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Record 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 on OpenAlexaboutno aff
Umesh Kapil, Ajeet Singh Bhadoria, Supreet Kaur

Bibliographic record

VenueJournal of Family Medicine and Primary Care · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObesityEnvironmental healthCalorieDemographyPediatricsEndocrinology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.278
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2014
Admission routes1
Has abstractyes

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