Alcohol drinking in one's thirties and forties is associated with body mass index in men, but not in women: A longitudinal analysis of the 1970 British Cohort Study

Our objective was to investigate longitudinal associations between alcohol drinking and body mass index (BMI). Alcohol drinking (exposure), BMI (outcome), smoking habit, occupation, longstanding illness, and leisure time physical activity (potential confounders) were assessed at ages 30, 34, 42, and 46 in the 1970 British Birth Cohort Study. Multilevel models were used to cope with the problem of correlated observations. There were 15,708 observations in 5931 men and 14,077 observations in 5656 women. Drinking was associated with BMI in men. According to the regression coefficients, BMI was expected to increase by 0.36 (95% confidence interval: 0.11, 0.60) kg/m2 per year in men who drank once a week and by 0.40 (0.14, 0.15) kg/m2 per year in men who drank most days. In ten years, BMI was expected to increase by 5.4 kg/m2 in men who drank and by 2.9 kg/m2 in men who drank and were physically active. Drinking was not associated with BMI in women. Rather, BMI was expected to increase by 0.25 (0.07, 0.43) kg/m2 per year in women who were former smokers. In ten years, BMI was expected to increase by 4.3 kg/m2 in women who were former smokers and by 0.8 kg/m2 in women who were former smokers and who were physically active. Associations between drinking and BMI were similar after further adjustment for problematic drinking and diet. These longitudinal data suggest that drinking is associated with BMI in men and that drinking is not associated with BMI in women independent of other lifestyle risk factors

Alcohol intake and mortality risk of COVID-19, pneumonia, and other infectious diseases: An analysis of 437191 UK biobank participants

This study aims to investigate the association between alcohol consumption and COVID-19, infectious diseases, and pneumonia mortality. This is a prospective analysis of 437,191 UK Biobank participants (age 56.3 years, 54% female). The main exposure was self-reported alcohol consumption. In addition to never and previous drinkers, we applied quartiles-based and UK guidelines-based criteria to divide current drinkers by weekly consumption into four groups. Outcomes included COVID-19, infectious diseases, and pneumonia mortality, obtained from the national death registries until May 2020. After an 11-year follow-up, compared to never drinkers, previous drinkers had higher mortality risks of infectious diseases and pneumonia (adjusted HR: 1.29 [95% CI 1.06–1.57] and 1.35 [1.07–1.70], respectively), but not COVID-19. There was a curvilinear association of alcohol consumption with infectious diseases and pneumonia mortality. Drinking within-guidelines (<14 UK units/wk) and amounts up to double the recommendation (14 to < 28 UK units/wk) was associated with the lowest mortality risks of infectious diseases (0.70 [0.59–0.83] and 0.70 [0.59–0.83], respectively) and pneumonia (0.71 [0.58–0.87] and 0.72 [0.58–0.88], respectively). Alcohol consumption was associated with lower risks of COVID-19 mortality (e.g., drinking within-guidelines: 0.53 [0.33–0.86]). Drinkers reporting multiples of the recommended alcohol drinking amounts did not have higher mortality risks of COVID-19 and other infectious diseases than never drinkers. Despite the well-established unfavorable effects on general health, we found no deleterious associations between alcohol consumption and the risk of infectious diseases, including COVID-19. Future research with other study designs is needed to confirm the causality

Comparison of a thigh worn accelerometer algorithm with diary estimates of time in bed and time asleep: the 1970 British Cohort Study

Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level

Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement

Introduction: The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting.Methods: (1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6).Results: (1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than 'physical activity' for the data we are collecting (Q1); make only minor changes to ProPASS's accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6).Conclusions: Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS's strategic directions-indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.</p

Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle : a scoping review and expert statement

The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting. (1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6). (1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than 'physical activity' for the data we are collecting (Q1); make only minor changes to ProPASS's accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6). Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS's strategic directions-indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing. [Abstract copyright: © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Effect of severe versus moderate energy restriction on physical activity among postmenopausal female adults with obesity: a pre-specified secondary analysis of the TEMPO Diet randomized controlled Trial

