Examining the use of loyalty point incentives to encourage health and fitness centre participation
Bibliographic record
Abstract
A unique financial incentive intervention was conducted in Canada, where YMCA members were offered loyalty points (Air Miles Reward Miles) to encourage visits to YMCA Health and Fitness Centres. The purpose of this evaluation study was to determine if YMCA members would participate in a loyalty point incentive program and if the weekly YMCA visit rates differed between Air Miles collectors and non-collectors. YMCA swipe data were collected from 2012 to 2016, including 12 months pre-program (baseline data), 36 months during the intervention period, and 3 months post-program. The final analyses, conducted in 2017, included 459,146 participants from 13 YMCA locations. Quasi-Poisson regression models were used to compare the weekly visit rates between Air Miles collectors and non-collectors. Of the 459,146 participants, 6.4% (n = 29,449) registered their Air Miles card with their YMCA membership (Air Miles collectors). Average weekly visit rates over the entire study period were significantly higher (1.37 to 3.84 times) among the Air Miles collector group than those in the non-collector group, but there was no evidence that incentives were associated with increased YMCA visits when adjusting for the pre-program period. This research demonstrated that incentives are a practical and acceptable public health strategy in Canada. More research is needed into how to harness the reach of loyalty point providers such as Air Miles, and how incentive-based programs should be optimally designed and delivered (e.g., type, timing, and magnitude of incentive).
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 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) | 0.001 | 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".