Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting

Abstract

BackgroundCellular 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.ObjectiveThe 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.MethodsA 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.ResultsWe 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.ConclusionsContinuous 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 measur...

Extended, continuous measures of functional status in community dwelling persons with Alzheimer’s and related dementia: Infrastructure, performance, tradeoffs, preliminary data, and promise

“…Details regarding how these tasks are implemented and validated are beyond the scope of this text; details regarding validation of step count (Carlson et al, 2012; Rye-Hanton et al, 2017) and GPS (Wan et al, 2013; Wan and Lin 2013) data have been previously published…”

Using Sports Tracker: Evidences on Dependence, Self-Regulatory Modes and Resilience in a Sample of Competitive Runners

“…Wearable technology is spreading more and more in the global market, achieving considerable success, especially in the sports and fitness sectors, becoming essential for those who love sports and need, in real time, to learn fundamental information such as the time and distance traveled, the position in which he/she is located, the heart rate and effort made, the calories consumed, and other spe-cific parameters to check the reactions of the body subjected to prolonged effort and monitor individual athletic performance (Wang et al, 2016;Coughlin & Stewart, 2016;Henriksen et al 2018;Godfrey et al, 2018). The wearable tech is currently offered in various forms, from the classic watch to the smart bracelet, from fitness bands to sensors of every shape and nature, to monitors indicating the physical performances useful in different sports, and thanks to their ability to connect to digital multimedia technology or to a smartphone, they are able to manage the data relating to the activity so as to be able to keep a real archive, with which to constantly monitor the progress achieved and obtainable (Rabin & Bock, 2011;Kinnunen et al, 2012;Kaewkannate & Kim, 2016;Hanton et al, 2017;Leone, 2018). Among the lovers of the race can be distinguished two categories of runners, the "jogger" and the "runners" that, unlike the first, train intensely in order to compete in sporting events in a systematic way (Schenkenfelder & Selinger, 2016).…”

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

“…Although these works offer valuable contributions toward sensor-based behavioral monitoring, the use of specialized systems or strict protocols regarding device placement to measure behavior under experimental conditions is unrealistic for long-term everyday use, therefore difficult to replicate in a real-world setting. This motivates a growing interest in leveraging the wide availability and acceptance of today’s personal devices [8,13-15]. Smartphones and wearables have successfully been applied to measure activity among older adults under free-living conditions [13], daily step count and distance covered among people with dementia [14], and life space among people with Parkinson disease and mild-to-moderate Alzheimer disease [8,15].…”

“…This motivates a growing interest in leveraging the wide availability and acceptance of today’s personal devices [8,13-15]. Smartphones and wearables have successfully been applied to measure activity among older adults under free-living conditions [13], daily step count and distance covered among people with dementia [14], and life space among people with Parkinson disease and mild-to-moderate Alzheimer disease [8,15]. System design considerations for real-world use are addressed in a previous study [14], which demonstrates how adequate data are recorded over an extended period (5 months) to reveal behavioral patterns.…”

“…System design considerations for real-world use are addressed in a previous study [14], which demonstrates how adequate data are recorded over an extended period (5 months) to reveal behavioral patterns. In some studies [8,13,15], the sensor-based approach is evaluated by comparing measures between experimental and control groups, indicating that significant change in sensor-based behavioral measures might be detected with disease onset/progression; however, no comparison is made with manually reported data. The behavioral measures used vary as follows: activity measures range from daily steps to more detailed descriptions of active and sedentary states; and life space measures range from basic distances to trips or time frames away from home but without extraction of travel trajectories in their estimation.…”