BackgroundAn under-explored strategy for increasing physical activity is the dietary treatment of obesity, but empirical evidence is lacking.ObjectivesTo compare the effects of weight loss via severe versus moderate energy restriction on physical activity over 36 months.Methods101 postmenopausal female adults (45–65 years, 30–40 kg/m2, ResultsCompared to the moderate group, the severe group exhibited greater mean levels of: total volume of physical activity; duration of moderate-to-vigorous-intensity physical activity (MVPA); duration of light-intensity physical activity; and step counts, as well as lower mean duration of sedentary time. All these differences (except step counts) were apparent at 6 months (e.g., 1006 [95% confidence interval 564, 1449] MET-minutes per week for total volume of physical activity), and some were also apparent at 4 and/or 12 months. There were no differences between groups in the two other outcomes investigated (self-efficacy to regulate exercise; and proportion of participants meeting the World Health Organization's 2020 Physical Activity Guidelines for MVPA). When the analyses were adjusted for weight at each time point, the differences between groups were either attenuated or abolished.ConclusionsAmong female adults with obesity, including a dietary component to reduce excess body weight—notably one involving severe energy restriction—could potentially enhance the effectiveness of physical activity interventions

Joint associations of device-measured physical activity and sleep duration with cardiometabolic health in the 1970 British Cohort Study

OBJECTIVES: Multiple unhealthy lifestyle behaviors could synergistically exaggerate unfavorable health outcomes. The present study aimed to investigate the joint associations of device-measured sleep duration and physical activity with cardiometabolic health markers. DESIGN: A cross-sectional analysis embedded in the 46-48 years wave of the 1970 British Cohort Study. METHODS: 4756 participants wore an activPAL3 micro accelerometer to measure physical activity and sleep duration. Outcomes included body mass index (BMI), glycated hemoglobin, triglycerides, c-reactive protein, systolic blood pressure, and total-to-high-density lipoprotein (HDL) cholesterol ratio, hypertension, and diabetes. We examined the joint associations of sleep (9h, long) and physical activity (median cut of step counts, 9480 steps/d; or moderate-to-vigorous physical activity, MVPA, 085h/d) with outcomes by generalized linear models or logistic regression. RESULTS: Low physical activity combined with either short or long sleep was associated with higher BMI (e.g., 2.32 [1.42, 3.23] (kg/m2) for short sleep) compared to the referent medium sleep and high physical activity combination. Low physical activity combined with long sleep was associated with a higher total-to-HDL cholesterol ratio (e.g., 0.31 [0.12, 0.49] for low step counts). Short sleep combined with low step counts showed higher odds for hypertension and diabetes (1.34 [1.06, 1.69] and 1.98 [1.07, 3.68], respectively), while short sleep combined with either low or high MVPA had higher odds for diabetes (2.04 [1.09, 3.82] and 2.07 [1.04, 4.15], respectively). CONCLUSIONS: Low physical activity may exaggerate the detrimental associations between inadequate sleep with BMI, blood lipids, hypertension, and diabetes

Joint associations of device-measured physical activity and sleep duration with cardiometabolic health in the 1970 British Cohort Study.

OBJECTIVES: Multiple unhealthy lifestyle behaviors could synergistically exaggerate unfavorable health outcomes. The present study aimed to investigate the joint associations of device-measured sleep duration and physical activity with cardiometabolic health markers. DESIGN: A cross-sectional analysis embedded in the 46-48 years wave of the 1970 British Cohort Study. METHODS: 4756 participants wore an activPAL3 micro accelerometer to measure physical activity and sleep duration. Outcomes included body mass index (BMI), glycated hemoglobin, triglycerides, c-reactive protein, systolic blood pressure, and total-to-high-density lipoprotein (HDL) cholesterol ratio, hypertension, and diabetes. We examined the joint associations of sleep (9h, long) and physical activity (median cut of step counts, 4740 steps/d; or moderate-to-vigorous physical activity, MVPA, 085h/d) with outcomes by generalized linear models or logistic regression. RESULTS: Low physical activity combined with either short or long sleep was associated with higher BMI (e.g., 2.32 [1.42, 3.23] (kg/m2) for short sleep) compared to the referent medium sleep and high physical activity combination. Low physical activity combined with long sleep was associated with a higher total-to-HDL cholesterol ratio (e.g., 0.31 [0.12, 0.49] for low step counts). Short sleep combined with low step counts showed higher odds for hypertension and diabetes (1.34 [1.06, 1.69] and 1.98 [1.07, 3.68], respectively), while short sleep combined with either low or high MVPA had higher odds for diabetes (2.04 [1.09, 3.82] and 2.07 [1.04, 4.15], respectively). CONCLUSIONS: Low physical activity may exaggerate the detrimental associations between inadequate sleep with BMI, blood lipids, hypertension, and diabetes