Dataset Information

Children's Body Mass Index Depending on Dietary Patterns, the Use of Technological Devices, the Internet and Sleep on BMI in Children.

ABSTRACT: Due to the increase in overweight as well as obesity in children, the researchers undertook the studies to determine the occurrence of these irregularities and identify the factors leading to them. The study aimed to assess the body mass index of the children subcategorized and compared depending on the dietary patterns, the use of technical devices, the Internet, and sleeping habits. The study group consisted of 376 children (189 girls and 187 boys) aged 6 to 15. The body composition estimates were obtained with the use of a foot-to-foot bioelectrical impedance analysis, the body height was measured by means of a stadiometer, and blood pressure was monitored. The research questionnaire was distributed among the surveyed and included the question concerning the children's lifestyle, eating and sleeping habits, the use of electronic devices, the Internet, and socio-demographic data. As indicated, the number of sleeping hours per day significantly negatively correlated with body mass index (BMI), whereas the frequency of using the smartphone had a positive correlation with BMI. The children who sleep less and spend more time using the smartphone had higher BMI values. Bearing in mind the conditions that may have an influence on the BMI of early-school age children, the emphasis must be placed on healthy lifestyle education among children and parents alike.

SUBMITTER: Bartosiewicz A

PROVIDER: S-EPMC7650693 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Children's Body Mass Index Depending on Dietary Patterns, the Use of Technological Devices, the Internet and Sleep on BMI in Children.

Bartosiewicz Anna A Łuszczki Edyta E Kuchciak Maciej M Bobula Gabriel G Oleksy Łukasz Ł Stolarczyk Artur A Dereń Katarzyna K

International journal of environmental research and public health 20201015 20

Due to the increase in overweight as well as obesity in children, the researchers undertook the studies to determine the occurrence of these irregularities and identify the factors leading to them. The study aimed to assess the body mass index of the children subcategorized and compared depending on the dietary patterns, the use of technical devices, the Internet, and sleeping habits. The study group consisted of 376 children (189 girls and 187 boys) aged 6 to 15. The body composition estimates ...[more]

PMID: 33076248

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Project description:BackgroundAdolescents who receive an adequate amount of sleep benefit from a positive health status. Previous studies have documented several health consequences connected with obesity as well as short sleep duration among adolescents. Poor sleep quality with obesity and uncontrolled diet can lead to chronic diseases in the future. This study aimed to examine the link between eating habits, sleep duration, and body mass index (BMI) among King Saud University (KSU) students.MethodsThe study was cross-sectional and conducted from February to May 2021 on 311 recruited students (male and female) of KSU premises. Pittsburgh Sleep Quality Index questionnaire was used to describe sleep duration linked with a dietary pattern that included fruit and vegetable intake. The questionnaire consists of two sections of 15 and 10 questions each. The questionnaire was created using the Google Forms tool and distributed through social media platforms like Twitter and WhatsApp. The obtained data was transferred into excel to perform the statistical analysis.ResultsThe mean total of students who participated in this study was 21.45 ± 23.11. Female students (72.3%) were actively involved in this study. About 30.2% of students were found to be overweight and obese. Around 67.8% of students had insufficient sleep, 32.2% had adequate sleep, and over 70% of students fell asleep within 30 min of going to bed. A total of 71.7% of students showed good sleep quality, whereas 28.3% reported poor sleep quality. BMI was categorized into four groups: 17.7% of individuals were underweight, 52.1% were of normal weight threshold, 20.6% were overweight, and 9.6% were obese. On a regular basis, 12.5% of students consume vegetables and 6.4% fruits daily. The results of this study show that only 8% of students eat breakfast, whereas 62.1% eat lunch, and 29.9% eat dinner.ConclusionThis study concludes that short sleep duration was associated with obesity among KSU students. This association was also found between sleep duration and dietary factors, specifically in the consumption of fruits and vegetables in terms of eating behaviour.

