Using instrumented everyday gait to predict falls in older adults using the WHS cohort

在社区生活的老年人中，福尔斯是伤害、残疾、与伤害相关的死亡的主要原因， 医疗费用。尽管经过几十年的研究，老年人跌倒的比例并没有下降。识别 有福尔斯跌倒危险的老年人仍然是一个主要的公共卫生优先事项。运动和其他干预措施可以降低跌倒 然而，需要新的工具来确定谁最有可能从早期干预中受益。 早期研究将跌倒风险与临床获得的步态测量结果联系起来（例如，平均速度，步幅变异性） 对理解跌倒风险的预测有很大贡献。研究还表明， 更活跃的成年人降低了福尔斯和与跌倒有关的伤害的风险。然而， 保持。数字医疗和使用可穿戴设备的远程监控的令人兴奋的进步为我们提供了新的 以及更广泛的评估日常生活步态（DLG）和 日常生活体力活动（DLPA）对老年人伤害性福尔斯跌倒的影响。DLG的度量（例如，步态速度，节奏， 变异性，以及这些在一周内如何变化）和DLPA的测量（例如，活动水平和活动 碎片）都可以从佩戴1周的单个加速度计导出。尽管越来越多的证据表明 DLG和DLPA在预测福尔斯方面比传统的临床测量更好，迄今为止的研究表明， 相对较小，并且没有集中于预测伤害性福尔斯。此外，人们对 结合DLG和DLPA措施预测伤害性福尔斯的效用。 为了解决这些差距，我们将利用：1）现有的大型老年妇女数据集， 健康研究（WHS）和2）可穿戴技术和机器学习的进展。2011年至2015年，17，466人 WHS的女性在清醒的时候戴着一个三轴加速计一周;他们还定期自我报告 他们的身体活动水平和健康史我们建议评估，第一次，如果和如何DLG和 DLPA测量预测了该老年队列中与跌倒相关的损伤（平均年龄=72岁， 加速计磨损）使用来自医疗保险和医疗补助服务中心（CMS）的伤害性福尔斯记录。 主要目的1和2将评价DLG和DLPA的哪些特定指标与以下风险相关： 有害的福尔斯在评估后的下一年，使用统计和机器学习方法， 使用至事件发生时间分析（有和无协变量调整）。主要目标3将评估是否 使用DLG和DLPA的措施与伤害性福尔斯的风险比使用 这些措施中的每一项。我们还将确定自我报告的运动史是否与DLG相关 和DLPA，并探讨DLG和DLPA标记物是否与伤害性福尔斯跌倒的风险相关， 延长5年和10年，作为次要和探索性目标。通过利用一个独特的， 数据集，我们的多学科团队将确定潜在的“签名”，以确定可能受益的高风险成年人 从早期跌倒预防策略，并显着加快使用跌倒风险的数字标记的潜力。

Among community-living older adults, falls are a leading cause of injury, disability, injury-related death, and high medical costs. Despite decades of research, the proportion of older adults who fall has not declined. Identifying older adults at risk of falls remains a major public health priority. Exercise and other interventions can lower fall risk; however, new tools are needed to determine who is most likely to benefit from early interventions. Early research linking fall risk to gait measures obtained in the clinic (e.g., average speed, stride variability) contributed significantly to the understanding of the prediction of fall risk. Studies have also shown that older adults who are more active have reduced risks of falls and fall-related injury. However, critical gaps remain. Exciting advances in digital medicine and remote monitoring using wearable devices have afforded new and more widely accessible opportunities for evaluating the relationships between Daily Living Gait (DLG) and Daily Living Physical Activity (DLPA) to injurious falls in older adults. Measures of DLG (e.g., gait speed, cadence, variability, and how these vary throughout the week) and measures of DLPA (e.g., activity levels and activity fragmentation) can all be derived from a single accelerometer worn for 1 week. While growing evidence suggests that DLG and DLPA do a better job at predicting falls than conventional in-clinic measures, studies to date have been relatively small and have not focused on the prediction of injurious falls. Moreover, little is known about the utility of combining DLG and DLPA measures to predict injurious falls. To address these gaps, we will leverage: 1) an existing large dataset of older women enrolled in the Women’s Health Study (WHS) and 2) advances in wearable technology and machine learning. From 2011 to 2015, 17,466 WHS women wore a tri-axial accelerometer during waking hours for a week; they also regularly self-reported their physical activity levels and health history. We propose to evaluate, for the first time, if and how DLG and DLPA measures predict fall-related injuries in this aging cohort (average age=72 years at the time of accelerometer wear) using records of injurious falls from the Centers for Medicare & Medicaid Services (CMS). Primary Aims 1 and 2 will evaluate which specific measures of DLG and DLPA are associated with the risk of injurious falls in the subsequent year after assessment, using statistical and machine learning approaches that use time-to-event analyses (with and without adjustments for covariates). Primary Aim 3 will evaluate whether utilizing measures of both DLG and DLPA is more strongly associated with the risk of injurious falls than utilizing each of these measures alone. We will also determine if self-reported exercise history is associated with DLG and DLPA, and explore whether markers of DLG and DLPA are associated with risks of injurious falls over more extended periods of 5 and 10 years, as secondary and exploratory aims. By taking advantage of a unique, large dataset, our multi-disciplinary team will identify potential “signatures” to identify high-risk adults who may benefit from early fall prevention strategies and markedly accelerate the potential of using digital markers of fall risk.