Assessment and Disruption of Ruminative Episodes to Enhance Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive Interventions in Clinical Depression: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: A just-in-time adaptive intervention (JITAI) is "designed to address the dynamically changing needs of individuals via the provision of the type or amount of support needed, at the right time when needed." If and how rumination-focused cognitive behavioral therapy (RFCBT), the gold standard, blocks emotional cascades underlying rumination is unclear. Furthermore, cognitive behavioral therapy has been successfully used as a mobile variant, but RFCBT has not been adapted for a mobile variant (mobile RFCBT [MRFCBT]) or for a JITAI variant. OBJECTIVE: This study aimed to pilot-test a fully automated JITAI leveraging RFCBT and ways to identify and block cascading depressive rumination. METHODS: Patients in therapy for clinical depression were recruited for a randomized controlled trial (RCT). After consenting to be part of the RCT, they were randomly assigned to either of the 2 mobile versions of the RFCBT conditions personalized to the individual's rumination timing patterns (JITAI-MRFCBT) or a no-treatment control condition through a double-blind procedure. Although the initial design was to have a 3-armed trial with 2 JITAI conditions (a JITAI and a narrative JITAI condition), we later opted to collapse those 2 conditions into 1 JITAI condition because of the low number of participants. All participants were recruited and participated through their smartphones, receiving 5 SMS text message reminders on each of the 35 days to self-report their rumination-related symptoms (eg, rumination episodes and duration). In the JITAI-MRFCBT condition, they also received treatment materials. The first 7 days provided a rumination baseline, and the last 7 days provided a postintervention rumination value. In total, 42% (25/59) of volunteers were eligible and provided their phone numbers, 20% (5/25) of whom never replied to the SMS text message reminding them to start the RCT. A total of 90% (18/20) of volunteers completed it (ie, finishing, as prespecified, 80% of the questionnaires and training tasks) and, therefore, were included in the analysis. RESULTS: Using independent 2-tailed t tests with bootstrapping, results showed that participants in the JITAI-MRFCBT condition, compared with those in the control condition, reported a greater reduction in counts of rumination episodes (mean -25.28, SD 14.50 vs mean 1.44, SD 4.12, P<.001) and greater reduced average time (minutes) spent in rumination (mean -21.53, SD 17.6 vs mean 1.47, SD 1.5; P=.04). Results also suggest that, compared with those in the control group, those in treatment reduced ruminative carryover from one episode to the next. CONCLUSIONS: The results suggest that JITAI-MRFCBT may reduce negative rumination by providing RFCBT just in time following rumination, thereby blocking the next rumination episode using the same trigger. This study supports a subsequent, full-scale JITAI and the importance of leveraging mobile smartphone technology with MRFCBT to curb depressive symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT04554706; https://clinicaltrials.gov/ct2/show/NCT04554706.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".