Financial Incentives for Physical Activity and Heart Health (FIPAHH): exploring the usability and feasibility of an eight-week financial incentive and physical activity mHealth intervention
Bibliographic record
Abstract
Background: Hypertension is the leading modifiable risk factor for cardiovascular disease and mortality. Physical activity (PA) is critical for hypertension prevention, however scalable PA solutions are warranted. Previous studies have shown the potential of mHealth lifestyle interventions to be an effective and scalable strategy to improve PA outcomes, however, engagement and PA adherence remain a challenge. Financial incentives have the potential to overcome these challenges, by providing immediate behaviour reinforcement, but the effectiveness of a pay-per-minute (PPM) versus a modified social impact bond (SIB) financial incentive framework is unknown. Objective: The objective of Study 1 was to co-create and determine the usability of Healthy Hearts, an eight-week mHealth financial incentive hypertension education program. The objectives of Study 2 were to determine the feasibility (recruitment, engagement, and acceptability) and evaluate the preliminary efficacy of eight-week financial incentive PA interventions of PPM and SIB relative to control. Methods: In Studies 1 and 2, adults aged 40-65 who were not meeting the Canadian PA Guidelines were recruited online. Study 1: The IDEAS framework was used to guide the development of Healthy Hearts. The development process consisted of intervention planning, development, and usability testing. For usability testing, participants completed online questionnaires and I conducted semi-structured interviews to assess Healthy Hearts and gather feedback to further enhance the user experience. Descriptive analyses were used to evaluate the online questionnaire data and thematic analysis was conducted for semi-structured interviews analysis. Study 2: An eight-week feasibility study was conducted to explore the feasibility of a financial incentive PA intervention using the Healthy Hearts program. Study recruitment, retention and acceptability were evaluated following the intervention. Changes in PA outcomes (MVPA, daily steps), BP, and PA motivation were evaluated between PPM, SIB, and control groups using linear regressions. Results: Study 1: Six participants were recruited to gather feedback to enhance the content, layout, and design of the Healthy Hearts program to prepare the program to be employed in Study 2. Study 2: 55 participants were recruited and randomized to the PPM (n=19), SIB (n=18), or control (n=18) groups. Recruitment, engagement, and acceptability were successful, with a recruitment rate of 77%, a 65% engagement rate, and overall positive feedback on the acceptability of the program. Relative to control at four weeks, the PPM and SIB arms increased their MVPA with medium effect (η2p= 0.06 and η2p=0.08, respectively). At eight weeks, relative to the control arm, the SIB arm increased their MVPA with medium effect (η2p=0.07) and no effect was noted between the PPM and control arm. There were small effects in PA outcomes, BP, and PA motivation. Conclusion: Study 1: The necessity for co-creating physical activity interventions was emphasized in the process of creating Healthy Hearts. Through the creation and usability testing of this program, valuable feedback was collected and integrated from the participants, thus preparing the program for Study 2. Study 2: Per the high recruitment, engagement, and acceptability results, after minor changes are made, this study recommends a full-scale randomized control trial after appropriate power calculations.
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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 | 0.024 | 0.034 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".