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Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.
Abstract
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.
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.
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.
This article provides details on the development of a statistical learning algorithm developed for constructing personalized treatment plans for psychotherapy. The algorithm takes data collected via Ecological Momentary Assessment (EMA) as an input. From this, it constructs an idiographic disorder model that reflects the latent dimensions of this patient’s psychopathology and their temporal interrelations. The priority of individual problems is derived from this statistical model. Based on this, treatment modules from cognitive-behavioral therapy are ranked so that the problems with the highest priority are dealt with first. A case study is used to illustrate the different analysis steps of the algorithm from data collection to the treatment plan.
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.
Abstract
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.
Physiological and neural synchrony in emotional and neutral stimulus processing: A study protocol
(2023)
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.