Novel computational techniques to detect the relationship between sitting patterns and metabolic syndrome in existing cohort studies.

在现有队列研究中检测坐姿模式与代谢综合征之间关系的新计算技术。

• 批准号: 10228732
• 负责人: Loki Natarajan
• 金额: $ 60.76万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228732
• 关键词: Accelerometer; Acute; Adolescent; Adult; Age; Algorithms; American Heart Association; Behavior; Blood Glucose; Blood Pressure; Body Patterning; Body fat; Calibration; Cholesterol; Chronic Disease; Classification; Clinical; Cohort Studies; Computational Technique; Data; Data Set; Development; Devices; Diabetes Mellitus; Elderly; Ethnic Origin; Frequencies; Gender; Guidelines; Health; Health behavior; Hip region structure; Intervention; Intervention Trial; Laboratories; Longitudinal cohort study; Machine Learning; Measurement; Measures; Metabolic; Metabolic syndrome; Methods; National Health and Nutrition Examination Survey; Obesity; Outcome; Pattern; Physical activity; Population Group; Posture; Process; Recommendation; Reporting; Research Personnel; Risk Factors; Sampling; Stroke; Techniques; Testing; Thigh structure; Time; Training; Triglycerides; Use of New Techniques; Validation; Variant; Youth; age group; algorithm training; cohort; epidemiology study; heart disease risk; improved; indexing; machine learning algorithm; novel; older women; performance tests; response; scale up; sedentary; young adult

Abstract Metabolic syndrome is a cluster of conditions (increased blood pressure, high blood sugar, excess body fat around the waist, and abnormal cholesterol or triglyceride levels) that occur together, increasing risk of heart disease, stroke and diabetes. Epidemiological studies have shown that prolonged sitting is deleterious to metabolic indicators, even after adjusting for physical activity (PA). Acute laboratory trials have shown that breaking up sitting time can improve metabolic factors. Sitting is a prevalent behavior in all population groups by age, gender and ethnicity. Associations with metabolic syndrome factors, such as obesity, have also been shown in all population groups. Epidemiological studies have mostly depended on reported sitting time, especially TV reviewing. More recently large cohort studies have collected data from hip worn accelerometers and applied a cut point (e.g., 100 counts per minute) on single axis data to estimate sedentary time. Such devices have been included in numerous studies, principally because of their accuracy to measure PA intensity. Primarily used in intervention trials to reduce sitting, the thigh worn ActivPAL has been shown to more accurately assess posture and provide valid measures of sitting, standing, and sit-stand transitions. To date, very few health outcome cohort studies have included the ActivPAL. Compared to the ActivPAL and free living observations of sitting time, the 100 count cut point has been shown to underestimate prolonged sitting by substantially overestimating sit-stand transitions. New studies are showing that how we accumulate sitting time (i.e. in long or short bouts) is associated with metabolic health outcomes, and may be independent of total sitting time and PA. Study results on prolonged sitting and metabolic risk factors from accelerometer data are inconsistent and may be due to measurement error in the cut points employed. In a small sample of older women, adults, and youth we have demonstrated that novel machine learned methods can greatly improve estimates of prolonged sitting and transitions. Further development and testing of these methods would support valid applications to existing large cohort studies with raw accelerometer data to improve estimates of associations between sitting patterns and metabolic health. There are also many large cohorts (e.g. NHANES 2003/6), with quality health outcomes, but non raw accelerometer count data, so calibration methods to adjust non raw estimates of sitting time are also needed and would be attractive to researchers not yet familiar with the machine learning process. We proposed to employ 7 existing data sets (N=20,000) matched for age and spanning youth, adults and older adults. We will scale up our training and test the performance of the refined algorithms to detect sit-stand frequencies, prolonged sitting, usual bout duration and Alpha (a combination of duration & frequency). We will test performance of the algorithms against ActivPAL (ground truth) and in new samples assess predictive validity with objective health outcomes. We will test differences between the existing and new techniques using R2 and mean-squared error of prediction (via bootstrapping) and GEE techniques.

抽象的 代谢综合征是一系列病症（血压升高、高血糖、体内脂肪过多） 腰部周围的胆固醇或甘油三酯水平异常）同时发生，会增加心脏病的风险 疾病、中风和糖尿病。流行病学研究表明，久坐有害健康 代谢指标，即使在调整体力活动 (PA) 后也是如此。急性实验室试验表明 打破久坐的时间可以改善代谢因素。久坐是所有人群中普遍存在的行为 按年龄、性别和种族。与肥胖等代谢综合征因素的关联也已被证实 显示在所有人口群体中。流行病学研究主要依赖于报告的坐着时间， 尤其是电视评论。最近的大型队列研究收集了来自髋部佩戴的加速度计的数据 并对单轴数据应用切点（例如，每分钟 100 次计数）来估计久坐时间。这样的 设备已被纳入众多研究中，主要是因为它们测量 PA 的准确性 强度。大腿佩戴的 ActivPAL 主要用于减少久坐的干预试验，已被证明可以 更准确地评估姿势并提供坐、站和坐站转换的有效测量。到 迄今为止，很少有健康结果队列研究纳入 ActivPAL。与 ActivPAL 和免费相比 对坐着时间的实时观察，100 计数切点已被证明低估了长时间坐着的情况 通过大大高估坐站转换。新的研究表明我们如何积累久坐的习惯 时间（即长或短时间）与代谢健康结果相关，并且可能与总次数无关 坐时间和 PA。来自加速度计数据的久坐和代谢危险因素的研究结果是 不一致，可能是由于所采用的切割点的测量误差造成的。在较旧的小样本中 我们已经证明，新颖的机器学习方法可以极大地改善妇女、成人和青少年的生活质量 长时间坐着和转换的估计。这些方法的进一步开发和测试将 使用原始加速度计数据支持现有大型队列研究的有效应用，以改进对 坐姿模式与代谢健康之间的关联。还有许多大型队列（例如 NHANES 2003/6），具有高质量的健康结果，但非原始加速度计计数数据，因此校准方法需要调整 还需要对坐着时间的非原始估计，这对尚不熟悉的研究人员很有吸引力 机器学习过程。我们建议采用 7 个现有数据集（N=20,000），与年龄和年龄相匹配。 涵盖青少年、成人和老年人。我们将扩大培训规模并测试精炼后的性能 检测坐站频率、长时间坐着、通常的持续时间和 Alpha 的算法（ 持续时间和频率）。我们将根据 ActivPAL（地面事实）和新的环境测试算法的性能 样本评估客观健康结果的预测有效性。我们将测试现有的差异 以及使用 R2 和预测均方误差（通过自举）以及 GEE 技术的新技术。

项目成果

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

国际儿童肥胖、生活方式和环境研究 (ISCOLE) 中的低运动、深入的坐姿模式和久坐行为。

• DOI: 10.1038/s41366-023-01364-8
• 发表时间: 2023
• 期刊: International journal of obesity (2005)
• 作者: Hibbing,PaulR;Carlson,JordanA;Steel,Chelsea;Greenwood-Hickman,MikaelAnne;Nakandala,Supun;Jankowska,MartaM;Bellettiere,John;Zou,Jingjing;LaCroix,AndreaZ;Kumar,Arun;Katzmarzyk,PeterT;Natarajan,Loki
• 通讯作者: Natarajan,Loki