scholarly journals A Wrinkle in Measuring Time Use for Cognitive Health: How should We Measure Physical Activity, Sedentary Behaviour and Sleep?

2021 ◽  
pp. 155982762110314
Author(s):  
Ryan S. Falck ◽  
Jennifer C. Davis ◽  
Karim M. Khan ◽  
Todd C. Handy ◽  
Teresa Liu-Ambrose
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Research Question ◽  
Cognitive Ageing ◽  
Cognitive Health ◽  
Dynamic Relationship ◽  
Subjective Estimates ◽  
Analytical Approaches ◽  
Behavioural Strategies

One new case of dementia is detected every 4 seconds and no effective drug therapy exists. Effective behavioural strategies to promote healthy cognitive ageing are thus essential. Three behaviours related to cognitive health which we all engage in daily are physical activity, sedentary behaviour and sleep. These time-use activity behaviours are linked to cognitive health in a complex and dynamic relationship not yet fully elucidated. Understanding how each of these behaviours is related to each other and cognitive health will help determine the most practical and effective lifestyle strategies for promoting healthy cognitive ageing. In this review, we discuss methods and analytical approaches to best investigate how these time-use activity behaviours are related to cognitive health. We highlight four key recommendations for examining these relationships such that researchers should include measures which (1) are psychometrically appropriate; (2) can specifically answer the research question; (3) include objective and subjective estimates of the behaviour and (4) choose an analytical method for modelling the relationships of time-use activity behaviours with cognitive health which is appropriate for their research question.

scholarly journals Compositional analysis of the association between mortality and 24-hour movement behaviour from NHANES

2019 ◽  
pp. 204748731986778 ◽  
Author(s):  
Duncan E McGregor ◽  
Javier Palarea-Albaladejo ◽  
Philippa M Dall ◽  
Borja del Pozo Cruz ◽  
Sebastien FM Chastin
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Compositional Data ◽  
Cox Regression ◽  
Vigorous Physical Activity ◽  
Lifestyle Factors ◽  
Accelerometer Data ◽  
Cox Regression Analysis ◽  
Daily Time

Aims Previous prospective studies of the association between mortality and physical activity have generally not fully accounted for the interplay between movement behaviours. A compositional data modelling approach accounts for relative scale and co-dependency in time-use data across physical activity behaviours of the 24-hour day. Methods A prospective analysis of the National Health and Nutrition Examination Survey 2005–2006 on N = 1468 adults ( d = 135 deaths) in ages 50–79 years was undertaken using compositional Cox regression analysis. Daily time spent in sedentary behaviour, light intensity (LIPA) and moderate-to-vigorous physical activity (MVPA) was determined from waist-mounted accelerometer data (Actigraph 7164) and supplemented with self-reported sleep data to determine the daily time-use composition. Results The composition of time spent in sedentary behaviour, LIPA, MVPA and sleep was associated with mortality rate after allowing for age and sex effects ( p < 0.001), and remained significant when other lifestyle factors were added ( p < 0.001). This was driven primarily by the preponderance of MVPA; however, significant changes are attributable to LIPA relative to sedentary behaviour and sleep, and sedentary behaviour relative to sleep. The final ratio ceased to be statistically significant after incorporating lifestyle factors. The preponderance of MVPA ceased to be statistically significant after incorporating health at outset and physical limitations on movement. Conclusions An association is inferred between survival rate and the physical activity composition of the day. The MVPA time share is important, but time spent in LIPA relative to sedentary behaviour and sleep is also a significant factor. Increased preponderance of MVPA may have detrimental associations at higher levels of MVPA.

scholarly journals Standardised criteria for classifying the International Classification of Activities for Time-use Statistics (ICATUS) activity groups into sleep, sedentary behaviour, and physical activity

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Nucharapon Liangruenrom ◽  
Melinda Craike ◽  
Dorothea Dumuid ◽  
Stuart J. H. Biddle ◽  
Catrine Tudor-Locke ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Classification System ◽  
Vigorous Physical Activity ◽  
Delphi Survey ◽  
International Classification ◽  
Expert Assessment ◽  
Activity Groups

Abstract Background Globally, the International Classification of Activities for Time-Use Statistics (ICATUS) is one of the most widely used time-use classifications to identify time spent in various activities. Comprehensive 24-h activities that can be extracted from ICATUS provide possible implications for the use of time-use data in relation to activity-health associations; however, these activities are not classified in a way that makes such analysis feasible. This study, therefore, aimed to develop criteria for classifying ICATUS activities into sleep, sedentary behaviour (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), based on expert assessment. Method We classified activities from the Trial ICATUS 2005 and final ICATUS 2016. One author assigned METs and codes for wakefulness status and posture, to all subclass activities in the Trial ICATUS 2005. Once coded, one author matched the most detailed level of activities from the ICATUS 2016 with the corresponding activities in the Trial ICATUS 2005, where applicable. The assessment and harmonisation of each ICATUS activity were reviewed independently and anonymously by four experts, as part of a Delphi process. Given a large number of ICATUS activities, four separate Delphi panels were formed for this purpose. A series of Delphi survey rounds were repeated until a consensus among all experts was reached. Results Consensus about harmonisation and classification of ICATUS activities was reached by the third round of the Delphi survey in all four panels. A total of 542 activities were classified into sleep, SB, LPA, and MVPA categories. Of these, 390 activities were from the Trial ICATUS 2005 and 152 activities were from the final ICATUS 2016. The majority of ICATUS 2016 activities were harmonised into the ICATUS activity groups (n = 143). Conclusions Based on expert consensus, we developed a classification system that enables ICATUS-based time-use data to be classified into sleep, SB, LPA, and MVPA categories. Adoption and consistent use of this classification system will facilitate standardisation of time-use data processing for the purpose of sleep, SB and physical activity research, and improve between-study comparability. Future studies should test the applicability of the classification system by applying it to empirical data.

