A Quasi-Experimental Study Examining the Impact and Challenges of Implementing a Fitness-Based Health Risk Assessment and a Physical Activity Counseling Intervention in the Workplace Setting
Bibliographic record
Abstract
Objectives: Few adults participate in enough physical activity for health benefits. The workplace provides a unique environment to deliver heath interventions and can be beneficial to the employee and the employer. The purpose of the study was to explore the use of a physical activity counseling (PAC) program and a fitness-based health risk assessment (fHRA) in the hospital workplace. Methods: A workplace-based intervention was developed utilizing a PAC program and an fHRA to improve physical activity levels of employees. Hospital employees were enrolled in a 4-month PAC program and given the option to also enroll in an fHRA program (PAC + fHRA). Physical activity was assessed by accelerometry and measured at baseline, 2 months, and 4 months. Changes in musculoskeletal fitness for those in the fHRA program were assessed at baseline and 2 months. Results: For both groups (PAC n = 22; PAC + fHRA n = 16), total and moderate to vigorous physical activity in bouts of 10 minutes or more increased significantly by 18.8 ( P = .004) and 10.2 ( P = .048) minutes per week at each data collection point, respectively. Only participants with gym memberships demonstrated increases in light physical activity over time. Those in the fHRA group significantly increased their overall musculoskeletal fitness levels from baseline levels (18.2 vs 21.7, P < .001). There was no difference in the change in physical activity levels between the groups. Conclusions: A PAC program in the workplace may increase physical activity levels within 4 months. The addition of an fHRA does not appear to further increase physical activity levels; however, it may improve overall employee musculoskeletal fitness levels.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".