Examining the Impact of a Mobile Health App on Functional Movement and Physical Fitness: Pilot Pragmatic Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Numerous mobile apps available for download are geared toward health and fitness; however, limited research has evaluated the real-world effectiveness of such apps. The movr app is a mobile health app designed to enhance physical functioning by prescribing functional movement training based on individualized movement assessments. The influence of the movr app on functional movement and physical fitness (flexibility, strength, and cardiovascular fitness) has not yet been established empirically. OBJECTIVE: This study aims to examine the real-world impact of the movr app on functional movement, flexibility, strength, and cardiovascular fitness. METHODS: A total of 48 healthy adults (24 women and 24 men; mean age 24, SD 5 years) completed an 8-week pilot pragmatic randomized controlled trial in which they were randomly assigned to either 8-week use of the movr app (n=24) or 8-week waitlist control (n=24). Measures of functional movement (Functional Movement Screen [FMS]), strength (push-ups, handgrip strength, and countermovement jump), flexibility (shoulder flexibility, sit and reach, active straight leg raise [ASLR], and half-kneeling dorsiflexion), and cardiovascular fitness (maximal oxygen uptake []) were collected at baseline and the 8-week follow-up. RESULTS: Repeated measures analyses of variance revealed significant group-by-time interactions for the 100-point FMS (P<.001), shoulder flexibility (P=.01), ASLR (P=.001), half-kneeling dorsiflexion (P<.001), and push-up tests (P=.03). Pairwise comparisons showed that FMS scores increased from pre- to postintervention for those in the movr group (P<.001) and significantly decreased for those in the control group (P=.04). For shoulder flexibility, ASLR, half-kneeling dorsiflexion, and push-up tests, improvements from pre- to postintervention were found in the movr group (all values of P<.05) but not in the control group (all values of P>.05). There were no changes in the sit and reach or handgrip strength test scores for either group (all values of P>.05). A significant main effect of time was found for the countermovement jump (P=.02), such that scores decreased from pre- to postintervention in the control group (P=.02) but not in the movr group (P=.38). Finally, a significant group-by-time interaction was found for (P=.001), revealing that scores decreased pre- to postintervention in the control group (P<.001), but not in the movr group (P=.54). CONCLUSIONS: The findings revealed that movr improved indices of functional movement (FMS), flexibility (shoulder, ASLR, and dorsiflexion), and muscular endurance (push-ups) over an 8-week period compared with the control group while maintaining handgrip strength, lower body power (countermovement jump), and cardiovascular fitness (). Thus, this study provides initial evidence of the effectiveness of the movr app for enhancing functional movement and physical fitness among healthy adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT04865666; https://clinicaltrials.gov/ct2/show/NCT04865666.
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 | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».