Feasibility, Usability, and Safety of ParaGym, an Intelligent Mobile Exercise App for Individuals With Paraplegia: Protocol for a Pilot Block-Randomized Controlled Trial
Notice bibliographique
Résumé
Background Exercise is crucial for individuals with paraplegia to reduce the risk of secondary diseases and improve independence and quality of life. However, numerous barriers such as inadequate accessibility restrict their participation in exercise programs. Digital exercise apps can help overcome these barriers. Personalization is considered a crucial feature of mobile exercise apps, as people with paraplegia have individual requirements regarding exercise programs depending on their level of impairment. Despite the increasing popularity of mobile exercise apps, there are none available that target the individual needs of this cohort. The ParaGym mobile exercise app prototype was designed to automatically tailor exercise sessions to the individual needs of users with paraplegia. Objective This study aims to evaluate the feasibility, usability, safety, and preliminary effectiveness of the ParaGym mobile exercise app prototype. Methods This pilot block-randomized controlled feasibility trial will include 45 adult participants with paraplegia. Eligible participants will be block randomized to either the intervention or waitlist control group. The intervention group will perform a 6-week exercise program using the ParaGym mobile exercise app, comprising three 35-minute exercise sessions per week. The waitlist control group will continue their usual care and receive access to the app after study completion. Participants will record all exercise sessions conducted with the app as well as additional exercise sessions conducted during the study period using exercise diaries. The primary outcomes include feasibility, usability, and safety. Feasibility will be assessed through semistructured interviews, study adherence, and retention rates. Usability will be measured using the System Usability Scale. Safety will be determined by the occurrence of adverse events. Secondary outcomes include the effects of the intervention on peak exercise capacity (VO2 peak); handgrip strength; independence, which will be measured using the Spinal Cord Independence Measure III (SCIM III); and health-related quality of life, which will be measured using the Short Form–36 Health Survey (SF-36). Results Recruitment commenced in November 2022. Overall, 12 participants were enrolled at the time of submission. Data collection commenced in January 2023, with completion expected in April 2023. Conclusions To the best of our knowledge, this is the first study to assess the feasibility, usability, and safety of an intelligent mobile exercise app for individuals with paraplegia. Thereafter, the app should be adapted according to the findings of this trial. Future trials with an updated version of the app should aim for a larger sample size, longer intervention duration, and more diverse target group. In the long term, a fully marketable version of the ParaGym app should be implemented. This would increase the access to personalized, independent, and evidence-based exercise training for this cohort and, in the future, other people who use wheelchairs. Trial Registration German Clinical Trials Register DRKS00030370; https://drks.de/search/de/trial/DRKS00030370 International Registered Report Identifier (IRRID) DERR1-10.2196/45652
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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 | 0,036 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,060 | 0,010 |
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 ».