Older adults, clinicians, and researchers’ preferences for measuring adherence to resistance and balance exercises
Notice bibliographique
Résumé
BACKGROUND: Resistance and balance training are important exercise interventions for older populations living with chronic diseases. Accurately measuring if an individual is adhering to exercises as prescribed is important to determine if lack of improvement in health outcomes is because of issues with adherence. Measuring adherence to resistance and balance exercises is limited by current methods that depend heavily on self-report and are often better at and tailored towards capturing aerobic training parameters (e.g., step count, minutes of moderate to vigorous physical activity). Adherence measures must meet users' needs to be useful. METHODS: Using a Dillman tailored study design, we surveyed researchers who conduct exercise trials, clinicians who prescribe exercise for older adults, and older adults to determine: (1) how they are currently measuring adherence; (2) barriers and facilitators they have experienced to measurement; and (3) the information they would like collected about adherence (e.g., repetitions, sets, intensity, duration, frequency, quality). Surveys were disseminated internationally through professional networks, professional organizations, and social media. Participants completed an online survey between August 2021 and April 2022. RESULTS: Eighty-eight older adults, 149 clinicians, and 41 researchers responded to the surveys. Most clinicians and researchers were between the ages of 30 and 39 years, and 70.0% were female. Most older adults were aged 70-79 years, and 46.6% were female. Diaries and calendars (either analog or digital) were the most common current methods of collecting adherence data. Users would like information about the intensity and quality of exercises completed that are presented in clear, easy to use formats that are meaningful for older adults where all data can be tracked in one place. Most older adults did not measure adherence because they did not want to, while clinicians most frequently reported not having measurement tools for adherence. Time, resources, motivation, and health were also identified as barriers to recording adherence. CONCLUSIONS: Our work provides information about current methods of measuring exercise adherence and suggestions to inform the design of future adherence measures. Future measures should comprehensively track adherence data in one place, including the intensity and quality of exercises.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».