Field-Based Assessment of Energy Expenditure in Spinal Cord Injury

描述（由申请人提供）： 在过去的几年中，使用基于加速度计的监测器来测量身体活动（PA）已经大幅增加。这些设备通常佩戴在身体上，并且具有内部时钟和大的存储容量，使得它们非常适合作为跟踪身体活动模式数天至数周的非侵入性客观方法。通常，通过使用单一线性回归方程将加速度计数据转换为能量消耗（EE）的估计。然而，这些方程是专门开发来捕捉流动人口的下肢运动，因此不适用于依靠上肢移动的轮椅使用者。我们最近评估了三个现成的加速度计为基础的监视器，估计EE之间的45个全职手动轮椅使用者脊髓损伤（SCI）。结果显示，对于轮椅相关活动（如轮椅推进和手臂测力计），每个设备的输出和间接测热法之间存在很大差异。在此之前的工作的基础上，该建议解决了需要一个PA评估工具，适合于跟踪自由生活的PA和EE的退伍军人脊髓损伤和疾病（SCI/D），特别是那些使用手动轮椅作为其主要的移动手段。我们将在本拟议项目中组建两个VA中心（即，宾夕法尼亚州匹兹堡市轮椅和相关康复工程卓越中心和纽约州布朗克斯的SCI医学后果中心）为SCI/D退伍军人开发一个生态有效的EE预测模型，并在自然条件下广泛评估其性能。本研究的具体目的是：1）开发基于机器学习的SCI/D MWU EE预测模型，该模型基于加速度计的活动监测器，使用从久坐行为到剧烈PA的各种活动（n=84）; 2）在半干旱条件下，评估新EE预测模型在SCI/D MWU单独样本中的准确性和精度。自然和自然条件下（n=24）;和3）评估新的EE预测模型的长期有效性在一个单独的样本的MWU与SCI/D在自然条件下（n=16）。我们希望这一建议将导致一个准确和精确的评估工具，估计自由生活EE和时间花在轻，中度和积极的PA在退伍军人SCI/D。SCI/D退伍军人可以使用该工具定期准确地自我监测他们的日常PA和EE，以提高他们的自我意识-这是健康决策和长期生活方式改变的先决条件。研究人员和临床医生可以使用该工具客观准确地评估PA干预措施，并确定适当的体重管理干预措施。所提出的工具可以促进更好的个人和临床决策PA，能量平衡，以及退伍军人和非退伍军人SCI/D人群的健康生活方式。

DESCRIPTION (provided by applicant): The use of accelerometer-based monitors for the measurement of physical activity (PA) has increased substantially over the last several years. These devices are typically worn on the body and have internal clocks and large memory capacities making them well suited as a non- invasive objective method of tracking physical activity patterns for days to weeks. Typically, accelerometer data are converted to estimates of energy expenditure (EE) by using single linear regression equations. However, such equations are exclusively developed to capture the lower extremity movements of ambulatory populations and thus are not applicable to wheelchair users who rely on their upper extremities for mobility. We recently evaluated three off-the-shelf accelerometer-based monitors in estimating EE among 45 full-time manual wheelchair users with spinal cord injury (SCI). The results revealed large discrepancies between each device output and indirect calorimetry for wheelchair related activities such as wheelchair propulsion and arm ergometry. Building upon this previous work, this proposal addresses the need for a PA assessment tool suitable for tracking free-living PA and EE among veterans with spinal cord injury & disorders (SCI/D), especially those who use manual wheelchairs as their primary means of mobility. We will team up two VA centers in this proposed project (i.e., the Center of Excellence on Wheelchairs and Associated Rehabilitation Engineering in Pittsburgh, PA and the Center for Medical Consequences of SCI in Bronx, NY) to develop an ecologically valid EE prediction model for veterans with SCI/D and evaluate its performance extensively under naturalistic conditions. The specific aims of the study are to: 1) to develop machine-learning based prediction models of EE in MWUs with SCI/D based on accelerometer-based activity monitors using a variety of activities that will range from sedentary behaviors to vigorous PAs (n=84); 2) to evaluate the accuracy and precision of the new EE prediction model in a separate sample of MWUs with SCI/D under semi-naturalistic and naturalistic conditions (n=24); and 3) to evaluate the long-term validity of the new EE prediction model in a separate sample of MWUs with SCI/D under naturalistic conditions (n=16). We expect this proposal will result in an accurate and precise assessment tool for estimating free-living EE and time spent in light, moderate, and vigorous PA in veterans with SCI/D. Veterans with SCI/D could use the tool to regularly and accurately self-monitor their everyday PA and EE to increase their self-awareness - a prerequisite for healthy decision making and long-term lifestyle change. Researchers and clinicians could use the tool to objectively and accurately assess PA interventions and determine appropriate interventions for weight management. The proposed tool could facilitate better personal and clinical decisions on PA, energy balance, and healthier lifestyle for both veteran and non-veteran populations with SCI/D.