Technological advances in physical activity measurement have increased the development and utilization of accelerometers and pedometers for assessing physical activity in controlled and free-living conditions. Individual differences in leg length, stride length and stride frequency may affect the reliability and validity of accelerometers in estimating energy expenditure. To address this theory, this thesis investigated the influence of leg length, stride length and stride frequency on accelerometer counts and energy expenditure using four accelerometers (AMP, Actical, MTI, and RT3) and one pedometer (Yamax). Eighty-six participants, age 8 to 40 (17.6 ± 8.0) years performed three ten-minute bouts of treadmill activity at self-selected speeds (4 to 12 km/h). Energy expenditure (kcal/min) was measured through expired gas analysis and used as the criterion standard to compare physical activity data from activity monitors. A 3 (models) x 2 (duplicates of each model) x 3 (speeds) x 7 (minutes) repeated measures ANOVA was used to assess intra-device, inter-device, and inter-model reliability. Coefficients of variation were calculated to compare within-device variation and between-device variation in accelerometer counts. Differences between measured and predicted energy expenditure were assessed across five height categories to determine the influence of leg length on the validity of accelerometer/pedometer data. Regression equations for each model were developed using mean activity counts/steps generated for each speed, adjusting for various predictor variables (i.e., age, weight, leg length). These were compared to model-specific equations to determine whether the addition of certain variables might explain more variance in energy expenditure. Leg length and stride frequency directly influenced variability in accelerometer data and thus predicted energy expenditure. At high speeds and stride frequencies counts began to level off in the Actical, however this did not occur in the other devices. Intra-device and inter-device variation in accelerometer counts was less than 10% and was lowest at very high speeds for the Actical, MTI, and RT3 (p<0.05). When compared to measured values, energy expenditure was consistently underestimated by the AMP, Actical, and Yamax models and consistently overestimated by the RT3 across speed. The MTI underestimated and overestimated energy expenditure depending on speed. Energy expenditure was both underestimated and overestimated to the greatest extent during the treadmill run for the tallest participants (p<0.05). Accelerometer counts or pedometer steps, when entered into regression equations with age, weight and leg length, explained from 85 to 94 % of the variance in measured energy expenditure, supporting the inclusion of these variables within manufacturer-based equations. These results suggest that individual differences in leg length and stride frequency affect the reliability and validity of accelerometer data and therefore must be controlled for when using accelerometry to predict energy expenditure.

Technological advances in physical activity measurement have increased the development and utilization of accelerometers and pedometers for assessing physical activity in controlled and free-living conditions. Individual differences in leg length, stride length and stride frequency may affect the reliability and validity of accelerometers in estimating energy expenditure. To address this theory, this thesis investigated the influence of leg length, stride length and stride frequency on accelerometer counts and energy expenditure using four accelerometers (AMP, Actical, MTI, and RT3) and one pedometer (Yamax). Eighty-six participants, age 8 to 40 (17.6 ± 8.0) years performed three ten-minute bouts of treadmill activity at self-selected speeds (4 to 12 km/h). Energy expenditure (kcal/min) was measured through expired gas analysis and used as the criterion standard to compare physical activity data from activity monitors. A 3 (models) x 2 (duplicates of each model) x 3 (speeds) x 7 (minutes) repeated measures ANOVA was used to assess intra-device, inter-device, and inter-model reliability. Coefficients of variation were calculated to compare within-device variation and between-device variation in accelerometer counts. Differences between measured and predicted energy expenditure were assessed across five height categories to determine the influence of leg length on the validity of accelerometer/pedometer data. Regression equations for each model were developed using mean activity counts/steps generated for each speed, adjusting for various predictor variables (i.e., age, weight, leg length). These were compared to model-specific equations to determine whether the addition of certain variables might explain more variance in energy expenditure. Leg length and stride frequency directly influenced variability in accelerometer data and thus predicted energy expenditure. At high speeds and stride frequencies counts began to level off in the Actical, however this did not occur in the other devices. Intra-device and inter-device variation in accelerometer counts was less than 10% and was lowest at very high speeds for the Actical, MTI, and RT3 (p<0.05). When compared to measured values, energy expenditure was consistently underestimated by the AMP, Actical, and Yamax models and consistently overestimated by the RT3 across speed. The MTI underestimated and overestimated energy expenditure depending on speed. Energy expenditure was both underestimated and overestimated to the greatest extent during the treadmill run for the tallest participants (p<0.05). Accelerometer counts or pedometer steps, when entered into regression equations with age, weight and leg length, explained from 85 to 94 % of the variance in measured energy expenditure, supporting the inclusion of these variables within manufacturer-based equations. These results suggest that individual differences in leg length and stride frequency affect the reliability and validity of accelerometer data and therefore must be controlled for when using accelerometry to predict energy expenditure.

