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The difference in sleep, sedentary behaviour, and physical activity between older adults with 'healthy' and 'unhealthy' cardiometabolic profiles: a cross-sectional compositional data analysis approach.

ABSTRACT: Background:Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults' cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60-89?years), with 'low' compared to those with 'high' concentrations of endocrine cardio-metabolic disease risk markers, using CoDA. Method:Ninety-three participants (55% female) wore a thigh-mounted triaxial accelerometer for seven consecutive free-living days. Accelerometer estimates of daily average hours of engagement in sedentary behaviour (SB), standing, light-intensity physical activity (LIPA), sporadic moderate-vigorous physical activity (sMVPA, accumulated with bouts between 1 and 10?min), 10-min moderate-vigorous physical activity (10MVPA, accumulated with bouts ?10?min), in addition to self-reported sleeping hours were reported. Fasted whole blood concentrations of total cholesterol, triglyceride, glucose, and glycated haemoglobin, and serum lipoprotein lipase (LPL), interleukin-6 (IL-6), and procollagen III N-terminal propeptide were determined. Results:Triglyceride concentration appeared to be highly dependent on 10MVPA engagement as the 'low' and 'high' concentration groups engaged in 48% more and 32% less 10MVPA, respectively, relative to the geometric mean of the entire study sample. Time-use composition of the 'low' LPL group's engagement in 10MVPA was 26% less, while the 'high' LPL group was 7.9% more, than the entire study sample. Time-use composition of the 'high' glucose and glycated haemoglobin groups appeared to be similar as both engaged in more Sleep and SB, and less 10MVPA compared to the study sample. Participants with a 'low' IL-6 concentration engaged in 4.8% more Sleep and 2.7% less 10MVPA than the entire study sample. Time-use composition of the Total Cholesterol groups was mixed with the 'low' concentration group engaging in more Standing and 10MVPA but less Sleep, SB, LIPA, and sMVPA than the entire study sample. Conclusion:Older adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile.

SUBMITTER: Ryan DJ

PROVIDER: S-EPMC6909533 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The difference in sleep, sedentary behaviour, and physical activity between older adults with 'healthy' and 'unhealthy' cardiometabolic profiles: a cross-sectional compositional data analysis approach.

Ryan Declan John DJ Wullems Jorgen Antonin JA Stebbings Georgina Kate GK Morse Christopher Ian CI Stewart Claire Elizabeth CE Onambele-Pearson Gladys Leopoldine GL

European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity 20191212

<h4>Background</h4>Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults' cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60-89 years), with 'low' compared to those with 'high' concentrations of endocrine cardio-metabolic disease risk markers, using CoDA.<h4>Method</h4>Ninety-three parti ...[more]

PMID: 31890050

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Project description:BackgroundDaily time spent in sleep, sedentary behaviour (SED), light intensity physical activity (LIPA), and moderate-to-vigorous intensity physical activity (MVPA) are compositional, co-dependent variables. The objectives of this study were to use compositional data analysis to: (1) examine the relationship between the movement behaviour composition (daily time spent in sleep, SED, LIPA and MVPA) and all-cause mortality risk, and (2) estimate the extent to which changing time spent in any given movement behaviour (sleep, SED, LIPA, or MVPA) within the movement behaviour composition was associated with changes in risk of all-cause mortality.Methods2838 adult participants from the 2005-2006 cycle of the U.S. National Health and Nutrition Examination Survey were studied using a prospective cohort design. Daily time spent in SED, LIPA and MVPA were determined by accelerometer. Nightly time spent sleeping was self-reported. Survey data were linked with mortality data through to the end of December 2015. Compositional data analysis was used to investigate relationships between the movement behaviour composition and mortality.ResultsThe movement behaviour composition was significantly associated with mortality risk. Time spent in MVPA relative to other movement behaviours was negatively associated with mortality risk (HR = .74; 95% CI [.67, .83]) while relative time spent in SED was positively associated with mortality risk (HR = 1.75; 95% CI [1.10, 2.79]). Time displacement estimates revealed that the greatest estimated changes in mortality risk occurred when time spent in MVPA was decreased and replaced with sleep, SED, LIPA or a combination of these behaviours (HRs of 1.76 to 1.80 for 15 min/day displacements).ConclusionsThe daily movement behaviour composition was related to mortality. Replacing time in MVPA or SED with equivalent time from any other movement behaviour was associated with an increase and decrease in mortality risk, respectively.

