Can fitness apps work long term? A 24-month quasiexperiment of 516 818 Canadian fitness app users
Bibliographic record
Abstract
OBJECTIVE: To examine whether a multicomponent commercial fitness app with very small ('micro') financial incentives (FI) increased population-level device-assessed physical activity (PA) over 2 years. The secondary objective was to explore the influence of select covariates on longitudinal effects. METHODS: This 24 month pre-post quasiexperiment was conducted in Ontario, Canada's largest province (December 2016-June 2019). Following a 1-to-2 week baseline period, users earned micro-FIs ($0.04 CAD/day) for achieving daily step goals. Multiple linear regression models estimated changes in weekly mean daily step count from baseline to key timepoints (eg, 24 months). To address the secondary objective, separate models were developed for each level of the selected covariates (eg, start season, baseline PA). RESULTS: The sample included 516 818 users (% female: 62.83; age (SD): 33.46 (12.65) years). Half were 'low' active at baseline (<5000 daily steps; 47.15%). Overall, daily step counts were greater than baseline at all key timepoints (eg, 242 steps/day at 24 months; p<0.001). Users from earlier start seasons and longer FI exposure exhibited larger differences from baseline (eg, 758 steps/day at 24 months; p<0.001). Differences were also more pronounced among 'low' active users (eg, 1986 steps/day at 24 months; p<0.001). Substantial daily step count reductions were observed among 'very high' active users (≥10 000 daily steps; eg, -3969 steps/day at 24 months; p<0.001). CONCLUSION: Modest PA increases of about 250 steps per day were sustained over 2 years. For important subgroups (ie, earlier start seasons, 'low' active) increases approached or surpassed 1000 steps/day-a level indicative of clinical significance. Substantial daily step count reductions among higher active users were also observed.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".