INTRODUCTION: Direct observation (DO) systems have been used for decades to assess free-living PA in children. These traditional DO systems identify the highest intensity observed during alternating observe-and-record periods. Using video-taped DO would allow researchers to code activities and contextual information each time the participant changes their behavior. PURPOSE: To develop and test a novel video-based DO system for children’s free-play activity. METHODS: Following iterative DO system development (The Observer XT, Noldus), 28 children (age=8.4 ± 1.5 years) participated in a 30-minute simulated free-play session that was recorded with a GoPro camera. Participants wore a portable indirect calorimetry (IC) device and an accelerometer on the hip (AG-H) and non-dominant wrist (AG-W). The DO system includes Whole Body Movement (body position, main movement pattern) that was further described with four modifiers: 1) Locomotion, 2) Limb Movement, 3) Activity Type, and 4) MET value. To assess intrarater reliability, an expert coder coded six randomly selected videos from the main sample and recoded the same videos one week later. Six novice coders were trained and coded three videos from the subsample to assess interrater reliability. To assess construct validity, total energy expenditure and time spent in activity intensity categories from DO were compared with IC and accelerometer estimates. RESULTS: Percent agreement for intrarater reliability was above 80% except for Locomotion (47%; video 3) and Limb Movement, MET value and Locomotion (19%, 78%, 26%), respectively, video 4). Across all variables, percent agreement for interrater reliability ranged widely from 12%-96%, 0-100%, and 36%-97% for videos 1, 2 and 3, respectively. Mean estimated time spent in PA intensity categories from AG-H overestimated sedentary (SED) and underestimated light, moderate, and moderate-to-vigorous PA (LPA, MPA, and MVPA; p < 0.001-0.008). AG-W and IC underestimated SED (p=0.03, p=0.03) and LPA (p< 0.001, pONCLUSION: The current DO system is feasible for observing detailed changes in children’s free-play activity. However, refinement to the system must be made to improve reliability before it is adopted as a criterion measure for free-play activity in children.

Pain and movement are intimately connected and nearly universal human experiences. However, our understanding of the extent, significance, and mechanisms of pain-movement relationships is limited. While pain is a normal, protective response to injury and potentially harmful stimuli, prolonged or dysfunctional neuromuscular adaptions in response to pain can contribute to a variety of pain conditions. Alternatively, movement (in the form of global physical activity, individual exercise programs, and/or specific motor learning/functional tasks) is often prescribed to help decrease pain and improve function. While attempts have been made to show an effect of movement on pain or to better understand altered movement strategies in response to pain, much of the research has been limited to animal models or to those with specific persistent or chronic pain conditions limiting generalizability and interpretability. Therefore, this research sought to advance current understanding of the relationships between physical activity and normal variability in centrally- and peripherally-mediated pain in healthy adults. Additionally, we sought to characterize changes in reflexive motor responses in the upper extremity to an endogenous, naturally-occurring, long-lasting acute muscle pain. The results of these investigations indicate that greater, self-reported intense (i.e. vigorous) and leisure activity are more strongly associated with decreased pain sensitivity than is pain modulation or measured activity (via accelerometry). Future research is needed to determine directionality of these relationships. Further, reflexive motor responses to endogenous, acute muscle pain in the upper extremity were not significantly altered indicating that changes in pain-related, movement strategies may be more strongly influenced by supraspinal adaptations. These results may have value in improving understanding of pain-related, movement sequelae and directing future research in this area.