This research studied the effects of cerebral palsy on the ability to plan and execute a one-handed aiming task. Simple reaction time (SRT) was fractionated into its premotor (PMT) and motor (MOT) components. Subjects were 20 youths, 10 with cerebral palsy and 10 nonhandicapped youths. The effect of accuracy demands on the planning and execution time was also studied by manipulating endpoint target size. Significant differences in PMT but not in MOT were obtained between groups, indicating that spastic hemiplegic cerebral palsied youths require more time to plan a simple aiming movement due to central processing limitations. Although manipulation of endpoint target size did not affect programming time for either group, the time to execute the movement increased significantly. This finding suggests that instead of incorporating the parameter of movement accuracy in the selected motor program, subjects adopted a feedback driven strategy to achieve greater endpoint accuracy.
Susan Parks, Debra J. Rose and John M. Dunn
Lindsay P. Toth, Susan Park, Whitney L. Pittman, Damla Sarisaltik, Paul R. Hibbing, Alvin L. Morton, Cary M. Springer, Scott E. Crouter and David R. Bassett
Purpose: To examine the effect of brief, intermittent stepping bouts on step counts from 10 physical activity monitors (PAMs). Methods: Adults (N = 21; M ± SD, 26 ± 9.0 yr) wore four PAMs on the wrist (Garmin Vivofit 2, Fitbit Charge, Withings Pulse Ox, and ActiGraph wGT3X-BT [AG]), four on the hip (Yamax Digi-Walker SW-200 [YX], Fitbit Zip, Omron HJ-322U, and AG), and two on the ankle (StepWatch [SW] with default and modified settings). AG data were processed with and without the low frequency extension (AGL) and with the Moving Average Vector Magnitude algorithm. In Part 1 (five trials), walking bouts were varied (4–12 steps) and rest intervals were held constant (10 s). In Part 2 (six trials), walking bouts were held constant (4 steps) and rest intervals were varied (1–10 s). Percent of hand-counted steps and mean absolute percentage error were calculated. One sample t-test was used to compare percent of hand-counted steps to 100%. Results: In Parts 1 and 2, the SWdefault, SWmodified, YX, and AGLhip captured within 10% of hand-counted steps across nearly all conditions. In Part 1, estimates of most methods improved as the number of steps per bout increased. In Part 2, estimates of most methods decreased as the rest duration increased. Conclusion: Most methods required stepping bouts of >6–10 consecutive steps to record steps. Rest intervals of 1–2 seconds were sufficient to break up walking bouts in many methods. The requirement for several consecutive steps in some methods causes an underestimation of steps in brief, intermittent bouts.
Susan Park, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter and David R. Bassett
It has become common to wear physical activity monitors on the wrist to estimate steps per day, but few studies have considered step differences between monitors worn on the dominant and non-dominant wrists. Purpose: The purpose of this study was to compare four step counting methods on the dominant versus non-dominant wrist using the Fitbit Charge (FC) and ActiGraph GT9X (GT9X) across all waking hours of one day. Methods: Twelve participants simultaneously wore two monitors (FC and GT9X) on each wrist during all waking hours for an entire day. GT9X data were analyzed with three step counting methods: ActiLife algorithm with default filter (AG-noLFE), ActiLife algorithm with low-frequency extension (AG-LFE), and the Moving Average Vector Magnitude (AG-MAVM) algorithm. A 2-way repeated measures ANOVA (method × wrist) was used to compare step counts. Results: There was a significant main effect for wrist placement (F(1,11) = 11.81, p = .006), with the dominant wrist estimating an average of 1,253 more steps than the non-dominant wrist. Steps differed between the dominant and non-dominant wrist for three of the step methods: AG-noLFE (1,327 steps), AG-LFE (2,247 steps), AG-MAVM (825 steps), and approached statistical significance for FC (613 steps). No significant method x wrist placement interaction was found (F(3,9) = 2.62, p = .115). Conclusion: Findings suggest that for step counting algorithms, it may be important to consider the placement of wrist-worn monitors since the dominant wrist location tended to yield greater step estimates. Alternatively, standardizing the placement of wrist-worn monitors could help to reduce the differences in daily step counts across studies.