Introducing novel approaches for examining the variability of individuals' physical activity

Tudor-Locke and colleagues previously assessed steps/day for 1 year. The aim of this study was to use this data set to introduce a novel approach for the investigation of whether individual's physical activity exhibits periodicity fluctuating round a mean and, if so, the degree of fluctuation and whether the mean changes over time. Twenty-three participants wore a pedometer for 365 days, recorded steps/day and whether the day was a workday. Fourier transform of each participant's daily steps data showed the physical activity had a periodicity of 7 days in half of the participants, matching the periodicity of the workday pattern. Activity level remained stable in half of the participants, decreased in ten participants and increased in two. In conclusion, the 7-day periodicity of activity in half of the participants and correspondence with the workday pattern suggest a social or environmental influence. The novel analytical approach introduced herein allows the determination of the periodicity of activity, the degree of variability in activity that is tolerated during day-to-day life and whether the activity level is stable. Results from the use of these methodologies in larger data sets may enable a more focused approach to the design of interventions that aim to increase activity

Thresholds of physical activity associated with obesity by level of sedentary behaviour in children

BACKGROUND: It is unknown whether moderate-to-vigorous physical activity (MVPA) thresholds for obesity should be adapted depending on level of sedentary behaviour in children.OBJECTIVE: The objective of the study is to determine the MVPA thresholds that best discriminate between obese and non-obese children, by level of screen time and total sedentary time in 12 countries.METHODS: This multinational, cross-sectional study included 6522 children 9-11 years of age. MVPA and sedentary time were assessed using waist-worn accelerometry, while screen time was self-reported. Obesity was defined according to the World Health Organization reference data.RESULTS: Receiver operating characteristic curve analyses showed that the best thresholds of MVPA to predict obesity ranged from 53.8 to 73.9 min d-1in boys and from 41.7 to 58.7 min d-1in girls, depending on the level of screen time. The MVPA cut-offs to predict obesity ranged from 37.9 to 75.9 min d-1in boys and from 32.5 to 62.7 min d-1in girls, depending on the level of sedentary behaviour. The areas under the curve ranged from 0.57 to 0.73 ('fail' to 'fair' accuracy), and most sensitivity and specificity values were below 85%, similar to MVPA alone. Country-specific analyses provided similar findings.CONCLUSIONS: The addition of sedentary behaviour levels to MVPA did not result in a better predictive ability to classify children as obese/non-obese compared with MVPA alone.</p

Inequality in physical activity, sedentary behaviour, sleep duration and risk of obesity in children : a 12-country study

ObjectiveStudies examining associations between movement behaviours (i.e. physical activity, sedentary behaviour and sleep duration) and obesity focus on average values of these movement behaviours, despite important within-country and between-country variability. A better understanding of movement behaviour inequalities is important for developing public health policies and behaviour-change interventions. The objective of this ecologic analysis at the country level was to determine if inequality in movement behaviours is a better correlate of obesity than average movement behaviour volume in children from all inhabited continents of the world. MethodsThis multinational, cross-sectional study included 6,128 children 9-11years of age. Moderate-to-vigorous physical activity (MVPA), total sedentary time (SED) and sleep period time were monitored over 7 consecutive days using waist-worn accelerometry. Screen time was self-reported. Inequality in movement behaviours was determined using Gini coefficients (ranging from 0 [complete equality] to 1 [complete inequality]). ResultsThe largest inequality in movement behaviours was observed for screen time (Gini of 0.32; medium inequality), followed by MVPA (Gini of 0.21; low inequality), SED (Gini of 0.07; low inequality) and sleep period time (Gini of 0.05; low inequality). Average MVPA (hd(-1)) was a better correlate of obesity than MVPA inequality (r=-0.77 vs. r=0.00, p=0.03). Average SED (hd(-1)) was also a better correlate of obesity than SED inequality (r=0.52 vs. r=-0.32, p=0.05). Differences in associations for screen time and sleep period time were not statistically significant. MVPA in girls was found to be disproportionally lower in countries with more MVPA inequality. ConclusionsFindings from this study show that average MVPA and SED should continue to be used in population health studies of children as they are better correlates of obesity than inequality in these behaviours. Moreover, the findings suggest that MVPA inequality could be greatly reduced through increases in girls' MVPA alone.Peer reviewe

Manatee (Trichechus manatus) vocalization usage in relation to environmental noise levels

