Increasing Physical Activity via Provider Support and Engagement Using a Digital Health Platform in Adults With Multiple Sclerosis: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The benefits of physical activity are well established in people with multiple sclerosis (MS); yet, most people with MS are insufficiently active. Although many apps and devices are available to promote physical activity, these are not connected to electronic health records (EHRs), making it difficult for health care providers to prescribe and monitor their patients' physical activity. The ExerciseRx platform is an innovative cloud-based, Health Insurance Portability and Accountability Act-compliant software platform (app+provider dashboard) that was developed to bridge the gap between consumer activity tracking devices, such as personal smartphones, and the EHR. The ExerciseRx app tracks a patient's physical activity using their existing personal smartphone and provides a personalized graded progression in step count goals to increase step count gradually and safely over time. The ExerciseRx app also translates the activity data into actionable metrics on a provider dashboard within the EHR that the provider can use to make activity recommendations and monitor patients' progress; they can also support patients by providing semiautomated weekly feedback and encouragement in meeting physical activity goals. OBJECTIVE: This paper describes the protocol for a randomized controlled trial designed to understand whether the ExerciseRx digital health platform improves physical activity, symptoms, and functioning in adults with MS relative to a waitlist, usual care control. METHODS: Participants are ambulatory adults with MS (n=106) who engage in <150 minutes per week of moderate to vigorous intense physical activity. Enrolled participants are assigned to use the ExerciseRx app for 12 weeks, versus a waitlist, usual care control. Participants allocated to the intervention condition have access to the ExerciseRx app, and their providers have access to the participants' activity data via a provider dashboard, connected to the EHR. Participants allocated to usual care receive the care they would normally obtain at the MS Center, including encouragement to participate in and increase physical activity as tolerated from their clinical provider and a handout that describes the current physical activity recommendations for adults with MS and links to local and web-based resources suitable for MS. The primary outcome is the change in average daily step count throughout the 12-week intervention. Secondary outcomes include symptoms (fatigue intensity, pain intensity, sleep, and depressive symptoms), patient-reported functional outcomes (physical functioning, fatigue interference, pain interference, falls, and social participation), and qualitative analysis of participant and provider interviews on usability and acceptability of the ExerciseRx platform. RESULTS: The study was funded in October 2023. Participant enrollment began in March 2024 and will continue through December 2025. As of July 25, 2025, a total of 85 participants have been enrolled in the trial. Data analysis and dissemination preparations will begin in January 2026. CONCLUSIONS: Results of this trial will provide important new information on the efficacy of an innovative digital health intervention tool for physical activity promotion. TRIAL REGISTRATION: ClinicalTrials.gov NCT06270641; https://clinicaltrials.gov/study/NCT06270641. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72213.
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 | 0,039 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,011 | 0,007 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,012 |
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 ».