Regional variation in lifestyle patterns and BMI in young children: the GECKO Drenthe cohort.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Rikstje Wiersma, Richard H Rijnks, Gianni Bocca, H Marike Boezen, Esther Hartman, Eva Corpeleijn
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引用次数: 3

Abstract

Background: A better understanding of lifestyle behaviours of children < 7 years and the relation with childhood overweight is needed. The aim of our prospective study was to examine how lifestyle patterns in young children are associated with the development of childhood overweight. As ecological models suggest focusing on not only the child as an individual, but also their environment, we also considered the role of socio-economic status (SES) and spatial clustering of lifestyle and body mass index (BMI).

Methods: In 1792 children (aged 3-6 years) participating in the GECKO Drenthe cohort, diet, screen time, outdoor play and sleep were assessed by questionnaires and moderate-to-vigorous physical activity and sedentary time by accelerometry (Actigraph GT3X). At 10-11 years, height and weight were measured to calculate age- and sex-specific standardized BMI z-scores (zBMI). Lifestyle patterns were identified using principal component analysis. To assess spatial clustering for the lifestyle patterns and zBMI, we calculated the Global Moran's I statistic. Linear- and logistic regression models, taking into account SES, were performed to examine the association between the lifestyle patterns and the development of overweight. For the spatial analyses, we added spatial terms for the determinants, the outcome, and the error term.

Results: Three lifestyle patterns were identified: (1) 'high activity', (2) 'low screen time, high sleep and healthy diet', and (3) 'high outdoor play'. No associations were observed between the 'high activity' or 'high outdoor play' patterns at young age with the development of childhood overweight (all p > 0.05). In contrast, children who adhered to the 'low screen time, high sleep and healthy diet' pattern had lower odds to become overweight and a lower zBMI at 10-11 years (odds ratio [95% CI] = 0.766 [0.65; 0.90]). These findings remained similar after taking SES into account. Regarding the spatial analyses, we found spatial clustering of zBMI, but no spatial clustering of the lifestyle patterns.

Conclusions: Low screen time, high sleep duration and a healthy diet cluster into a pattern that seems favourable in the prevention of childhood overweight, independent of individual SES. The spatial analyses suggest that there are likely other neighbourhood factors that contribute to the spatial clustering of childhood overweight.

Abstract Image

Abstract Image

Abstract Image

幼儿生活方式和BMI的区域差异:GECKO - Drenthe队列。
方法:对参加GECKO Drenthe队列的1792名儿童(3-6岁)的饮食、屏幕时间、户外游戏和睡眠进行问卷调查,并通过加速度计(Actigraph GT3X)评估中高强度身体活动和久坐时间。在10-11岁时,测量身高和体重以计算年龄和性别特异性标准化BMI z分数(zBMI)。使用主成分分析确定生活方式。为了评估生活方式和zBMI的空间聚类,我们计算了Global Moran's I统计量。考虑到社会经济地位,采用线性和逻辑回归模型来检验生活方式与超重发展之间的关系。对于空间分析,我们为决定因素、结果和误差项添加了空间项。结果:确定了三种生活方式:(1)“高活动”,(2)“少看屏幕,高睡眠和健康饮食”,以及(3)“高户外游戏”。幼儿时期“高活动”或“高户外游戏”模式与儿童超重发展之间没有关联(均p > 0.05)。相比之下,坚持“少屏幕时间,高睡眠和健康饮食”模式的儿童在10-11岁时超重的几率较低,zBMI也较低(优势比[95% CI] = 0.766 [0.65;0.90])。在考虑了SES因素后,这些发现仍然相似。在空间分析方面,我们发现zBMI存在空间聚类,但生活方式没有空间聚类。结论:低屏幕时间,高睡眠时间和健康饮食似乎有利于预防儿童超重,独立于个体SES。空间分析表明,可能还有其他邻里因素导致儿童超重的空间聚类。
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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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