Examining The Potential Efficacy Of An Incentive-based Mhealth Application: A Proof-of-concept Study In Leeds, Uk
Bibliographic record
Abstract
PURPOSE: The cost of delivering financial incentive (FI) based mobile health applications (mHealth apps) can be prohibitive. Aligning with the ORBIT Model for Developing Behavioral Treatments, the aim of this Phase IIa preliminary, proof-of-concept study was to test the potential efficacy of a mHealth app offering very small FIs on physical activity (PA) in a small, select sample of adults living in Leeds, UK. METHODS: A five-week quasi-experimental, within-subjects study was conducted between September 12 and December 13, 2022. Adults providing informed consent and access to smartphone-assessed daily step count data were included. Participants were rewarded for achieving realistic, personalized daily step goals and completing four short educational health quizzes per week (worth about $0.10 USD per day step goal reached and $0.25 per quiz completed, respectively). The first personalized goal was set using the median from a 5-day baseline period; thereafter, goals were set using the median from the previous 14-days. FIs were provided in the form of “points” that could be redeemed for movies or gym passes. Paired-sample t-tests were used to examine change in weekly mean daily step count from baseline (vs. Study Week 5) for the total sample, as well as by engagement and physical activity subgroups (i.e., high engagement = completing >50% of quizzes; low PA = taking <5,000 daily steps at baseline). Descriptive daily step goal achievements are also reported. RESULTS: Fourteen participants were included in the study (age: 37.2 ± 9.0 yrs, 71% female, BMI: 27.8 ± 6.5 kg/m2, baseline daily step count: 5127 ± 2736). Weekly mean daily step count increased from baseline by 1670 ± 2221 steps per day (p = 0.024; Cohen’s d = 0.699). This mean increase was more pronounced among high engagers (2190 ± 2005 steps/day, p = 0.11, d = 0.986) and lower active participants (2536 ± 2394 steps/day, p = 0.048, d = 0.891). Daily step goals were achieved 38.8 ± 24.5% of the time over the five-week study period (13.6 ± 8.6 out of 35), with 42.8% of participants reaching their goal at least half the time. CONCLUSION: The statistically and clinically significant PA improvements observed here justify moving forward with more rigorous pilot testing using a randomized design with a larger and more representative sample (ORBIT Phase IIb).
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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 | 0.006 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".