Cross-sectional associations between sleep duration, sedentary time, physical activity, and adiposity indicators among Canadian preschool-aged children using compositional analyses

Abstract Background Sleep duration, sedentary behaviour, and physical activity are three co-dependent behaviours that fall on the movement/non-movement intensity continuum. Compositional data analyses provide an appropriate method for analyzing the association between co-dependent movement behaviour data and health indicators. The objectives of this study were to examine: (1) the combined associations of the composition of time spent in sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with adiposity indicators; and (2) the association of the time spent in sleep, sedentary behaviour, LPA, or MVPA with adiposity indicators relative to the time spent in the other behaviours in a representative sample of Canadian preschool-aged children. Methods Participants were 552 children aged 3 to 4 years from cycles 2 and 3 of the Canadian Health Measures Survey. Sedentary time, LPA, and MVPA were measured with Actical accelerometers (Philips Respironics, Bend, OR USA), and sleep duration was parental reported. Adiposity indicators included waist circumference (WC) and body mass index (BMI) z-scores based on World Health Organization growth standards. Compositional data analyses were used to examine the cross-sectional associations. Results The composition of movement behaviours was significantly associated with BMI z-scores (p = 0.006) but not with WC (p = 0.718). Further, the time spent in sleep (BMI z-score: γ sleep  = −0.72; p = 0.138; WC: γ sleep  = −1.95; p = 0.285), sedentary behaviour (BMI z-score: γ SB  = 0.19; p = 0.624; WC: γ SB  = 0.87; p = 0.614), LPA (BMI z-score: γ LPA  = 0.62; p = 0.213, WC: γ LPA  = 0.23; p = 0.902), or MVPA (BMI z-score: γ MVPA  = −0.09; p = 0.733, WC: γ MVPA  = 0.08; p = 0.288) relative to the other behaviours was not significantly associated with the adiposity indicators. Conclusions This study is the first to use compositional analyses when examining associations of co-dependent sleep duration, sedentary time, and physical activity behaviours with adiposity indicators in preschool-aged children. The overall composition of movement behaviours appears important for healthy BMI z-scores in preschool-aged children. Future research is needed to determine the optimal movement behaviour composition that should be promoted in this age group

Active video games and health indicators in children and youth: a systematic review

BACKGROUND: Active video games (AVGs) have gained interest as a way to increase physical activity in children and youth. The effect of AVGs on acute energy expenditure (EE) has previously been reported; however, the influence of AVGs on other health-related lifestyle indicators remains unclear. OBJECTIVE: This systematic review aimed to explain the relationship between AVGs and nine health and behavioural indicators in the pediatric population (aged 0–17 years). DATA SOURCES: Online databases (MEDLINE, EMBASE, psycINFO, SPORTDiscus and Cochrane Central Database) and personal libraries were searched and content experts were consulted for additional material. DATA SELECTION: Included articles were required to have a measure of AVG and at least one relevant health or behaviour indicator: EE (both habitual and acute), adherence and appeal (i.e., participation and enjoyment), opportunity cost (both time and financial considerations, and adverse events), adiposity, cardiometabolic health, energy intake, adaptation (effects of continued play), learning and rehabilitation, and video game evolution (i.e., sustainability of AVG technology). RESULTS: 51 unique studies, represented in 52 articles were included in the review. Data were available from 1992 participants, aged 3–17 years, from 8 countries, and published from 2006–2012. Overall, AVGs are associated with acute increases in EE, but effects on habitual physical activity are not clear. Further, AVGs show promise when used for learning and rehabilitation within special populations. Evidence related to other indicators was limited and inconclusive. CONCLUSIONS: Controlled studies show that AVGs acutely increase light- to moderate-intensity physical activity; however, the findings about if or how AVG lead to increases in habitual physical activity or decreases in sedentary behaviour are less clear. Although AVGs may elicit some health benefits in special populations, there is not sufficient evidence to recommend AVGs as a means of increasing daily physical activity

Light-intensity physical activity and cardiometabolic biomarkers in US adolescents