Project description:BACKGROUND:With approximately 8 hours of one's waking day spent at work, occupational tasks and environments are important influencers on an individual's physical activity (PA) and sedentary behaviours. Little research has compared device-measured physical activity, sedentary behaviour and cardiometabolic outcomes between occupational groups. OBJECTIVE:To compare device-measured movement (sedentary time [ST], light intensity physical activity [LPA], moderate-to-vigorous intensity physical activity [MVPA], and steps) across occupations. The secondary objective was to examine whether cardiometabolic and fitness outcomes differed by occupation. METHODS:Five bibliographic databases were searched to identify all studies which included working age, employed adults from high-income countries, and reported on device-measured movement within occupations. Risk of bias within and across studies was assessed. Results were synthesized using meta-analyses and narrative syntheses. RESULTS:The review includes 132 unique studies with data from 15,619 participants. Working adults spent ~ 60% of their working and waking time engaged in sedentary behaviour; a very small proportion (~ 4%) of the day included MVPA. On average, workers accumulated 8124 steps/day. Office and call center workers' steps/day were among the lowest, while those of postal delivery workers were highest. Office workers had the greatest ST and the lowest time in LPA both at work and during wakeful time. However, office workers had the greatest minutes sent in MVPA during wakeful hours. Laborers had the lowest ST and spent a significantly greater proportion of their work time in LPA and MVPA. Healthcare and protective services workers had higher levels of LPA at work compared to other occupations. Workers in driving-based occupations tended to have a higher body mass index and blood pressure. CONCLUSION:This review identifies that occupational and wakeful time PA and ST differed between occupations. Future studies are needed to assess whether patterns differ by age and sex, describe leisure-time movement and movement patterns, and the relationship with cardiometabolic health. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42017070448 .

Project description:BackgroundDaily activity data are by nature compositional data. Accordingly, they occupy a specific geometry with unique properties that is different to standard Euclidean geometry. This study aimed to estimate the difference in adiposity associated with isotemporal reallocation between daily activity behaviours, and to compare the findings from compositional isotemporal subsitution to those obtained from traditional isotemporal substitution.MethodsWe estimated the differences in adiposity (body fat%) associated with reallocating fixed durations of time (isotemporal substitution) between accelerometer-measured daily activity behaviours (sleep, sedentary time and light and moderate-to-vigorous physical activity (MVPA)) among 1728 children aged 9-11 years from Australia, Canada, Finland and the UK (International Study of Childhood Obesity, Lifestyle and the Environment, 2011-2013). We generated estimates from compositional isotemporal substitution models and traditional non-compositional isotemporal substitution models.ResultsBoth compositional and traditional models estimated a positive (unfavourable) difference in body fat% when time was reallocated from MVPA to any other behaviour. Unlike traditional models, compositional models found the differences in estimated adiposity (1) were not necessarily symmetrical when an activity was being displaced, or displacing another (2) were not linearly related to the durations of time reallocated, and (3) varied depending on the starting composition.ConclusionThe compositional isotemporal model caters for the constrained and therefore relative nature of activity behaviour data and enables all daily behaviours to be included in a single statistical model. The traditional model treats data as real variables, thus the constrained nature of time is not accounted for, nor reflected in the findings. Findings from compositional isotemporal substitution support the importance of MVPA to children's health, and suggest that while interventions to increase MVPA may be of benefit, attention should be directed towards strategies to avoid decline in MVPA levels, particularly among already inactive children. Future applications of the compositional model can extend from pair-wise reallocations to other configurations of time-reallocation, for example, increasing MVPA at the expense of multiple other behaviours.

Dataset Information

The difference in sleep, sedentary behaviour, and physical activity between older adults with 'healthy' and 'unhealthy' cardiometabolic profiles: a cross-sectional compositional data analysis approach.

Publications

The difference in sleep, sedentary behaviour, and physical activity between older adults with 'healthy' and 'unhealthy' cardiometabolic profiles: a cross-sectional compositional data analysis approach.

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