Process Evaluation And Uptake Of The Incentive-based Caterpillar Mhealth App
Bibliographic record
Abstract
PURPOSE: The cost of delivering financial incentive (FI) based mHealth apps can be prohibitive. The main objective was to evaluate uptake of an mHealth app offering very small financial incentives during an exclusive 3-month launch period in Leeds, UK. METHODS: A 12-week single cohort process evaluation was conducted. Adults downloaded the app between September 12 and December 12, 2022 and provided informed consent. Users were rewarded for achieving personalized daily step goals and completing two short educational health quizzes per week (worth about $0.10 USD per day for step goal reached, and $0.25 per quiz completed, respectively). FIs were provided in the form of “points” that could be redeemed for movie passes and gym discounts. Aligning with dimensions of the RE-AIM framework (i.e., Reach and Implementation), information regarding uptake, sociodemographic and health characteristics, as well as engagement and acceptability (Mobile Application Rating Scale (MARS)) were collected. Data are presented descriptively. RESULTS: The app was promoted via email campaign (~25,000 sent by app partners to Leeds residents, e.g., Leeds gym members, Leeds City Council), paid social media advertisements (27 advertisements with 3484 impressions (number of app views on platform)) and posts via the app’s social media accounts (28 across three platforms i.e., 341 followers). A total of 204 app store “visits” (viewed store listing) were recorded, leading to 106 app downloads (52.2% iOS users). Forty-six provided consent (36.4 ± 13.5 years; 69.6% female; 18.9% classified as lower income). Most users indicated living with at least one chronic disease (70.7%). On average, users accumulated 5003 ± 3329 steps per day at baseline. Among those using the app for 30+ days (n = 22), 47.1% opened the app at least once per week (8.2 app opens per week). Participants completing the MARS (n = 28) provided a high overall app rating (3.7/5) with most likely to recommend the app to others (68.8%). CONCLUSION: Early results suggest more must be done to promote app downloads (e.g., optimizing email campaigns). The app was downloaded by younger females especially, with lower income adults well represented. Last, it appears that very small incentives contributed to high early engagement in a field where attrition is the norm. (Sponsored by MF Mottola FACSM)
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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 | 0.048 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".