背景:这项研究的重点是肥胖与休息活动节律(RAR)之间的关系,利用基于参数余弦的模型和非参数算法。重点是不同的测量周期(7-28天)对这种关系的影响。
方法:我们从两个数据集中检索了活动记录数据,包括从肥胖门诊诊所和工作场所健康促进计划招募的多样化队列。参与者被要求连续佩戴研究级腕动描记器至少四周。最终的数据集包括115名个体(平均年龄40.7±9.5岁,51%女性)。我们采用参数和非参数方法使用六个标准变量量化RAR。此外,这项研究评估了三个关键肥胖指数之间的相关性——体重指数(BMI),内脏脂肪组织(VAT)面积,和身体脂肪百分比(BF%)-和昼夜节律指标,控制身体活动等因素,年龄,和性别。
结果:根据非参数算法结果,肥胖组显示出明显较低的相对振幅(RA),在参数算法分析中注意到振幅减小,与超重组和对照组相比。研究了7至28天的昼夜节律指标与肥胖指标之间的关系。RA与BMI和VAT呈显著负相关,而肥胖指标与非参数昼夜节律参数之间的相关系数随着活动记录数据持续时间的延长而增加。具体来说,28天期间的RA与BF%显着相关,在多元线性回归的7天测量中未发现趋势(p=0.094)。BF%与28天RA之间的相关性强度比7天期间更为明显(p=0.044)。然而,根据参数余弦拟合将RA替换为振幅,对于任何测量时段均未产生显著相关性.
结论:该研究得出的结论是,28天的测量周期更有效地捕获了昼夜节律紊乱与肥胖之间的联系。非参数算法,特别是,更有效地表征被破坏的昼夜节律,特别是当延长测量周期超过标准7天。
BACKGROUND: This study focused on the relationship between adiposity and Rest-Activity Rhythms (RAR), utilizing both parametric cosine-based models and non-parametric algorithms. The emphasis was on the impact of varying measurement periods (7-28 days) on this relationship.
METHODS: We retrieved
actigraphy data from two datasets, encompassing a diverse cohort recruited from an obesity outpatient clinic and a workplace health promotion program. Participants were required to wear a research-grade wrist
actigraphy device continuously for a minimum of four weeks. The final dataset included 115 individuals (mean age 40.7 ± 9.5 years, 51 % female). We employed both parametric and non-parametric methods to quantify RAR using six standard variables. Additionally, the study evaluated the correlations between three key adiposity indices - Body Mass Index (BMI), Visceral Adipose Tissue (VAT) area, and Body Fat Percentage (BF%) - and circadian rhythm indicators, controlling for factors like physical activity, age, and gender.
RESULTS: The obesity group displayed a significantly lower relative amplitude (RA) as per non-parametric algorithm findings, with a decreased amplitude noted in the parametric algorithm analysis, in comparison to the overweight and control groups. The relationship between circadian rhythm indicators and adiposity metrics over 7- to 28-day periods was examined. A notable negative correlation was observed between RA and both BMI and VAT, while correlation coefficients between adiposity indicators and non-parametric circadian parameters increased with extended durations of
actigraphy data. Specifically, RA over a 28-day period was significantly correlated with BF%, a trend not seen in the 7-day measurement (p = 0.094) in multivariate linear regression. The strength of the correlation between BF% and 28-day RA was more pronounced than that in the 7-day period (p = 0.044). However, replacing RA with amplitude as per parametric cosinor fitting yielded no significant correlations for any of the measurement periods.
CONCLUSIONS: The study concludes that a 28-day measurement period more effectively captures the link between disrupted circadian rhythms and adiposity. Non-parametric algorithms, in particular, were more effective in characterizing disrupted circadian rhythms, especially when extending the measurement period beyond the standard 7 days.