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.

Physical inactivity is a modifiable risk factor for cardiovascular diseases and other chronic diseases of life. In countries undergoing economic transition from underdeveloped to being developed, there is an increasing rate of physical inactivity. Accurate assessment of physical activity behaviours is important for determining the presence of physical inactivity, for setting goals for physical therapy interventions to increase physical activity and to utilize physical activity as an outcome measure for physical therapy interventions. There are different techniques used to measure physical activity, namely questionnaires, motion sensors (pedometers and accelerometers) and doubly labelled water. The most used method in large epidemiological research is questionnaires because of their affordability and feasibility. Limitations of physical activity questionnaires include the exclusion of house-hold activities, intensity of work done, bicycling, duration and frequency of leisure time activities. Motion sensors have been mostly used in developed and westernized countries. In the North West Province (NWP) of South Africa the only method that has been used to determine physical activity among the Tswana speaking people was the Transition of Health during urbanization physical activity questionnaire (THUSA-PAQ). The application of other methods such as the motion sensors has never been done. Objectives: The study comprised two major objectives: The first objective was to determine the physical activity levels of the rural and urban Tswana speaking people of the NWP using the THUS A questionnaire and pedometers. The second objective was to determine whether there is a relationship in physical activity determined by the THUSA-PAQ, promotional pedometer and an accelerometer determined activity. Methods The participants recruited for this study form part of the larger prospective urban and rural epidemiology (PURE) longitudinal study running over 12 years which started in 2005. A subsample of 200 was randomly selected of which hundred and eighty signed the informed consent (90 urban and 90 rural) to participate in the study. The participants completed the THUSA-PAQ with the assistance help of the fieldworkers in their native language and wore pedometers for seven consecutive days. The number of steps taken per day distance travelled and energy expenditure were recorded in a logbook. Another thirty eight participants from a co-hort in the same geographical area were issued with accelerometers to wear simultaneously with pedometers for a period of twenty four hours and also completed the THUSA-PAQ. Results The rural male and female participants reported higher average physical activity index (PAT) with the THUSA questionnaire (9.49 ± 3.67 and 8.10 ± 1.26) than urban male and female participants (8.13 ± 2.47 and 7.51 ± 1.65) respectively. The same trend was observed with the objectively determined physical activity with the pedometers. A partial correlation adjusted for age and gender showed no statistical significance between the subjectively determined physical activity index (PAT) and the objectively determined activity (average steps per day). Results from the co-hort participants indicated that both male and female participants spent a larger percentage of their time on sedentary activities (66.45 ± 15.84% and 70.13 ± 8.39%) respectively. Most of the participants, 64.7% females and 52.1% males, recorded fewer than 5000 steps per day with a pedometer and reported high PAI (9.61 ± 1.83 males and 7.79 ± 1.26 females) with the THUSA-PAQ. On this population partial correlation analyses that was adjusted for age and body mass index (BMT) showed a statistical significant relationship between (p<0.05) time spent on vigorous activities and commute index between male and female participants. There was no statistical significant relationship between the PAI (THUSA-PAQ), activity energy expenditure (AEE) determined with an accelerometer and the number of steps per day determined with a pedometer. Conclusion The major conclusion that can be drawn from this study is that the participants did not meet the recommended physical activity levels (30 min moderate physical activity or 10 000 pedometer determined steps per day). The participants reported high subjective physical activity index (PAI) with the THXJSA-PAQ which did not correlate with the low objectively determined number of steps per day using the pedometer and AEE. Possible reasons for this include the influence of perception toward physical activity, social desrrabiUty, seasonal changes, reactivity and time of the year. Motion sensors gave a better indication of habitual physical activity among the Tswana speaking people of the NWP and should be considered for further research. / Thesis (M.Sc. (Human Movement Science))--North-West University, Potchefstroom Campus, 2009.

