Calibrating free-living physical activity characteristics across functionally-limited populations using machine-learned accelerometer approaches

项目摘要/摘要 五分之一的美国成年人被认为患有残疾/残疾，这是一种复杂和多方面的疾病 影响运动模式，相关医疗费用每年超过3,000亿美元。精确度和 准确评估残疾人的体力活动(PA)和久坐行为(SB) 损害是准确测量PA/SB患病率和基于行为的有效性的关键 PA/SB干预，并充分阐明PA/SB剂量-反应健康关系。科学的进步已经 在这一领域取得了进展，先进的分析和数据处理技术应用于可穿戴设备 来自实验室校准研究的加速度计。有科学上的需要将校准研究从 固定持续时间的实验室模拟日常生活活动到具有自然观察的自由生活校准 以及加速度计算法的培训和验证。这项提议的目的满足了这一基本的科学 知识鸿沟。具体目标是：1)评估和改进用于预测能源的机器学习算法 24小时呼吸量热计停留期间的成本和活动类型；2)验证机器学习的加速度计 具有现场派生的、视频记录的直接观测的算法；以及3)验证机器学习的算法 使用双标记水技术。我们高素质的研究团队将通过以下方式实现上述目标 使用简短的可翻译功能测试将行动不便的人群分为健康、 上半身损害、下半身损害和上下半身损害。免费最佳实践- 然后，将使用现场校准方案来训练、改进和评估特定于功能集群的功能 用于预测活动能源成本、活动类型、活动转换和活动的加速度计算法 域。这些拟议研究的结果将首次提供一个具有创新性和可译性的 对残疾和行动障碍者的自由生活能力进行分类和评估的方法。

PROJECT SUMMARY/ ABSTRACT One in 5 U.S. adults are thought to be living with a disability/impairment, a complex and multifaceted condition affecting movement patterns with related medical care costs exceeding $300 billion annually. Precise and accurate assessment of physical activity (PA) and sedentary behavior (SB) in individuals with disabiliy/ impairment is essential to accurately measure PA/SB prevalence rates and effectiveness of behavioral based PA/SB interventions, and to fully elucidate PA/SB dose-response health relationships. Scientific progress has been made in this area with advanced analytics and data processing techniques applied to wearable accelerometers from laboratory calibration studies. There is a scientific need to extend calibration studies from fixed-duration laboratory simulated activities of daily living to free-living calibrations with natural observation and accelerometer algorithm training and validation. The aims of this proposal fill this essential scientific knowledge gap. The specific aims are: 1) To evaluate and refine machine-learned algorithms to predict energy cost and activity type during a 24-hr respiratory calorimeter stay; 2) To validate machine-learned accelerometer algorithms with field-derived, video-recorded direct observation; and 3) To validate machine-learned algorithms using the doubly labeled water technique. Our highly qualified research team will address the above aims by using brief translatable functional tests to cluster movement-impaired populations into groups of healthy, upper-body impairment, lower-body impairment, and upper- and lower-body impairment. Best practice free- living calibration protocols will then be used to train, refine, and evaluate functional clustered-specific accelerometer algorithms for predicting activity energy cost, activity type, activity transitions, and activity domain. The results of these proposed studies will for the first time provide an innovative and translatable approach to categorize and assess free-living PA/SB in persons with disability and movement impairment.