Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting.
Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting.
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DOI：
10.2196/mhealth.5090
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发表时间：
2017-10-03
影响因子：
5
通讯作者：
Bonasera SJ
中科院分区：
文献类型：
--
作者：
Rye Hanton C;Kwon YJ;Aung T;Whittington J;High RR;Goulding EH;Schenk AK;Bonasera SJ
蜂窝移动的电话技术显示出以具有成本效益的方式提供和评估医疗保健干预措施的巨大潜力，并且访问障碍最小。几乎没有数据表明这些设备可以在实验室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，来自无处不在的移动的电话技术的数据，使用我们的实验室在受控环境中开发和先前验证的算法，可以用于连续和非侵入性测量参与者（受试者）的健康状况，包括步数，步态速度和活动水平，在一个自然的社区设置。第二个目标是将我们基于移动的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的还是独立的结构。从内布拉斯加州医学中心招募了43名非卧床、独立居住的老年人，其中包括25名（58%，25/43）来自我们的Engage Wellness Center的健康对照个体和18名（42%，18/43）来自我们的非卧床老年病诊所的功能受损、认知完整的个体（至少符合5项虚弱标准中的3项）。在研究第1天获得以下先前验证的调查：（1）晚年生活功能和残疾量表（LLFDI）;（2）老年人活动和跌倒恐惧调查（SAFFE）;（3）患者报告结局测量信息系统（PROMIS），简表1.0版身体功能10a（PROMIS-PF）;和（4）PROMIS全球卫生，简表1.1版（PROMIS-GH）。此外，还获得了虚弱（10英尺起床行走、4米步行和8字形步行[F8 W]）的临床身体性能测量值。将这些指标与我们在初始评估后1周内从社区参与者收集的24小时内基于移动的电话的指标进行比较。我们确定了功能完整和虚弱的参与者在移动的手机衍生的活动百分比（P= 0.002，t检验），活动与非活动状态（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）的测量中存在统计学显著差异。在功能完好的个体中，上述移动的电话指标评估了独立于基于调查和/或性能电池的功能测量的功能状态方面（Bland-Altman和相关性分析）。相比之下，在体弱的人，上述移动的电话指标与SAFFE和PROMIS-GH的子措施。连续移动的手机为基础的措施，参与社区活动和流动性强烈区分人与完整的功能状态和人与脆弱的表型。这些指标评估功能状态的维度，独立于使用当前经验证的问卷和身体表现评估进行测量的维度，以识别功能受损。基于移动的电话的步态测量可以提供更容易获得且耗时更少的步态测量，同时还为临床医生提供当前难以获得的纵向步态测量。
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.
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影响因子：
2.9
作者：
Castell, Maria-Victoria;Sanchez, Mercedes;Otero, Angel
通讯作者：
Otero, Angel
DOI：
10.1093/gerona/glu227
发表时间：
2015-05-01
影响因子：
5.1
作者：
Beauchamp, Marla K.;Jette, Alan M.;Bean, Jonathan F.
通讯作者：
Bean, Jonathan F.
影响因子：
1.8
作者：
Barnes, Lisa L.;Wilson, Robert S.;Bennett, David A.
通讯作者：
Bennett, David A.
影响因子：
2.4
作者：
Balasubramanian, Chitralakshmi K.
通讯作者：
Balasubramanian, Chitralakshmi K.
影响因子：
5.7
作者：
Hardy, Susan E.;Kang, Yihuang;Degenholtz, Howard B.
通讯作者：
Degenholtz, Howard B.