The accurate measurement of habitual physical activity is fundamental to the study of the relationship between physical activity and health. However, many physical activity measurement techniques produce variables accurate to only the day level, such as total energy expenditure via self-report questionnaire, pedometer step counts, or accelerometer measurements of minutes of moderate to vigorous physical activity. Monitoring technologies providing more detailed information on physical activity and inactivity behaviour can now be used to explore the relationships between health and movement frequency, intensity, and duration more comprehensively. This paper explores the activity-inactivity profile that can be acquired through objective monitoring, with a focus on accelerometry. Using previously collected objective data, a detailed physical activity profile is presented and case study examples of data utilization and interpretation are provided. The rich detail captured through comprehensive profiling creates new surveillance and study possibilities and could possibly inform new physical activity guidelines. Data are presented in various formats to demonstrate the dangers of misinterpretation when monitoring population adherence to Canada's physical activity guidelines. Recommendations for physical activity-inactivity profiling are provided and future research needs identified.
Movement/step frequency has been shown (at least in one accelerometer model) to influence the prediction of accelerometry-based energy expenditure (EE). Uniaxial accelerometers are also limited in their ability to distinguish the intensity of faster running speeds and their data outputs may be confounded by a frequency (i.e., step) dependant filtering limitation within the device. These observations have called into question the validity of accelerometers for physical activity monitoring, particularly among individuals of varying height and consequently stride frequency. PUrPose: To determine whether, or the degree to which, the concurrent validity of three popular accelerometer models (Actical, Actigraph, and RT3) are affected by differences in subject height. METHODS: Eighty six participants age 8 to 40 (17.6 ± 8.0) performed three 10-minute bouts of treadmill activity (speed 1 = walk; 2 = walk/jog; 3 = run) ranging in speed from 4 to 12 km/hr. Height quintiles (1 = 125.5–138.7 cm, 2=139.0–155.3 cm, 3=156-166.2 cm, 4=166.8–179.6 cm, 5=181.0–196.1 cm) were created to investigate mean differences between accelerometer predicted EE and measured EE from respiratory gas analysis across speed categories. Validity coefficients between models and within height categories across speed were determined to assess which models provided the most valid EE estimates for individuals of different heights and whether validity changed at different speeds. All analyses were performed using ANOVA and a Bonferroni correction for multiple comparisons (p<0.05). RESULTS: All speeds considered, each model provided the most valid estimate of EE for height categories 3 and 4 (r2 = 0.54 to 0.86). For the tallest individuals, validity coefficients from all models were weak during treadmill running (r2 = 0.14 to 0.39) and mean differences between estimated and measured EE were significantly greater for the tallest individuals in comparison to the shortest individuals across all speeds. All height categories combined, the Actical underestimated EE while the RT3 overestimated EE across all speeds. The Actigraph underestimated EE during treadmill walking, however overestimated EE for taller individuals (i.e., categories 3 to 5) as speed increased. Across all conditions and heights, the RT3 provided the most valid estimate of EE(r2 = 0.58). CONCLUSIONS: When using accelerometry to estimate EE, validity may be affected by changes in treadmill speed and individual differences in height. These factors should be considered when using accelerometry to predict EE or time spent in physical activity, especially in groups with varied heights. Funded in part by statistics Canada
No abstract is provided for this article.
The Canadian Society for Exercise Physiology (CSEP), in partnership with Health Canada and others, released Canada's first physical activity guide for adults in 1998, with specific versions for older adults in 1999 and for children and youth in 2002. Research in the physical activity sciences (e.g., basic science, behavioural assessment, dose-response relationships, epidemiology, health messaging, physical activity measurement) has advanced rapidly since these publications. A detailed review of relevant current research is thus required, to assess whether the existing guidelines and resulting guides need revision or renewal. This introductory paper provides a brief chronology of events leading to the preparation of this journal supplement, including a statement of purpose and an overview of organization and content. A brief discussion of the purpose of the physical activity guidelines and guides, intended biological, psychological, and behavioural outcomes, and the way in which guidelines relate to on-going measurement and surveillance is provided as a context for the papers that follow.
