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Full-Text Articles in Kinesiology

Biomechanical Risk Factors For Knee Osteoarthritis In Young Adults: The Influence Of Obesity And Gait Instruction, Julia Ann Freedman Dec 2010

Biomechanical Risk Factors For Knee Osteoarthritis In Young Adults: The Influence Of Obesity And Gait Instruction, Julia Ann Freedman

Doctoral Dissertations

With increasing rates of obesity, research has begun to focus of co-morbidities of obesity such as osteoarthritis. The majority of existing research has focused on older adults as the group most likely to suffer from osteoarthritis. The purpose of this study was to determine if overweight and obese young adults exhibit biomechanical risk factors for knee osteoarthritis, and to determine if young adults with biomechanical risk factors of osteoarthritis can modify these with instruction. This purpose was divided into two separate studies.

Study 1: Thirty adults between 18-35 years old were recruited into three groups according to body mass index: …


A Comparison Of Commonly Used Accelerometer Based Activity Monitors In Controlled And Free-Living Environment, Yuri Feito Dec 2010

A Comparison Of Commonly Used Accelerometer Based Activity Monitors In Controlled And Free-Living Environment, Yuri Feito

Doctoral Dissertations

This dissertation was designed to determine the effects of body mass index (BMI) and walking speed on activity monitor outputs. A secondary purpose was to compare the activity monitors’ performance in a free-living environment. In the first experiment, 71 participants wore three waist-mounted activity monitors (Actical, ActiGraph, and NL-2000) and an ankle-mounted device (StepWatch 3) while walking on a treadmill (40, 67 and 94 m/min). The tilt angle of each device was measured. The Actical recorded 26% higher activity counts (P < 0.01) in obese persons with a tilt <10 degrees, compared to normal weight persons. The ActiGraph was unaffected by BMI or tilt angle. In the second experiment, the steps recorded by the devices were compared to actual steps. Speed had the greatest influence on the accuracy these devices. At 40 m/min, the ActiGraph was the least accurate device for normal weight (38%), overweight (46%) and obese (48%) individuals. The Actical, NL-2000 and StepWatch averaged 65%, 73% and 99% of steps taken, respectively. Lastly, several generations of the ActiGraph (7164, GT1M, and GT3X), and other research grade activity monitors (Actical; ActivPAL; and Digi-Walker) were compared to a criterion measure of steps. Fifty-six participants performed treadmill walking (40, 54, 67, 80 and 94 m/min) and wore the devices for 24-hours under free-living conditions. BMI did not affect step count accuracy during treadmill walking. The StepWatch, PAL, and the AG7164 were the most accurate across all speeds; the other devices were only accurate at the faster speeds. In the free-living environment, all devices recorded about 75% of StepWatch-determined steps, except the AG7164 (99%). Based on these findings, we conclude that BMI does not affect the output of these activity monitors. However, waist-borne activity monitors are highly susceptible to under-counting steps at walking speeds below 67 m/min, or stepping rates below 100 steps/min. An activity monitor worn on the ankle is less susceptible to these speed effects and provides the greatest accuracy for step counting.


A Comparison Of The Actigraph 7164 And The Actigraph Gt1m During Self-Paced Locomotion, Sarah Kozey, John Staudenmayer, Richard Troiano, Patty Freedson May 2010

A Comparison Of The Actigraph 7164 And The Actigraph Gt1m During Self-Paced Locomotion, Sarah Kozey, John Staudenmayer, Richard Troiano, Patty Freedson

Patty S. Freedson

Purpose—This study compared the ActiGraph accelerometer model 7164 (AM1) to the ActiGraph GT1M (AM2) during self-paced locomotion. Methods—Participants n = 116, 18–73y, mean BMI = 26.1) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60 second epochs. To compare differences between monitors, the average difference (bias) in count output and steps output were computed at each speed. Time spent in different activity intensities (light, moderate, vigorous) based on the Freedson et al. cut-points was compared for each …


Walking Class Step Average, Step Intensity, Distance Covered And Leisure Physical Activity Of College Students, Kimberly D. Tallent Mar 2010

Walking Class Step Average, Step Intensity, Distance Covered And Leisure Physical Activity Of College Students, Kimberly D. Tallent

International Journal of Exercise Science: Conference Proceedings

PURPOSE. The purpose of this study was to examine the relationship between stepping levels in and outside of a prescribed walking class and leisure time physical activity (PA) of college students. METHOD. Participants in the study were twenty three male (n = 9) and female (n = 14) enrolled in a walking class that met Monday through Friday for five weeks during the summer. Students completed the short version of the International Physical Activity Questionnaire (IPAQ) at the beginning and end of the course and followed the prescribed walking times during class. Step counts were ascertained both in and outside …


Neighborhood Design And Perceptions: Relationship With Active Commuting, Carolyn C. Voorhees, J. Scott Ashwood, Kelly R. Evenson, John R. Sirard, Ariane L. Rung, Marsha Dowda, Thomas L. Mckenzie Dec 2009

Neighborhood Design And Perceptions: Relationship With Active Commuting, Carolyn C. Voorhees, J. Scott Ashwood, Kelly R. Evenson, John R. Sirard, Ariane L. Rung, Marsha Dowda, Thomas L. Mckenzie

John Sirard

Purpose—Walking to and from school contributes to total physical activity levels. This study investigated whether perceived and actual neighborhood features were associated with walking to or from school among adolescent girls. Methods—A sample of geographically diverse 8th grade girls (N=890) from the Trial for Activity in Adolescent Girls (TAAG) study living within 1.5 miles of their middle school were recruited. Participants completed a self-administered survey on their neighborhood and walking behavior. Geographic information system (GIS) data were used to assess objective neighborhood features. Nested multivariable logistic regression analyses were conducted to determine the contribution of perceived and objective measures of …