■通常使用髋部佩戴的加速度计,但是使用每分钟100计数切点处理的数据不能准确测量坐姿。我们开发并验证了一个模型,该模型可以使用来自广泛年龄范围的老年人的臀部佩戴加速度计数据对坐姿和坐姿模式进行准确分类。
■深度学习模型以30Hz三轴髋关节佩戴加速度计数据作为输入,并以activPAL坐姿/非坐姿事件作为地面实况进行训练。来自两大洲队列的981名35-99岁成年人的数据用于训练模型,我们称之为CHAP-成人(卷积神经网络髋关节加速度计姿势-成人)。在419个随机选择的未包括在模型训练中的成年人中进行验证。
■平均误差(activPAL-CHAP-Adult)和95%的一致性限制为:久坐时间-10.5(-63.0,42.0)分钟/天,久坐时间休息1.9(-9.2,12.9)休息/天,平均回合持续时间-0.6(-4.0,2.7)分钟,通常回合持续时间-1.4(-8.3,5.4)分钟,阿尔法.00(-.04,.04),时间≥30分钟,-15.1(-84.3,54.1)分钟/天。各自的平均(和绝对)百分比误差为:-2.0%(4.0%),-4.7%(12.2%),4.1%(11.6%),-4.4%(9.6%),0.0%(1.4%),和5.4%(9.6%)。皮尔森的相关性分别为:.96、.92、.86、.92、.78和.96。错误在不同年龄之间基本一致,性别,和体重指数≥30kg/m2的体重指数组的偏差最大。
■总的来说,这些有力的验证结果表明,CHAP-Adult在使用髋部佩戴加速度计进行坐姿和坐姿模式的动态测量方面取得了显著进步.等待外部验证,它可以广泛应用于来自世界各地的数据,以扩大对坐姿的流行病学和健康后果的理解。
UNASSIGNED: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults.
UNASSIGNED: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training.
UNASSIGNED: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson\'s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2.
UNASSIGNED: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.