HabitWalk: A micro‐randomized trial to understand and promote habit formation in physical activity
Bibliographic record
Abstract
Habit is a key psychological determinant for physical activity behavior change and maintenance. This study aims to deepen the understanding of habit formation in physical activity and identify promotion strategies. We examined the habit formation trajectory and its relationships with cue-behavior repetition (a cue-triggered 15-minute brisk walk) and unconditional physical activity (daily steps). We also tested whether the behavior change techniques (BCTs) 'commitment' and 'prompts and cues' promote habit, cue-behavior repetition, and daily steps within persons. This micro-randomized trial included a 7-day preparatory and a 105-day experimental phase delivered via the HabitWalk app. Participants (N = 24) had a 50% probability of receiving each BCT daily, leading to four conditions. Habit strength was assessed daily using the Self-Report Behavioral Automaticity Index, while cue-behavior repetition and steps were measured via an activity tracker. Person-specific growth functions indicated that habit strength trajectories were highly idiosyncratic. Multilevel models indicated a positive effect of cue-behavior repetition on habit strength, but not vice versa. The effect of habit strength on daily steps varied by the operationalization of cue-behavior repetition. Tentative findings suggest that commitment and prompts and cues are effective habit-promotion strategies when delivered together.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".