Aim: To analyze the data from the World Health Organization Nepal STEPS survey 2013 to determine the prevalence of total and domain-specific physical activity (PA) and associated factors among Nepalese adults. Methods: A multistage cluster sampling technique was used to proportionately select participants from the 3 ecological zones (Mountain, Hill, and Terai) in Nepal. The Global PA Questionnaire was used to assess PA. The data were analyzed using quantile and ordinary least square regression. Results: Only 4% of the adults did not meet the World Health Organization PA guidelines. Age had a negative monotonic association with total PA and occupational PA, with the highest difference at the upper tails of the PA distribution. Lower total PA and occupational PA were associated with secondary or higher education, being retired or in unpaid employment, living in Terai or urban areas, and nonsmoking. Age, higher education, unpaid employment, and Terai or urban residence were negatively associated, while being currently married was positively associated with transport-related PA. Conclusion: Increasing age, higher education, unpaid employment, unemployment or retirement, and urban residence were associated with lower PA, with the stronger association at the upper tails of the distribution. The correlates had dissimilar associations across the quantiles of PA distribution.
Susan Paudel, Alice J. Owen, Stephane Heritier and Ben J. Smith
Keith R. Lohse
Ignacio Perez-Pozuelo, Thomas White, Kate Westgate, Katrien Wijndaele, Nicholas J. Wareham and Soren Brage
Background: Wrist-worn accelerometry is the commonest objective method for measuring physical activity in large-scale epidemiological studies. Research-grade devices capture raw triaxial acceleration which, in addition to quantifying movement, facilitates assessment of orientation relative to gravity. No population-based study has yet described the interrelationship and variation of these features by time and personal characteristics. Methods: 2,043 United Kingdom adults (35–65 years) wore an accelerometer on the non-dominant wrist and a chest-mounted combined heart-rate-and-movement sensor for 7 days free-living. From raw (60 Hz) wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (forearm) angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from combined sensing and sedentary time from approximate horizontal arm angle coupled with low movement. Results: Movement differences by time-of-day and day-of-week were associated with forearm angles; more movement in downward forearm positions. Mean (SD) movement was similar between sexes ∼31 (42) mg, despite higher PAEE in men. Women spent longer with the forearm pitched >0°, above horizontal (53% vs 36%), and less time at <0° (37% vs 53%). Diurnal pitch was 2.5–5° above and 0–7.5°below horizontal during night and daytime, respectively; corresponding roll angles were ∼0° (hand flat) and ∼20° (thumb-up). Differences were more pronounced in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 vs 15 hours/day) in obese vs normal-weight individuals. Conclusions: More movement occurred in forearm positions below horizontal, commensurate with activities including walking. Findings suggest time-specific population differences in behaviors by age, sex, and BMI.
Emma L. Sweeney, Daniel J. Peart, Irene Kyza, Thomas Harkes, Jason G. Ellis and Ian H. Walshe
Experimental sleep restriction (SR) has demonstrated reduced insulin sensitivity in healthy individuals. Exercise is well-known to be beneficial for metabolic health. A single bout of exercise has the capacity to increase insulin sensitivity for up to 2 days. Therefore, the current study aimed to determine if sprint interval exercise could attenuate the impairment in insulin sensitivity after one night of SR in healthy males. Nineteen males were recruited for this randomized crossover study which consisted of four conditions—control, SR, control plus exercise, and sleep restriction plus exercise. Time in bed was 8 hr (2300–0700) in the control conditions and 4 hr (0300–0700) in the SR conditions. Conditions were separated by a 1-week entraining period. Participants slept at home, and compliance was assessed using wrist actigraphy. Following the night of experimental sleep, participants either conducted sprint interval exercise or rested for the equivalent duration. An oral glucose tolerance test was then conducted. Blood samples were obtained at regular intervals for measurement of glucose and insulin. Insulin concentrations were higher in SR than control (p = .022). Late-phase insulin area under the curve was significantly lower in sleep restriction plus exercise than SR (862 ± 589 and 1,267 ± 558; p = .004). Glucose area under the curve was not different between conditions (p = .207). These findings suggest that exercise improves the late postprandial response following a single night of SR.
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.