Evaluation of Various Support Intensities of Digital Mental Health Treatment for Reducing Anxiety and Depression in Adults: Protocol for a Mixed Methods, Adaptive, Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression are leading causes of disease worldwide, requiring timely access to evidence-based treatment. Digital mental health (dMH) interventions increase accessibility to evidence-based psychological services delivered in a variety of web-based formats (eg, self-help and therapist-assisted interventions). Robust and rigorous studies of adaptive web-based intervention designs are scarce. No identified randomized clinical trial has investigated the efficacy of a 2-stage adaptive design, whereby the program-only condition or no support dMH treatment program is augmented by either low or high therapist assistance, if a participant does not improve or engage in the program-only condition. OBJECTIVE: The primary objective is to assess whether low or high therapist-assisted support delivered via video chat is more effective in reducing anxiety and depressive symptoms compared with a dMH program-only condition. The secondary objective is to evaluate the role of motivation; self-efficacy; and preferences in participant engagement, adherence, and clinical outcomes (anxiety and depression symptoms) among the 3 treatment conditions (program only, low-intensity therapist assistance, and high-intensity therapist assistance). A mixed methods analysis of factors affecting participant attrition, participant reasons for nonengagement and withdrawal, and therapist training and implementation of dMH interventions will be completed. Qualitative data regarding participant and therapist experiences and satisfaction with video chat assessment and treatment will also be analyzed. METHODS: Australian adults (N=137) with symptoms or a diagnosis of anxiety or depression will be screened for eligibility and given access to the 8-module Life Flex dMH treatment program. On day 15, participants who meet the augmentation criteria will be stepped up via block randomization to receive therapist assistance delivered via video chat for either 10 minutes (low intensity) or 50 minutes (high intensity) per week. This adaptive trial will implement a mixed methods design, with outcomes assessed before the intervention (week 0), during the intervention (weeks 3 and 6), after the intervention (week 9), and at the 3-month follow-up (week 21). RESULTS: The primary outcome measures are for anxiety (Generalized Anxiety Disorder-7) and depression severity (Patient Health Questionnaire-9). Measures of working alliance, health status, health resources, preferences, self-efficacy, and motivation will be used for secondary outcomes. Qualitative methods will be used to explore participant and therapist experiences of video chat assessment and treatment, participant reasons for withdrawal and nonengagement, and therapist training and implementation experiences. Data collection commenced in November 2020 and was completed at the end of March 2022. CONCLUSIONS: This is the first mixed methods adaptive trial to explore the comparative efficacy of different intensity levels of self-help and a therapist-assisted dMH intervention program delivered via video chat for adults with anxiety or depression. Anticipated results may have implications for the implementation of dMH interventions. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry 12620000422921; https://tinyurl.com/t9cyu372. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/45040.
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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 | 0.066 | 0.061 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".