Department of Rehabilitation and Mental Health Counseling, USF College of Behavioral and Community Sciences, Tampa, FL 33612, USA.
J Am Med Dir Assoc. 2012 Sep;13(7):665.e7-665.e13. doi: 10.1016/j.jamda.2012.06.010. Epub 2012 Aug 9.
We hypothesized that variability in voluntary movement paths of assisted living facility (ALF) residents would be greater in the week preceding a fall compared with residents who did not fall.
Prospective, observational study using telesurveillance technology.
Two ALFs.
The sample consisted of 69 older ALF residents (53 female) aged 76.9 (SD ± 11.9 years).
Daytime movement in ALF common use areas was automatically tracked using a commercially available ultra-wideband radio real-time location sensor network with a spatial resolution of approximately 20 cm. Movement path variability (tortuosity) was gauged using fractal dimension (fractal D). A logistic regression was performed predicting movement related falls from fractal D, presence of a fall in the prior year, psychoactive medication use, and movement path length. Fallers and non-fallers were also compared on activities of daily living requiring supervision or assistance, performance on standardized static and dynamic balance, and stride velocity assessments gathered at the start of a 1-year fall observation period. Fall risk due to cognitive deficit was assessed by the Mini Mental Status Examination (MMSE), and by clinical dementia diagnoses from participant's activities of daily living health record.
Logistic regression analysis revealed odds of falling increased 2.548 (P = .021) for every 0.1 increase in fractal D, and having a fall in the prior year increased odds of falling by 7.36 (P = .006). There was a trend for longer movement paths to reduce the odds of falling (OR .976 P = .08) but it was not significant. Number of psychoactive medications did not contribute significantly to fall prediction in the model. Fallers had more variable stride-to-stride velocities and required more activities of daily living assistance.
High fractal D levels can be detected using commercially available telesurveillance technologies and offers a new tool for health services administrators seeking to reduce falls at their facilities.
我们假设，与未跌倒的居民相比，辅助生活设施（ALF）居民在跌倒前一周的自愿运动路径变化更大。
使用远程监控技术的前瞻性观察研究。
两个辅助生活设施。
样本包括 69 名年龄在 76.9（SD ± 11.9 岁）的老年 ALF 居民（53 名女性）。
使用商用超宽带无线电实时位置传感器网络自动跟踪 ALF 公共使用区域的日间运动，该网络的空间分辨率约为 20 厘米。使用分形维数（分形 D）来衡量运动路径变化（曲折度）。进行逻辑回归，从分形 D、前一年是否有跌倒、使用精神药物以及运动路径长度预测与运动相关的跌倒。还比较了跌倒者和非跌倒者在日常生活活动中需要监督或协助的活动、标准化静态和动态平衡以及在为期 1 年的跌倒观察期开始时进行的步速评估。使用 Mini 精神状态检查（MMSE）和参与者日常生活健康记录中的临床痴呆诊断评估认知缺陷导致的跌倒风险。
逻辑回归分析显示，分形 D 每增加 0.1，跌倒的几率就会增加 2.548（P=0.021），前一年有跌倒的几率会增加 7.36 倍（P=0.006）。运动路径较长有降低跌倒几率的趋势（OR.976 P=0.08），但无统计学意义。精神药物的数量对模型中的跌倒预测没有显著贡献。跌倒者的步速变化更不规则，需要更多的日常生活活动协助。
可以使用商用远程监控技术检测到高分形 D 水平，为寻求降低设施跌倒风险的卫生服务管理人员提供了一种新工具。
