The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time ...spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005-6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.
The purpose of this study was to examine the relationships between movement behaviours (sleep duration, sedentary time, physical activity) and health indicators in a representative sample of children ...and youth using compositional analyses. Cross-sectional findings are based on 4169 children and youth (aged 6-17 years) from cycles 1 to 3 of the Canadian Health Measures Survey. Sedentary time (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) were accelerometer-derived. Sleep duration was subjectively measured. Body mass index z scores, waist circumference, blood pressure, behavioural strengths and difficulties, and aerobic fitness were measured in the full sample. Triglycerides, high-density lipoprotein-cholesterol, C-reactive protein, and insulin were measured in a fasting subsample. The composition of movement behaviours was entered into linear regression models via an isometric log ratio transformation and was found to be associated with all health indicators (p < 0.01). Relative to other movement behaviours, time spent in SB or LPA was positively associated (p < 0.04) and time spent in MVPA or sleep was negatively associated (p < 0.02) with obesity risk markers. Similarly, LPA was positively associated (p < 0.005) and sleep was negatively associated (p < 0.03) with unfavourable behavioural strengths and difficulties scores and systolic blood pressure. Relative to other movement behaviours, time spent in SB was negatively associated (p < 0.001) and time spent in MVPA (p < 0.001) was positively associated with aerobic fitness. Likewise, MVPA was also negatively associated with several cardiometabolic risk markers (p < 0.008). Compositional data analyses provide novel insights into collective health implications of 24-h movement behaviours and can facilitate interesting avenues for future investigations.
High amounts of time spent sitting can increase cardiovascular disease risk and are deleteriously associated cardio-metabolic risk biomarkers. Though evidence suggests that accruing sitting time in ...prolonged periods may convey additional risk, verification using high-quality measures is needed. We examined this issue in adults from the Australian Diabetes, Obesity and Lifestyle Study, using accurate measures of sitting accumulation.
In 2011/12, 739 adults aged 36 to 89 years (mean±SD 58±10 years) wore activPAL3™ monitors (which provide accurate objective measures of sitting); 678 provided ≥4 valid days of monitor data and complete cardio-metabolic biomarker and confounder data. Multivariable linear regression models examined associations of sitting time, sitting time accrued in ≥30 minute bouts (prolonged sitting time), and three measures of sitting accumulation patterns with cardio-metabolic risk markers: body mass index (BMI), waist circumference, blood pressure, high- and low- density lipoprotein (HDL and LDL) cholesterol, triglycerides, glycated haemoglobin (HbA1c), fasting plasma glucose (FPG) and 2-hour post-load glucose (PLG). Interactions tests examined whether associations of sitting time with biomarkers varied by usual sitting bout duration.
Adjusted for potential confounders, greater amounts of sitting time and prolonged sitting time were significantly (p<0.05) deleteriously associated with BMI, waist circumference, HDL cholesterol, and triglycerides. Total sitting time was also significantly associated with higher PLG. Sitting accumulation patterns of frequently interrupted sitting (compared to patterns with relatively more prolonged sitting) were significantly beneficially associated with BMI, waist circumference, HDL cholesterol, triglycerides, PLG, and with FPG. Effect sizes were typically larger for accumulation patterns than for sitting time. Significant interactions (p<0.05) showed that associations of sitting time with HDL, triglycerides and PLG became more deleterious the longer at a time sitting was usually accumulated.
Adding to previous evidence reliant on low-quality measures, our study showed that accumulating sitting in patterns where sitting was most frequently interrupted had significant beneficial associations with several cardio-metabolic biomarkers and that sitting for prolonged periods at a time may exacerbate some of the effects of sitting time. The findings support sedentary behavior guidelines that promote reducing and regularly interrupting sitting.
Recent research shows that sedentary behaviour is associated with adverse cardio-metabolic consequences even among those considered sufficiently physically active. In order to successfully develop ...interventions to address this unhealthy behaviour, factors that influence sedentariness need to be identified and fully understood. The aim of this review is to identify individual, social, environmental, and policy-related determinants or correlates of sedentary behaviours among adults aged 18-65 years.
