Anticoagulant Medication in Endoprosthetically Treated Proximal Femur Fracture - Complications and Mortality Considering the Time of Treatment as a Quality Criterion. / Gerinnungsaktive Medikation bei endoprothetisch versorgter proximaler Femurfraktur - Komplikationen und Mortalität unter Berücksichtigung des Versogungszeitpunktes als Qualitätskriterium.

There is an international debate on the optimal time to surgery following hip fracture in older patients. Pre-existing anticoagulation seems to be a major concern when it comes to a delay in operative fracture treatment. The aim of this study was to examine complication and mortality rates for elderly anticoagulated hip fracture patients considering early (< 24 h) vs. delayed (> 24 h) surgery.Our Analysis is based on data of the external inpatient quality assurance of North Rhine Westphalia as the most populous German federal state. We identified 13,201 hip fracture patients with antithrombotic medication and a minimum age of 65 years treated from January 2015 to December 2017.Delayed surgery was associated with significantly higher rates of general and surgical complications as well as mortality. Except for pre-existing heart failure, we were not able to identify certain comorbidities that could clearly indicate, why there might have been a delay.In most cases, patients with antithrombotic medication have a poor outcome to be expected due to serious comorbidity. If there was a delay in surgery for those patients, treatment results were even worse. Thus, surgery with a delay of > 24 h must be avoided to reduce the risk of complications.

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers.

BACKGROUND: Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation. METHODS: In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software. RESULTS: Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset. DISCUSSION: Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Physiological and neural synchrony in emotional and neutral stimulus processing: A study protocol.

Background: As psychotherapy involves at least two individuals, it is essential to include the interaction perspective research. During interaction, synchrony, i.e., the occurrence of simultaneous responses, can be observed at the physiological, neural, and behavioral level. Physiological responses include heart rate and electrodermal activity; neural markers can be measured using electroencephalogram. Emotionally arousing stimuli are allocated more attentional resources (motivated attention), which is reflected in physiological activation and brain potentials. Here we present a protocol for a pilot study implementing a new research methodology, and replication of the motivated attention to emotion effect in in dyads. There is evidence that higher synchrony is associated with more positive (therapeutic) relationships. Thus, the secondary outcome will be the association between physiological and neural synchrony and subjective ratings. Methods and design: Individuals (18-30 years) will participate in same-sex pairs in two experiments. In the first experiment (triadic interaction), both participants attentively watch unpleasant, neutral and pleasant pictures, and read/listen to standardized scripts (unpleasant, neutral, and pleasant, respectively) for the imagination task. In the second experiment, participants will read out three scripts (unpleasant, neutral, pleasant) to each other, followed by a joint imagination period. Stimuli will be presented in counterbalanced orders. After each picture and imagination, participants rate their subjective arousal and valence. In the beginning and in the end of the procedure, dyads rate their relationship, sympathy, and bonds (Working Alliance Inventory subscale). Heart rate, electrodermal activity and electroencephalogram will be continuously measured during both experiments using portable devices (EcgMove4 and EdaMove4, nine-channel B-Alert X-Series mobile-wireless EEG). Synchrony analyses will include the dual electroencephalography analysis pipeline, correlational analyses and Actor-Partner Interdependence Models. Discussion: The present study protocol provides an experimental approach to investigate interpersonal synchrony during emotion processing, allowing for the establishment of research methods in a pilot study, which can later be translated into real-life psychotherapy research. In the future, fundamental understanding of such mechanisms in dyadic interactions is essential in order to promote therapeutic relationships, and thus, treatment effectiveness and efficiency.

What if we wait? Using synthetic waiting lists to estimate treatment effects in routine outcome data.

Objective: Due to the lack of randomization, pre-post routine outcome data precludes causal conclusions. We propose the "synthetic waiting list" (SWL) control group to overcome this limitation.Method: First, a step-by-step introduction illustrates this novel approach. Then, this approach is demonstrated using an empirical example with data from an outpatient cognitive-behavioral therapy (CBT) clinic (N = 139). We trained an ensemble machine learning model ("Super Learner") on a data set of patients waiting for treatment (N = 311) to make counterfactual predictions of symptom change during this hypothetical period.Results: The between-group treatment effect was estimated to be d = 0.42. Of the patients who received CBT, 43.88% achieved reliable and clinically significant change, while this probability was estimated to be 14.54% in the SWL group. Counterfactual estimates suggest a clear net benefit of psychotherapy for 41% of patients. In 32%, the benefit was unclear, and 27% would have improved similarly without receiving CBT.Conclusions: The SWL is a viable new approach that provides between-group outcome estimates similar to those reported in the literature comparing psychotherapy with high-intensity control interventions. It holds the potential to mitigate common limitations of routine outcome data analysis.

