Nutritional status, physical activity level, physical fitness level and cognitive status among older adults in Kuala Selangor / Fatin Nabilah Samsudin … [et al.]
Notice bibliographique
Résumé
Ageing population has increased significantly as observed in developing countries. This increase has highlighted the importance of health-related quality of life through healthy ageing. Older adults who do not practice healthy lifestyle are at higher risk to develop deterioration in physical and mental capacity. In Malaysia, there are limited studies that address relationship between nutritional status, physical activity and its effect on physical fitness function and cognitive function among older adults. This study is in congruent with The Academy of Nutrition and Dietetics of United States (2012) which stress the role of nutritious diet and physical active lifestyle as the key influential factors in reducing the physical and mental deterioration associated with ageing. Thus, this research can provide better understanding on the current diet and physical activity practices among Kuala Selangor older adult and its outcome to their fitness and cognitive function. The purpose of this was to assess the nutritional status, physical activity level, physical fitness function and cognitive function among older adults in Kuala Selangor. Besides that, this study was carried out to determine the correlation between nutritional status, physical activity and cognitive function. Participants for this study were 40 males and 117 female community-dwelling older adults aged from 60 to 85years old from several villages in Jeram district, Kuala Selangor. Their nutritional status was assessed by using Diet History Questionnaire, Mini Nutritional Assessment and anthropometry data. The physical activity was assessed by using International. Physical Activity Questionnaire and for physical fitness; it was assessed by using hand grip test and Senior Fitness Test. For cognitive function, it was assessed by using Montreal Cognitive Assessment. The mean age for males was 67.0 ±9.00, and for females was 68.0 ±9.00, respectively, while BMI (kg/m2) for male and female respectively 23.9 (5.30), 24.3 (6.00), waist circumference (cm) for male and female were = 89.4 (15.10), 90.7 (14.45), respectively. Fat percentage (%) for males and females were 29.5 (6.50), 33.3 (9.40)) respectively. There was a correlation between energy, protein with right hand grip strength among female older adults. There were significant differences seen in physical activity and age in female (p =0.007).
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».