Author Posting. © Acoustical Society of America, 2009. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 125 (2009): 1806-1815, doi:10.1121/1.3068455.Noise can interfere with acoustic communication by masking signals that contain biologically important information. Communication theory recognizes several ways a sender can modify its acoustic signal to compensate for noise, including increasing the source level of a signal, its repetition, its duration, shifting frequency outside that of the noise band, or shifting the timing of signal emission outside of noise periods. The extent to which animals would be expected to use these compensation mechanisms depends on the benefit of successful communication, risk of failure, and the cost of compensation. Here we study whether a coastal marine mammal, the manatee, can modify vocalizations as a function of behavioral context and ambient noise level. To investigate whether and how manatees modify their vocalizations, natural vocalization usage and structure were examined in terms of vocalization rate, duration, frequency, and source level. Vocalizations were classified into two call types, chirps and squeaks, which were analyzed independently. In conditions of elevated noise levels, call rates decreased during feeding and social behaviors, and the duration of each call type was differently influenced by the presence of calves. These results suggest that ambient noise levels do have a detectable effect on manatee communication and that manatees modify their vocalizations as a function of noise in specific behavioral contexts.This research was supported by a P.E.O. Scholar Award and National Defense Science and Engineering Fellowship awarded to Jennifer Miksis

Temporal and bi-directional associations between sleep duration and physical activity/sedentary time in children:An international comparison

The purpose of this multinational and cross-sectional study was to investigate whether nighttime sleep duration was associated with physical activity (PA) and sedentary time (SED) the following day, whether daytime PA/SED were associated with sleep duration the subsequent night, and whether the associations were modified by sex and study sites. Data from 5779 children aged 9-11years were analyzed. A waist-worn Actigraph GT3X+ accelerometer was used to assess children's 24-h movement behaviours for 7days, i.e. sleep duration, total SED, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA). Multilevel linear regression models were used to account for the repeated measures nested within participants (there were up to 7 sleep→PA/SED and PA/SED→sleep pairings per participant) and schools, and adjusted for covariates. To facilitate interpretation, all sleep and PA/SED variables were standardized. Results showed that the relationship between sleep and PA/SED is bi-directional in this international sample of children. Specifically, for each one standard deviation (SD) unit increase in sleep duration, SED the following day decreased by 0.04 SD units, while LPA and MVPA increased by 0.04 and 0.02 SD units, respectively. Sleep duration decreased by 0.02 SD units and increased by 0.04 SD units for each one SD unit increase in SED and MVPA, respectively. Sleep duration was not affected by changes in LPA. These associations differed across sex and study sites in both directions. However, since the observed effect sizes are subtle, public health initiatives should consider the clinical and practical relevance of these findings.</p

Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study

BACKGROUND: The Canadian 24-h movement guidelines were developed with the hope of improving health and future health outcomes in children and youth. The purpose of this study was to evaluate adherence to the 3 recommendations most strongly associated with health outcomes in new 24-h movement guidelines and their relationship with adiposity (obesity and body mass index z-score) across countries participating in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).METHODS: Cross-sectional results were based on 6128 children aged 9-11 years from the 12 countries of ISCOLE. Sleep duration and moderate-to-vigorous physical activity (MVPA) were assessed using accelerometry. Screen time was measured through self-report. Body weight and height were measured. Body mass index (BMI, kg · m(-2)) was calculated, and BMI z-scores were computed using age- and sex-specific reference data from the World Health Organization. Obesity was defined as a BMI z-score &gt; +2 SD. Meeting the overall 24-h movement guidelines was defined as: 9 to 11 h/night of sleep, ≤2 h/day of screen time, and at least 60 min/day of MVPA. Age, sex, highest parental education and unhealthy diet pattern score were included as covariates in statistical models. Associations between meeting vs. not meeting each single recommendation (and combinations) with obesity were assessed with odds ratios calculated using generalized linear mixed models. A linear mixed model was used to examine the differences in BMI z-scores between children meeting vs. not meeting the different combinations of recommendations.RESULTS: The global prevalence of children meeting the overall recommendations (all three behaviors) was 7%, with children from Australia and Canada showing the highest adherence (15%). Children meeting the three recommendations had lower odds ratios for obesity compared to those meeting none of the recommendations (OR = 0.28, 95% CI 0.18-0.45). Compared to not meeting the 24-h movement recommendations either independently or combined, meeting them was significantly associated with a lower BMI z-score. Whenever the MVPA recommendation was included in the analysis the odds ratios for obesity were lower.CONCLUSIONS: For ISCOLE participants meeting these 3 healthy movement recommendations the odds ratios of being obese or having high BMI z-scores were lower. However, only a small percentage of children met all recommendations. Future efforts should aim to find promising ways to increase daily physical activity, reduce screen time, and ensure an adequate night's sleep in children.TRIAL REGISTRATION: The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) was registered at ClinicalTrials.gov (Identifier NCT01722500) (October 29, 2012).</p

Improving the identification of bone‐specific physical activity using wrist‐worn accelerometry: A cross‐sectional study in 11–12‐year‐old Australian children

