Background College students are at elevated risk for developing mental health problems and face specific barriers around accessing evidence-based treatment. Web-based interventions that focus on mental health promotion and strengthening resilience represent one possible solution. Providing support to users has shown to reduce dropout in these interventions. Further research is needed to assess the efficacy and acceptability of these interventions and explore the viability of automating support. Objective This study investigated the feasibility of a new web-based resilience program based on positive psychology, provided with human or automated support, in a sample of college students. Methods A 3-armed closed pilot randomized controlled trial design was used. Participants were randomized to the intervention with human support (n=29), intervention with automated support (n=26), or waiting list (n=28) group. Primary outcomes were resilience and well-being, respectively measured by the Connor–Davidson Resilience Scale and Pemberton Happiness Index. Secondary outcomes included measures of depression and anxiety, self-esteem, and stress. Outcomes were self-assessed through online questionnaires. Intention-to-treat and per-protocol analyses were conducted. Results All participants demonstrated significant improvements in resilience and related outcomes, including an unexpected improvement in the waiting list group. Within- and between-group effect sizes ranged from small to moderate and within-group effects were typically larger for the human than automated support group. A total of 36 participants began the program and completed 46.46% of it on average. Participants were generally satisfied with the program and found it easy to use. Conclusions Findings support the feasibility of the intervention. Preliminary evidence for the equal benefit of human and automated support needs to be supported by further research with a larger sample. Results of this study will inform the development of a full-scale trial, from which stronger conclusions may be drawn. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN) 11866034; http://www.isrctn.com/ISRCTN11866034 International Registered Report Identifier (IRRID) RR2-10.1016/j.invent.2019.100254
Background Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. Methods A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. Results Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). Conclusions An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.
Background Exposure to new stressors places college students at increased risk for developing mental health problems. Preventive interventions aimed at enhancing resilience have the potential to improve mental health and well-being in college students and internet-delivery may improve access to these interventions. However, few studies have evaluated the efficacy of online interventions for resilience in college students. The present study seeks to assess the feasibility [initial efficacy and acceptability] of a newly developed internet-delivered intervention for resilience provided with human or automated support, in a sample of college students. Method A pilot randomised controlled trial including three groups: 1) an intervention group with human support; 2) an intervention group with automated support; and 3) a waiting list control group. The intervention, Space for Resilience , is based on positive psychology and consists of seven modules, delivered over a period of eight weeks. Primary outcomes measures will include the Connor-Davidson Resilience Scale (CD-RISC) and the Pemberton Happiness Index (PHI). Secondary outcomes measures will include the Brief Resilience Scale (BRS), the Patient Health Questionnaire – 4 items (PHQ-4), the Rosenberg Self-Esteem Scale (RSES), and the Perceived Stress Scale – 4 items (PSS-4). Acceptability will be examined using the Satisfaction with Treatment (SAT) questionnaire. Analysis will be conducted on an intention-to-treat basis. Discussion The study seeks to establish the initial efficacy and acceptability of an internet-delivered intervention for resilience with human support and automated support. Apart from determining the impact of the intervention on acceptability and effectiveness, this study will be a first to explore more clearly the relative benefits of different support modes.
Objective: Prior studies have found metacognitive impairments are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance (‘metacognitive bias’). However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves.Methods: Data were collected as part of the ‘Precision in Psychiatry’ study, a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and four-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=88) and a control group receiving no intervention (n=82).Results: Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence (B=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. In the antidepressant arm, anxious-depression reduced (B=-0.61, SE=0.09, p<0.001) and confidence increased (B=0.31, SE=0.08, p<0.001). Among controls, confidence remained stable from baseline to follow-up (B=0.11, SE=0.07, p=0.103). Conclusions: Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.
Elevated emotion network connectivity is thought to leave people vulnerable to become and stay depressed. The mechanism through which this arises is however unclear. Here, we test the idea that the connectivity of emotion networks causes people to suffer more extreme fluctuations in depression over time, rather than necessarily more severe depression. We gathered data from 2 independent samples of N=155 paid students and N=194 citizen scientists who rated their positive and negative emotions on a smartphone app twice a day and completed a weekly depression questionnaire for 8 weeks. We constructed thousands of personalised emotion networks for each participant and tested if connectivity was associated with severity of depression or its variance over 8 weeks. Network connectivity was positively associated with baseline depression severity in citizen scientists, but not paid students. In contrast, 8-week variance of depression was correlated with network connectivity in both samples. When controlling for depression variance, the association between connectivity and baseline depression severity in citizen scientists was no longer significant. We replicated these findings in an independent community sample (N=519). We conclude that elevated network connectivity primarily leads to greater variability in depression symptoms. This variability only translates into increased severity in samples where depression is on average low and positively skewed, causing mean and variance to be more strongly correlated. These findings underscore how emotional network connectivity has bi-directional effects; risk for severe bouts of depression, but also the potential to be harnessed to bring about improvements.
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