Is personalized treatment selection a promising avenue in bpd research? A meta-regression estimating treatment effect heterogeneity in RCTs of BPD.

OBJECTIVE: Psychotherapy is the first-line treatment for borderline personality disorder (BPD) in evidence-based care. The effects are, on average, medium; however, nonresponse rates point to differential treatment effects. Personalized treatment selection has the potential to improve outcomes, but they depend on the heterogeneity of treatment effects (HTEs), which this article seeks to establish. METHOD: Using an extensive database of randomized controlled trials on psychotherapy for BPD, we determined a reliable estimate of this heterogeneity in treatment effects by (a) applying Bayesian variance ratio meta-analysis and (b) estimating the HTE. In total, 45 studies were included in our study. HTE was found for all psychological treatments, although with low degrees of certainty. RESULTS: Across all psychological treatment and control group types, the estimate for the intercept was 0.10, indicating a 10% higher variance of endpoint values in the intervention groups after controlling for differences in posttreatment means. CONCLUSIONS: The results suggest that, while there might be sufficient heterogeneity in treatment effects, the estimates are uncertain, and future research is needed to gain more accurate boundaries for HTE. Personalizing psychological treatments for BPD by using treatment selection approaches could have positive effects, but the current evidence does not allow for a precise estimate of potential outcome improvement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Toward Individualized Prediction of Binge-Eating Episodes Based on Ecological Momentary Assessment Data: Item Development and Pilot Study in Patients With Bulimia Nervosa and Binge-Eating Disorder.

BACKGROUND: Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail. OBJECTIVE: We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data. METHODS: We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors). RESULTS: On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of rD 0.40, SD 0.13; and mean rCV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models. CONCLUSIONS: Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

Assessing Dysfunctional Expectations in Posttraumatic Stress Disorder: Development and Validation of the Posttraumatic Expectations Scale (PTES).

Dysfunctional expectations are a particularly important subset of cognitions that influence the development and maintenance of various mental disorders. This study aimed to develop and validate a scale to assess dysfunctional expectations in posttraumatic stress disorder (PTSD), the "Posttraumatic Expectations Scale" (PTES). In a cross-sectional study, 70 PTSD patients completed the PTES, the Posttraumatic Cognitions Inventory (PTCI), as well as measures of the severity of symptoms of PTSD and depression. The results show that the PTES has excellent internal consistency and correlates significantly with the PTCI and PTSD symptom severity. A regression analysis revealed that the PTES explained variance of PTSD symptom severity above the PTCI, supporting the incremental validity of the PTES. While the original version of the PTES comprises 81 items, short scales were constructed using the BISCUIT (best items scales that are cross-validated, unit-weighted, informative and transparent) method. The current findings provide preliminary psychometric evidence suggesting that the PTES is an internally consistent and valid novel self-report measure in patients with PTSD. However, conclusions about the psychometric properties of the PTES are limited because of the absence of criterion-related validity, factor structure evidence, variability over time/response to intervention, and test-retest reliability. Future research should use the PTES in large-scale longitudinal studies to address these aspects to further validate the scale.

Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD.

Several evidence-based treatments for posttraumatic stress disorder (PTSD) are recommended by international guidelines (e.g., APA, NICE). While their average effects are in general high, non-response rates indicate differential treatment effects. Here, we used a large database of RCTs on psychotherapy for PTSD to determine a reliable estimate of this heterogeneity in treatment effects (HTE) by applying Bayesian variance ratio meta-analysis. In total, 66 studies with a total of 8803 patients were included in our study. HTE was found for all psychological treatments, with varying degrees of certainty, only slight differences between psychological treatments, and active control groups yielding a smaller variance ratio compared to waiting list control groups. Across all psychological treatment and control group types, the estimate for the intercept was 0.12, indicating a 12% higher variance of posttreatment values in the intervention groups after controlling for differences in treatment outcomes. This study is the first to determine the maximum increase in treatment effects of psychological treatments for PTSD by personalization. The results indicate that there is comparatively high heterogeneity in treatment effects across all psychological treatment and control groups, which in turn allow personalizing psychological treatments by using treatment selection approaches.

