Rye Hanton Cassia, Kwon Yong-Jun, Aung Thawda, Whittington Jackie, High Robin R, Goulding Evan H, Schenk A Katrin, Bonasera Stephen J
Department of Internal Medicine, Division of Geriatrics, University of Nebraska Medical Center, Omaha, NE, United States.
Department of Physics, Randolph College, Lynchburg, VA, United States.
JMIR Mhealth Uhealth. 2017 Oct 3;5(10):e104. doi: 10.2196/mhealth.5090.
Cellular mobile telephone technology shows much promise for delivering and evaluating healthcare interventions in cost-effective manners with minimal barriers to access. There is little data demonstrating that these devices can accurately measure clinically important aspects of individual functional status in naturalistic environments outside of the laboratory.
The objective of this study was to demonstrate that data derived from ubiquitous mobile phone technology, using algorithms developed and previously validated by our lab in a controlled setting, can be employed to continuously and noninvasively measure aspects of participant (subject) health status including step counts, gait speed, and activity level, in a naturalistic community setting. A second objective was to compare our mobile phone-based data against current standard survey-based gait instruments and clinical physical performance measures in order to determine whether they measured similar or independent constructs.
A total of 43 ambulatory, independently dwelling older adults were recruited from Nebraska Medicine, including 25 (58%, 25/43) healthy control individuals from our Engage Wellness Center and 18 (42%, 18/43) functionally impaired, cognitively intact individuals (who met at least 3 of 5 criteria for frailty) from our ambulatory Geriatrics Clinic. The following previously-validated surveys were obtained on study day 1: (1) Late Life Function and Disability Instrument (LLFDI); (2) Survey of Activities and Fear of Falling in the Elderly (SAFFE); (3) Patient Reported Outcomes Measurement Information System (PROMIS), short form version 1.0 Physical Function 10a (PROMIS-PF); and (4) PROMIS Global Health, short form version 1.1 (PROMIS-GH). In addition, clinical physical performance measurements of frailty (10 foot Get up and Go, 4 Meter walk, and Figure-of-8 Walk [F8W]) were also obtained. These metrics were compared to our mobile phone-based metrics collected from the participants in the community over a 24-hour period occurring within 1 week of the initial assessment.
We identified statistically significant differences between functionally intact and frail participants in mobile phone-derived measures of percent activity (P=.002, t test), active versus inactive status (P=.02, t test), average step counts (P<.001, repeated measures analysis of variance [ANOVA]) and gait speed (P<.001, t test). In functionally intact individuals, the above mobile phone metrics assessed aspects of functional status independent (Bland-Altman and correlation analysis) of both survey- and/or performance battery-based functional measures. In contrast, in frail individuals, the above mobile phone metrics correlated with submeasures of both SAFFE and PROMIS-GH.
Continuous mobile phone-based measures of participant community activity and mobility strongly differentiate between persons with intact functional status and persons with a frailty phenotype. These measures assess dimensions of functional status independent of those measured using current validated questionnaires and physical performance assessments to identify functional compromise. Mobile phone-based gait measures may provide a more readily accessible and less-time consuming measure of gait, while further providing clinicians with longitudinal gait measures that are currently difficult to obtain.
蜂窝移动电话技术在以具有成本效益的方式提供和评估医疗保健干预措施方面显示出很大的前景，且获取障碍极小。几乎没有数据表明这些设备能够在实验室之外的自然环境中准确测量个体功能状态的临床重要方面。
本研究的目的是证明，利用我们实验室在受控环境中开发并先前验证过的算法，从无处不在的移动电话技术中获取的数据，可用于在自然社区环境中持续、无创地测量参与者（受试者）的健康状况，包括步数、步态速度和活动水平。第二个目的是将我们基于手机的数据与当前基于标准调查的步态仪器和临床身体性能测量进行比较，以确定它们测量的是相似还是独立的结构。
从内布拉斯加医学中心招募了43名能够行走、独立居住的老年人，其中包括来自我们参与式健康中心的25名（58%，25/43）健康对照个体，以及来自我们门诊老年病诊所的18名（42%，18/43）功能受损但认知完好的个体（符合虚弱5项标准中的至少3项）。在研究的第1天获得了以下先前验证过的调查：（1）晚年功能与残疾量表（LLFDI）；（2）老年人活动与跌倒恐惧调查（SAFFE）；（3）患者报告结局测量信息系统（PROMIS），简版1.0身体功能10a（PROMIS-PF）；以及（4）PROMIS全球健康，简版1.1（PROMIS-GH）。此外，还获得了虚弱的临床身体性能测量（10英尺起身行走、4米行走和8字形行走[F8W]）。将这些指标与在初始评估后1周内的24小时内从社区参与者收集的基于手机的指标进行比较。
我们发现，在功能完好和虚弱的参与者之间，基于手机的活动百分比测量（P=0.002，t检验）、活跃与不活跃状态（P=0.02，t检验）、平均步数（P<0.001，重复测量方差分析[ANOVA]）和步态速度（P<0.001，t检验）存在统计学显著差异。在功能完好的个体中，上述基于手机的指标评估的功能状态方面与基于调查和/或性能组的功能测量无关（布兰德-奥特曼分析和相关性分析）。相比之下，在虚弱个体中，上述基于手机的指标与SAFFE和PROMIS-GH的子测量相关。
基于手机的参与者社区活动和移动性的连续测量能够有力地区分功能状态完好的人和具有虚弱表型的人。这些测量评估的功能状态维度独立于使用当前验证问卷和身体性能评估所测量的维度，以识别功能损害。基于手机的步态测量可能提供一种更容易获得且耗时更少的步态测量方法，同时为临床医生提供目前难以获得的纵向步态测量。
