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Additional file 1: Supplementary Figure 1. Weekday rest-activity profiles of 24-hour actigraphy data from adults in the National Health and Nutrition Examination Survey (2011-2014). Each panel depicts the mean 24-hour activity patterns for participants with high (solid line) and low (dotted) eigenvalues of the first four components derived from the functional principal component analysis (PCA): A) The first component (48.5% variance), with higher eigenvalues representing a higher amplitude; B) the second component (20.6% variance), with higher eigenvalues representing earlier rise time; C) the third component (10.1% variance), with higher eigenvalues representing a more prolonged daytime activity window; D) the forth component (6.2% variance), with higher eigenvalues representing a more pronounce biphasic pattern characterized by a mid-day dip in activity. Supplementary Figure 2. Weekend rest-activity profiles of 24-hour actigraphy data from adults in the National Health and Nutrition Examination Survey (2011-2014). Each panel depicts the mean 24-hour activity patterns for participants with high (solid line) and low (dotted) eigenvalues of the first four components derived from the functional principal component analysis (PCA): A) The first component (42.4% variance), with higher eigenvalues representing a higher amplitude; B) the second component (21.8% variance), with higher eigenvalues representing earlier rise time; C) the third component (10.1% variance), with higher eigenvalues representing a more prolonged daytime activity window; D) the forth component (6.7% variance), with higher eigenvalues representing a later activity peak. Supplementary Table 1. Distribution of study characteristics among 7,657 participants aged 25 or older in the National Health and Nutrition Examination Survey (2011-2014). Supplementary Table 2. Pearson correlation coefficients between overall, weekday and weekend rest-activity profiles among adults in the National Health and Nutrition Examination Survey (2011-2014).

Additional File 1: Fig. S1. Assessment of frequency and volume of intentional weight loss. Table S1. All-Cause Mortality HRs for Frequency of Weight Loss Attempts; overall and by sex. Table S2. All-Cause Mortality HRs for Frequency of Weight Loss Attempts; by historical BMI, weight change, age, and smoking status. Table S3. Mortality HRs for Joint Effects of Total Weight loss and frequency of Weight Loss Attempts. Table S4. Sub-distribution cause-specific Mortality HRs for Frequency of Weight Loss Attempts. Table S5. All-Cause Mortality HRs for Frequency of Weight Loss Attempts with imputed missing data. Table S6. Comparison of Weight Change Definitions for Stratified All-Cause Mortality HRs for Frequency of Weight Loss Attempts.

Additional file 1.

Background High amounts of sedentary behaviour have been associated with increased risks of several chronic conditions and mortality. However, it is unclear whether physical activity attenuates or even eliminates the detrimental effects of prolonged sitting. We examined the associations of sedentary behaviour and physical activity with all-cause mortality. Methods We did a systematic review, searching six databases (PubMed, PsycINFO, Embase, Web of Science, Sport Discus, and Scopus) from database inception until October, 2015, for prospective cohort studies that had individual level exposure and outcome data, provided data on both daily sitting or TV-viewing time and physical activity, and reported effect estimates for all-cause mortality, cardiovascular disease mortality, or breast, colon, and colorectal cancer mortality. We included data from 16 studies, of which 14 were identified through a systematic review and two were additional unpublished studies where pertinent data were available. All study data were analysed according to a harmonised protocol, which categorised reported daily sitting time and TV-viewing time into four standardised groups each, and physical activity into quartiles (in metabolic equivalent of task [MET]-hours per week). We then combined data across all studies to analyse the association of daily sitting time and physical activity with all-cause mortality, and estimated summary hazard ratios using Cox regression. We repeated these analyses using TV-viewing time instead of daily sitting time. Findings Of the 16 studies included in the meta-analysis, 13 studies provided data on sitting time and all-cause mortality. These studies included 1 005 791 individuals who were followed up for 2–18·1 years, during which 84 609 (8·4%) died. Compared with the referent group (ie, those sitting 35·5 MET-h per week]), mortality rates during follow-up were 12–59% higher in the two lowest quartiles of physical activity (from HR=1·12, 95% CI 1·08–1·16, for the second lowest quartile of physical activity [8 h/day). Daily sitting time was not associated with increased all-cause mortality in those in the most active quartile of physical activity. Compared with the referent (35·5 MET-h per week]), there was no increased risk of mortality during follow-up in those who sat for more than 8 h/day but who also reported >35·5 MET-h per week of activity (HR=1·04; 95% CI 0·99–1·10). By contrast, those who sat the least (