Therapy from my point of view: A case illustration of routine outcome monitoring and feedback in psychotherapeutic interventions.

Lately, the use of patient-reported outcome measures (PROM) to adapt and improve ongoing psychotherapeutic treatments has become more widespread. Their main purpose is to support data-informed, collaborative treatment decisions which include the patient's point of view on their progress. In case of nonresponse or deterioration, these systems are able to warn clinicians and guide the process "back on track" in treatment. In this case illustration, the Greifswald Psychotherapy Navigator System (GPNS) detected the deterioration of 19-year-old Sarah during the first eight sessions of cognitive-behavioral therapy for social anxiety and depression. Here, the GPNS helped the therapist gain insight as to how Sarah's social anxiety affected their treatment and adjust her strategy accordingly. Using the symptom curves and progress scales of the GPNS, the therapist was able to then address her patient's struggles in detail during their sessions and with her supervisor. After adapting her therapeutic approach, the patient's deterioration could be averted while simultaneously strengthening their communication in the process. Clinical implications and the benefits of using PROM systems for evidence-based personalization of psychotherapy are presented.

Clustering individuals' temporal patterns of affective states, hunger, and food craving by latent class vector-autoregression.

BACKGROUND: Eating plays an important role in mental and physical health and is influenced by affective (e.g., emotions, stress) and appetitive (i.e., food craving, hunger) states, among others. Yet, substantial temporal variability and marked individual differences in these relationships have been reported. Exploratory data analytical approaches that account for variability between and within individuals might benefit respective theory development and subsequent confirmatory studies. METHODS: Across 2 weeks, 115 individuals (83% female) reported on momentary affective states, hunger, and food craving six times a day. Based on these ecological momentary assessment (EMA) data we investigated whether latent class vector-autoregression (LCVAR) can identify different clusters of participants based on similarities in their temporal associations between these states. RESULTS: LCVAR allocated participants into three distinct clusters. Within clusters, we found both positive and negative associations between affective states and hunger/food craving, which further varied temporally across lags. Associations between hunger/food craving and subsequent affective states were more pronounced than vice versa. Clusters differed on eating-related traits such as stress-eating and food craving as well as on EMA completion rates. DISCUSSION: LCVAR provides novel opportunities to analyse time-series data in affective science and eating behaviour research and uncovers that traditional models of affect-eating relationships might be overly simplistic. Temporal associations differ between subgroups of individuals with specific links to eating-related traits. Moreover, even within subgroups, differences in associations across time and specific affective states can be observed. To account for this high degree of variability, future research and theories should consider individual differences in direction and time lag of associations between affective states and eating behaviour, daytime and specific affective states. In addition to that, methodological implications for EMA research are discussed.

Fostering compliance with physical distancing by interactive feedback in the context of the COVID-19 pandemic: A web-based randomized controlled trial.

Background: To slow down the spread of COVID-19, the observance of basic hygiene measures, and physical distancing is recommended. Initial findings suggest that physical distancing in particular can prevent the spread of COVID-19. Objectives: To investigate how information to prevent the spread of infectious diseases should be presented to increase willingness to comply with preventive measures. Methods: In a preregistered online experiment, 817 subjects were presented with either interactively controllable graphics on the spread of COVID-19 and information that enable them to recognize how much the spread of COVID-19 is reduced by physical distancing (experimental group) or text-based information about quantitative evidence (control group). It was hypothesized that participants receiving interactive information on the prevention of COVID-19 infections show a significantly higher willingness to comply with future containment measures than participants reading the text-based information. Explorative analyses were conducted to examine whether other factors influence compliance. Results: As predicted, we found a small effect (d = 0.22, 95% CI: 0.11; 0.23, p < .001) for the tested intervention. The exploratory analysis suggests a decline in compliance later in the study (r = -0.10, 95% CI: -0.15; -0.07). Another significant predictor of change in compliance was health-related anxiety, but the effect was trivial. Conclusions: When presented interactively, information on how the own behavior can help prevent infectious diseases can lead to slightly stronger changes in attitude towards behavioral prevention measures than just text-based information. Given the scalability of this simple internet-based intervention, it could play a role in fostering compliance during a pandemic within universal prevention strategies. Future work on the predictive validity of self-reported compliance and the real-world effects on the intervention is needed.

