Do incentives increase action planning in a web-based walking intervention?
Bibliographic record
Abstract
Objective: Examining the use of incentives to promote action planning in university employees participating in the web-based RISE@Work walking intervention. Methods: Lower-active, full-time University of Toronto employees were invited to participate in RISE@Work. The 11-week program engages participants in evidence-based behavioural support strategies with the goal of increasing their daily step counts by 3000 steps over 5-Phases. Half of the recruited participants were randomly assigned to an incentive condition where they received a $5 Starbucks e-gift card each week for completing at least 1 new action plan during weeks 3-7. Intention to treat was conducted and repeated measures ANCOVAs were used to analyze mean step count differences between the two groups, during the 4-week incentive period, controlling for baseline step counts. Results: The final sample (n=55; age=41.07 years±10.62; BMI=25 kg/m2±SD4.99; 85.5%Female) experienced a significant increase of 1728±2875 daily steps over baseline steps (p=.04). During the 4-week incentive period, 57% participants in the incentive group and 8% in the control group completed action plans; only 6% of the incentive group continued planning at post-intervention. At the end of the incentive period (Phase 3) the incentive group accumulated more steps, representing a small effect size (d=.37). Although there were no significant between group differences at post-intervention, 73% of the incentive group versus 43% of the control group increased from low-active (5,000-7,500 steps/day) to somewhat-active (7,500-9,999 steps/day). Conclusions: Offering incentives resulted in increased planning, but only short-term increases in steps. Research is needed in identifying the best way to incorporate incentives into the design of web-based physical activity behaviour change interventions. Acknowledgments: GEF is supported by a Canadian Institutes of Health Research-Public Health Agency of Canada (CIHR-PHAC) Chair in Applied Public Health. MM is supported by the Canadian Institutes of Health Research (CIHR) [grant number 305843].
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".