Just-in-Time Adaptive Intervention to Promote Walking Behavior and Reduce Stationary Time in Physically Inactive Adults: Protocol for the Walking With JITAIs Study
Bibliographic record
Abstract
BACKGROUND: A Just-in-Time Adaptive Intervention (JITAI) recognizes the dynamic nature of individuals' states and contexts, predicts support needs, and sends tailored support at more opportune, actionable times. OBJECTIVE: This paper outlines the application architecture and protocol for the pilot "Walking With Just-in-Time Adaptive Interventions" (WWJ) study, which uses a JITAI approach to improve walking behavior-duration, speed, and distance-and reduce stationary time, defined as idle sitting or standing. METHODS: This study targets 20 adults who are physically inactive and leverages the Apple Watch to deliver fully automated tailored intervention notifications to "walk faster," "walk longer," or "stand up and move around" based on real-time data and contextual factors, including time-of-day activity patterns, geographic locations (eg, home, work, park, and gymnasium), weather conditions (eg, precipitation, wind speed, and humidity), and receptiveness. The protocol involves a preintervention assessment of demographics, behavior change constructs, anthropometrics, and resting vital signs; a 2-week observation period to establish walking behavior and stationary time baselines; a 2-week just-in-time learning period to evaluate receptiveness to untailored prompts at all applicable times; the 2-week JITAI intervention phase; and a postintervention assessment. Feasibility will be evaluated through protocol fidelity, participant adherence, Apple Watch wear-time compliance, user burden, acceptability ratings, and perceptions of benefits and preferences. RESULTS: The WWJ architecture development began in spring 2021 and concluded in fall 2022. Participant recruitment and enrollment began in fall 2022. A total of 18 participants were recruited. Upon completion of the analyses, the results of this study are expected to be submitted for publication. CONCLUSIONS: Distinctively, the WWJ just-in-time learning period aims to train the learner based on user receptiveness within contexts by sending interventions whenever a participant meets the predetermined thresholds regardless of the likelihood that the user will be receptive to the notification to prune out nonopportune or "nonactionable" times. This approach may allow for greater customization during the JITAI period. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/79022.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".