Efficacy of Augmentation of Cognitive Behavioral Therapy With Transcranial Direct Current Stimulation for Depression: A Randomized Clinical Trial.

Importance: Major depressive disorder (MDD) affects approximately 10% of the population globally. Approximately 20% to 30% of patients with MDD do not sufficiently respond to standard treatment. Therefore, there is a need to develop more effective treatment strategies. Objective: To investigate whether the efficacy of cognitive behavioral therapy (CBT) for the treatment of MDD can be enhanced by concurrent transcranial direct current stimulation (tDCS). Design, Setting, and Participants: The double-blind, placebo-controlled randomized clinical trial PsychotherapyPlus was conducted at 6 university hospitals across Germany. Enrollment took place between June 2, 2016, and March 10, 2020; follow-up was completed August 27, 2020. Adults aged 20 to 65 years with a single or recurrent depressive episode were eligible. They were either not receiving medication or were receiving a stable regimen of antidepressant medication (selective serotonin reuptake inhibitor and/or mirtazapine). A total of 148 women and men underwent randomization: 53 individuals were assigned to CBT alone (group 0), 48 to CBT plus tDCS (group 1), and 47 to CBT plus sham-tDCS (group 2). Interventions: Participants attended a 6-week group intervention comprising 12 sessions of CBT. If assigned, tDCS was applied simultaneously. Active tDCS included stimulation with an intensity of 2 mA for 30 minutes (anode over F3, cathode over F4). Main Outcomes and Measures: The primary outcome was the change in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to posttreatment in the intention-to-treat sample. Scores of 0 to 6 indicate no depression; 7 to 19, mild depression; 20 to 34, moderate depression; and 34 and higher, severe depression. Results: A total of 148 patients (89 women, 59 men; mean [SD] age, 41.1 [13.7] years; MADRS score at baseline, 23.0 [6.4]) were randomized. Of these, 126 patients (mean [SD] age, 41.5 [14.0] years; MADRS score at baseline, 23.0 [6.3]) completed the study. In each of the intervention groups, intervention was able to reduce MADRS scores by a mean of 6.5 points (95% CI, 3.82-9.14 points). The Cohen d value was -0.90 (95% CI, -1.43 to -0.50), indicating a significant effect over time. However, there was no significant effect of group and no significant interaction of group × time, indicating the estimated additive effects were not statistically significant. There were no severe adverse events throughout the whole trial, and there were no significant differences of self-reported adverse effects during and after stimulation between groups 1 and 2. Conclusions and Relevance: Based on MADRS score changes, this trial did not indicate superior efficacy of tDCS-enhanced CBT compared with 2 CBT control conditions. The study confirmed that concurrent group CBT and tDCS is safe and feasible. However, additional research on mechanisms of neuromodulation to complement CBT and other behavioral interventions is needed. Trial Registration: ClinicalTrials.gov Identifier: NCT02633449.

Out of sight, out of mind? High discrepancy between observer- and patient-reported outcome after routine inpatient treatment for depression.

BACKGROUND: Divergent outcomes of treatment for depression occur regularly, but often go undetected by clinical judgment alone. To date, no comprehensive studies are available on what the detection rate of divergent outcomes is in routine care. METHOD: We analyzed a large (N = 20,882) database of clinician-rated and patient-reported outcomes from routine inpatient treatment for depression. RESULTS: There was little agreement (57.7% on the GAF, 7.8% on the CGI-I) between clinician ratings and patients not showing clinically significant change. There was virtually no agreement (0.6% on the GAF, 2% on the CGI-I) between clinician ratings and self-report scales in deteriorated patients. Multiple regression showed that clinician ratings of change were influenced primarily by symptom severity at discharge, rather than change from admission. LIMITATIONS: Only symptom scales were available as patient-reported outcomes, although clinician ratings may be based on other sources of information. In addition, no information was available on clinicians' experience with the rating scales used, nor is it clear how carefully the ratings were made. DISCUSSION: It can be concluded that failure to achieve treatment success and worsening after routine treatment for depression often go undetected on clinical rating scales, suggesting that such cases frequently remain undetected. Clinicians should generally obtain patient-reported outcomes during treatment to detect these cases.

