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疾病（血压升高，高血糖，多余的身体脂肪 腰部周围，胆固醇或甘油三酸酯水平异常），增加了心脏的风险 疾病，中风和糖尿病。流行病学研究表明，长时间的坐姿是有害的 代谢指标，即使在调整了体育活动（PA）之后。急性实验室试验表明 分解坐姿可以改善代谢因素。坐着是所有人口群体中普遍的行为 按年龄，性别和种族。与肥胖等代谢综合征因素的关联也已经存在 显示在所有人口群体中。流行病学研究主要取决于报告的坐姿， 特别是电视评论。最近，大型队列研究已从臀部磨损的加速度计收集数据 并在单轴数据上应用一个切点（例如每分钟100次计数）以估计久坐时间。这样的 设备已包含在许多研究中，主要是因为它们的准确性测量PA 强度。大腿磨损的ActivPal已显示用于减少坐姿的主要用于减少坐姿 更准确地评估姿势，并提供有效的坐姿，站立和坐姿过渡。到 日期，很少有健康结果队列研究包括ActivPal。与ActivPal相比 坐着时间的生活观察结果已显示出100个计数切口点可低估长时间的坐姿 通过实质上高估了坐姿台过渡。新研究表明，我们如何积累坐着 时间（即长或短次回合）与代谢健康结果有关，可能独立于总数 坐着时间和PA。加速度计数据的长时间坐姿和代谢风险因素的研究结果是 不一致，可能是由于所使用的切口的测量误差所致。在一小部分旧样本中 妇女，成人和青年，我们已经证明了新颖的机器学习方法可以大大改善 估计长时间的坐姿和过渡。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，以改善估计值 坐姿模式与代谢健康之间的关联。也有许多大型队列（例如Nhanes 2003/6），具有质量的健康结果，但非原始加速度计数数据，因此可以调整校准方法 还需要对坐时间的非原始估计，并且对尚不熟悉的研究人员有吸引力 机器学习过程。我们建议使用7个现有数据集（n = 20,000），并且 跨越青年，成人和老年人。我们将扩大培训并测试精致的表现 检测坐姿的算法，长时间坐着，通常的回合持续时间和alpha（组合 持续时间和频率）。我们将测试针对ActivPal（地面真相）和新算法的性能 样品通过客观健康结果评估预测有效性。我们将测试现有的差异 以及使用R2和于点的预测误差（通过引导）和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