (e.g. remission from binge eating, EDE shape concern) were better for CBT-L than for CBT-S. Dropout rates were significantly higher in CBT-L (35%) than in CBT-S (14%). Moderator analyses revealed that treatment efficacy for rapid responders and individuals exhibiting high scores on the mixed dietary negative affect subtype differed between the CBT-L and CBT-S with respect to objective binges, restraint eating and eating concern. CONCLUSION: Findings suggest that CBT in general represents an effective treatment for BED, but that subgroups of patients might profit more from a prolonged treatment. Short, less-intensive CBT treatments could nevertheless be a viable option in the treatment of BED

On the importance of non-linear relationships between landscape patterns and the sustainable provision of ecosystem services

Marginal land use changes can abruptly result in non-marginal and irreversible changes in ecosystem functioning and the economic values that the ecosystem generates. This challenges the traditional ecosystem services (ESS) mapping approach, which has often made the assumption that ESS can be mapped uniquely to land use and land cover data. Using a functional fragmentation measure, we show how landscape pattern changes might lead to changes in the delivery of ESS. We map changes in ESS of dry calcareous grasslands under different land use change scenarios in a case study region in Switzerland. We selected three ESS known to be related to species diversity including carbon sequestration and pollination as regulating values and recreational experience as cultural value, and compared them to the value of two production services including food and timber production. Results show that the current unceasing fragmentation is particularly critical for the value of ESS provided by species-rich habitats. The article concludes that assessing landscape patterns is key for maintaining valuable ESS in the face of human use and fluctuating environment

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial

Background Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. Objective This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. Methods This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. Results The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. Conclusions The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. Trial Registration ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617 International Registered Report Identifier (IRRID) DERR1-10.2196/4254

Effects of the study design and analysis on the spatial community structure detected by multiscale ordination

This is the accepted manuscript of an article published by Wiley.Question: What is the effect of different aspects of data collection and analysis on the spatial partitioning of ordination results by multiscale ordination? Location: Heterogeneous pasture at Marchairuz in the Jura mountains, Switzerland. Mixed hardwood-pine forest in Oostings Natural Area, NC, USA. Methods: We evaluated the efficiency of different sampling designs for identifying the spatial structure of plant communities and analyzed two data sets with multiscale ordination. We compared the effects of quadrat size, the number of species included in the analysis, data type, detrending and ordination method on the shape and precision of the community variogram summarizing spatial community structure. Results: A three-block sampling design provided a more even distribution of the number of pairs of observation per distance class than random, transect or grid designs. The precision of variogram estimates depended more strongly on the number of species than on the number of quadrats. In contrary, the choice of data type (abundance transformation) had little influence on the shape of the variogram. Detrending reduced the range of spatial autocorrelation. An increase in quadrat size resulted in a smoother variogram and stronger spatial autocorrelation. Principal components analysis (PCA) and redundancy analysis (RDA) resulted in a larger range of spatial autocorrelation than using correspondence analysis (CA) and canonical correspondence analysis (CCA), but the shape of the variograms was rather similar. Conclusion: Random samples as well as transects and regular grids may not be efficient sampling designs for spatial analysis of community structure. While the number of species considered strongly affects the precision of a community variogram, its shape depends on the size of the sampling units. Earlier studies may have overestimated the spatial scale of internal organization in plant communities.This research is part of a project funded by the Swiss National Science Foundation (SNF) under the NCCR Plant Survival

Validity of the "Diagnostisches Interview bei psychischen Störungen" (DIPS fur DSM-IV-TR)

The "Diagnostisches Interview bei psychischen Störungen" (DIPS fur DSM-IV-TR; Schneider & Margraf, 2006) is a structured interview that has been expanded and adapted to DSM-IV-TR criteria. Objective: The purpose of this study was to validate the DIPS for DSM-IV-TR. Methods: The validity of the DIPS diagnoses was verified using a questionnaire battery on a sample of 194 patients from various clinical facilities. Results: Results indicate good validity in the categories of anxiety disorders, mood disorders, somatoform disorders, eating disorders, substance disorders, and for the exclusion of mental disorders. Inadequate validity was found solely for sleep disorders and generalized anxiety disorder. Conclusions: The DIPS for DSM-IV-TR has proven to be a valid tool (except for generalized anxiety disorder and sleep disorders) for use with outpatients and inpatients from psychiatric facilities