It is unclear if children of different weight status differ in their nutritional habits while watching television. The objective of the present paper was to determine if children who are overweight or obese differ in their frequency of consumption of six food items while watching television compared with their normal-weight counterparts. A cross-sectional study of 550 children (57·1 % female; mean age = 10 years) from Ottawa, Canada was conducted. Children's weight status was categorised using the Centers for Disease Control and Prevention cut-points. Questionnaires were used to determine the number of hours of television watching per day and the frequency of consumption of six types of foods while watching television. Overweight/obese children watched more television per day than normal-weight children (3·3 v. 2·7 h, respectively; P = 0·001). Obese children consumed fast food and fruits/vegetables more frequently while watching television than normal-weight or overweight children (P < 0·05). Children who watched more than 4 h of television per d had higher odds (OR 3·21; 95% CI 1·14, 9·03; P = 0·03) of being obese, independent of several covariates, but not independent of moderate-to-vigorous physical activity. The finding that both television watching and the frequency of consumption of some food items during television watching are higher in children who are obese is concerning. While the nature of the present study does not allow for the determination of causal pathways, future research should investigate these weight-status differences to identify potential areas of intervention.
I thank Dr. Viir and Dr. Veraksits for their interest in sedentary behaviours and for their letter. The Sedentary Behaviour Research Network (SBRN) is an emerging international network of individual members who are interested in research, leadership, education, and advocacy related to the consequences and possible benefits of sedentary behaviour. The mission of SBRN is to advance our understanding of the implications of sedentary behaviours through research, education, and advocacy. One of the greatest barriers to progress toward this mission is the inconsistent definition of “sedentary behaviour” as discussed in our earlier letter (Sedentary Behaviour Research Network 2012). I appreciate the concerns expressed by Drs. Viir and Veraksits. They raise an important point regarding the effects of gravity on physiological functions while in different postures. Furthermore, nuances on how to define “reclining” are raised. The proposal by SBRN that sedentary behaviour be defined as any waking behaviour characterized by an energy expenditure 1.5 METs while in a sitting or reclining posture does not disregard the potential importance of gravity or other mechanical (e.g., pressure on tissues of contact points from sitting or type of clothing being worn) or environmental (e.g., indoor vs. outdoor) factors that may provoke, mediate, or moderate physiological or even cognitive functions. Nor does the definition preclude the study of sedentary behaviours such that perhaps a whole “family” of sedentary behaviour subcategories may emerge based on their physiological influence. The fact that this discussion is occurring is evidence that our earlier letter is achieving its objective and that the mission of SBRN is being successfully pursued. Thanks and please keep the discussion going!
Summary Cross‐sectional associations between objectively‐measured sleep duration, sleep efficiency and sleep timing with adiposity and physical activity were examined in a cohort of 567 children from Ottawa, Canada. Five‐hundred and fifteen children (58.8% female; age: 10.0 ± 0.4 years) had valid sleep measurements and were included in the present analyses. Physical activity, sedentary time and sleep parameters were assessed over 7 days (actigraphy). Height, weight and waist circumference were measured according to standardized procedures. Percentage body fat was assessed using bioelectric impedance analysis. Light physical activity and sedentary time were greater in children with the shortest sleep durations ( P < 0.0001), whereas children with the highest sleep efficiencies had lower light physical activity and more sedentary time across tertiles ( P < 0.0001). In multivariable linear regression analyses, and after adjusting for a number of covariates, sleep efficiency was inversely related to all adiposity indices ( P < 0.05). However, sleep duration and sleep timing were not associated with adiposity indices after controlling for covariates. Inverse associations were noted between sleep duration and light physical activity and sedentary time ( P < 0.0001). Sleep efficiency ( P < 0.0001), wake time and sleep timing midpoint ( P < 0.05) were negatively associated with light physical activity, but positively associated with sedentary time. In conclusion, only sleep efficiency was independently correlated with adiposity in this sample of children. Participants with the shortest sleep durations or highest sleep efficiencies had greater sedentary time. More research is needed to develop better sleep recommendations in children that are based on objective measures of sleep duration, sleep efficiency and sleep timing alike.