BackgroundThe minimal physical activity intensity that would confer health benefits among adolescents is unknown. The purpose of this study was to examine the associations of accelerometer-derived light-intensity (split into low and high) physical activity, and moderate- to vigorous-intensity physical activity with cardiometabolic biomarkers in a large population-based sample.MethodsThe study is based on 1,731 adolescents, aged 12&ndash;19 years from the 2003/04 and 2005/06 National Health and Nutrition Examination Survey. Low light-intensity activity (100&ndash;799 counts/min), high light-intensity activity (800 counts/min to &lt;4 METs) and moderate- to vigorous-intensity activity (&ge;4 METs, Freedson age-specific equation) were accelerometer-derived. Cardiometabolic biomarkers, including waist circumference, systolic blood pressure, diastolic blood pressure, HDL-cholesterol, and C-reactive protein were measured. Triglycerides, LDL- cholesterol, insulin, glucose, and homeostatic model assessments of &beta;-cell function (HOMA-%B) and insulin sensitivity (HOMA-%S) were also measured in a fasting sub-sample (n=807).ResultsAdjusted for confounders, each additional hour/day of low light-intensity activity was associated with 0.59 (95% CI: 1.18&ndash;0.01) mmHG lower diastolic blood pressure. Each additional hour/day of high light-intensity activity was associated with 1.67 (2.94&ndash;0.39) mmHG lower diastolic blood pressure and 0.04 (0.001&ndash;0.07) mmol/L higher HDL-cholesterol. Each additional hour/day of moderate- to vigorous-intensity activity was associated with 3.54 (5.73&ndash;1.35) mmHG lower systolic blood pressure, 5.49 (1.11&ndash;9.77)% lower waist circumference, 25.87 (6.08&ndash;49.34)% lower insulin, and 16.18 (4.92&ndash;28.53)% higher HOMA-%S.ConclusionsTime spent in low light-intensity physical activity and high light-intensity physical activity had some favorable associations with biomarkers. Consistent with current physical activity recommendations for adolescents, moderate- to vigorous-intensity activity had favorable associations with many cardiometabolic biomarkers. While increasing MVPA should still be a public health priority, further studies are needed to identify dose-response relationships for light-intensity activity thresholds to inform future recommendations and interventions for adolescents.</div

Managing sedentary behavior to reduce the risk of diabetes and cardiovascular disease

Modern human environments are vastly different from those of our forebears. Rapidly advancing technology in transportation, communications, workplaces, and home entertainment confer a wealth of benefits, but increasingly come with costs to human health. Sedentary behavior—too much sitting as distinct from too little physical activity—contributes adversely to cardiometabolic health outcomes and premature mortality. Findings from observational epidemiology have been synthesized in meta-analyses, and evidence is now shifting into the realm of experimental trials with the aim of identifying novel mechanisms and potential causal relationships. We discuss recent observational and experimental evidence that makes a compelling case for reducing and breaking up prolonged sitting time in both the primary prevention and disease management contexts. We also highlight future research needs, the opportunities for developing targeted interventions, and the potential of population-wide initiatives designed to address too much sitting as a health risk

Physical activity and aerobic fitness in children after liver transplantation

To determine physical activity (PA), aerobic fitness, muscle strength, health-related quality of life (HRQOL), fatigue, and participation in children after liver transplantation. Children, 6-12 years, at least one year after liver transplantation, participated in this cross-sectional study. Measurements: Time spent in moderate to vigorous PA (MVPA) was measured using an accelerometer, and aerobic fitness (VO2 peak) was measured by cardiopulmonary exercise testing. Muscle strength was measured by hand-held dynamometry. Fatigue was measured using the multidimensional fatigue scale, and HRQOL with the Pediatric Quality of life Core scales and leisure activities was measured using the Children's Assessment of Participation and Enjoyment. Outcomes (medians and interquartile range (IQR)) were compared to norm values. Twenty-six children participated in this study (14 boys, age 9.7 years, IQR 7.7;11.4). Children spent 0.8 hours/d (IQR 0.6;1.1) on MVPA. One child met the recommendation of at least 1 hour of MVPA every day of the week. Aerobic fitness was similar to norms (VO2 peak 1.4 (L)(/min), IQR 1.1;1.7, Z-score -0.3). Z-scores of muscle strength ranged between -1.4 and -0.4 and HRQOL and fatigue between -2.3 and -0.4. Participation was similar to published norms (Z-scores between -0.6 and 0.6). Young children after liver transplantation have similar MVPA patterns and aerobic fitness compared to published norms. Despite lower HRQOL, more fatigue, and less muscle strength, these children have similar participation in daily activities. Although children do well, it remains important to stimulate PA in children after liver transplantation in the context of long-term management

Physiological and molecular responses to an acute bout of reduced-exertion high-intensity interval training (REHIT)

PurposeWe have previously shown that 6 weeks of reduced-exertion high-intensity interval training (REHIT) improves V˙O2V˙O2 max in sedentary men and women and insulin sensitivity in men. Here, we present two studies examining the acute physiological and molecular responses to REHIT.MethodsIn Study 1, five men and six women (age: 26 ± 7 year, BMI: 23 ± 3 kg m−2, V˙O2V˙O2 max: 51 ± 11 ml kg−1 min−1) performed a single 10-min REHIT cycling session (60 W and two 20-s ‘all-out’ sprints), with vastus lateralis biopsies taken before and 0, 30, and 180 min post-exercise for analysis of glycogen content, phosphorylation of AMPK, p38 MAPK and ACC, and gene expression of PGC1α and GLUT4. In Study 2, eight men (21 ± 2 year; 25 ± 4 kg·m−2; 39 ± 10 ml kg−1 min−1) performed three trials (REHIT, 30-min cycling at 50 % of V˙O2V˙O2 max, and a resting control condition) in a randomised cross-over design. Expired air, venous blood samples, and subjective measures of appetite and fatigue were collected before and 0, 15, 30, and 90 min post-exercise.ResultsAcutely, REHIT was associated with a decrease in muscle glycogen, increased ACC phosphorylation, and activation of PGC1α. When compared to aerobic exercise, changes in V˙O2V˙O2 , RER, plasma volume, and plasma lactate and ghrelin were significantly more pronounced with REHIT, whereas plasma glucose, NEFAs, PYY, and measures of appetite were unaffected.ConclusionsCollectively, these data demonstrate that REHIT is associated with a pronounced disturbance of physiological homeostasis and associated activation of signalling pathways, which together may help explain previously observed adaptations once considered exclusive to aerobic exercise

