Motivation Predicts Change in Nurses’ Physical Activity Levels During a Web-Based Worksite Intervention: Results From a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Low physical activity levels can negatively affect the health of nurses. Given the low physical activity levels reported by nurses, there is a clear need for brief and economical interventions designed to increase physical activity levels in this population. We developed a web-based intervention that used motivational strategies to increase nurses' physical activity levels. The intervention provided the nurses with feedback from an activity monitor coupled with a web-based individual, friend, or team physical activity challenge. OBJECTIVE: In this parallel-group randomized trial, we examine whether nurses' motivation at baseline predicted changes in objectively measured physical activity levels during the 6-week intervention. METHODS: The participants were 76 nurses (n=74, 97% female; mean age 46, SD 11 years) randomly assigned to 1 of 3 physical activity challenge conditions: (1) individual, (2) friend, or (3) team. The nurses completed a web-based questionnaire designed to assess motivational regulations for physical activity levels before the intervention and wore a Tractivity activity monitor before and during the 6-week intervention. We analyzed data using multilevel modeling for repeated measures. RESULTS: The nurses' physical activity levels increased (linear estimate=10.30, SE 3.15; P=.001), but the rate of change decreased over time (quadratic estimate=-2.06, SE 0.52; P<.001). External and identified regulations (ß=-2.08 to 11.55; P=.02 to .04), but not intrinsic and introjected regulations (ß=-.91 to 6.29; P=.06 to .36), predicted changes in the nurses' physical activity levels. CONCLUSIONS: Our findings provide evidence that an intervention that incorporates self-monitoring and physical activity challenges can be generally effective in increasing nurses' physical activity levels in the short term. They also suggest that drawing solely on organismic integration theory to predict changes in physical activity levels among the nurses participating in web-based worksite interventions may have been insufficient. Future research should examine additional personal (eg, self-efficacy) and occupational factors (eg, shift length and shift type) that influence physical activity levels to identify potential targets for intervention among nurses. TRIAL REGISTRATION: ClinicalTrials.gov NCT04524572; https://clinicaltrials.gov/ct2/show/NCT04524572.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".