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

使用 WHS 队列，使用仪器化的日常步态来预测老年人跌倒

• 批准号: 10657828
• 负责人: JEFFREY M HAUSDORFF
• 金额: $ 65.81万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657828
• 关键词: Acceleration; Accelerometer; Activities of Daily Living; Address; Adult; Affect; Age; Aging; Body mass index; Brain; Cardiovascular system; Characteristics; Clinic; Clinical; Clinical Management; Cognition; Cognitive; Cohort Studies; Complex; Data; Dementia; Developed Countries; Digital biomarker; Dual-Energy X-Ray Absorptiometry; Early Intervention; Early identification; Elderly; Enrollment; Event; Exercise; Fall prevention; Future; Gait; Gait speed; Health; Hip region structure; Hour; Human Resources; Hypertension; Individual; Injury; Intervention; Knowledge; Laboratories; Link; Machine Learning; Measures; Medical Care Costs; Metabolic; Motion; Motivation; Muscle; Musculoskeletal; Nursing Homes; Occupations; Outcome; Patient Self-Report; Performance; Persons; Physical activity; Physiological; Pilot Projects; Prevention strategy; Public Health; Recording of previous events; Records; Research; Risk; Risk Estimate; Smoking; Societies; Speed; System; Time; Training; United States Centers for Medicare and Medicaid Services; Visit; Walking; Woman; Women' s Health; Work; age related; cohort; community living; cost; digital assessment; digital medicine; disability; evidence base; fall injury; fall risk; falls; high risk; injury-related death; instrument; large datasets; lifestyle factors; mortality; multidisciplinary; muscle form; older women; outcome prediction; public health priorities; remote monitoring; respiratory; skeletal; statistical and machine learning; time use; tool; wearable device; wearable monitor

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.

在社区生活的老年人中，跌倒是伤害、残疾、与伤害相关的死亡和高血压的主要原因 医疗费用。尽管进行了数十年的研究，跌倒的老年人比例并没有下降。识别 有跌倒风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 风险；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上获得的步态测量(例如，平均速度、步幅变异性)联系起来 对理解跌倒风险的预测有很大贡献。研究还表明，年龄较大的人 成年人越活跃，摔倒和摔倒相关伤害的风险就越低。然而，关键差距 留下来。数字医疗和使用可穿戴设备进行远程监控的令人兴奋的进步为新的 更广泛的机会评估日常生活步态(DLG)和 日常生活体力活动(DLPA)对老年人的破坏性跌倒。DLG的测量(例如，步速、节奏、 可变性，以及这些指标在一周内如何变化)和DLPA的衡量标准(例如，活动水平和活动 碎裂)均可由佩戴1周的单个加速度计得出。虽然越来越多的证据表明 到目前为止的研究表明，DLG和DLPA在预测跌倒方面比传统的临床测量方法更好 规模相对较小，并未侧重于预测伤害性跌倒。此外，人们对此知之甚少 结合DLG和DLPA方法预测损伤性跌倒的有效性。 为了解决这些差距，我们将利用：1)现有的大型数据集，老年妇女登记在妇女 健康研究(WHS)和2)可穿戴技术和机器学习的进展。从2011年到2015年，17466人 WHS女性在一周的清醒时间里佩戴三轴加速度计；她们还定期自我报告 他们的体力活动水平和健康史。我们建议第一次评估DLG和 DLPA测量预测这一老龄化队列中与跌倒相关的伤害(平均年龄=72岁 加速度计磨损)使用来自医疗保险和医疗补助服务中心(CMS)的伤害跌落记录。 主要目标1和2将评估DLG和DLPA的哪些具体测量与 在评估后的下一年，使用统计和机器学习方法 使用事件发生时间分析(对协变量进行调整和不调整)。主要目标3将评估 使用DLG和DLPA的测量方法比使用DLG和DLPA测量方法与伤害跌倒风险的相关性更强 这些措施中的每一项都是单独的。我们还将确定自我报告的运动史是否与DLG相关 和DLPA，并探索DLG和DLPA的标志物是否与更多的损伤性跌倒风险相关 延长5年和10年，作为次要和探索性目标。通过利用独特的、大型的 数据集，我们的多学科团队将识别潜在的“签名”，以识别可能受益的高危成年人 从早秋预防战略，并明显加快了使用跌倒风险的数字标记的潜力。