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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Assessing Smart Phones for Generating Life-space Indicators.

Life-space is a promising method for estimating older adults' functional status. However, traditional life-space measures are costly and time consuming because they often rely on active subject participation. This study assesses the feasibility of using the global positioning system (GPS) function of smart phones to generate life-space indicators. We first evaluated the location accuracy of smart phone collected GPS points versus those acquired by a commercial GPS unit. We then assessed the specificity of the smart phone processed life-space information against the traditional diary method. Our results suggested comparable location accuracy between the smart phone and the standard GPS unit in most outdoor situations. In addition, the smart phone method revealed more comprehensive life-space information than the diary method, which leads to higher and more consistent life-space scores. We conclude that the smart phone method is more reliable than traditional methods for measuring life-space. Further improvements will be required to develop a robust application of this method that is suitable for health-related practices.

Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

A number of important health-related outcomes are directly related to a person's ability to maintain normal gait speed. We hypothesize that cellular telephones may be repurposed to measure this important behavior in a noninvasive, continuous, precise, and inexpensive manner. The purpose of this study was to determine if physical activity (PA) counts collected by cell phone accelerometers could measure treadmill gait speeds. We also assessed how cell phone placement influenced treadmill gait speed measures. Participants included 55 young, middle-aged, and older community-dwelling men and women. We placed cell phones as a pendant around the neck, and on the left and right wrist, hip, and ankle. Subjects then completed an individualized treadmill protocol, alternating 1 min rest periods with 5 min of walking at different speeds (0.3-11.3 km/h; 0.2-7 mi/h). No persons were asked to walk at speeds faster than what they would achieve during day-to-day life. PA counts were calculated from all sensor locations. We built linear mixed statistical models of PA counts predicted by treadmill speeds ranging from 0.8 to 6.4 km/h (0.5-4 mi/h) while accounting for subject age, weight, and gender. We solved linear regression equations for treadmill gait speed, expressed as a function of PA counts, age, weight, and gender. At all locations, cell phone PA counts were strongly associated with treadmill gait speed. Cell phones worn at the hip yielded the best predictive model. We conclude that in both men and women, cell phone derived activity counts strongly correlate with treadmill gait speed over a wide range of subject ages and weights.