This is the final version. Available from Wiley via the DOI in this record. DATA AVAILABILITY STATEMENT: The LSAC and CheckPoint data are available under license at https:// growingupinaustralia.gov.au/data‐and‐documentation/accessing‐lsac ‐data. To access raw accelerometry data, please contact the MCRI's LifeCourse initiative https://lifecourse.melbournechildrens.comPhysical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone‐ relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11–12‐ year‐olds. Participants (n = 770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress‐strain index. Seven‐day wrist‐worn raw acceleration data averaged over 1‐s epochs was used to estimate time accumulated above incremental PA intensities (50 milli‐gravitational unit (mg) increments from 200 to 3000 mg). Associations between time spent above each 50 mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross‐sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone‐related outcomes as the intensity increased in 50 mg increments from >200 to >700 mg. All outcomes became significant at >700 mg (R2 change = 0.6%–1.3% and p = 0.001–0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non‐significant for all outcomes >1500 mg. Using more appropriate accelerometry methods (1‐s epochs; no a priori application of traditional cut‐points) enabled us to identify that ~10 min/day of PA >700 mg (equivalent to running ~10 km/h) was positively associated with pQCT‐derived measures of bone density, geometry and strength in 11–12‐year‐ olds.Australian National Health and Medical Research CouncilAustralian National Health and Medical Research CouncilRoyal Children's Hospital FoundationNIHR Leicester Biomedical Research CentreVictorian Deaf Education InstituteEconomic and Social Research CouncilUniversity of MelbourneNational Heart Foundation of AustraliaCollaboration for Leadership in Applied Health Research and Care East MidlandsFinancial Markets Foundation for ChildrenFinancial Markets Foundation for ChildrenNHMCR Principal Research FellowshipMurdoch Children's Research Institut

Estimating the number of children exposed to parental psychiatric disorders through a national health survey

<p>Abstract</p> <p>Objective</p> <p>Children whose parents have psychiatric disorders experience an increased risk of developing psychiatric disorders, and have higher rates of developmental problems and mortality. Assessing the size of this population is important for planning of preventive strategies which target these children.</p> <p>Methods</p> <p>National survey data (CCHS 1.2) was used to estimate the number of children exposed to parental psychiatric disorders. Disorders were diagnosed using the World Psychiatric Health Composite International Diagnostic Interview (WMH-CIDI) (12 month prevalence). Data on the number of children below 12 years of age in the home, and the relationship of the respondents with the children, was used to estimate exposure. Parent-child relations were identified, as was single parenthood. Using a design-based analysis, the number of children exposed to parental psychiatric disorders was calculated.</p> <p>Results</p> <p>Almost 570,000 children under 12 live in households where the survey respondent met criteria for one or more mood, anxiety or substance use disorders in the previous 12 months, corresponding to 12.1% of Canadian children under the age of 12. Almost 3/4 of these children have parents that report receiving no mental health care in the 12 months preceding the survey. For 17% of all Canadian children under age 12, the individual experiencing a psychiatric disorder is the only parent in the household.</p> <p>Conclusion</p> <p>The high number of children exposed causes major concern and has important implications. Although these children will not necessarily experience adversities, they possess an elevated risk of accidents, mortality, and of developing psychiatric disorders. We expect these estimates will promote further research and stimulate discussion at both health policy and planning tables.</p

Sleep characteristics and health-related quality of life in 9- to 11-year-old children from 12 countries

IntroductionPrevious studies have linked short sleep duration, poor sleep quality, and late sleep timing with lower health-related quality of life (HRQoL) in children. However, almost all studies relied solely on self-reported sleep information, and most studies were conducted in high-income countries. To address these gaps, we studied both device-measured and self-reported sleep characteristics in relation to HRQoL in a sample of children from 12 countries that vary widely in terms of economic and human development.MethodsThe study sample included 6,626 children aged 9-11 years from Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the United Kingdom, and the United States. Waist-worn actigraphy was used to measure total sleep time, bedtime, wake-up time, and sleep efficiency on both weekdays and weekends. Children also reported ratings of sleep quantity and quality. HRQoL was measured by the KIDSCREEN-10 survey. Multilevel regression models were used to determine the relationships between sleep characteristics and HRQoL.ResultsResults showed considerable variation in sleep characteristics, particularly duration and timing, across study sites. Overall, we found no association between device-measured total sleep time, sleep timing or sleep efficiency, and HRQoL. In contrast, self-reported ratings of poor sleep quantity and quality were associated with HRQoL.ConclusionsSelf-reported, rather than device-based, measures of sleep are related to HRQoL in children. The discrepancy related to sleep assessment methods highlights the importance of considering both device-measured and self-reported measures of sleep in understanding its health effects