Is it worth it? Cost-effectiveness analysis of a commercial physical activity app
Bibliographic record
Abstract
BACKGROUND: Government interest in investing in commercial physical activity apps has increased with little evidence of their cost-effectiveness. This is the first study to our knowledge to examine the cost-effectiveness of a commercial physical activity app (Carrot Rewards) despite there being over 100,000 in the major app stores. METHODS: A cost-effectiveness analysis was performed to calculate the incremental cost-effectiveness ratio (ICER) of the app compared to a no-intervention reference scenario using a five-year time horizon. Primary data was collected between 2016 and 2017. Data synthesis, model creation, and statistical analyses were conducted between 2019 and 2020. An age-, sex-, and geography-dependent Markov model was developed assuming a public healthcare payer perspective. A closed cohort (n = 38,452) representing the population reached by Carrot Rewards in two Canadian provinces (British Columbia, Newfoundland & Labrador) at the time of a 12-month prospective study was used. Costs and effects were both discounted at 1.5% and expressed in 2015 Canadian dollars. Subgroup analyses were conducted to compare ICERs between provinces, sexes, age groups, and engagement levels. RESULTS: Carrot Rewards had an ICER of $11,113 CAD per quality adjusted life year (QALY), well below a $50,000 CAD per QALY willingness-to-pay (WTP) threshold. Subgroup analyses revealed that the app had lower ICERs for British Columbians, females, highly engaged users, and adults aged 35-64 yrs., and was dominant for older adults (65 + yrs). Deterministic sensitivity analyses revealed that the ICER was most influenced by the relative risk of diabetes. Probabilistic sensitivity analyses revealed varying parameter estimates predominantly resulted in ICERs below the WTP threshold. CONCLUSIONS: The Carrot Rewards app was cost-effective, and dominant for older adults. These results provide, for the first time, rigorous health economic evidence for a commercial physical activity app as part of public health programming.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".