D'Souza Ninoshka J, Zheng Miaobing, Abbott Gavin, Lioret Sandrine, Hesketh Kylie D
Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, VIC 3125, Australia.
Research Center in Epidemiology and Biostatistics (CRESS), Université de Paris, INSERM, INRAE, 75004 Paris, France.
Children (Basel). 2021 Nov 7;8(11):1023. doi: 10.3390/children8111023.
Identifying correlates of behavioural patterns are important to target population sub-groups at increased health risk. The aim was to investigate correlates of behavioural patterns comprising four behavioural domains in children. Data were from the HAPPY study when children were 6-8 years (n = 335) and 9-11 years (n = 339). Parents reported correlate and behavioural data (dietary intake, physical activity, sedentary behaviour, and sleep). Behavioural data were additionally captured using accelerometers. Latent profile analysis was used to derive patterns. Patterns were identified as healthy, unhealthy, and mixed at both time points. Multinomial logistic regression tested for associations. Girls were more likely to display healthy patterns at 6-8 years and display unhealthy and mixed patterns at 9-11 years than boys, compared to other patterns at the corresponding ages. Increased risk of displaying the unhealthy pattern with higher age was observed at both timepoints. At 9-11 years, higher parental working hours were associated with lower risk of displaying mixed patterns compared to the healthy pattern. Associations observed revealed girls and older children to be at risk for unhealthy patterns, warranting customisation of health efforts to these groups. The number of behaviours included when deriving patterns and the individual behaviours that dominate each pattern appear to be drivers of the associations for child level, but not for family level, correlates.
确定行为模式的相关因素对于针对健康风险增加的人群亚组至关重要。目的是调查儿童中包括四个行为领域的行为模式的相关因素。数据来自HAPPY研究，研究对象为6至8岁（n = 335）和9至11岁（n = 339）的儿童。父母报告了相关因素和行为数据（饮食摄入、身体活动、久坐行为和睡眠）。此外，使用加速度计收集行为数据。采用潜在剖面分析来得出模式。在两个时间点，模式均被确定为健康、不健康和混合模式。使用多项逻辑回归检验关联性。与相应年龄的其他模式相比，女孩在6至8岁时更有可能表现出健康模式，而在9至11岁时更有可能表现出不健康和混合模式。在两个时间点均观察到随着年龄增长表现出不健康模式的风险增加。在9至11岁时，与健康模式相比，父母工作时间较长与表现出混合模式的风险较低相关。观察到的关联性表明女孩和年龄较大的儿童有出现不健康模式的风险，因此有必要针对这些群体定制健康促进措施。得出模式时所纳入的行为数量以及主导每种模式的个体行为似乎是儿童层面而非家庭层面相关因素之间关联性的驱动因素。
