The activPAL monitor, often worn 24 h d, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to participants and researchers-are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18-40 years) then evaluated in AusDiab 2011/12 participants (n  =  741, 44% men, aged >35 years, mean  ±  SD 58.5  ±  10.4 years) who wore the activPAL3 (7 d, 24 h d protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ > 0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p < 0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d higher than by the diary method, with 95% limits of agreement of approximately this amount  ±2 h d. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.

AimsTo quantify the association of self-reported walking pace and handgrip strength with all-cause, cardiovascular, and cancer mortality.Methods and resultsA total of 230 670 women and 190 057 men free from prevalent cancer and cardiovascular disease were included from UK Biobank. Usual walking pace was self-defined as slow, steady/average or brisk. Handgrip strength was assessed by dynamometer. Cox-proportional hazard models were adjusted for social deprivation, ethnicity, employment, medications, alcohol use, diet, physical activity, and television viewing time. Interaction terms investigated whether age, body mass index (BMI), and smoking status modified associations. Over 6.3 years, there were 8598 deaths, 1654 from cardiovascular disease and 4850 from cancer. Associations of walking pace with mortality were modified by BMI. In women, the hazard ratio (HR) for all-cause mortality in slow compared with fast walkers were 2.16 [95% confidence interval (CI): 1.68–2.77] and 1.31 (1.08–1.60) in the bottom and top BMI tertiles, respectively; corresponding HRs for men were 2.01 (1.68–2.41) and 1.41 (1.20–1.66). Hazard ratios for cardiovascular mortality remained above 1.7 across all categories of BMI in men and women, with modest heterogeneity in men. Handgrip strength was associated with cardiovascular mortality in men only (HR tertile 1 vs. tertile 3 = 1.38; 1.18–1.62), without differences across BMI categories, while associations with all-cause mortality were only seen in men with low BMI. Associations for walking pace and handgrip strength with cancer mortality were less consistent.ConclusionA simple self-reported measure of slow walking pace could aid risk stratification for all-cause and cardiovascular mortality within the general population.

BackgroundBoth physical activity and sedentary behaviour have been individually associated with health, however, the extent to which the combination of these behaviours influence health is less well-known. The aim of this study was to examine the associations of four mutually exclusive categories of objectively measured physical activity and sedentary time on markers of cardiometabolic health in a nationally representative sample of English adults.MethodsUsing the 2008 Health Survey for England dataset, 2131 participants aged ≥18 years, who provided valid accelerometry data, were included for analysis and grouped into one of four behavioural categories: (1) ‘Busy Bees’: physically active & low sedentary, (2) ‘Sedentary Exercisers’: physically active & high sedentary, (3) ‘Light Movers’: physically inactive & low sedentary, and (4) ‘Couch Potatoes’: physically inactive & high sedentary. ‘Physically active’ was defined as accumulating at least 150 min of moderate-to-vigorous physical activity (MVPA) per week. ‘Low sedentary’ was defined as residing in the lowest quartile of the ratio between the average sedentary time and the average light-intensity physical activity time. Weighted multiple linear regression models, adjusting for measured confounders, investigated the differences in markers of health across the derived behavioural categories. The associations between continuous measures of physical activity and sedentary levels with markers of health were also explored, as well as a number of sensitivity analyses.ResultsIn comparison to ‘Couch Potatoes’, ‘Busy Bees’ [body mass index: −1.67 kg/m2 (p < 0.001); waist circumference: −1.17 cm (p = 0.007); glycated haemoglobin: −0.12 % (p = 0.003); HDL-cholesterol: 0.09 mmol/L (p = 0.001)], ‘Sedentary Exercisers’ [body mass index: −1.64 kg/m2 (p < 0.001); glycated haemoglobin: −0.11 % (p = 0.009); HDL-cholesterol: 0.07 mmol/L (p < 0.001)] and ‘Light Movers’ [HDL-cholesterol: 0.11 mmol/L (p = 0.004)] had more favourable health markers. The continuous analyses showed consistency with the categorical analyses and the sensitivity analyses indicated robustness and stability.ConclusionsIn this national sample of English adults, being physically active was associated with a better health profile, even in those with concomitant high sedentary time. Low sedentary time independent of physical activity had a positive association with HDL-cholesterol.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-2694-9) contains supplementary material, which is available to authorized users.