The daily enactment of behavior change techniques among physically active university students with physical disabilities and chronic conditions
Bibliographic record
Abstract
BACKGROUND: While behavior change techniques (BCTs) support physical activity (PA) behavior change, further examination aimed at understanding their enactment is needed. PURPOSE: This study aimed to examine the daily enactment of BCTs and the factors that impact their daily usage. METHODS: Using experience sampling methods, we sampled 53 university students (Mage = 22 years; SDage = 3.93 years) from McGill (n = 40) and Queen's (n = 13) University to examine BCT usage among university students with physical disabilities and chronic conditions. A daily online survey was delivered for 10 consecutive days where participants answered a checklist of 28 BCTs, a modified 6-item Capability, Opportunity, and Motivation (COM) questionnaire and questions on situational disruptions. Descriptive analysis summarized BCT usage, COM, and contextual factors. A generalized linear mixed model examined the relationship between demographic variables, COM, and contextual factors on the usage of BCTs. RESULTS: On average, participants used 11 BCTs daily, while 22 distinct BCTs were used over the 10 days. The most frequently used BCTs were task crafting and goal integration. The least used BCTs included self-monitoring and obtaining information on how to perform PA. Women were more likely than men to enact certain BCTs, while students with physical disabilities were less likely than those with chronic conditions to enact others. Participants reported higher daily capability and opportunity to use BCTs compared to motivation. Situational disruptions such as personal commitments (eg, work) followed by flare-ups (eg, pain) were frequently reported. Motivation and opportunity predicted the usage of 15 to 24 BCTs, while capability predicted the usage of 2 BCTs. CONCLUSION: Our findings provide valuable context on BCT enactment, thereby improving the structure of future PA behavior change interventions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".