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

ABSTRACT: 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.

SUBMITTER: Carlson RH

PROVIDER: S-EPMC3387318 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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

Carlson Richard H RH Huebner Derek R DR Hoarty Carrie A CA Whittington Jackie J Haynatzki Gleb G Balas Michele C MC Schenk Ana Katrin AK Goulding Evan H EH Potter Jane F JF Bonasera Stephen J SJ

Gait & posture 20120402 2

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 spee ...[more]

PMID: 22475727

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Project description:BACKGROUND AND OBJECTIVES:Virtual reality (VR) is increasingly often finding applications in physiotherapy and health promotion. Recent years have seen the use of advanced technologies in the promotion of physical activity (PA) in society. New simulators, e.g., treadmills, enable the performance of PA (e.g., locomotive movements) in VR (artificially created virtual world). The question of how such movements are similar to natural forms of human locomotion (march, run) inspired the comparative analysis of physiological gait and gait in VR on a multidirectional Omni treadmill. MATERIALS AND METHODS:The tests involved the use of the BTS Smart system for the triplanar analysis of motion. The test involved 10 healthy females aged 20-24 (weight: 52 ± 3.1 kg, height 162 ± 5.4 cm). Measurements were performed at two stages. The first stage involved the standard assessment of physiological gait, whereas the second was focused on gait forced by the Omni treadmill. The following gait parameters were analyzed: Flexion-extension in the ankle, knee joint and hip joint, rotation in the hip joint and knee joint, foot progression, adduction-abduction in the knee joint and hip joint, pelvic obliquity, pelvic tilt, pelvic rotation as well as energy expenditure and the movement of the body center of mass. RESULTS:The analysis of the test results revealed the existence of differences in the kinematics of physical gait and gait on the treadmill. The greatest differences were recorded in relation to the dorsal-plantar flexion in the ankle, the foot progression, the rotation of the knee joint, pelvic tilt and rotation. In addition, the gait on the treadmill is characterized by the longer duration of the stance phase and reduced ranges of the following movements: Flexion-extension in the ankle, knee joint and hip joint, adduction-abduction in the hip joint as well as rotation in the ankle and hip joint. The values of potential, kinetic and total energy recorded in relation to forced gait are significantly lower than those of physiological gait. CONCLUSIONS:Because of the fact that the parameters of gait on the Omni platform vary significantly from the parameters of physical gait, the application of the Omni treadmill in the re-education of gait during rehabilitation should be treated with considerable care. Nonetheless, the treadmill has adequate potential to become a safe simulator enabling active motion in VR using locomotive movements.

Project description:Background:Although physical activity (PA) is known to be beneficial in improving motor symptoms of people with Parkinson's disease (pwPD), little is known about the relationship between gait patterns and features of PA performed during daily life. Objective:To verify the existence of possible relationships between spatiotemporal and kinematic parameters of gait and amount/intensity of PA, both instrumentally assessed. Methods:Eighteen individuals affected by PD (10F and 8M, age 68.0 ± 10.8 years, 1.5 ? Hoehn and Yahr (H&Y) < 3) were required to wear a triaxial accelerometer 24?h/day for 3 consecutive months. They also underwent a 3D computerized gait analysis at the beginning and end of the PA assessment period. The number of daily steps and PA intensity were calculated on the whole day, and the period from 6:00 to 24:00 was grouped into 3 time slots, using 3 different cut-point sets previously validated in the case of both pwPD and healthy older adults. 3D gait analysis provided spatiotemporal and kinematic parameters of gait, including summary indexes of quality (Gait Profile Score (GPS) and Gait Variable Score (GVS)). Results:The analysis of hourly trends of PA revealed the existence of two peaks located in the morning (approximately at 10) and in the early evening (between 18 and 19). However, during the morning time slot (06:00-12:00), pwPD performed significantly higher amounts of steps (4313 vs. 3437 in the 12:00-18:00 time slot, p < 0.001, and vs. 2889 in the 18:00-24:00 time slot, p=0.021) and of moderate-to-vigorous PA (43.2% vs. 36.3% in the 12:00-18:00 time slot, p=0.002, and vs. 31.4% in the 18:00-24:00 time slot, p=0.049). The correlation analysis shows that several PA intensity parameters are significantly associated with swing-phase duration (rho = -0.675 for sedentary intensity, rho = 0.717 for moderate-to-vigorous intensity, p < 0.001), cadence (rho = 0.509 for sedentary intensity, rho = -0.575 for moderate-to-vigorous intensity, p < 0.05), and overall gait pattern quality as expressed by GPS (rho = -0.498 to -0.606 for moderate intensity, p < 0.05) and GVS of knee flexion-extension (rho = -0.536 for moderate intensity, p < 0.05). Conclusions:Long-term monitoring of PA integrated by the quantitative assessment of spatiotemporal and kinematic parameters of gait may represent a useful tool in supporting a better-targeted prescription of PA and rehabilitative treatments in pwPD.

Dataset Information

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

Publications

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

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