Physical inactivity is a modifiable risk factor for cardiovascular diseases and other chronic diseases of life. In countries undergoing economic transition from underdeveloped to being developed, there is an increasing rate of physical inactivity. Accurate assessment of physical activity behaviours is important for determining the presence of physical inactivity, for setting goals for physical therapy interventions to increase physical activity and to utilize physical activity as an outcome measure for physical therapy interventions. There are different techniques used to measure physical activity, namely questionnaires, motion sensors (pedometers and accelerometers) and doubly labelled water. The most used method in large epidemiological research is questionnaires because of their affordability and feasibility. Limitations of physical activity questionnaires include the exclusion of house-hold activities, intensity of work done, bicycling, duration and frequency of leisure time activities. Motion sensors have been mostly used in developed and westernized countries. In the North West Province (NWP) of South Africa the only method that has been used to determine physical activity among the Tswana speaking people was the Transition of Health during urbanization physical activity questionnaire (THUSA-PAQ). The application of other methods such as the motion sensors has never been done. Objectives: The study comprised two major objectives: The first objective was to determine the physical activity levels of the rural and urban Tswana speaking people of the NWP using the THUS A questionnaire and pedometers. The second objective was to determine whether there is a relationship in physical activity determined by the THUSA-PAQ, promotional pedometer and an accelerometer determined activity. Methods The participants recruited for this study form part of the larger prospective urban and rural epidemiology (PURE) longitudinal study running over 12 years which started in 2005. A subsample of 200 was randomly selected of which hundred and eighty signed the informed consent (90 urban and 90 rural) to participate in the study. The participants completed the THUSA-PAQ with the assistance help of the fieldworkers in their native language and wore pedometers for seven consecutive days. The number of steps taken per day distance travelled and energy expenditure were recorded in a logbook. Another thirty eight participants from a co-hort in the same geographical area were issued with accelerometers to wear simultaneously with pedometers for a period of twenty four hours and also completed the THUSA-PAQ. Results The rural male and female participants reported higher average physical activity index (PAT) with the THUSA questionnaire (9.49 ± 3.67 and 8.10 ± 1.26) than urban male and female participants (8.13 ± 2.47 and 7.51 ± 1.65) respectively. The same trend was observed with the objectively determined physical activity with the pedometers. A partial correlation adjusted for age and gender showed no statistical significance between the subjectively determined physical activity index (PAT) and the objectively determined activity (average steps per day). Results from the co-hort participants indicated that both male and female participants spent a larger percentage of their time on sedentary activities (66.45 ± 15.84% and 70.13 ± 8.39%) respectively. Most of the participants, 64.7% females and 52.1% males, recorded fewer than 5000 steps per day with a pedometer and reported high PAI (9.61 ± 1.83 males and 7.79 ± 1.26 females) with the THUSA-PAQ. On this population partial correlation analyses that was adjusted for age and body mass index (BMT) showed a statistical significant relationship between (p<0.05) time spent on vigorous activities and commute index between male and female participants. There was no statistical significant relationship between the PAI (THUSA-PAQ), activity energy expenditure (AEE) determined with an accelerometer and the number of steps per day determined with a pedometer. Conclusion The major conclusion that can be drawn from this study is that the participants did not meet the recommended physical activity levels (30 min moderate physical activity or 10 000 pedometer determined steps per day). The participants reported high subjective physical activity index (PAI) with the THXJSA-PAQ which did not correlate with the low objectively determined number of steps per day using the pedometer and AEE. Possible reasons for this include the influence of perception toward physical activity, social desrrabiUty, seasonal changes, reactivity and time of the year. Motion sensors gave a better indication of habitual physical activity among the Tswana speaking people of the NWP and should be considered for further research. / Thesis (M.Sc. (Human Movement Science))--North-West University, Potchefstroom Campus, 2009.

