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

基本信息

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
项目类别：
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&apos 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

项目摘要

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，瀑布是受伤，残疾，与伤害有关的主要原因，高医疗费用。尽管进行了数十年的研究，但跌倒的老年人的比例并未下降。识别有跌倒风险的老年人仍然是主要的公共卫生优先事项。运动和其他干预措施可以降低秋天风险;但是，需要新的工具来确定谁最有可能受益于早期干预措施。早期研究将跌倒风险联系起来，以收集诊所获得的措施（例如，平均速度，步幅可变性）为理解跌倒风险的预测做出了重大贡献。研究还表明，年龄较大更活跃的成年人降低了跌倒和与跌倒有关的伤害的风险。但是，关键差距保持。使用可穿戴设备的数字医学和远程监控的令人兴奋的进步为新的以及评估日常生活步态（DLG）和老年人受伤的日常体育锻炼（DLPA）跌落。 DLG的度量（例如步态速度，节奏，可变性，以及这些在整个星期内如何变化）和DLPA的度量（例如，活动水平和活动水平和活动碎片化）可以从单个加速度计磨损中得出1周。虽然越来越多的证据表明 DLG和DLPA在预测下降方面做得比传统的临界措施更好，迄今为止的研究已有我们相对较小，并没有专注于预测有害瀑布。而且，对组合DLG和DLPA措施预测有害跌倒的效用。为了解决这些差距，我们将利用：1）现有的大型妇女的大型数据集入学健康研究（WHS）和2）可穿戴技术和机器学习的进步。从2011年到2015年，17,466 WHS妇女在一周醒来时穿了三轴加速度计；他们也经常自我报告他们的体育锻炼水平和健康史。我们建议第一次评估DLG和如何评估 DLPA措施预测该老化队列中与跌倒有关的伤害（平均年龄= 72岁时加速度计）使用Medicare＆Medicaid Services（CMS）中心的有害跌倒记录。主要目的1和2将评估哪些DLG和DLPA的特定度量与在评估后的下一年，有害的跌倒，使用统计和机器学习方法使用事件时间分析（有和没有调整协变量）。主要目标3将评估是否利用DLG和DLPA的措施与有害跌倒的风险更密切相比这些措施中的每一个。我们还将确定自我报告的运动历史是否与DLG相关和DLPA，并探索DLG和DLPA的标记是否与有害的风险相关作为次要和探索目的，长达5年和10年的时间。通过利用独特的大型数据集，我们的多学科团队将确定潜在的“签名”，以识别可能受益的高风险成年人从初期预防策略开始，并明显加快了使用跌倒风险的数字标记的潜力。