What Motivates Nurses to Exercise? Determinants of Physical Activity Among Canadian Nurses Using Self-Determination Theory
Bibliographic record
Abstract
BACKGROUND: Nurses' suboptimal physical activity (PA) levels place them at high risk for cardiovascular diseases. Little is known about the motivational factors that influence their PA behavior. PURPOSE: This study drew on the Self-Determination Theory (SDT) to investigate whether associations between nurses' levels of mood disturbance, psychological need satisfaction (competence, autonomy, and relatedness), and self-determined motivation predict levels of objectively assessed PA. METHODS: A total of 363 nurses recruited from 14 hospitals in the Champlain region of Ontario, Canada, wore ActiGraph GT3X accelerometers and completed standardized questionnaires assessing sociodemographic and work characteristics, mood disturbance, and SDT variables. Levels of moderate-to-vigorous intensity PA (MVPA) were measured in minutes/week in bouts ≥10 min. Data were analyzed using path analysis and multiple mediational model. RESULTS: The model predicting MVPA showed good fit to the data, χ 2 (4, n = 363) = 7.82, p = .10; comparative fit index = .991; Tucker-Lewis Index = .967; root mean square error of approximation = .051. Higher mood disturbance was associated with lower perceived competence (β = -.29, p = .002), autonomy (β = -.29, p = .002), and relatedness (β = -.19, p = .002). Lower perceived competence (β = .46, p = .003) and autonomy (β = .14, p = .011), as well as higher mood disturbance (β = -.16, p = .016), were associated with less self-determined motivation for PA. Lower self-determined motivation was associated with lower levels of MVPA among nurses. CONCLUSIONS: Interventions targeting low mood, as well as perceived competence and autonomy in exercise, may promote MVPA among nurses and reduce cardiac risk.
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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 | 0.000 | 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 | 0.000 |
| 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 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".