Factors associated with change in physical activity among nurses participating in a web-based worksite intervention
Bibliographic record
Abstract
Background: Web-based worksite interventions represent a feasible, acceptable, and cost-effective approach to address the physical inactivity burden among nurses. We delivered a 6-week web-based worksite intervention to promote physical activity (PA) among nurses, and assessed whether demographic (age, sex), occupational (weekly hours, shift schedule, work role), and psychological (mood states) factors predicted changes in weekly moderate-to-vigorous intensity PA (MVPA) and steps. Methods: Nurses (N=76; 97% female; Mage = 46±11 years) from the University of Ottawa Heart Institute were recruited into this parallel-group trial and randomized to an individual, friend, or team PA challenge group. Pre-randomization, nurses completed self-report questionnaires and were given a PA monitor to wear prior to and during the intervention. We analyzed data using multilevel modeling for repeated measures. Results: Changes in weekly MVPA were significant and followed an inverted U-shape. Changes in weekly steps were also non-linear, but non-significant. Shift schedule (rotating vs. fixed) by time (estimate=-17.62, SE=5.39) and by time-squared (estimate=2.66, SE=0.86) and work role (clinical only vs. other) by time (estimate=19.47, SE=5.69) and by time-squared (estimate=-3.11, SE=0.91) predicted MVPA. Work role by time (estimate=1096.85, SE=522.50) and by time-squared (estimate=-218.40, SE=97.31) predicted step count. Age, sex, weekly hours, and mood states did not predict weekly MVPA or steps. Conclusions: Strategies should be employed to help nurses maintain increases in PA observed during a web-based worksite intervention. As nurses working rotating shifts and completing clinical work only showed less improvements in PA, collaborating with such nurses to inform the design of future interventions is recommended.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".