Acceptability of a computer-tailored and pedometer-based socio-cognitive intervention in a secondary coronary heart disease prevention program: A qualitative study
Bibliographic record
Abstract
When developing an innovative intervention, its acceptability to patients, health care professionals and managers must be considered to ensure the implementation into practice. This study aims to identify factors influencing the acceptability of a computer-tailored and pedometer-based socio-cognitive intervention for patients with heart disease. Focus group interviews were conducted in two outlying regions of the province of Quebec (Canada). The Theory of Planned Behavior formed the theoretical basis of the interview guide. Two researchers performed verbatim analysis independently until consensus was achieved. The sample included 44 participants divided into six groups (patients n = 7 + 8, health care professionals n = 8 + 8, managers n = 6 + 7). Health care professionals and managers mentioned benefits concerning partners’ opportunity to improve assessment and monitoring. Patients believed the intervention could be useful to improve adherence to physical activity. Additional benefits indicated were self-monitoring behavior and improved health-related outcomes. However, patients expressed concern about the online security, fearing possible data breach. Some clinicians felt the pedometer may not be able to evaluate physical activities other than walking. With regard to behavioral control, a web application and pedometer must be easy to use and compatible with services already in place. Further barriers include level of literacy, cost and the various difficulties associated with wearing a pedometer. Findings suggest that, to improve the acceptability of a computer-tailored and pedometer-based socio-cognitive intervention, users must be assured of a secure website, validated, affordable and easy-to-use pedometers, and an intervention adapted to their level of literacy.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".