Widespread increases in the prevalence of childhood obesity have raised the prospect of serious public health consequences in many countries. New Zealand is no exception; according to the most recent national estimates, approximately one in three children is overweight or obese. As a consequence, an understanding of the specific risk factors that predict this condition in children is becoming increasingly important. It is generally accepted that the promotion of physical activity is a key strategy for reducing the risk of childhood obesity. However, there is limited information describing physical activity and its relationship with body fatness in young New Zealanders. The overall aim of this thesis was to gain insight into the associations between excess fatness and physical activity in New Zealand children from a diverse range of socio-demographic groups. Three related studies were conducted to achieve this aim: a large descriptive survey of obesity and physical activity patterns in primary-aged children, and two preceding studies which develop the methodology for objective assessment of physical activity in this population. The first study provided the only validation data for the NL-2000 multiday memory (MDM) pedometer in children. In a sample of 85 participants aged 5-7 and 9-11 years, the NL-2000 offered similar accuracy and better precision than the widely used SW-200 pedometer (NL-2000: mean bias = -8.5 ± 13.3%; SW-200: mean bias = -8.6 ± 14.7%). The second study investigated reactivity to wearing pedometers over four 24-hour testing periods in 62 children aged 5-11 years. The sample was divided into two groups: one was given a full explanation of the function of the pedometer, while the other received no information prior to testing. The absence of significant differences in step counts between the first and last test periods indicated that there was no evidence of reactivity to this device for either preparation procedure. The central study presented in this thesis was the measurement of physical activity, body composition, and dietary patterns in 1,226 children aged 5-12 years, from which four chapters (4-7) were derived. The sample was ethnically diverse, with 46.8% European, 33.1% Polynesian, 15.9% Asian, and 4.1% from other ethnicities. Physical activity levels over three weekdays and two weekend days were assessed using NL 2000 pedometers. Percentage body fat (%BF) was determined using hand-to-foot bioelectrical impedance analysis with a prediction equation previously developed for New Zealand children. Waist and hip girths, height, and weight were measured using standard anthropometric techniques. Parent proxy questionnaires were used to assess demographic and lifestyle factors and pedometer compliance. The first reported analyses of this dataset (Chapter 4) examined the effect of weather conditions on children’s activity levels. In boys, a 10ºC rise in ambient temperature was associated with a 10.5% increase in weekday steps and a 26.4% increase in weekend steps. Equivalent temperature changes affected girls’ step counts on weekdays only (16.2% increase). Precipitation also had a substantial impact, with decreases in weekday and weekend step counts during moderate rainfall ranging from 8.3% to 16.3% across all sex, age, and socioeconomic (SES) groups. The aim of Chapter 5 was to understand the relationship between children’s step counts and their body mass index (BMI), waist circumference (WC), and %BF. Mean step counts for this sample were 16,133 ± 3,864 (boys) and 14,124 ± 3,286 (girls) on weekdays, and 12,702 ± 5,048 (boys) and 11,158 ± 4,309 (girls) on weekends. Significant associations were detected between steps.day-1 and both WC and %BF, but not between steps.day-1 and BMI. The findings in Chapter 6 extended these results by estimating the number of steps required to reduce the risk of excess adiposity in children (16,000 and 13,000 steps.day-1 for boys and girls, respectively). Finally, the study described in Chapter 7 examined the associations between excess adiposity and a series of demographic and lifestyle variables, providing the first assessment of body fat correlates in young New Zealanders. Our results indicated that children aged 11-12 years were 15.4 times more likely to be overfat (boys, %BF ≥ 25%; girls, %BF ≥ 30%) than those aged 5-6 years. In addition, the odds of overfat were 1.8 times greater in Asian children than in European children, and 2.7 times greater in the low SES group when compared with the high SES group. Three modifiable behaviours related to fat status were also identified: low physical activity, skipping breakfast, and insufficient sleep on weekdays. Clustering of these risk factors resulted in a cumulative increase in the prevalence of overfat.

No description available.

