A Fully Automated Self-help Biopsychosocial Transdiagnostic Digital Intervention to Reduce Anxiety and/or Depression and Improve Emotional Regulation and Well-being: Pre–Follow-up Single-Arm Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. OBJECTIVE: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. METHODS: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). RESULTS: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR]<.001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=-0.50 to -0.63) and optimism (range Cohen d=-0.72 to -0.79) and small-to-medium treatment effect size change for EQ-5D-3L Health Rating (range Cohen d=-0.34 to -0.58). Changes across all outcome variables were generally strongest for participants with preintervention clinical comorbid anxiety and depression presentations (range |d|=0.58 to 2.01) and weakest for participants presenting with nonclinical anxiety and/or depressive symptoms (|d|=0.05 to 0.84). Life Flex was rated as acceptable at the postintervention time point, and participants indicated that they enjoyed the transdiagnostic program and biological, wellness, and lifestyle-focused content and strategies. CONCLUSIONS: Given the paucity of evidence on fully automated self-help transdiagnostic digital interventions for anxiety and/or depressive symptomatology and general treatment accessibility issues, this study provides preliminary support for biopsychosocial transdiagnostic interventions, such as Life Flex, as a promising future mental health service delivery gap filler. Following large-scale, randomized controlled trials, the potential benefits of fully automated self-help digital health programs, such as Life Flex, could be considerable. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12615000480583; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368007.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".