Barriers and enablers to the use of activity trackers in cardiac rehabilitation programs: a multi-national study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».