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Enregistrement W1991915489 · doi:10.4103/2249-4863.148157

Dietary pattern amongst obese and nonobese children in national capital territory of Delhi: A case control study

2014· article· en· W1991915489 sur OpenAlexaboutno aff
Umesh Kapil, Ajeet Singh Bhadoria, Supreet Kaur

Notice bibliographique

RevueJournal of Family Medicine and Primary Care · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueObesity, Physical Activity, Diet
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineObesityEnvironmental healthCalorieDemographyPediatricsEndocrinology

Résumé

récupéré en direct d'OpenAlex

Sir, Obesity is mainly caused by a chronic imbalance between energy intake and actual energy need of the body. A change in dietary patterns and eating habits has been considered as one of the important predisposing factor. Limited data is available on dietary habits among obese and nonobese children. Hence, we conducted the study on assessment of dietary habits among obese and nonobese children in National Capital Territory (NCT) of Delhi. A total of 16,595 children in NCT of Delhi in the age group 6–18 years were surveyed to assess the prevalence of obesity. All the schools in the NCT of Delhi were enlisted and 30 schools were selected utilizing probability proportionate to size sampling methodology covering children from different socio-economic groups. The findings of this large survey have been published earlier.[1] A sub sample of obese children and their matched control were included for this study. International Obesity Task Force classification was utilized for the estimation of obese subjects.[2] Dietary pattern was compared between obese children (451 cases) and nonobese children (451 controls). The cases and control groups were matched for their age (±2 years), sex and socio-economic status. Dietary intake and dietary consumption pattern was assessed utilizing the 24-h dietary recall method and food frequency questionnaire method, respectively.[3] Recommended Dietary Allowances (RDA) suggested by Indian Council of Medical Research was utilized to assess the calorie intake of each child.[4] Distribution of obese cases and their matched controls according to different food patterns and energy intake is depicted in Table 1.Table 1: Distribution of obese cases and matched controls according to pattern of food consumptionHigher consumption of energy-dense fast foods, eating snacks in between meals and higher energy intake (percentage RDA intake) were found to be significantly associated with obesity (P < 0.05). However, higher consumption of green leafy vegetables and fruits were found to be protective against obesity (P < 0.05). About 80% of children in both groups (obese and nonobese) were bringing packed lunch to the school (P > 0.05). The results of this study were supported by a study conducted on children in the age group of 6-16 years residing in urban and semi-urban areas of Bangalore. It showed that increased consumption of fried and fast foods were associated with overweight amongst children. They also showed that the risk of overweight increased to 3.1 (95% confidence interval: 1.3-7.6) times among children who consumed fried foods > 6 times/week.[5] Similar results were seen in a study conducted on children aged 9-14 years, where high consumption of fried foods was associated with greater total energy intake, poor diet quality and excessive weight gain.[6] In another study, it was reported that higher consumption of fried foods from outside home was associated with greater total energy intakes and excessive weight gain.[7] In the same study conducted on about 15,000 school children in the age group of 9-14 years in Boston, USA showed that the body mass index (BMI) of the children who consumed fried foods 4-7 times in a week was statistically higher when compared with children who consumed fried foods less than once a week, that is, increase consumption of fried foods was associated with increasing BMI.[7] In another study, conducted among children in the age group of 2-18 years participating in a nationally representative survey, reported that children residing in Cebu consumed 40% of total calories from fast foods and energy-dense snacks.[8] A study conducted among 4,966 school children of Nova Scotia in Canada, on consumption of fried foods and fast foods showed that consumption of large portions of potato chips and French fries resulted in poor diet quality and increased energy intake and thus lead to obesity.[9] Similar findings were observed in the present study. In a study conducted by Amin et al. among children in the age of 10-14 years from Saudi Arabia revealed that frequent consumption of fast foods and carbonated beverages along with low serving of fruits and vegetables were predictors of obesity and overweight.[10] Likewise, Nicklas et al. from Bogalusa heart study reported that consumption of fruit and vegetables had significantly decreased amongst children during the study period of 1973-1994.[11] The worldwide childhood dietary patterns have been changed and it found associated with an increase in energy intake and a higher percentage of calories from energy-dense, nutrient poor fast foods.[121314] It is now well-established fact that dietary trends and type of food intake are major contributors of epidemic of childhood obesity.[1516] Feeding practices right from childhood and early years of adolescence play an important role in developing eating behavior, which has a direct relationship with childhood obesity. Exhaustive and comprehensive health promotion strategies at school level are required to introduce healthy eating habits amongst children.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,044

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,278
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2014
Routes d'admission1
Résumé présentoui

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Même revueJournal of Family Medicine and Primary CareMême sujetObesity, Physical Activity, DietTravaux en français237 207