Combining a Hudl App With Telehealth to Increase Home Exercise Program Adherence in People With Chronic Diseases Experiencing Financial Distress: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Patients with chronic diseases often need to adhere to long-term individualized home exercise programs (HEPs). Limited adherence to long-term exercise given during physical therapy (PT) visits reduces the capacity of exercise to manage or improve symptoms related to chronic disease. In addition, a lower socioeconomic status negatively impacts exercise adherence. To mitigate this, apps that motivate people to exercise could be a viable option. Using an app through telehealth may help adults with chronic diseases to achieve long-term HEP adherence. However, because apps for rehabilitation are an emerging field, the feasibility of the app needs to be evaluated. OBJECTIVE: To address HEP adherence in participants with chronic diseases who are experiencing financial distress, we aim to evaluate the feasibility of and satisfaction with the Hudl Technique app and telehealth and satisfaction with PT care and to monitor HEP adherence and compliance (ie, percentage of participant-recorded videos sent) in participants using the app with telehealth compared with those using standard HEPs on paper. METHODS: We recruited patients scheduled for outpatient PT. We performed a randomized controlled trial in which the experimental group received weekly HEP demonstrations through app videos on a tablet with feedback on their self-recorded HEP video performance from the telehealth physical therapist. The control group received HEPs on paper without feedback, as is customary in PT practice. Demographic, clinical, and health coverage information was collected for screening and baseline measurements. Adherence and compliance were evaluated. Both groups completed surveys at 8 and 24 weeks on their satisfaction with PT care, and the experimental group also completed a survey on their satisfaction with the app with telehealth use. Descriptive and nonparametric statistics were used for within-group and between-group comparisons and analyzed with JMP, version 13. RESULTS: Overall, 45 adults with chronic diseases who were experiencing financial distress were randomized into experimental (23/45, 51%) and control (22/45, 49%) groups, with 74% (17/23) and 86% (19/22) participants completing the 24-week HEP, respectively. The experimental group had an HEP adherence frequency of 4 (SD 2) to 5 (SD 2) times per week at 8 and 24 weeks (P=.14), whereas HEP adherence decreased in the control group from 4 (SD 2) to 3 (SD 2) times per week (P=.07), with a significant difference (P=.01) between groups at 24 weeks. Of the total participants, 68% (15/22) sent videos. They sent 68% (16/24) of the requested number of videos on average. The average score for PT care satisfaction was maintained at 87% in the experimental group (P=.99), whereas it decreased from 89% at 8 weeks to 74% at 24 weeks (P=.008) in the control group. App-related adverse events were not observed. CONCLUSIONS: The Hudl app/telehealth platform is feasible for delivering HEPs and maintaining HEP adherence in participants with chronic diseases who are experiencing financial distress. TRIAL REGISTRATION: ClinicalTrials.gov NCT02659280; https://clinicaltrials.gov/ct2/show/NCT02659280.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
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 ».