Improving Maximal Strength in the Initial Postoperative Phase After Anterior Cruciate Ligament Reconstruction Surgery: Randomized Controlled Trial of an App-Based Serious Gaming Approach
Notice bibliographique
Résumé
BACKGROUND: Anterior cruciate ligament reconstruction surgery is one of the most common orthopedic procedures. One of the main factors that influence the outcome is regaining strength in the postoperative phase. Because anterior cruciate ligament reconstruction surgeries are often performed in young patients, we combined the concept of prehabilitation with an app-based serious gaming approach to improve maximal strength postoperatively. OBJECTIVE: Our objective was to conduct a prospective randomized trial to evaluate whether an app-based active muscle training program (GenuSport Knee Trainer) can improve postoperative strength by starting rehabilitation immediately after primary anterior cruciate ligament reconstruction surgery. METHODS: We designed a pilot study in which we randomly assigned patients receiving primary anterior cruciate ligament reconstruction to either the serious gaming training (intervention) group or a conventional rehabilitation (control) group. Except for the serious gaming-based training, both groups followed the same postoperative treatment protocol. Outcome parameters were absolute and relative change in maximal strength, as well as the International Knee Documentation Committee Subjective Knee evaluation form, Knee Injury and Osteoarthritis Outcome Score, and Lysholm Knee Score. RESULTS: In total 26 patients agreed to participate (14 patients in the intervention group and 12 patients in the control group, 1 of whom was lost to follow-up). We noted a difference in absolute maximum strength between the exergaming intervention and the control groups. Mean maximum strength preoperatively was 155.1 (SD 79.2) N in the intervention group (n=14) and 157.0 (SD 40.8) N in the control group (n=11). Postoperative mean maximum strength was 212.8 (SD 78.5) N in the intervention group and 154.5 (SD 27.1) N in the control group. Mean absolute change in maximum strength was 57.7 (SD 95.2) N in the intervention group and -4.8 (22.2) N in the control group. The analysis of covariance model with absolute change as the dependent variable and treatment group and baseline maximum strength as covariates showed a relevant difference in relative change between treatment groups (intervention - control) of 59.7 N (95% CI 10.1-109.3; P=.02). Similarly to the absolute increase, the relative change in maximum strength was relevantly higher in the exergaming group. The mean relative change in maximum strength was 1.7 (SD 1.17) in the intervention group and 1 (SD 0.13) in the control group. No adverse events or problems were reported during the study period. CONCLUSIONS: Implementation of an app-based active muscle training program in the early postoperative therapy scheme was associated with an improvement in maximal strength. Therefore, we considered the use of GenuSport training after anterior cruciate ligament reconstruction to be a helpful complement to rehabilitation after anterior cruciate ligament reconstruction surgery to improve strength in the early postoperative phase. To our knowledge this was the first study to analyze immediate postoperative serious gaming-based training with the GenuSport device based on strength improvement.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».