Cognitive Change and Relaxation as Key Mechanisms of Treatment Outcome in Chronic Pain: Evidence From Routine Care.

Despite effective treatment approaches within the cognitive behavioral framework general treatment effects for chronic pain are rather small to very small. Translation from efficacy trials to naturalistic settings is questionable. There is an urgent need to improve the effectiveness of well-established treatments, such as cognitive-behavior therapy (CBT) and the investigation of mechanisms of change is a promising opportunity. We performed secondary data analysis from routine data of 1,440 chronic pain patients. Patients received CBT in a multidisciplinary setting in two inpatient clinics. Effect sizes and reliable change indices were computed for pain-related disability and depression. The associations between changes in the use of different pain coping skills (cognitive restructuring, activity despite pain, relaxation techniques and mental distraction) and changes in clinical outcomes were analyzed in structural equation models. Pre-post effect sizes range from g = 0.47 (disability) to g = 0.89 (depression). Changes in the use of cognitive restructuring, relaxation and to a lesser degree mental distraction were associated with changes in disability and depression. Effects from randomized trials can be translated to naturalistic settings. The results complement experimental research on mechanisms of change in the treatment of chronic pain and indicate an important role of cognitive change and relaxation as mechanisms of change. Our findings cautiously suggest that clinicians should optimize these processes in chronic pain patients to reduce their physical and emotional disability.

Maintaining Outcomes of Internet-Delivered Cognitive-Behavioral Therapy for Depression: A Network Analysis of Follow-Up Effects.

Background: Depression is a highly prevalent mental disorder, but only a fraction of those affected receive evidence-based treatments. Recently, Internet-based interventions were introduced as an efficacious and cost-effective approach. However, even though depression is a heterogenous construct, effects of treatments have mostly been determined using aggregated symptom scores. This carries the risk of concealing important effects and working mechanisms of those treatments. Methods: In this study, we analyze outcome and long-term follow-up data from the EVIDENT study, a large (N = 1,013) randomized-controlled trial comparing an Internet intervention for depression (Deprexis) with care as usual. We use Network Intervention Analysis to examine the symptom-specific effects of the intervention. Using data from intermediary and long-term assessments that have been conducted over 36 months, we intend to reveal how the treatment effects unfold sequentially and are maintained. Results: Item-level analysis showed that scale-level effects can be explained by small item-level effects on most depressive symptoms at all points of assessment. Higher scores on these items at baseline predicted overall symptom reduction throughout the whole assessment period. Network intervention analysis offered insights into potential working mechanisms: while deprexis directly affected certain symptoms of depression (e.g., worthlessness and fatigue) and certain aspects of the quality of life (e.g., overall impairment through emotional problems), other domains were affected indirectly (e.g., depressed mood and concentration as well as activity level). The configuration of direct and indirect effects replicates previous findings from another study examining the same intervention. Conclusions: Internet interventions for depression are not only effective in the short term, but also exert long-term effects. Their effects are likely to affect only a small subset of problems. Patients reporting these problems are likely to benefit more from the intervention. Future studies on online interventions should examine symptom-specific effects as they potentially reveal the potential of treatment tailoring. Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT02178631.

Sample size, sample size planning, and the impact of study context: systematic review and recommendations by the example of psychological depression treatment.

