Just-in-Time Delivery of Cognitive Behavioral Therapy–Based Exercises: Single-Case Experimental Design With Random Multiple Baselines
Bibliographic record
Abstract
Background: Just-in-time adaptive interventions (JITAIs) are a promising approach in mental health care given the potential scalability (ie, interventions are offered automatically and remotely) and preciseness (ie, the right interventions are offered at the right moments). Typically, a smartphone app is programmed to assess users' psychological states in daily life; when a particular state is detected, the app prompts users to engage in specific behaviors. Conceptually, JITAIs hold significant potential for precision health, although there is currently limited evidence in the literature. Objective: We implemented this scheme as a smartphone intervention for daily stress management, based on cognitive behavioral therapy (CBT), and evaluated its feasibility and efficacy using a single-case experimental design. Methods: A total of 8 Japanese adults (community sample: 4 women; mean 37.6, SD 13.1 y) were recruited. An AB phase design with multiple random baselines was used, where "A" represents the baseline phase and "B" represents the intervention phase. Throughout the study period (28 d), participants were prompted to indicate their momentary levels of stress (range 0-100) using a smartphone thrice a day. The baseline phase duration was randomly varied among participants, lasting between 7 and 14 days. The remaining period was used as the intervention phase (14-21 d), where 6 CBT-based exercises (ie, breath control, mindfulness, relaxation, self-talk, cognitive defusion, and cognitive restructuring) were offered depending on the reported levels of stress. Results: Approximately 70% (6/8) of the participants perceived the intervention to be useful and helpful. A randomization test detected a statistically significant decrease in reported stress levels after the intervention began (P=.005), though this effect was less pronounced when analyzed individually for each participant. Multilevel model analysis detected a significant acute reduction in the momentary level of stress right after completing a CBT-based exercise (pre-exercise: mean 47.98, SD 21.65; post exercise: mean 42.13, SD 19.88; P=.03; Cohen dz=0.58). Also, a significant reduction in depressive rumination was observed in the postintervention assessment (preintervention: mean 13.00, SD 3.21; post intervention: mean 9.25, SD 2.60; P=.01, Cohen dz=1.17). Conclusions: The intervention was feasible and effective in reducing subjective stress (and rumination) in the study sample. The small sample size and the nonclinical nature of the sample may limit the generalizability and implications of the study findings for clinical practice. More evidence should be collected to draw solid conclusions for technical and technological as well as clinical aspects of mobile interventions. Accumulating exemplars with different implementations will clarify how a JITAI can be designed and developed on a mobile platform and how the program can be delivered in the prevention and treatment of mental ill health.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".