An eHealth intervention (ManGuard) to reduce cardiovascular disease risk in male taxi drivers: protocol for a feasibility randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Men are at higher risk then women of developing cardiovascular disease (CVD), and male taxi drivers are a particularly high-risk group because of their typically unhealthy behaviours, such as poor eating habits, smoking and sedentary lifestyle. However, only two studies of behavioural interventions targeting taxi drivers have been identified, one of which reported a high attrition rate. Therefore, an eHealth intervention co-designed by taxi drivers may prove more acceptable and effective. The aim of this study is to assess the feasibility an eHealth intervention (ManGuard) to reduce CVD risk in male taxi drivers. METHODS: A randomised wait-list controlled trial will be conducted with a sample of 30 male taxi drivers to establish feasibility, including recruitment, engagement, and retention rates. Program usability and participant satisfaction will be assessed by a survey completed by all participants at 3 months after allocation. Additionally, an in-depth qualitative process evaluation to explore acceptability of the intervention will be conducted with a subset of participants by semi-structured telephone interviews. Preliminary efficacy of ManGuard for improving key CVD-related outcomes will be assessed, including biomarkers (total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and total/HDL cholesterol ratio), blood pressure, anthropometry (body mass index, body fat percentage, and waist circumference), physical activity (accelerometery, and self-report) and psychosocial status (health-related quality of life, self-efficacy, and social support). Outcomes will be assessed at baseline, 7 weeks, and 3 months after group allocation. The wait-list control group will be offered access to the intervention at the completion of data collection. DISCUSSION: eHealth interventions show potential for promoting behaviour change and reducing CVD risk in men, yet there remains a paucity of robust evidence pertaining to male taxi drivers, classified as a high-risk group. This study uses a randomised controlled trial to assess the feasibility of ManGuard for reducing CVD risk in male taxi drivers. It is envisaged that this study will inform a fully powered trial that will determine the effectiveness of eHealth interventions for this high risk and underserved population. TRIAL REGISTRATION: This trial has been registered prospectively on the ISRCTN registry on 5 January 2022, registration number ISRCTN29693943.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".