An Evaluation of a Commercialized mHealth Intervention to Promote Physical Activity in the Workplace
Bibliographic record
Abstract
Background UPnGO with ParticipACTION (UPnGO) was a commercialized 12-month workplace physical activity intervention, aimed at encouraging employees to sit less and move more at work. Its design took advantage of the ubiquitous nature of mobile fitness trackers and aimed to be implemented in any office-based workplace in Canada. The program was available at cost from June 2017 to April 2020. The objectives of this study are to evaluate the program and identify key lessons from the commercialization of UPnGO. Methods Using a quasi-experimental design over 3 time points: baseline, 6 months, 12 months, five evaluation indicators were measured as guided by the RE-AIM framework. Reach was defined as the number and percentage of employees who registered for UPnGO and the number and percentage of sedentary participants registered. Effectiveness was assessed through average daily step count. Adoption was determined by workplace champion and senior leadership responses to the off-platform survey. Implementation was assessed as the percentage of participants who engaged with specific program elements at the 3-evaluation time points. Maintenance was assessed by the number of companies who renewed their contracts for UPnGO. Results Reach across 17 organizations, 1980 employees participated in UPnGO, with 27% of participants identified as sedentary at baseline. Effectiveness Daily step count declined from 7,116 ± 3,558 steps at baseline to 6,969 ± 6,702 (p = <0.001) at 12 months. Adoption Workplace champion and senior leadership engagement declined from 189 to 21 and 106 to 5 from baseline to 12 months, respectively. Maintenance Two companies renewed their contracts beyond the first year. Conclusions The commercialization of UPnGO was an ambitious initiative that met with limited success; however, some key lessons can be generated from the attempt. The workplace remains an important environment for PA interventions but effective mHealth PA programs may be difficult to implement and sustain long-term.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".