John R. Sirard

Physical Activity Assessment in Children and Adolescents

Chronic disease risk factors, including a sedentary lifestyle, may be present even in young children, suggesting that early prevention programmes may be critical to reducing the rates of chronic disease. Accurate assessment of physical activity in children is necessary to identify current levels of activity and to assess the effectiveness of intervention programmes designed to increase physical activity. This article summarises the strengths and limitations of the methods used to evaluate physical activity in children and adolescents. MEDLINE searches and journal article citations were used to locate 59 articles that validated physical activity measurement methods in children and adolescents. Only those methods that were validated against a more stringent measure were included in the review.Based on the definition of physical activity as any bodily movement resulting in energy expenditure (EE), direct observation of the individual’s movement should be used as the gold standard for physical activity research. The doubly labelled water technique and indirect calorimetry can also be considered criterion measures for physical activity research, because they measure EE, a physiologic consequence closely associated with physical activity. Devices such as heart rate monitors, pedometers and accelerometers have become increasingly popular as measurement tools for physical activity. These devices reduce the subjectivity inherent in survey methods and can be used with large groups of individuals. Heart rate monitoring is sufficiently valid to use in creating broad physical activity categories (e.g. highly active, somewhat active, sedentary) but lacks the specificity needed to estimate physical activity in individuals. Laboratory and field validations of pedometers and accelerometers yield relatively high correlations using oxygen consumption (r = 0.62 to 0.93) or direct observation (r = 0.80 to 0.97) as criterion measures, although, they may not be able to capture all physical activity.Physical activity has traditionally been measured with surveys and recall instruments. These techniques must be used cautiously in a paediatric population that has difficulty recalling such information. Still, some studies have reported 73.4% to 86.3% agreement between these instruments and direct observation. Future investigations of physical activity instruments should validate the novel instrument against a higher standard. Additional studies are needed to investigate the possibility of improving the accuracy ofmeasurement by combining 2 or more techniques. The accurate measurement of physical activity is critical for determining current levels of physical activity, monitoring compliance with physical activity guidelines, understanding the dose-response relationship between physical activity and health and determining the effectiveness of intervention programmes designed to improve physical activity.

Compliance with physical activity guidelines: Prevalence in a population of children and youth

PURPOSE To use objective monitoring of physical activity to determine the percentages of children and youth in a population that met physical activity guidelines. METHODS A total of 375 students in grades 1-12 wore an accelerometer (CSA 7164) for seven consecutive days. Bouts of continuous activity and accumulation of minutes spent in physical activity at various intensities were calculated to determine how many students met three physical activity guidelines. RESULTS Over 90% of students met Healthy People 2010, Objective 22.6 and nearly 70% met the United Kingdom Expert Consensus Group guideline, both of which recommend daily accumulation of moderate physical activity. Less than 3% met Healthy People 2010, Objective 22.7, which calls for bouts of continuous vigorous physical activity. For the United Kingdom Expert Consensus Group guideline, compliance decreased markedly with age, but gender differences were not statistically significant. CONCLUSIONS Prevalence estimates for compliance with national physical activity guidelines varied markedly for the three guidelines examined. Objective monitoring of physical activity in youth appears to be feasible and may provide more accurate prevalence rates than self-report measures.

INFLUENCES OF PRESCHOOL POLICIES AND PRACTICES ON CHILDREN'S PHYSICAL ACTIVITY

The objective of this study was to determine if moderate to vigorous physical activity (MVPA) of 3–5 year old preschool children varied with differences in policies/practices, and overall quality of preschools. A total of 266 children (47% males, 60% African American) from 9 preschools were observed for 1 hour on 3 different days. PA of children was observed twice per minute and scored as 1–5, with 1 for stationary/motionless and 5 for fast movement. Summary MVPA was calculated over the 3 days as percent of times observed at levels of 4 or 5, and percent of time at levels 1 or 2 as sedentary activity. A structured interview about PA policies was conducted with an administrator at each preschool and overall quality of the preschool was assessed using Early Childhood Environment Rating Scale-Revised Edition (ECERS-R). Preschools were divided into groups according to whether a specific policy/practice that would be logically hypothesized to promote PA was in place at the school. MVPA differences between groups of children was assessed using mixed ANOVA controlling for preschool. When preschools offered more field trips, and more college educated teachers, the children participated in more MVPA. Children who attended preschools with lower quality spent more time in sedentary activity. In conclusion, children in preschools which may have more resources and better quality appear to show both more sedentary behavior and more MVPA.

