Barriers and enablers to the use of activity trackers in cardiac rehabilitation programs: a multi-national study
Bibliographic record
Abstract
Abstract Background Wearable activity trackers can improve cardiorespiratory fitness by promoting physical activity in patients with heart disease participating in cardiac rehabilitation (CR). However, these trackers are not consistently or widely implemented in CR programs where physical activity promotion is crucial. This study aimed to identify barriers and enablers associated with the use of activity trackers in CR programs and provide considerations for implementation in clinical practice. Methods This multi-national cross-sectional study was conducted in Australia, Brazil, Canada, Norway and the United Kingdom from April 2023 to December 2024. Multidisciplinary clinicians working in CR completed a purpose-built online survey using the Research Electronic Data Capture (REDCap) platform. The survey included questions about (1) sociodemographic details, (2) personal and professional use of activity trackers, (3) perspectives on the use of activity trackers for CR, and (4) perceptions of factors affecting the use of activity trackers in CR. Descriptive statistics were used to analyse the data. Results A total of 308 clinicians participated in the study. Most were women (77%) with a median age of 40 years (range 22-71). The most common profession was physiotherapy (36%) and median length of time working in CR was 6 years (range 1-48). Of the participants, 67% personally used activity trackers and 69% recommended their use in their clinical practice. Clinicians perceived activity trackers as useful for engaging patients in managing their own health (95%), boosting patient adherence to prescribed exercise (86%) and helping patients understand their health condition (74%). Common barriers for implementing activity trackers in CR included limited or no funding (77%), lack of support from leadership (70%), and absence of relevant policies (54%). A minority (22%) were concerned about data security, privacy and confidentiality issues. Key enablers for clinicians included confidence to integrate activity trackers in directed exercise (73%), having the time to familiarize themselves with activity trackers (67%), being motivated to use activity trackers (57%), and having proper training about the use of activity trackers in clinical practice (36%). Considerations for implementation of activity trackers in clinical practice are summarised in Figure 1. Conclusion Barriers and enablers identified in this study were both related to system and clinician level factors. Strategies to increase the use of activity trackers in clinical practice that address these factors must be tested using robust methods. The development of specific advice for clinical practice using implementation science approaches is needed to ensure effective integration of activity trackers into clinical practice.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".