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Elizabeth Lawinger, Tim L. Uhl, Mark Abel and Srinath Kamineni


The overarching goal of this study was to examine the use of triaxial accelerometers in measuring upper-extremity motions to monitor upper-extremity-exercise compliance. There were multiple questions investigated, but the primary objective was to investigate the correlation between visually observed arm motions and triaxial accelerometer activity counts to establish fundamental activity counts for the upper extremity.

Study Design:

Cross-sectional, basic research.


Clinical laboratory.


Thirty healthy individuals age 26 ± 6 y, body mass 24 ± 3 kg, and height 1.68 ± 0.09 m volunteered.


Participants performed 3 series of tasks: activities of daily living (ADLs), rehabilitation exercises, and passive shoulder range of motion at 5 specific velocities on an isokinetic dynamometer while wearing an accelerometer on each wrist. Participants performed exercises with their dominant arm to examine differences between sides. A researcher visually counted all arm motions to correlate counts with physical activity counts provided by the accelerometer.

Main Outcome Measure:

Physical activity counts derived from the accelerometer and visually observed activity counts recorded from a single investigator.


There was a strong positive correlation (r = .93, P < .01) between accelerometer physical activity counts and visual activity counts for all ADLs. Accelerometers activity counts demonstrated side-to-side difference for all ADLs (P < .001) and 5 of the 7 rehabilitation activities (P < .003). All velocities tested on the isokinetic dynamometer were shown to be significantly different from each other (P < .001).


There is a linear relationship between arm motions counted visually and the physical activity counts generated by an accelerometer, indicating that arm motions could be potentially accounted for if monitoring arm usage. The accelerometers can detect differences in relatively slow arm-movement velocities, which is critical if attempting to evaluate exercise compliance during early phases of shoulder rehabilitation. These results provide fundamental information that indicates that triaxial accelerometers have the potential to objectively monitor and measure arm activities during rehabilitation and ADLs.

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Stamatis Agiovlasitis, Robert W. Motl, John T. Foley and Bo Fernhall

This study examined the relationship between energy expenditure and wrist accelerometer output during walking in persons with and without Down syndrome (DS). Energy expenditure in metabolic equivalent units (METs) and activity-count rate were respectively measured with portable spirometry and a uniaxial wrist accelerometer in 17 persons with DS (age: 24.7 ± 6.9 years; 9 women) and 21 persons without DS (age: 26.3 ± 5.2 years; 12 women) during six over-ground walking trials. Combined groups regression showed that the relationship between METs and activity-count rate differed between groups (p < .001). Separate models for each group included activity-count rate and squared activity-count rate as significant predictors of METs (p ≤ .005). Prediction of METs appeared accurate based on Bland-Altman plots and the lack of between-group difference in mean absolute prediction error (DS: 17.07%; Non-DS: 18.74%). Although persons with DS show altered METs to activity-count rate relationship during walking, prediction of their energy expenditure from wrist accelerometry appears feasible.

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Jocelyn F. Hafer, Mark S. Miller, Jane A. Kent and Katherine A. Boyer

. Participants wore accelerometers (GT3X; ActiGraph, Pensacola, FL) at the hip for at least 5 days (including at least 1 weekend day). Weekly time spent in MVPA 43 and weekly activity counts were determined for all participants. Gait analyses were performed from data collected during walking overground at 1.4 m

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Christopher Kuenze, Lisa Cadmus-Bertram, Karin Pfieffer, Stephanie Trigsted, Dane Cook, Caroline Lisee and David Bell

period. Freedson Adult VM3 cut points were then used to categorize physical activity as light, moderate, vigorous, or very vigorous based on the number of activity counts that occurred per minute during periods of wear time. 33 Based on the use of these cut points, we were able to calculate the amount

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Jaehun Jung, Willie Leung, Bridgette Marie Schram and Joonkoo Yun

sample and age), publication year, number of days for data collection, and physical activity levels data. Physical activity levels data were included if it was provided as either (a) average minutes spent in physical activity or (b) physical activity counts measured by accelerometers, so that the effect

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Xihe Zhu and Justin A. Haegele

with oxygen consumption ( Kelly, McMillan, Anderson, Fippinger, Fillerup, & Rider, 2013 ). The ActiGraph GT3x output represents data across three axes (mediolateral, vertical, and anteroposterior) separately and provides activity counts as a composite vector magnitude of these three axes. This specific