Assessing the use of activity trackers in clinical practice: a survey of cardiac rehabilitation clinicians from Australia, Brazil, and Canada
Bibliographic record
Abstract
Abstract Background The use of wearable activity trackers has been found to significantly improve health profile and cardiorespiratory fitness, as well as to reinforce positive health behaviours in patients participating in cardiac rehabilitation (CR) programs. However, clinicians’ perceptions of activity trackers and their use in clinical practice have not been widely explored. Purpose To describe perceptions, attitudes, and behaviours of CR clinicians towards the use and usefulness of activity trackers in CR programs, and identify barriers and enablers associated with their personal and clinical use. Methods Descriptive cross-sectional survey. Data were collected using Research Electronic Data Capture (REDCap) from April to December 2023. Clinicians working in CR programs were recruited in each country via social media, email and digital flyers, group chats and author networks. A purpose-built 44-item digital survey comprising four sections was constructed: (1) socio-demographic 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. Results In total, 199 clinicians from Australia (n=44), Brazil (n=102) and Canada (n=53) responded to the survey. Most were women (74%), physiotherapists (37%), working at a metropolitan hospital (55%), with a median age of 35 years (range 22-71). The majority found activity trackers helpful for patients with goal setting and monitoring exercise (89%) and promoting patient engagement and autonomy beyond structured, supervised CR (75%). Activity trackers were also perceived to be useful in engaging patients in their own health (94%), improving patient-provider communication (73%), boosting patient adherence with directed exercise (87%), and improving patient’s understanding of their own health conditions (79%). Furthermore, activity trackers were perceived to enable a more personalised care (69%), increase accessibility to CR (45%) and be time- and cost-effective for CR programs (49%). Sixty percent were motivated to use activity trackers and 69% recommended the use of trackers to their patients. On the other hand, the use of activity trackers was reported to be related to dependence (44%) and excessive obsession of one’s own health (55%); 50% reported a lack of relevant policies on activity trackers for clinical use in their respective institutions and limited funding for purchasing activity trackers by health services (78%). Only 30% reported that there was support from leadership and/or peers for the use of activity trackers. Conclusion In general, clinicians held positive attitudes towards the use of activity trackers in CR. However, a lack of relevant policies, funding and support from leadership are important barriers to the adoption and use of activity trackers in CR programs. Development of guidelines for the use of activity trackers in clinical practice is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".