BACKGROUND: Sample size planning (SSP) is vital for efficient studies that yield reliable outcomes. Hence, guidelines, emphasize the importance of SSP. The present study investigates the practice of SSP in current trials for depression. METHODS: Seventy-eight randomized controlled trials published between 2013 and 2017 were examined. Impact of study design (e.g. number of randomized conditions) and study context (e.g. funding) on sample size was analyzed using multiple regression. RESULTS: Overall, sample size during pre-registration, during SSP, and in published articles was highly correlated (r's ≥ 0.887). Simultaneously, only 7-18% of explained variance related to study design (p = 0.055-0.155). This proportion increased to 30-42% by adding study context (p = 0.002-0.005). The median sample size was N = 106, with higher numbers for internet interventions (N = 181; p = 0.021) compared to face-to-face therapy. In total, 59% of studies included SSP, with 28% providing basic determinants and 8-10% providing information for comprehensible SSP. Expected effect sizes exhibited a sharp peak at d = 0.5. Depending on the definition, 10.2-20.4% implemented intense assessment to improve statistical power. CONCLUSIONS: Findings suggest that investigators achieve their determined sample size and pre-registration rates are increasing. During study planning, however, study context appears more important than study design. Study context, therefore, needs to be emphasized in the present discussion, as it can help understand the relatively stable trial numbers of the past decades. Acknowledging this situation, indications exist that digital psychiatry (e.g. Internet interventions or intense assessment) can help to mitigate the challenge of underpowered studies. The article includes a short guide for efficient study planning.

Unraveling the comorbidity of depression and anxiety in a large inpatient sample: Network analysis to examine bridge symptoms.

BACKGROUND: Comorbidities in mental disorders are often understood by assuming a common cause. The network theory of mental disorders offers an alternative to this assumption by understanding comorbidities as mutually reinforced problems. In this study, we used network analysis to examine bridge symptoms between anxiety and depression in a large sample. METHOD: Using data from a sample of patients diagnosed with both depression and an anxiety disorder before and after inpatient treatment (N = 5,614, mean age: 42.24, 63.59% female, average treatment duration: 48.12 days), network models of depression and anxiety symptoms are estimated. Topology, the centrality of nodes, stability, and changes in network structure are analyzed. Symptoms that drive comorbidity are determined by bridge node analysis. As an alternative to network communities based on categorical diagnosis, we performed a community analysis and propose empirically derived symptom subsets. RESULTS: The obtained network models are highly stable. Sad mood and the inability to control worry are the most central. Psychomotor agitation or retardation is the strongest bridge node between anxiety and depression, followed by concentration problems and restlessness. Changes in appetite and suicidality were unique to depression. Community analysis revealed four symptom groups. CONCLUSION: The estimated network structure of depression and anxiety symptoms proves to be highly accurate. Results indicate that some symptoms are considerably more influential than others and that only a small number of predominantly physical symptoms are strong candidates for explaining comorbidity. Future studies should include physiological measures in network models to provide a more accurate understanding.

Networks of stress, affect and eating behaviour: anticipated stress coping predicts goal-congruent eating in young adults.

BACKGROUND: Many people aim to eat healthily. Yet, affluent food environments encourage consumption of energy dense and nutrient-poor foods, making it difficult to accomplish individual goals such as maintaining a healthy diet and weight. Moreover, goal-congruent eating might be influenced by affects, stress and intense food cravings and might also impinge on these in turn. Directionality and interrelations of these variables are currently unclear, which impedes targeted intervention. Psychological network models offer an exploratory approach that might be helpful to identify unique associations between numerous variables as well as their directionality when based on longitudinal time-series data. METHODS: Across 14 days, 84 diet-interested participants (age range: 18-38 years, 85.7% female, mostly recruited via universities) reported their momentary states as well as retrospective eating episodes four times a day. We used multilevel vector autoregressive network models based on ecological momentary assessment data of momentary affects, perceived stress and stress coping, hunger, food craving as well as goal-congruent eating behaviour. RESULTS: Neither of the momentary measures of stress (experience of stress or stress coping), momentary affects or craving uniquely predicted goal-congruent eating. Yet, temporal effects indicated that higher anticipated stress coping predicted subsequent goal-congruent eating. Thus, the more confident participants were in their coping with upcoming challenges, the more they ate in line with their goals. CONCLUSION: Most eating behaviour interventions focus on hunger and craving alongside negative and positive affect, thereby overlooking additional important variables like stress coping. Furthermore, self-regulation of eating behaviours seems to be represented by how much someone perceives a particular eating episode as matching their individual eating goal. To conclude, stress coping might be a potential novel intervention target for eating related Just-In-Time Adaptive Interventions in the context of intensive longitudinal assessment.