Project description:Dietary and physical activity behaviors formed early in life can increase risk for childhood obesity and have continued negative consequences for lifelong health. Previous research has highlighted the importance of both genetic and environmental (e.g., cultural environment or parental lifestyle) contributions to obesity risk, although these studies typically involve genetically-related individuals residing in the same household, where genetic similarity and rearing environment are inextricably linked. Here we utilize a sibling-adoption design to independently estimate genetic and environmental contributions to obesity risk in childhood and describe how these influences might vary as children age. As part of a prospective adoption study, the current investigation used data from biological siblings reared either apart or together, and nonbiological siblings reared together to estimate the contributions of genetics and environment to body mass indices (BMI) in a large cohort of children (N = 711). We used a variance partitioning model to allocate variation in BMI to that which is due to shared genetics, common environment, or unique environment in this cohort during middle childhood and adolescence. We found 63% of the total variance in BMI could be attributed to heritable factors in middle childhood sibling pairs (age 5-11.99; 95% CI [0.41,0.85]). Additionally, we observed that common environment explained 31% of variation in BMI in this group (95% CI [0.11,0.5]), with unique environment and error explaining the remaining variance. We failed to detect an influence of genetics or common environment in older sibling pairs (12-18) or pairs spanning childhood and adolescence (large sibling age difference), but home type (adoptive versus birth) was an important predictor of BMI in adolescence. The presence of strong common environment effects during childhood suggests that early interventions at the family level in middle childhood could be effective in mitigating obesity risk in later childhood and adolescence.

Project description:BackgroundOverweight/obesity is a well-defined risk factor for a variety of chronic cardiovascular and metabolic diseases. Sleep duration has been associated with overweight/obesity and other cardio metabolic and neurocognitive problems. Notably, overweight/obesity and many of the associated comorbidities are prevalent in Indigenous Australians. Generally, sleep duration has been associated with BMI for Australian adults but information about Australian Indigenous adults' sleep is scant. A recent report established that sleep is a weak predictor of obesity for Indigenous Australian adults.AimTo determine whether sleep remains a predictor of obesity when physical activity, diet and smoking status are accounted for; and to determine whether sleep duration plays a mediating role in the relationship between Indigenous status and BMI.MethodsStatistical analyses of 5,886 Australian adults: 5236 non-Indigenous and 650 Indigenous people aged over 18 years who participated in the Australian Health Survey 2011-2013. Demographic and lifestyle characteristics were described by χ2 and t-tests. ANOVA was used to determine the variables that significantly predicted BMI and sleep duration. Stepwise regression analyses were performed to determine the strongest significant predictors of BMI. Sleep duration was self-reported; BMI was calculated from measurement.ResultsThe study revealed two main findings: (i) short sleep duration was an independent predictor of obesity (adjusted-R2 = 0.056, p <0.0001); and (ii) controlling for sleep duration and other possible confounders, Indigenous status was a significant predictor of BMI overweight/obesity. Sleep duration played a weak, partial mediator role in this relationship. Increased BMI was associated with lower socioeconomic status and level of disadvantage of household locality for non-remote Indigenous and non-Indigenous people.ConclusionIndigenous status strongly predicted increased BMI. The effect was not mediated by the socioeconomic indicators but was partially mediated by sleep duration.

Project description:BackgroundIncreasing evidence supports the association between body mass index (BMI), Alzheimer's disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer's and vascular markers in relation to metabolic health status.MethodsWe recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aβ positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aβ positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores.ResultsBeing underweight increased the risk of Aβ positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13-4.98), whereas obesity decreased Aβ positivity risk (OR = 0.63, 95% CI 0.50-0.80). Especially, obesity decreased the risk of Aβ positivity (OR = 0.38, 95% CI 0.26-0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16-2.47). Decreased Aβ positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (β = 1.423, p = 0.037) compared to being normal weight, especially in the MH group.ConclusionsOur findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer's markers, which, in turn, lead to better clinical outcomes.

Dataset Information

Children's Body Mass Index Depending on Dietary Patterns, the Use of Technological Devices, the Internet and Sleep on BMI in Children.

Publications

Children's Body Mass Index Depending on Dietary Patterns, the Use of Technological Devices, the Internet and Sleep on BMI in Children.

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