Monte Carlo sampling techniques have broad applications in machine learning, Bayesian posterior inference, and parameter estimation. Often the target distribution takes the form of a product distribution over a dataset with a large number of entries. For sampling schemes utilizing gradient information it is cheaper for the derivative to be approximated using a random small subset of the data, introducing extra noise into the system. We present a new discretization scheme for underdamped Langevin dynamics when utilizing a stochastic (noisy) gradient. This scheme is shown to bias computed averages to second order in the stepsize while giving exact results in the special case of sampling a Gaussian distribution with a normally distributed stochastic gradient.

Changes in accelerometry-derived physical activity and sedentary time variables (baseline to 6-months) between HIGH and MODERATE groups in BETA, Alberta, Canada, 2010â 2014. (DOCX 18 kb)

Changes in self-reported physical activity and sedentary time variables (baseline to 24-months) between HIGH and MODERATE groups in BETA, Alberta, Canada, 2010â 2014. (DOCX 23 kb)

Changes in self-reported physical activity and sedentary time variables (baseline to 12-months) between HIGH and MODERATE groups in BETA, Alberta, Canada, 2010â 2014. (DOCX 23 kb)

For large-scale power networks, the failure of particular transmission lines can offload power to other lines and cause self-protection trips to activate, instigating a cascade of line failures. In extreme cases, this can bring down the entire network. Learning where the vulnerabilities are and the expected timescales for which failures are likely is an active area of research. In this article we present a novel stochastic dynamics model for a large-scale power network along with a framework for efficient computer simulation of the model including long timescale events such as cascade failure. We build on an existing Hamiltonian formulation and introduce stochastic forcing and damping components to simulate small perturbations to the network. Our model and simulation framework allow assessment of the particular weaknesses in a power network that make it susceptible to cascade failure, along with the timescales and mechanism for expected failures.

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by BMI at age 18Â years in postmenopausal women in the Womenâ s Health Initiative Observational Study. (PDF 94 kb)

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by current BMI (

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by current BMI in postmenopausal women not using menopausal hormone therapy among control subjects only in the Womenâ s Health Initiative Observational Study. (PDF 103 kb)

Percentages of parent estrogens (estradiol and estrone) and child estrogen metabolites (2-, 4-, and 16-hydroxylation pathway metabolites) out of summed estrogens/estrogen metabolites by current BMI among current menopausal hormone therapy users. (PPTX 63 kb)

Percentages of parent estrogens (estradiol and estrone) and child estrogen metabolites (2-, 4-, and 16-hydroxylation pathway metabolites) out of summed estrogens/estrogen metabolites by current BMI among current menopausal hormone therapy users. (PPTX 63 kb)

Percentages of each pathway estrogens/estrogen metabolites (2-catechols, methylated 2-catechols, 4-catechols, methylated 4-catechols, 16-pathway metabolites) out of summed child estrogen metabolites by current BMI among current menopausal hormone therapy users. (PPTX 70 kb)

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by waist-to-hip ratio in postmenopausal women currently using menopausal hormone therapy in the Womenâ s Health Initiative Observational Study. (PDF 95 kb)

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by BMI at age 18Â years in postmenopausal women in the Womenâ s Health Initiative Observational Study. (PDF 94 kb)

Geometric means (pmol/L) and 95% CIs of serum estrogens/estrogen metabolites by waist-to-hip ratio in postmenopausal women currently using menopausal hormone therapy in the Womenâ s Health Initiative Observational Study. (PDF 95 kb)