PubMed, Embase, CINAHL, PsycINFO and Web of Science were searched for articles published between January 2000 and September 2015. The search strategy was based on four key elements and their synonyms: (a) sedentary behaviour (b) correlates (c) types of sedentary behaviours (d) types of correlates. Articles were included if information relating to sedentary behaviour in adults (18-65 years) was reported. Studies on samples selected by disease were excluded. The full protocol is available from PROSPERO (PROSPERO 2014:CRD42014009823).
74 original studies were identified out of 4041: 71 observational, two qualitative and one experimental study. Sedentary behaviour was primarily measured as self-reported screen leisure time and total sitting time. In 15 studies, objectively measured total sedentary time was reported: accelerometry (n = 14) and heart rate (n = 1). Individual level factors such as age, physical activity levels, body mass index, socio-economic status and mood were all significantly correlated with sedentariness. A trend towards increased amounts of leisure screen time was identified in those married or cohabiting while having children resulted in less total sitting time. Several environmental correlates were identified including proximity of green space, neighbourhood walkability and safety and weather.
Results provide further evidence relating to several already recognised individual level factors and preliminary evidence relating to social and environmental factors that should be further investigated. Most studies relied upon cross-sectional design limiting causal inference and the heterogeneity of the sedentary measures prevented direct comparison of findings. Future research necessitates longitudinal study designs, exploration of policy-related factors, further exploration of environmental factors, analysis of inter-relationships between identified factors and better classification of sedentary behaviour domains.
Aging is associated with a progressive decrease in bone mass (BM), and being physical active is one of the main strategies to combat this continuous loss. Nonetheless, because daily time is limited, ...time spent on each movement behavior is co-dependent. The aim of this study was to determine the relationship between BM and movement behaviors in elderly people using compositional data analysis.
We analyzed 871 older people 395 men (76.9±5.3y) and 476 women (76.7±4.7y). Time spent in sedentary behavior (SB), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA), was assessed using accelerometry. BM was determined by bone densitometry (DXA). The sample was divided according to sex and bone health indicators.
The combined effect of all movement behaviors (PA and SB) was significantly associated with whole body, leg and femoral region BM in the whole sample (p≤0.05), with leg and pelvic BM (p<0.05) in men and, with whole body, arm and leg BM (p<0.05) in women. In men, arm and pelvic BM were negatively associated with SB and whole body, pelvic and leg BM were positively associated with MVPA (p≤0.05). In women, whole body and leg BM were positively associated with SB. Arm and whole body BM were positively associated and leg BM was negatively associated with LPA and arm BM was negatively associated with MVPA (p≤0.05). Women without bone fractures spent less time in SB and more in LPA and MVPA than the subgroup with bone fractures.
We identified that the positive effect of MVPA relative to the other behaviors on bone mass is the strongest overall effect in men. Furthermore, women might decrease bone fracture risk through PA increase and SB reduction, despite the fact that no clear benefits of PA for bone mass were found.
Background
Regular physical activity is the prime modality for the prevention of numerous non-communicable diseases and has also been advocated for resilience against COVID-19 and other infectious ...diseases. However, there is currently no systematic and quantitative evidence synthesis of the association between physical activity and the strength of the immune system.
Objective
To examine the association between habitual physical activity and (1) the risk of community-acquired infectious disease, (2) laboratory‐assessed immune parameters, and (3) immune response to vaccination.
Methods
We conducted a systemic review and meta-analysis according to PRISMA guidelines. We searched seven databases (MEDLINE, Embase, Cochrane CENTRAL, Web of Science, CINAHL, PsycINFO, and SportDiscus) up to April 2020 for randomised controlled trials and prospective observational studies were included if they compared groups of adults with different levels of physical activity and reported immune system cell count, the concentration of antibody, risk of clinically diagnosed infections, risk of hospitalisation and mortality due to infectious disease. Studies involving elite athletes were excluded. The quality of the selected studies was critically examined following the Cochrane guidelines using ROB2 and ROBINS_E. Data were pooled using an inverse variance random-effects model.
