Kinesiology Commons^™

Knoxville Moves: Log-In And Get Mobile, Courtney Marie Monroe

Doctoral Dissertations

This dissertation evaluated 1) the efficacy of a course-based Internet-technology intervention rooted in social cognitive theory (SCT) for increasing step counts in university faculty and staff, and 2) the effect of online social support tools on step counts among adults using a randomized control trial.

Thirty-six sedentary/insufficiently active university faculty and staff participated in an eight-week, Internet-delivered walking intervention. They received an Omron HJ-720ITC pedometer, personal step goals, and access to a Blackboard LearnTM website comprised of SCTbased features. Outcomes included daily steps, social support, self-regulation, self-efficacy, and outcome expectations. Participants significantly increased their average daily steps (p < 0.001) between baseline and week 1 by 1800. A similar increase in daily steps was observed between baseline and all other intervention weeks (p < 0.001). Social support and self-regulation significantly improved (p < 0.001). These findings helped inform the design of the second study.

In this …

Investigating The Role Of A Reduced-Instruction Approach In Implicit And Explicit Motor Learning Strategies, Kevin Michael Fisher

Doctoral Dissertations

Traditional explanations of motor learning contend that skills are learned explicitly in a process in which learners accumulate declarative knowledge and progress through distinct stages of learning (e.g., Fitts & Posner, 1967). More recently, implicit approaches to instruction have been used in an attempt to bypass accumulation of explicit knowledge. Such approaches have been shown to facilitate motor learning compared to explicit instruction by enhancing skill retention and transfer under conditions involving distraction, increased pressure, or physical stress (Masters & Poolton, 2012). One method thought to invoke implicit learning involves instructions in the form of an analogy (Liao & Masters, …

Active Families In The Great Outdoors: A Family-Centered Program To Increase Physical Activity Levels, Perceptions, And Behaviors, Jennifer Irene Flynn

Doctoral Dissertations

Physical activity levels of adults and children are low, and the amount of time children spend being active outdoors is decreasing. Parents play a critical role in developing health behaviors of children and ways to increase physical activity and encourage an active family culture are needed. Promotion of family outdoor physical activity, which includes at least one parent and one child from a home engaging in physical activity together, is understudied. Therefore, the purpose of this dissertation was to 1) describe the type, frequency, and duration of family physical activity, 2) determine whether changes in the duration and number of …

Associations Of Total Activity Counts And Physical Activity Intensity Levels With The Metabolic Syndrome: A Structural Equation Modeling Approach, Dana Lizbeth Wolff

Doctoral Dissertations

To clarify the protective benefits of physical activity (PA), epidemiologists and public health researchers continue to seek improved methods of assessing PA. In particular, accelerometers have gained acceptance with researchers as they provide reliable estimates of PA and can record both the amount and intensity of ambulatory movement. However, there is concern that accelerometer data reduction techniques may not provide quantitatively accurate measurements of time spent in various PA intensity categories. One way to circumvent these inaccuracies is to use the accelerometer-derived total activity counts (TAC), which is a more direct expression of what the monitor records.

In order to …

Development And Validation Of Accelerometer-Based Activity Classification Algorithms For Older Adults: A Machine Learning Approach, Jeffer Eidi Sasaki

Doctoral Dissertations

Machine learning algorithms to classify activity type from wearable accelerometers are important to improve our understanding of the relationship between physical activity (PA) and risk for physical disability in older adults. Therefore, the main objective of this dissertation was to develop and evaluate machine learning algorithms to predict activity type and intensity in older adults from a commercially available accelerometer (ActiGraph GT3X+). In Study 1, we developed machine learning algorithms to classify activity type and intensity from raw accelerometer data in older adults. Thirty-five older adults performed an activity routine comprised of different activities (5 min/activity) while wearing three ActiGraph …