Long non-coding RNAs and cancer: a new frontier of translational research?

Author manuscriptTiling array and novel sequencing technologies have made available the transcription profile of the entire human genome. However, the extent of transcription and the function of genetic elements that occur outside of protein-coding genes, particularly those involved in disease, are still a matter of debate. In this review, we focus on long non-coding RNAs (lncRNAs) that are involved in cancer. We define lncRNAs and present a cancer-oriented list of lncRNAs, list some tools (for example, public databases) that classify lncRNAs or that scan genome spans of interest to find whether known lncRNAs reside there, and describe some of the functions of lncRNAs and the possible genetic mechanisms that underlie lncRNA expression changes in cancer, as well as current and potential future applications of lncRNA research in the treatment of cancer.RS is supported as a fellow of the TALENTS Programme (7th R&D Framework Programme, Specific Programme: PEOPLE—Marie Curie Actions—COFUND). MIA is supported as a PhD fellow of the FCT (Fundação para a Ciência e Tecnologia), Portugal. GAC is supported as a fellow by The University of Texas MD Anderson Cancer Center Research Trust, as a research scholar by The University of Texas System Regents, and by the Chronic Lymphocytic Leukemia Global Research Foundation. Work in GAC’s laboratory is supported in part by the NIH/ NCI (CA135444); a Department of Defense Breast Cancer Idea Award; Developmental Research Awards from the Breast Cancer, Ovarian Cancer, Brain Cancer, Multiple Myeloma and Leukemia Specialized Programs of Research Excellence (SPORE) grants from the National Institutes of Health; a 2009 Seena Magowitz–Pancreatic Cancer Action Network AACR Pilot Grant; the Laura and John Arnold Foundation and the RGK Foundation

Translations equations to compare ActiGraph GT3X and Actical accelerometers activity counts

Background: This study aimed to develop a translation equation to enable comparison between Actical and ActiGraph GT3X accelerometer counts recorded minute by minute. Methods: Five males and five females of variable height, weight, body mass index and age participated in this investigation. Participants simultaneously wore an Actical and an ActiGraph accelerometer for two days. Conversion algorithms and R2 were calculated day by day for each subject between the omnidirectional Actical and three different ActiGraph (three-dimensional) outputs: 1) vertical direction, 2) combined vector, and 3) a custom vector. Three conversion algorithms suitable for minute/minute conversions were then calculated from the full data set. Results: The vertical ActiGraph activity counts demonstrated the closest relationship with the Actical, with consistent moderate to strong conversions using the algorithm: y = 0.905x, in the day by day data (R2 range: 0.514 to 0.989 and average: 0.822) and full data set (R2 = 0.865). Conclusions: The Actical is most sensitive to accelerations in the vertical direction, and does not closely correlate with three-dimensional ActiGraph output. Minute by minute conversions between the Actical and ActiGraphvertical component can be confidently performed between data sets and might allow further synthesis of information between studies

The contribution of office work to sedentary behaviour associated risk

Background: Sedentary time has been found to be independently associated with poor health and mortality. Further, a greater proportion of the workforce is now employed in low activity occupations such as office work. To date, there is no research that specifically examines the contribution of sedentary work to overall sedentary exposure and thus risk. The purpose of the study was to determine the total exposure and exposure pattern for sedentary time, light activity and moderate/vigorous physical activity (MVPA) of office workers during work and non-work time.Methods: 50 office workers from Perth, Australia wore an Actical (Phillips, Respironics) accelerometer during waking hours for 7 days (in 2008–2009). Participants recorded wear time, waking hours, work hours and daily activities in an activity diary. Time in activity levels (as percentage of wear time) during work and non-work time were analysed using paired t-tests and Pearson’s correlations.Results: Sedentary time accounted for 81.8% of work hours (light activity 15.3% and MVPA 2.9%), which was significantly greater than sedentary time during non-work time (68.9% p 30 minutes) and significantly less brief duration (0–10 minutes) light intensity activity during work hours compared to non-work time (p < 0.001). Further, office workers had fewer breaks in sedentary time during work hours compared to non-work time (p < 0.001).Conclusions: Office work is characterised by sustained sedentary time and contributes significantly to overall sedentary exposure of office workers