The Effect of Web-Based Culinary Medicine to Enhance Protein Intake on Muscle Quality in Older Adults: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The most common age-related musculoskeletal disorder is sarcopenia. Sarcopenia is the progressive and generalized loss of muscle mass, strength, and function. The causes of sarcopenia can include insufficient nutritional status, which may be due to protein-energy malnutrition, anorexia, limited food access and eating ability, or malabsorption. In the United States, 15.51% of older adults have been diagnosed with sarcopenia. Culinary medicine (CM) is a novel evidence-based medical field that combines the science of medicine with food and cooking to prevent and treat potential chronic diseases. CM helps individuals learn and practice culinary skills while tasting new recipes. Therefore, this program could successfully reduce barriers to protein intake, enabling older adults to enhance their diet and muscle quality. OBJECTIVE: This study aimed to examine how a web-based CM intervention, emphasizing convenient ways to increase lean red meat intake, could improve protein intake with the promotion of physical activity to see how this intervention could affect older adults' muscle strength and mass. METHODS: A 16-week, single-center, parallel-group, randomized controlled trial was conducted to compare a web-based CM intervention group (CMG) with a control group (CG) while monitoring each group's muscle strength, muscle mass, and physical activity for muscle quality. The CMG received weekly web-based cooking demonstrations and biweekly nutrition education videos about enhancing protein intake, whereas the CG just received the recipe handout. Anthropometrics, muscle mass, muscle strength, dietary habits, physical activity, and cooking effectiveness were established at baseline and measured after the intervention. The final number of participants for the data analysis was 24 in the CMG and 23 in the CG. RESULTS: No between-group difference in muscle mass (P=.88) and strength (dominant P=.92 and nondominant P=.72) change from the prestudy visit was detected. No statistically significant difference in protein intake was seen between the groups (P=.50). A nonsignificant time-by-intervention interaction was observed for daily protein intake (P=.08). However, a statistically significant time effect was observed (P≤.001). Post hoc testing showed that daily protein intake was significantly higher at weeks 1 to 16 versus week 0 (P<.05). At week 16, the intake was 16.9 (95% CI 5.77-27.97) g higher than that at the prestudy visit. CONCLUSIONS: This study did not affect protein intake and muscle quality. Insufficient consistent protein intake, low physical activity, intervention adherence, and questionnaire accuracy could explain the results. These studies could include an interdisciplinary staff, different recruitment strategies, and different muscle mass measurements. Future research is needed to determine if this intervention is sustainable in the long term and should incorporate a follow-up to determine program efficacy on several long-term behavioral and health outcomes, including if the participants can sustain their heightened protein intake and how their cooking skills have changed. TRIAL REGISTRATION: ClinicalTrials.gov NCT05593978; https://clinicaltrials.gov/ct2/show/NCT05593978.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».