TY - JOUR
T1 - Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers
AU - Carlson, Richard H.
AU - Huebner, Derek R.
AU - Hoarty, Carrie A.
AU - Whittington, Jackie
AU - Haynatzki, Gleb
AU - Balas, Michele C.
AU - Schenk, Ana Katrin
AU - Goulding, Evan H.
AU - Potter, Jane F.
AU - Bonasera, Stephen J.
N1 - Funding Information:
Sponsors: This work was funded by a grant to SJB from the Vada Kinman Oldfield Foundation. The Dr. and Mrs. Robert A. Steven Endowment for Research in Geriatrics, and startup support from the University of Nebraska Medical Center. EHG supported by K08 MH071671. Sponsors did not have any role in design, methods, subject recruitment, data collection, analysis or preparation of the manuscript.
PY - 2012/6
Y1 - 2012/6
N2 - 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.
AB - 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.
KW - Actimetry
KW - Cellular phone
KW - Gait speed
KW - Spatio-temporal organization of human behavior
KW - Treadmill locomotion
KW - Validation, actimetry
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U2 - 10.1016/j.gaitpost.2012.02.025
DO - 10.1016/j.gaitpost.2012.02.025
M3 - Article
C2 - 22475727
AN - SCOPUS:84862855760
SN - 0966-6362
VL - 36
SP - 241
EP - 248
JO - Gait and Posture
JF - Gait and Posture
IS - 2
ER -