Why Early Prevention of Childhood Obesity Is More Than a Medical Concern: A Health Economic Approach

Sonntag, Diana

doi:10.1159/000456554

Cited by 33 publications

(25 citation statements)

References 11 publications

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“…Preschool-aged children who are overweight or obese are at an increased risk of type 2 diabetes, cardiovascular disease, and mental health problems as they progress to pre-adolescence and adulthood [2,3]. Childhood obesity is the top contributor to health care cost across all decades of life, with a 3 to 5 times higher health-care cost burden in adults with a history of childhood obesity [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Ahmadi

Pavey

Trost

2020

Sensors

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Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Ahmadi

Pavey

Trost

2020

Sensors

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“…The prevalence of childhood obesity remains increasing in recent decades, which is alarming because it is also associated with health consequences such as metabolic syndrome, diabetes, cardiovascular diseases and even many types of cancers in adulthood 1 2. Once a child is obese that is more likely to remain during adulthood 3–5. With normal growth, children’s weight rises in proportion to height at an average age of 6 years, which is called adiposity rebound,6–8 and is thought to be a critical time of risk for adult obesity: obesity in this childhood period strongly predicts adult obesity 5 9–11.…”

Section: Introductionmentioning

confidence: 99%

Maternal postload 1-hour glucose level during pregnancy and offspring’s overweight/obesity status in preschool age

Shi¹,

Huang

Wang

et al. 2020

BMJ Open Diab Res Care

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BackgroundChildhood obesity is associated with adverse outcomes such as metabolic syndrome, diabetes, and cardiovascular diseases in adulthood. Identifying risk factors related to excessive adiposity in early childhood is of great importance for obesity intervention. The results of studies for associations between maternal with gestational diabetes and offspring obesity are conflicting. Nonetheless, the association of maternal glucose across a spectrum of glucose values with childhood adiposity outcomes is less clear.AimTo assess the association of maternal glucose across a spectrum of glucose values with childhood adiposity at age 5 years.MethodsA population-based cohort study was conducted between 2011 and 2018. Using the healthcare records data were from the Medical Birth Registry in Xiamen, China. The primary outcome was offspring obese/obesity. Primary predictors were maternal oral glucose tolerance test values during pregnancy.Results6090 mother–child pairs were analyzed. The mean age of the children at follow-up was 5.2 years. At multiple logistic regression, after adjustment for variables, including maternal pre-pregnancy body mass index (BMI), birth weight of offspring, and insulin therapy, ORs for offspring overweight/obesity were 1.13 (95% CI 0.90 to 1.42) for maternal fasting glucose levels, 1.12 (95% CI 1.04 to 1.22) for 1-hour glucose, and 1.04 (95% CI 0.95 to 1.14) for 2-hour glucose. The adjusted association of offspring BMI Z-score with maternal 1-hour glucose level remained significant. There were no significant associations between BMI Z-score and maternal fasting glucose and 2-hour glucose level. Exploratory sex-specific analyses indicated generally consistent associations for boys and girls.ConclusionMaternal postload 1-hour glucose across a spectrum of glucose values during pregnancy was an independent risk for offspring weight gain at age 5 years, indicating the importance of screen and management of maternal 1-hour glucose level, except for fasting glucose and 2-hour glucose level during pregnancy in order to prevent offspring weight gain in early childhood.

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“…The epidemic of childhood obesity has become an urgent public health priority due to its short and long-term development of diseases 1 and economic burdens 2 . Infancy is the period during which children experience most rapid growth and can play a vital role in the later childhood obesity 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Effect of maternal glycemia and weight status on offspring birth measures and BMI-z among Chinese population in the first year

Huang

Yin

Liang

et al. 2017

Sci Rep

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To investigate the effects of maternal fasting plasma glucose (FPG) and pre-pregnancy weight status (PPWS) on offspring birth measures and body mass index z-score (BMI-z) in the first year, we conducted a prospective study of 1,096 mother-infant dyads in Guangdong, China, 2014–2015. Multivariate logistic regression was used to test independent/interaction associations of maternal FPG and PPWS with macrosomia/large for gestational age (LGA). Association of PPWS and FPG with offspring BMI-z in the first year was assessed by the linear mixed effects models. For each 1-mmol/L increase in FPG, the risk of macrosomia and LGA was elevated by 2.74 and 2.01 (95% CI: 1.85, 7.60 and 1.54, 5.88), respectively. No main effect of PPWS or interaction association of FPG and PPWS on macrosomia/LGA was observed (P > 0.05). A relation between maternal FPG and PPWS was detected (P < 0.05). Infants of Q5 FPG mothers, those who were born to OWO mothers, had a 0.35 increase in the BMI-z (95% CI: 0.16, 0.55) compared with infants of NW mothers. In conclusion, maternal FPG is positively associated with macrosomia/LGA. Maternal PPWS and FPG considerably interacted for the association with the risk of offspring high BMI-z in the first year.

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Why Early Prevention of Childhood Obesity Is More Than a Medical Concern: A Health Economic Approach

Cited by 33 publications

References 11 publications

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Maternal postload 1-hour glucose level during pregnancy and offspring’s overweight/obesity status in preschool age

Effect of maternal glycemia and weight status on offspring birth measures and BMI-z among Chinese population in the first year

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