Adaptive Just-in-Time Intervention to Reduce Everyday Stress Responses: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Personalized approaches to behavior change to improve mental and physical health outcomes are needed. Reducing the intensity, duration, and frequency of stress responses is a mechanism for interventions to improve health behaviors. We developed an ambulatory, dynamic stress measurement approach that can identify personalized stress responses in the moments and contexts in which they occur; we propose that intervening in these stress responses as they arise (ie, just in time; JIT) will result in positive impacts on health behaviors. OBJECTIVE: This study aims to (1) use an experimental medicine approach to evaluate the impact of a smartphone-delivered JIT stress management intervention on the frequency and intensity of person-specific stress responses (ie, stress reactivity, nonrecovery, and pileup); (2) evaluate the impact of the JIT intervention on the enactment of health behaviors in everyday life (physical activity and sleep); and (3) explore whether changes in stress responses mediate the interventions' effects on health behaviors. METHODS: In a 2-arm phase 2 clinical trial, we will enroll 210 adults in either a JIT stress management intervention or an active control condition. For 4 weeks, participants will complete 8 brief smartphone surveys each day and wear devices to assess sleep and physical activity. After a 1-week run-in period, participants will be randomized into the JIT intervention or an active control condition for 2 weeks. Participants in the JIT intervention will receive very brief stress management activities when reporting greater than typical stress responses, whereas control participants will receive no personalized stress management activities. Participants enrolled in both conditions will engage in self-monitoring for the entire study period and have access to a general stress management education module. Self-report outcomes will be assessed again 1 month after the intervention. We will use mixed-effects models to evaluate differences in person-specific stress responses between the intervention and control groups. We will conduct parallel analyses to evaluate whether the intervention is associated with improvement in health behavior enactment (ie, sleep and physical activity). The Pennsylvania State University Institutional Review Board approved all study procedures (STUDY00012740). RESULTS: Initial participant recruitment for the trial was initiated on August 15, 2022, and enrollment was completed on June 9, 2023. A total of 213 participants were enrolled in this period. Data are currently being processed; analyses have not yet begun. CONCLUSIONS: We anticipate that this research will contribute to advancing stress measurement, thereby enhancing understanding of health behavior change mechanisms and, more broadly, providing a conceptual roadmap to advance JIT interventions aimed at improving stress management and health behaviors. TRIAL REGISTRATION: Clinicaltrials.gov NCT05502575; https://clinicaltrials.gov/study/NCT05502575. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58985.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.086 | 0.014 |
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".