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Enregistrement W3045630680 · doi:10.2196/19296

Telerehabilitation to Address the Rehabilitation Gap in Anterior Cruciate Ligament Care: Survey of Patients

2020· article· en· W3045630680 sur OpenAlexvenueno aff
Emma Dunphy, Elizabeth C. Gardner

Notice bibliographique

RevueJMIR Formative Research · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueKnee injuries and reconstruction techniques
Établissements canadiensnon disponible
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institute for Health and Care Research
Mots-clésTelerehabilitationRehabilitationAnterior cruciate ligamentAnterior cruciate ligament reconstructionMedicinePopulationChecklistPhysical therapyPhysical medicine and rehabilitationHealth carePsychologyTelemedicineSurgery

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Evidence shows that after anterior cruciate ligament (ACL) reconstruction, patients may have varied access to physical therapy. In particular, physical therapy input may end many months before patients reach full recovery. Telerehabilitation may provide an opportunity to address this rehabilitation gap and improve access to evidence-based rehabilitation alongside physical therapy at all stages of care. OBJECTIVE: This study aims to understand the opinions of patients who have undergone ACL surgery and rehabilitation on the use of telerehabilitation as part of ACL care and define the population and explore their experiences and views on the acceptability of telerehabilitation after ACL reconstruction. METHODS: This study was a cross-sectional, voluntary, web-based survey combining both closed and open questions. Ethical approval was obtained from the Yale School of Medicine Institutional Review Board. Participants were aged 16 years or older at the time of recruitment and had undergone ACL reconstruction within the past 5 years. A 26-item survey was developed using the Qualtrics survey platform. No items were mandatory. Responses were multiple choice, binary, and qualitative. The CHERRIES (Checklist for Reporting Results of Internet E-Surveys) was used to ensure the quality of reporting of surveys in the medical literature. Data were analyzed using Stata version 15. Qualitative data were analyzed using NVivo 11. The theoretical framework for this analysis is based on the Capability, Opportunity, and Motivation-Behavior model of behavior change. RESULTS: A total of 100 participants opened the survey. All completers were unique. The participation and completion rates were each 96% (96/100). Patients reported their physical therapy care ended at an average of 6.4 months and that they felt fully recovered at an average of 13.2 months. Only 26% (25/96) of patients felt fully recovered at the end of physical therapy. Of these 96 patients, 54 (60%) were younger than 30 years, 71 (74%) were recreational athletes, 24 (24%) were competitive athletes, 72 (75%) had private insurance, 74 (77%) were not familiar at all with telerehabilitation, and 89% (85/96) felt capable. They preferred to use telerehabilitation at different stages of care. Reported benefits included resource saving, improved access to care, improved learning, and greater engagement. Concerns included incorrect performance of exercises or unmanaged pain being missed and less access to manual therapy, motivation, and opportunities to ask questions. Participants' priorities for a future telerehabilitation intervention included its use as an adjunct to physical therapy rather than a replacement, with content available for each stage of care, especially return to sports. Participants stressed that the intervention should be personalized to them and include measures of progress. CONCLUSIONS: These findings helped understand and define the ACL reconstruction population. Participants found telerehabilitation acceptable in principle and highlighted the key user requirements and scope of future interventions.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,545
Score d'incertitude au seuil0,228

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,057
Tête enseignante GPT0,417
Écart entre enseignants0,360 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2020
Routes d'admission1
Résumé présentoui

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