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

摘要 代谢综合征是一组条件(血压升高、高血糖、体脂过多 腰围和胆固醇或甘油三酯水平异常)一起出现，增加心脏病的风险 疾病、中风和糖尿病。流行病学研究表明，久坐对人体有害。 代谢指标，即使在体力活动(PA)调整后也是如此。急性实验室试验表明， 打破坐着的时间可以改善代谢因素。久坐是所有人群中的一种普遍行为 按年龄、性别和种族分类。与代谢综合征因素，如肥胖，也有关联 在所有人口群体中都有显示。流行病学研究大多依赖于报告的坐着时间， 尤其是电视评论。最近，大型队列研究收集了臀部佩戴的加速度计的数据 并在单轴数据上应用切割点(例如，每分钟100个计数)来估计静坐时间。是这样的 许多研究中都包括了这些设备，主要是因为它们测量PA的准确性 强度。主要用于减少坐着的干预试验中，大腿佩戴的active PAL已被证明 更准确地评估姿势，并提供坐、站和坐-立转换的有效测量。至 迄今为止，很少有健康结局队列研究包括激活剂PAL。与活动PAL和FREE相比 对久坐时间的活生生的观察表明，100个数点的临界点低估了久坐的时间 因为大大高估了坐立转换。新的研究表明，我们是如何积累久坐的 时间(即长时间或短时间)与新陈代谢健康结果相关，并且可能与总结果无关 坐着的时间和PA。来自加速计数据的关于久坐和代谢风险因素的研究结果如下 不一致，可能是由于所采用的切割点的测量误差。在一个较老的小样本中 妇女、成年人和年轻人我们已经证明，新颖的机器学习方法可以极大地提高 对久坐和过渡的估计。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对现有大型队列研究的有效应用，以改进对 坐姿模式与新陈代谢健康之间的关系。也有许多大型队列(例如NHANE 2003/6)，具有高质量的健康结果，但非原始加速度计计数数据，因此校准方法进行调整 对坐着时间的非原始估计也是必要的，这对还不熟悉的研究人员来说是有吸引力的 机器学习过程。我们建议使用7个现有数据集(N=20,000)，这些数据集的年龄和 涵盖青年、成年人和老年人。我们将扩大我们的培训，并测试精炼的 检测坐立频率、延长坐姿、通常的比赛持续时间和Alpha(阿尔法组合)的算法 持续时间和频率)。我们将针对ActialPAL(基本事实)和在新的 样本用客观的健康结果评估预测的有效性。我们将测试现有的 以及使用R2和预测均方误差(通过自举)和GEE技术的新技术。

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.