Wang Yiru, Gangwani Rachana, Kannan Lakshmi, Schenone Alison, Wang Edward, Bhatt Tanvi
Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.
MS Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.
Front Sports Act Living. 2020 Jul 10;2:73. doi: 10.3389/fspor.2020.00073. eCollection 2020.
As age progresses, decline in physical function predisposes older adults to high fall-risk, especially on exposure to environmental perturbations such as slips and trips. However, there is limited evidence of association between daily community ambulation, an easily modifiable factor of physical activity (PA), and fall-risk. Smartphones, equipped with accelerometers, can quantify, and display daily ambulation-related PA simplistically in terms of number of steps. If any association between daily steps and fall-risks is established, smartphones due to its convenience and prevalence could provide health professionals with a meaningful outcome measure, in addition to existing clinical measurements, to identify older adults at high fall-risk. This study aimed to explore whether smartphone-derived step data during older adults' community ambulation alone or together with commonly used clinical fall-risk measurements could predict falls following laboratory-induced real-life like slips and trips. Relationship between step data and PA questionnaire and clinical fall-risk assessments were examined as well. Forty-nine community-dwelling older adults (age 60-90 years) completed Berg Balance Scale (BBS), Activities-specific Balance Confidence scale (ABC), Timed Up-and-Go (TUG), and Physical Activity Scale for the Elderly (PASE). One-week and 1-month smartphone steps data were retrieved. Participants' 1-year fall history was noted. All participants' fall outcomes to laboratory-induced slip-and-trip perturbations were recorded. Logistic regression was performed to identify a model that best predicts laboratory falls. Pearson correlations examined relationships between study variables. A model including age, TUG, and fall history significantly predicted laboratory falls with a sensitivity of 94.3%, specificity of 58.3%, and an overall accuracy of 85.1%. Neither 1-week nor 1-month steps data could predict laboratory falls. One-month steps data significantly positively correlated with BBS ( = 0.386, = 0.006) and ABC ( = 0.369, = 0.012), and negatively correlated with fall history ( = -0.293, = 0.041). Older participants with fall history and higher TUG scores were more likely to fall in the laboratory. No association between smartphone steps data and laboratory fall-risk was established in our study population of healthy community-dwelling older adults which calls for further studies on varied populations. Although modest, results do reveal a relationship between steps data and functional balance deficits and fear of falls.
随着年龄的增长，身体功能的衰退使老年人面临较高的跌倒风险，尤其是在遭遇滑倒和绊倒等环境干扰时。然而，日常社区行走作为身体活动（PA）的一个易于改变的因素，与跌倒风险之间的关联证据有限。配备加速度计的智能手机可以简单地根据步数来量化和显示与日常行走相关的PA。如果能确定每日步数与跌倒风险之间的任何关联，那么由于智能手机的便利性和普及性，除了现有的临床测量方法外，它还可以为健康专业人员提供一个有意义的结果指标，以识别跌倒风险较高的老年人。本研究旨在探讨仅通过老年人社区行走期间智能手机获取的步数数据，或者将其与常用的临床跌倒风险测量方法结合起来，是否能够预测在实验室诱导的类似现实生活中的滑倒和绊倒后发生的跌倒情况。同时还研究了步数数据与PA问卷以及临床跌倒风险评估之间的关系。49名社区居住的老年人（年龄在60 - 90岁之间）完成了伯格平衡量表（BBS）、特定活动平衡信心量表（ABC）、计时起立行走测试（TUG）和老年人身体活动量表（PASE）。收集了一周和一个月的智能手机步数数据。记录了参与者的1年跌倒史。记录了所有参与者在实验室诱导的滑倒和绊倒干扰下的跌倒结果。进行逻辑回归以确定最能预测实验室跌倒的模型。采用皮尔逊相关性分析研究变量之间的关系。一个包含年龄、TUG和跌倒史的模型能够显著预测实验室跌倒，其灵敏度为94.3%，特异度为58.3%，总体准确率为85.1%。一周和一个月的步数数据均无法预测实验室跌倒。一个月的步数数据与BBS显著正相关（r = 0.386，p = 0.006）和ABC显著正相关（r = 0.369，p = 0.012），与跌倒史显著负相关（r = -0.293，p = 0.041）。有跌倒史且TUG得分较高的老年参与者在实验室中更有可能跌倒。在我们健康的社区居住老年人研究人群中，未发现智能手机步数数据与实验室跌倒风险之间存在关联，这需要对不同人群进行进一步研究。尽管结果不显著，但确实揭示了步数数据与功能平衡缺陷以及跌倒恐惧之间的关系。
