Can Smartphone-Derived Step Data Predict Laboratory-Induced Real-Life Like Fall-Risk in Community- Dwelling Older Adults?
Can Smartphone-Derived Step Data Predict Laboratory-Induced Real-Life Like Fall-Risk in Community- Dwelling Older Adults?
复制标题
智能手机衍生的步骤数据可以预测实验室引起的现实生活，例如社区居住的老年人的跌倒风险？
DOI：
10.3389/fspor.2020.00073
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发表时间：
2020
影响因子：
2.7
通讯作者：
Bhatt T
中科院分区：
文献类型：
--
作者：
Wang Y;Gangwani R;Kannan L;Schenone A;Wang E;Bhatt T
背景资料：随着年龄的增长，身体功能的下降使老年人更容易跌倒，特别是在暴露于环境干扰（如滑倒和绊倒）时。然而，有有限的证据表明，日常社区活动，一个容易修改的因素的体力活动（PA），和跌倒风险之间的关联。配备加速度计的智能手机可以量化，并简单地以步数显示每日与走动相关的PA。如果日常步数和跌倒风险之间存在任何关联，那么智能手机由于其便利性和普及性，除了现有的临床测量外，还可以为卫生专业人员提供有意义的结果测量，以识别高跌倒风险的老年人。目的：本研究旨在探索老年人社区活动期间智能手机获取的步数数据单独或与常用的临床跌倒风险测量一起是否可以预测实验室诱导的现实生活中的滑倒和绊倒等福尔斯。步数据和PA问卷和临床跌倒风险评估之间的关系进行了检查。研究方法：49名居住在社区的老年人（年龄60 - 90岁）完成了贝格平衡量表（BBS），活动特定的平衡信心量表（ABC），定时起床和去（TUG），和老年人身体活动量表（PASE）。检索了一周和一个月的智能手机步数数据。记录了参与者1年的跌倒史。记录所有参与者对实验室诱导的滑倒和绊倒扰动的跌倒结果。进行逻辑回归以确定最佳预测实验室福尔斯的模型。Pearson相关性检验了研究变量之间的关系。结果如下：包括年龄、TUG和跌倒史的模型显著预测实验室跌倒，灵敏度为94.3%，特异性为58.3%，总体准确性为85.1%。无论是1周还是1个月的步数数据都不能预测实验室福尔斯。一个月的步数数据与BBS（r = 0.386，p = 0.006）和ABC（r = 0.369，p = 0.012）显著正相关，与跌倒史（rp =-0.293，p = 0.041）呈负相关。结论：有跌倒史和TUG评分较高的老年参与者更容易在实验室跌倒。在我们的健康社区居住老年人研究人群中，智能手机步数数据与实验室跌倒风险之间没有关联，这需要对不同人群进行进一步研究。虽然不多，但结果确实揭示了步数数据与功能平衡缺陷和对福尔斯的恐惧之间的关系。
Background: 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. Objective: 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. Methods: 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. Results: 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 (r = 0.386, p = 0.006) and ABC (r = 0.369, p = 0.012), and negatively correlated with fall history (rp = −0.293, p = 0.041). Conclusion: 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.
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