Questions on sleep habits and sleep disorders in the sleep disorder questionnaire, NHANES 2005–2006. Table S2. Population distribution by sleep characteristic in the analytic sample, NHANES 2005–2006. Table S3. Spearman correlation coefficient a among sleep variables, NHANES 2005–2006. Table S4. Spearman correlation coefficient a among measurements of adiposity, by sex, NHANES 2005–2006. Table S5. Study participant characteristics by snoring, NHANES 2005–2006. Table S6. Study participant characteristics by sleep quality, NHANES 2005–2006. Table S7. Multivariate a associations between sleep characteristics and measurements of adiposity in men, NHANES 2005–2006. (DOCX 51 kb)

Questions on sleep habits and sleep disorders in the sleep disorder questionnaire, NHANES 2005–2006. Table S2. Population distribution by sleep characteristic in the analytic sample, NHANES 2005–2006. Table S3. Spearman correlation coefficient a among sleep variables, NHANES 2005–2006. Table S4. Spearman correlation coefficient a among measurements of adiposity, by sex, NHANES 2005–2006. Table S5. Study participant characteristics by snoring, NHANES 2005–2006. Table S6. Study participant characteristics by sleep quality, NHANES 2005–2006. Table S7. Multivariate a associations between sleep characteristics and measurements of adiposity in men, NHANES 2005–2006. (DOCX 51 kb)

Supplementnal Methods and Results. (DOC 692 kb)

In recent years, there has been a growing body of public health research examining the role of community trails and paths in the promotion and maintenance of physical activity. However, little is known about how much activity occurs on trails, the impact of community trails on overall physical activity levels or about the relationships between specific trail characteristics and utilization. The integration of activity measurements technologies, specifically accelerometers and wearable global positioning system (GPS) units that can track spatial patterns of activity, provide a unique opportunity to study some of these issues. The current transdisciplinarystudy builds on a previous Active Living Research project that developed and evaluated objective geographic information system (GIS) measures of trail characteristics. This will be accomplished by objectively measuring activity of users with two devices and linking activity data to detailed environmental data on trails.

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Changes in self-reported physical activity and sedentary time variables (12- to 24-months) between HIGH and MODERATE groups in BETA, Alberta, Canada, 2010â 2014. (DOCX 22 kb)

Changes in self-reported physical activity and sedentary time variables (12- to 24-months) between HIGH and MODERATE groups in BETA, Alberta, Canada, 2010â 2014. (DOCX 22 kb)

Background: The last few years have seen renewed interest in use-of-time recalls in epidemiological studies, driven by a focus on the 24-h day [including sleep, sitting, and light physical activity (LPA)] rather than just moderate-vigorous physical activity (MVPA). This paper describes four different computerised use-of-time instruments (ACT24, PAR, MARCA and cpar24) and presents population time-use data from a collective sample of 8286 adults from different population studies conducted in Australia/New Zealand, Germany and the United States. Methods: The instruments were developed independently but showed a number of similarities: they were self administered through the web or used computer-assisted telephone interviews; all captured energy expenditure using variants of the Ainsworth Compendium; each had been validated against criterion measures; and they used a domain structure whereby activities were aggregated under categories such as Personal Care and Work. Results: Estimates of physical activity level (average daily rate of energy expenditure in METs) ranged from 1.53 to 1.78 in the four studies, strikingly similar to population estimates derived from doubly labelled water. There was broad agreement in the amount of time spent in sleep (7.2–8.6 h), MVPA (1.6–3.1 h), personal care (1.6–2.4 h), and transportation(1.1–1.8 h). There were consistent sex differences, with women spending 28–81% more time on chores, 8–40% more time in LPA, and 3–39% less time in MVPA than men. Conclusions: Although there were many similarities between instruments, differences in operationalizing definitions of sedentary behaviour and LPA resulted in substantive differences in the amounts of time reported in sedentary and physically active behaviours. Future research should focus on deriving a core set of basic activities and associated energy expenditure estimates, an agreed classificatory hierarchy for the major behavioural and activity domains, and systems to capture relevant social and environmental contexts. Refereed/Peer-reviewed