Physical activity in overweight and non-overweight pre-school children

OBJECTIVE To compare the physical activity levels of overweight and non overweight 3- to 5-y-old children while attending preschool. A secondary aim was to evaluate weight-related differences in hypothesized parental determinants of child physical activity behavior. DESIGN: Cross-sectional study. SUBJECTS A total of 245, 3- to 5-y-olds (127 girls, 118 boys) and their parent(s) (242 mothers, 173 fathers) recruited from nine preschools. Overweight status determined using the age- and sex-specific 85th percentile for body mass index (BMI) from CDC Growth Charts. MEASUREMENTS Physical activity during the preschool day was assessed on multiple days via two independent objective measuresFdirect observation using the observation system for recording activity in preschools (OSRAP) and real-time accelerometry using the MTI/CSA 7164 accelerometer. Parents completed a take-home survey assessing sociodemographic information, parental height and weight, modeling of physical activity, support for physical activity, active toys and sporting equipment at home, child’s television watching, frequency of park visitation, and perceptions of child competence. RESULTS Overweight boys were significantly less active than their nonoverweight peers during the preschool day. No significant differences were observed in girls. Despite a strong association between childhood overweight status and parental obesity, no significant differences were observed for the hypothesized parental influences on physical activity behavior. CONCLUSIONS Our results suggest that a significant proportion of overweight children may be at increased risk for further gains in adiposity because of low levels of physical activity during the preschool day.

CALIBRATION OF THE COMPUTER SCIENCE AND APPLICATIONS, INC. (CSA) ACCELEROMETER 256

Monitoring physical activity with electronic devices that use non-volatile memory is an attractive technology as it allows the user to examine patterns in physical activity as well as total activity. A new device that may be used for such purposes is the CSA accelerometer (model #7164). The device i

Prevalence of Active Commuting at Urban and Suburban Elementary Schools in Columbia, SC

We directly observed the prevalence of walking and bicycling (active commuting) to 8 randomly selected urban and suburban elementary schools. When school was used as the unit of analysis, only 5.0% of the students actively commuted to or from school across all observed trips. Active commuting was not affected (P >/=; 18) by school urbanization level, school socioeconomic status, time of day, day of week, weather conditions, or temperature. These results indicate a need for school- and community-based interventions.

Multilevel predictors of adolescent physical activity: a longitudinal analysis.

BackgroundTo examine how factors from a social ecologic model predict physical activity (PA) among adolescents using a longitudinal analysis.MethodsParticipants in this longitudinal study were adolescents (ages 10-16 at baseline) and one parent enrolled in the Transdisciplinary Research on Energetics and Cancer-Identifying Determinants of Eating and Activity (TREC-IDEA) and the Etiology of Childhood Obesity (ECHO). Both studies were designed to assess a socio-ecologic model of adolescent obesity risk. PA was collected using ActiGraph activity monitors at two time points 24 months apart. Other measures included objective height and weight, adolescent and parent questionnaires on multilevel psychological, behavioral and social determinants of PA, and a home PA equipment inventory. Analysis was conducted using SAS, including descriptive characteristics, bivariate and stepped multivariate mixed models, using baseline adjustment. Models were stratified by gender.ResultsThere were 578 adolescents with complete data. Results suggest few statistically significant longitudinal associations with physical activity measured as minutes of MVPA or total counts from accelerometers. For boys, greater self-efficacy (B = 0.75, p = 0.01) and baseline MVPA (B = 0.55, p < 0.01) remained significantly associated with MVPA at follow-up. A similar pattern was observed for total counts. For girls, baseline MVPA (B = 0.58, p = 0.01) and barriers (B = -0.32, p = 0.05) significantly predicted MVPA at follow-up in the full model. The full multilevel model explained 30% of the variance in PA among boys and 24% among girls.ConclusionsPA change in adolescents is a complex issue that is not easily understood. Our findings suggest early PA habits are the most important predictor of PA levels in adolescence. Intervention may be necessary prior to middle school to maintain PA through adolescence.

