权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，跌倒是造成伤害、残疾、伤害相关死亡和高血压的主要原因