Employee Use of a Wireless Physical Activity Tracker Within Two Incentive Designs at One Company
Bibliographic record
Abstract
Physical activity provides numerous health benefits, including reducing risk factors that contribute to the leading causes of morbidity and mortality. Many employers offer incentives to employees to motivate engagement in wellness program activities. Two incentive designs to reward employees for achieving step goals were evaluated. This study used a retrospective design and the study population consisted of benefit-eligible employees at American Specialty Health ages 18 to 65 years who completed a health assessment and biometric screening during 2011 (N=396) or 2012 (N=500). A total of 320 employees participated in both years. During 2011, the incentive goal was 500,000 steps per quarter. By comparison, a 3-tier step goal plan was implemented in 2012 (ie, 400,000; 650,000; or 900,000 steps/quarter). The prevalence of participants in the step program was 64.7% in 2011 and 72.8% in 2012. The percentage of employees who reached at least 1 quarterly incentive increased from 36.3% in 2011 to 51.4% in 2012. Average steps/day was higher in 2012 (mean [M]=3573, standard deviation [SD]=3010) compared to the same employees in 2011 (M=2817, SD=2654) (P<.001). The findings suggest that a tiered incentive design may be an effective population approach to engage employees in physical activity. A multitier incentive design offers participants choices for goal setting and may help shape behavior toward what may be perceived as a difficult goal to achieve. (Population Health Management 2016;19:88-94).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".