Adolescent Physical Activity and Screen Time: Associations With the Physical Home Environment

BackgroundPrevious research on the environment and physical activity has mostly focused on macro-scale environments, such as the neighborhood environment. There has been a paucity of research on the role of micro-scale and proximal environments, such as that of the home which may be particularly relevant for younger adolescents who have more limited independence and mobility. The purpose of this study was to describe associations between the home environment and adolescent physical activity, sedentary time, and screen time.MethodsA total of 613 parent-adolescent dyads were included in these analyses from two ongoing cohort studies. Parents completed a Physical Activity and Media Inventory (PAMI) of their home environment. Adolescent participants (49% male, 14.5 ± 1.8 years) self-reported their participation in screen time behaviors and wore an ActiGraph accelerometer for one week to assess active and sedentary time.ResultsAfter adjusting for possible confounders, physical activity equipment density in the home was positively associated with accelerometer-measured physical activity (p < 0.01) among both males and females. Most of the PAMI-derived measures of screen media equipment in the home were positively associated with adolescent females screen time behavior (p ≤ 0.03). In addition, the ratio of activity to media equipment was positively associated with physical activity (p = 0.04) in both males and females and negatively associated with screen time behavior for females (p < 0.01).ConclusionsThe home environment was associated with physical activity and screen time behavior in adolescents and differential environmental effects for males and females were observed. Additional research is warranted to more comprehensively assess the home environment and to identify obesogenic typologies of families so that early identification of at-risk families can lead to more informed, targeted intervention efforts.

Adolescent Physical Activity and the Built Environment: A Latent Class Analysis Approach

This study used latent class analysis to classify adolescent home neighborhoods (n=344) according to built environment characteristics, and tested how adolescent physical activity, sedentary behavior, and screen time differ by neighborhood type/class. Four distinct neighborhood classes emerged: (1) low-density retail/transit, low walkability index (WI), further from recreation; (2) high-density retail/transit, high WI, closer to recreation; (3) moderate-high-density retail/transit, moderate WI, further from recreation; and (4) moderate-low-density retail/transit, low WI, closer to recreation. We found no difference in adolescent activity by neighborhood class. These results highlight the difficulty of disentangling the potential effects of the built environment on adolescent physical activity.

Validation of Accelerometer Thresholds and Inclinometry for Measurement of Sedentary Behavior in Young Adult University Students.

Sedentary behavior (SB) is a major contributor to obesity and significant morbidity and mortality in adolescence and adulthood, yet measurement of SB is still evolving. The purpose of this study was to assess the degree of construct validity of the inclinometer function and single-axis and vector magnitude accelerometry metrics of the ActiGraph GT3X+ in objectively measuring SB and physical activity in 28 young adult university students who performed nine semi-structured activities, each for five minutes: lying, sitting, reading, seated video gaming, video watching, seated conversation, standing, stationary biking, and treadmill walking. Inclinometry and four output metrics from the ActiGraph were analyzed in comparison to direct observation by a researcher recorded each minute. For overall accuracy in measuring both SB and physical activity, all four accelerometer metrics (94.7-97.8%) outperformed the inclinometer function (70.9%). Vector magnitude accelerometry with a threshold of 150 counts per minute as the cut point for sedentary behavior was superior to other methods. While accelerometry was more accurate overall at detecting the behaviors tested, inclinometry had some advantages over accelerometry methods at detecting walking, biking, and standing. The findings support use of accelerometry as a valid objective measure of body movement, while use of inclinometry as a sole measure is not recommended. Additional research would be beneficial to improve the calibration of the inclinometer and explore ways of combining this with accelerometer data for objectively measuring SB and physical activity.Sedentary behavior (SB) is a major contributing factor to obesity and significant morbidity and mortality in adolescence and into adulthood, yet measuring SB is difficult. The purpose of this study was to assess the degree of construct validity of the ActiGraph GT3X+ inclinometer function and accelerometry metrics in objectively measuring SB and physical activity in young adult university students while performing semi-structured activities: lying, sitting, reading, playing seated video gaming, video watching, seated conversation, standing, stationary biking, and walking. These nine activities were objectively assessed using inclinometry and four output metrics from the ActiGraph based on the axis used to detect activity. Considering overall accuracy in measuring both sedentary behavior and physical activity, all four accelerometer metrics (94.7%-97.8%) outperformed the inclinometer function (70.9%). While accelerometry was overall more accurate at detecting the behaviors tested, inclinometry had similar or better accuracy than some accelerometry methods at detecting walking, biking, and standing. Using vector magnitude accelerometry with a sedentary threshold of 150 counts per minute was superior to other methods. The findings support use of accelerometry as a valid objective measure of body movement while use of inclinometry as a sole measure is not recommended. Additional research would be beneficial to improve the inclinometer and explore ways of combining this with accelerometer data for objectively measuring SB and physical activity.