The role of habit in different phases of exercise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The primary purpose of this study was to investigate how habit strength in a preparatory and performance phase predicts exercise while accounting for intention. The secondary purpose was to determine the strength of potential habit antecedents (affective judgement, perceived behavioural control, consistency, and cues) in both exercise phases. DESIGN: This was a prospective study with measures collected at baseline and week 6. METHODS: Participants (n = 181) were a sample of adults (18-65) recruited across nine gyms and recreation centres who completed baseline and follow-up questionnaires after 6 weeks. RESULTS: Intention (β = .28, p = .00) and habit preparation (β = .20, p = .03), predicted exercise, and change of exercise with coefficients of β = .25, (p = .00) and β = .18, (p = .04), respectively, across 6 weeks but not habit performance (p>.05). CONCLUSIONS: This study highlighted the distinction between the two phases of exercise and the importance of preparatory habit in predicting behaviour. Focusing on a consistent preparatory routine could be helpful in establishing an exercise habit. Statement of contribution What is already known on this subject? A recent meta-analysis found habit to correlate r = .43 with behaviour (Gardner, de Bruijn, & Lally, ). Verplanken and Melkevik () propose that habit in exercise should be measured in separate components. Phillips and Gardner () interpreted this as habitual instigation (thought) to exercise and execution. What does this study add? Extended pervious work and identified two distinct behavioural phases (preparation and performance) for exercise. Habit model revealed that temporal consistency was the strongest predictor in both phases of exercise. Intention and habit of preparatory behaviour predicted exercise fluctuations in gym members.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it