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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".