Objective: To determine the relationship between body mass index (BMI) and soft drink consumption among adolescents. It is hypothesized that soft drink consumption contributes to overweight and obesity among adolescents. Background: Research examining the relationship between body mass index and soft drink consumption is inconsistent. Several studies have found a negative association between total sugar intake and BMI; however, others have found a link between sugar-sweetened drinks and obesity. There are no known studies that have controlled for physical activity. Data and Methods: Data on approximately 225 adolescents were used. Frequency of soft drink consumption, type of milk, and calcium intake were assessed using the Youth and Adolescent Questionnaire (YAQ). Body Mass Index was calculated from height and weight measurements and adjusted for age. Physical activity levels were assessed using data recorded from the My Life Stepper 2515 digital pedometer. Age, birthday, grade, sex, and ethnicity were reported on the consent form. Results: When treated as a categorical variable, soft drink consumption was a marginal predictor of adjusted BMI (p = 0.0802). The relationship between soft drink consumption and adjusted BMI is not linear and does not follow a monotonic trend. Other variables found to significantly influence BMI were type of milk, total step mean, and calcium. Discussion and Conclusions: The results of this study conclude that soft drink consumption is related to BMI among adolescents. This relationship is marginally significant; it is significant at the 0.10 level but not at the 0.05 level. Those who were in the highest soft drink consumption category had a higher mean BMI than those in the other soft drink consumption categories. Soft drink consumption, type of milk, total step mean, and calcium together predict about 10% of the variability in BMI.

Music is used and can be found in everyday life and throughout society. With many studies pointing towards music being a motivating stimulus for exercise, it is plausible that music would positively affect the physical activity rates of junior high school students in physical education classes. Thus, the purpose of this study was to examine the effects of popular music on physical activity rates, via pedometry, and enjoyment levels of junior high physical education students. There were 305 junior high physical education students that participated in the study with 151 being male and 154 being female. This was a quasi-experimental study using a two conditions, with and without music, by two activities, basketball and volleyball, cross-over design. It is found that across all grades and gender, more steps were taken with music in both activities versus without music. No statistically significant differences are noted in time in activity between activities with music than without. When comparing the level of enjoyment of the activities with music versus without across genders and all grades, the level of enjoyment is higher with music than without, though the difference is not statistically significant. While statistically significant differences can be found and attributed to the very nature of the differences between volleyball and basketball, there are also several statistical significances found that can be described and attributed to the intervention of the use of music during that activity. Therefore, if teachers are looking for a way for their students to increase step counts and increase the level of enjoyment their students feel throughout an activity, adding music to the background of the activity will help teachers to achieve those goals.

No description available.

Physical activity promotion in schools is a critical component of adolescent health. The main purpose of this study was to test the efficacy of a school-based program to increase the physical activity of 6th grade students. A total of 113 students in a large suburban public middle school participated in the 11 week study. A quasi-experimental design was used. Physical education (PE) classes served as the unit of randomization. Six PE classes were assigned to the control condition and six PE classes to the experimental condition. Control group students were asked to wear unsealed pedometers throughout the day in school and at home and to record their daily step-counts in school. Experimental group students also wore unsealed pedometers throughout the day and logged their daily step-counts in school, but additionally received a 10,000 step per day goal, were asked to attain an increased step-count goal during PE class, and received an enhanced PE curriculum. Pre- and post-test data were gathered for all dependent measures including average daily step-counts by week, GPA, attendance, tardiness, attitude and self-efficacy toward physical activity, and Presidential Physical Fitness Tests. The data analysis was completed using analyses of variance (ANOVAs), analysis of covariance (ANCOVA), paired sample t-tests, and independent sample t-tests. Results revealed significant gains in physical activity for both treatment conditions. Both groups demonstrated significantly increased step-counts relative to their baseline step-counts. The intervention did not produce significant changes in attitude or self-efficacy. There were some significant improvements in physical fitness and the scholastic measures, but these changes were not attributed to the intervention. Very low attrition, a high compliance rate, and favorable participant feedback were also noted. Overall, this study revealed that, in the short-term, it is possible to significantly improve physical activity without changing an adolescent's self-efficacy or attitude. An important finding of this study was that multi-faceted self-monitoring was the most critical factor that contributed to increased physical activity. / Kinesiology