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  • Author: Jeffer E. Sasaki x
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Rachel E. Klaren, Jeffer E. Sasaki, Edward McAuley and Robert W. Motl

Background:

Physical inactivity is common in persons with multiple sclerosis (MS), but there is very little known about the pattern and predictors of changes in physical activity over time.

Purpose:

This study examined changes in moderate-to-vigorous physical activity (MVPA) over a 30-month time period and the demographic and clinical predictors of such changes in relapsing-remitting MS (RRMS).

Methods:

269 persons with MS wore an accelerometer for a 7-day period and completed a demographic/clinical scale every 6 months over a 30-month period. Data were analyzed using latent class growth modeling (LCGM).

Results:

LCGM identified a two-class model for changes in levels of MVPA over time. Class 1 involved higher initial levels of MVPA and linear decreases in MVPA over time, whereas Class 2 involved lower initial levels of MVPA and linear increases in MVPA over time. LCGM further indicated that males were more likely (OR = 5.8, P < .05) and those with higher disability status were less likely (OR = 0.51, P < .05) to belong to Class 1 than Class 2.

Conclusion:

Levels of MVPA change over time in persons with RRMS and the pattern of change suggests that behavioral physical activity interventions for persons with MS might target men and those with lower disability.

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Amanda Hickey, Dinesh John, Jeffer E. Sasaki, Marianna Mavilia and Patty Freedson

Background:

There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting.

Methods:

Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi-Walker SW-200, 2 ActiGraph GT3Xs (1 in “low-frequency extension” [AGLFE] and 1 in “normal-frequency” mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings.

Results:

Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW.

Conclusion:

This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.