Results
Higher level of habitual physical activity is associated with a 31% risk reduction (hazard ratio 0.69, 95% CI 0.61–0.78, 6 studies,
N
= 557,487 individuals) of community-acquired infectious disease and 37% risk reduction (hazard ratio 0.64, 95% CI 0.59–0.70, 4 studies,
N
= 422,813 individuals) of infectious disease mortality. Physical activity interventions resulted in increased CD4 cell counts (32 cells/µL, 95% CI 7–56 cells/µL, 24 studies,
N
= 1112 individuals) and salivary immunoglobulin IgA concentration (standardised mean difference 0.756, 95% CI 0.146–1.365, 7 studies,
N
= 435 individuals) and decreased neutrophil counts (704 cells/µL, 95% CI 68–1340, 6 studies,
N
= 704 individuals) compared to controls. Antibody concentration after vaccination is higher with an adjunct physical activity programme (standardised mean difference 0.142, 95% CI 0.021–0.262, 6 studies,
N
= 497 individuals).
Conclusion
Regular, moderate to vigorous physical activity is associated with reduced risk of community-acquired infectious diseases and infectious disease mortality, enhances the first line of defence of the immune system, and increases the potency of vaccination.
Protocol registration
The original protocol was prospectively registered with PROSPERO (CRD42020178825).
Sleep duration, sedentary behaviour, and physical activity are three co-dependent behaviours that fall on the movement/non-movement intensity continuum. Compositional data analyses provide an ...appropriate method for analyzing the association between co-dependent movement behaviour data and health indicators. The objectives of this study were to examine: (1) the combined associations of the composition of time spent in sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with adiposity indicators; and (2) the association of the time spent in sleep, sedentary behaviour, LPA, or MVPA with adiposity indicators relative to the time spent in the other behaviours in a representative sample of Canadian preschool-aged children.
Participants were 552 children aged 3 to 4 years from cycles 2 and 3 of the Canadian Health Measures Survey. Sedentary time, LPA, and MVPA were measured with Actical accelerometers (Philips Respironics, Bend, OR USA), and sleep duration was parental reported. Adiposity indicators included waist circumference (WC) and body mass index (BMI) z-scores based on World Health Organization growth standards. Compositional data analyses were used to examine the cross-sectional associations.
The composition of movement behaviours was significantly associated with BMI z-scores (p = 0.006) but not with WC (p = 0.718). Further, the time spent in sleep (BMI z-score: γ
= -0.72; p = 0.138; WC: γ
= -1.95; p = 0.285), sedentary behaviour (BMI z-score: γ
= 0.19; p = 0.624; WC: γ
= 0.87; p = 0.614), LPA (BMI z-score: γ
= 0.62; p = 0.213, WC: γ
= 0.23; p = 0.902), or MVPA (BMI z-score: γ
= -0.09; p = 0.733, WC: γ
= 0.08; p = 0.288) relative to the other behaviours was not significantly associated with the adiposity indicators.
This study is the first to use compositional analyses when examining associations of co-dependent sleep duration, sedentary time, and physical activity behaviours with adiposity indicators in preschool-aged children. The overall composition of movement behaviours appears important for healthy BMI z-scores in preschool-aged children. Future research is needed to determine the optimal movement behaviour composition that should be promoted in this age group.
To assess the relationship between time spent in light physical activity and cardiometabolic health and mortality in adults.
Systematic review and meta-analysis.
Searches in Medline, Embase, ...PsycInfo, CINAHL and three rounds of hand searches.
Experimental (including acute mechanistic studies and physical activity intervention programme) and observational studies (excluding case and case-control studies) conducted in adults (aged ≥18 years) published in English before February 2018 and reporting on the relationship between light physical activity (<3 metabolic equivalents) and cardiometabolic health outcomes or all-cause mortality.
Study quality appraisal with QUALSYST tool and random effects inverse variance meta-analysis.
