Assessing the use of activity trackers in clinical practice: a survey of cardiac rehabilitation clinicians from Australia, Brazil, and Canada
Notice bibliographique
Résumé
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.
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».