On the importance of non-linear relationships between landscape patterns and the sustainable provision of ecosystem services

Marginal land use changes can abruptly result in non-marginal and irreversible changes in ecosystem functioning and the economic values that the ecosystem generates. This challenges the traditional ecosystem services (ESS) mapping approach, which has often made the assumption that ESS can be mapped uniquely to land use and land cover data. Using a functional fragmentation measure, we show how landscape pattern changes might lead to changes in the delivery of ESS. We map changes in ESS of dry calcareous grasslands under different land use change scenarios in a case study region in Switzerland. We selected three ESS known to be related to species diversity including carbon sequestration and pollination as regulating values and recreational experience as cultural value, and compared them to the value of two production services including food and timber production. Results show that the current unceasing fragmentation is particularly critical for the value of ESS provided by species-rich habitats. The article concludes that assessing landscape patterns is key for maintaining valuable ESS in the face of human use and fluctuating environment

Self-efficacy effects on symptom experiences in daily life and early treatment success in anxiety patients

Self-efficacy is a key construct in behavioral science affecting mental health and psychopathology. Here, we expand on previously demonstrated between-persons self-efficacy effects. We prompted 66 patients five times daily for 14 days before starting cognitive behavioral therapy (CBT) to provide avoidance, hope, and perceived psychophysiological-arousal ratings. Multilevel logistic regression analyses confirmed self-efficacy’s significant effects on avoidance in daily life (odds ratio [OR] = 0.53, 95% confidence interval [CI] = [0.34, 0.84], p = .008) and interaction effects with anxiety in predicting perceived psychophysiological arousal (OR = 0.79, 95% CI = [0.62, 1.00], p = .046) and hope (OR = 1.21, 95% CI = [1.03, 1.42], p = .02). More self-efficacious patients also reported greater anxiety-symptom reduction early in treatment. Our findings assign a key role to self-efficacy for daily anxiety-symptom experiences and for early CBT success. Self-efficacy interventions delivered in patients’ daily lives could help improve treatment outcome

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial

Background: Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. Objective: This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. Methods: This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. Results: The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. Conclusions: The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment.ISSN:1929-074

Optimizing Outcomes in Psychotherapy for Anxiety Disorders (OPTIMAX) Protocol– A Randomized Controlled Trial on Efficacy and Response Prediction in a Transdiagnostic Psychotherapy Treatment for Anxiety Disorders

Background: This paper describes the study protocol for our clinical trial “Optimizing Outcomes in Psychotherapy for Anxiety Disorders (OPTIMAX)” funded by the Swiss National Science Foundation (10001C_169827). The study aims to establish predictive features for forecasting response to cognitive behavior therapy (CBT) and to investigate mechanisms underlying treatment response. Methods: OPTIMAX comprises a monocentric, randomized-controlled clinical trial. We employ the Unified Treatment Protocol (UP, Barlow, 2017), an established transdiagnostic CBT protocol for treating emotional disorders, to treat patients with anxiety disorders. We use psychological questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing data in order to derive a multimodal feature set for predictive modeling. We obtain assessments at different time points including baseline, mid-, and post- treatment as well as 6 and 12 months after treatment completion. Anxiety and depression symptom severity are indexed weekly during treatment. We aim to include 150 patients, randomized to CBT versus WAIT group in a 3:1 ratio. Machine learning (e.g., support vector machines, random forest) and linear regression modeling will be employed to establish predictive accuracy in forecasting treatment response. In addition to predictive modelling, we test mechanistic hypotheses, e.g., on the association between self-efficacy, dynamic symptom changes and treatment response, to elucidate mechanisms underlying treatment response. Discussion: The aim of the current trial is to improve current CBT treatment, such as the transdiagnostic unified treatment protocol employed here, by precise forecasting of treatment response and by understanding and, in the future, augmenting underpinning mechanisms and personalizing treatment. Registration: This study has been registered on clinicaltrials.gov (NCT03945617, 10 of May 2019, https://clinicaltrials.gov/ct2/show/NCT03945617