Seventy-two studies were eligible including 27 experimental studies (and 45 observational studies). Mechanistic experimental studies showed that short but frequent bouts of light-intensity activity throughout the day reduced postprandial glucose (-17.5%; 95% CI -26.2 to -8.7) and insulin (-25.1%; 95% CI -31.8 to -18.3) levels compared with continuous sitting, but there was very limited evidence for it affecting other cardiometabolic markers. Three light physical activity programme intervention studies (n ranging from 12 to 58) reduced adiposity, improved blood pressure and lipidaemia; the programmes consisted of activity of >150 min/week for at least 12 weeks. Six out of eight prospective observational studies that were entered in the meta-analysis reported that more time spent in daily light activity reduced risk of all-cause mortality (pooled HR 0.71; 95% CI 0.62 to 0.83).
Light-intensity physical activity could play a role in improving adult cardiometabolic health and reducing mortality risk. Frequent short bouts of light activity improve glycaemic control. Nevertheless, the modest volume of the prospective epidemiological evidence base and the moderate consistency between observational and laboratory evidence inhibits definitive conclusions.
Prolonged sedentary behaviour (SB) is associated with poor health. It is unclear which SB measure is most appropriate for interventions and population surveillance to measure and interpret change in ...behaviour in older adults. The aims of this study: to examine the relative and absolute reliability, Minimal Detectable Change (MDC) and responsiveness to change of subjective and objective methods of measuring SB in older adults and give recommendations of use for different study designs.
SB of 18 older adults (aged 71 (IQR 7) years) was assessed using a systematic set of six subjective tools, derived from the TAxonomy of Self report Sedentary behaviour Tools (TASST), and one objective tool (activPAL3c), over 14 days. Relative reliability (Intra Class Correlation coefficients-ICC), absolute reliability (SEM), MDC, and the relative responsiveness (Cohen's d effect size (ES) and Guyatt's Responsiveness coefficient (GR)) were calculated for each of the different tools and ranked for different study designs.
ICC ranged from 0.414 to 0.946, SEM from 36.03 to 137.01 min, MDC from 1.66 to 8.42 hours, ES from 0.017 to 0.259 and GR from 0.024 to 0.485. Objective average day per week measurement ranked as most responsive in a clinical practice setting, whereas a one day measurement ranked highest in quasi-experimental, longitudinal and controlled trial study designs. TV viewing-Previous Week Recall (PWR) ranked as most responsive subjective measure in all study designs.
The reliability, Minimal Detectable Change and responsiveness to change of subjective and objective methods of measuring SB is context dependent. Although TV viewing-PWR is the more reliable and responsive subjective method in most situations, it may have limitations as a reliable measure of total SB. Results of this study can be used to guide choice of tools for detecting change in sedentary behaviour in older adults in the contexts of population surveillance, intervention evaluation and individual care.
Sedentary behavior (SB), defined as sitting (nonexercising), reclining, and lying down (posture), or by low energy expenditure, is a public health risk independent to physical activity. The objective ...of this systematic literature review was to synthesize the available evidence on amount of SB reported by and measured in older adults.
Studies published between 1981 and 2014 were identified from electronic databases and manual searching. Large-scale population studies/surveys reporting the amount of SB (objective/ subjective) in older adults aged ≥ 60 years of age were included. Appraisal and synthesis was completed using MOOSE guidelines.
349,698 adults aged ≥ 60 within 22 studies (10 countries and 1 EU-wide) were included. Objective measurement of SB shows that older adults spend an average of 9.4 hr a day sedentary, equating to 65-80% of their waking day. Self-report of SB is lower, with average weighted self-reports being 5.3 hr daily. Within specific domains of SB, older adults report 3.3 hr in leisure sitting time and 3.3 hr watching TV. There is an association with more time spent in SB as age advances and a trend for older men to spend more time in SB than women. Conclusion/ implications: Time spent sedentary ranges from 5.3-9.4 hr per waking day in older adults. With recent studies suggesting a link between SB, health, and well-being, independent of physical activity, this is an area important for successful aging.
Different methodologies of measurement and different reporting methods of SB made synthesis difficult. Estimated SB time from self-report is half of that measured objectively; suggesting that most self-report surveys of SB will vastly underestimate the actual time spent in SB.
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