scholarly journals The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour

2017 ◽  
Vol 28 (3) ◽  
pp. 846-857 ◽  
Author(s):  
Dorothea Dumuid ◽  
Željko Pedišić ◽  
Tyman Everleigh Stanford ◽  
Josep-Antoni Martín-Fernández ◽  
Karel Hron ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Health Outcome ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Multiple Linear Regression Model ◽  
Health Messages ◽  
Compositional Data Analysis ◽  
Public Health Messages ◽  
Log Ratio

How people use their time has been linked with their health. For example, spending more time being physically active is known to be beneficial for health, whereas long durations of sitting have been associated with unfavourable health outcomes. Accordingly, public health messages have advocated swapping strategies to promote the reallocation of time between parts of the time-use composition, such as “Move More, Sit Less”, with the aim of achieving optimal distribution of time for health. However, the majority of research underpinning these public health messages has not considered daily time use as a composition, and has ignored the relative nature of time-use data. We present a way of applying compositional data analysis to estimate change in a health outcome when fixed durations of time are reallocated from one part of a particular time-use composition to another, while the remaining parts are kept constant, based on a multiple linear regression model on isometric log ratio coordinates. In an example, we examine the expected differences in Body Mass Index z-scores for reallocations of time between sleep, physical activity and sedentary behaviour.

scholarly journals GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies

2021 ◽  
pp. bjsports-2020-103604
Author(s):  
Jairo H Migueles ◽  
Eivind Aadland ◽  
Lars Bo Andersen ◽  
Jan Christian Brønd ◽  
Sebastien F Chastin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Sedentary Behaviour ◽  
Compositional Data ◽  
Epidemiological Studies ◽  
Future Research ◽  
Compositional Data Analysis ◽  
Accelerometer Data ◽  
Physical Behaviour ◽  
Analytical Approaches

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

scholarly journals Using Compositional Data Analysis to Explore Associations Between Accumulation of Sedentary Behaviour and Physical Activity and Biomarkers of Cardiometabolic Health in Children and Adolescents: A Cross-Sectional Study

2020 ◽  
Author(s):  
Simone Johanna Josefa Maria Verswijveren ◽  
Karen E. Lamb ◽  
Josep A. Martín-Fernández ◽  
Elisabeth Winkler ◽  
Rebecca M. Leech ◽  
...  
Keyword(s):  
Physical Activity ◽  
Waist Circumference ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Cardiometabolic Risk ◽  
Sedentary Time ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Intensity Physical Activity ◽  
Vigorous Intensity

Abstract Background The consequences for youth cardiometabolic risk might depend on whether sedentary time and physical activity are accumulated sporadically (in shorter bouts) or in a sustained pattern (in longer bouts). This study aimed to: 1) describe daily time-use compositions of youth, including time spent in shorter and longer bouts of sedentary behaviour and physical activity; and 2) examine associations between time-use compositions with cardiometabolic biomarkers.Methods Accelerometer and cardiometabolic biomarker data (adiposity, blood pressure, lipids) from 7–13 year olds (mean ± SD: 10.4 ± 1.7) from two Australian studies were pooled (complete cases adiposity n = 772). A time-use composition of nine components was formed using compositional data analysis: time in shorter and longer bouts of sedentary behaviour, light-, moderate- and vigorous-intensity physical activity, and “other time” (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as < 5 and ≥ 5 min, respectively. Longer light-, moderate- and vigorous-intensity physical activity bouts were defined as ≥ 1 min. Linear regression models examined associations between overall time-use composition and cardiometabolic biomarkers. Then, associations between ratios of longer relative to shorter activity patterns, and each intensity relative to more intense activities and/or “other time”, with cardiometabolic biomarkers were derived.Results Confounder-adjusted models showed that the overall time-use composition was associated with zBMI, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and a combined cardiometabolic risk score. Specifically, more time in longer relative to shorter bouts of light-intensity physical activity was associated with greater zBMI (β = 1.79, SE = 0.70, p = 0.010) and waist circumference (β = 17.28, SE = 4.87, p < 0.001). More time in longer relative to shorter bouts of vigorous-intensity physical activity was also associated with higher waist circumference (β = 2.54, SE = 1.14, p = 0.026). More relative time in total light- and vigorous-intensity physical activity (including longer and shorter bouts) was associated with lower waist circumference. In contrast, more relative time in sedentary behaviour and moderate-intensity physical activity was detrimental for waist circumference.Conclusions Accumulating physical activity in frequent short bursts may be beneficial for adiposity compared to engaging in the same amount of these intensities in longer bouts.Trial registration: 'Lifestyle Of Our Kids’ (ACTRN12615000066583 [23/01/2015]) and ‘Transform-Us!’ (ACTRN12609000715279 [19/08/2009], ISRCTN83725066 [30/06/2010]).

scholarly journals How are combinations of physical activity, sedentary behaviour and sleep related to cognitive function in older adults? A systematic review

2021 ◽  
Author(s):  
Maddison L Mellow ◽  
Alyson J Crozier ◽  
Dorothea Dumuid ◽  
Alexandra T Wade ◽  
Mitchell R Goldsworthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Older Adults ◽  
Cognitive Function ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Brain Function ◽  
Vigorous Physical Activity ◽  
Cognitive Outcomes ◽  
Statistical Methodology

AbstractThe relationships between cognitive function and each of physical activity, sleep and sedentary behaviour in older adults are well documented. However, these three “time use” behaviours are co-dependent parts of the 24-hour day (spending time in one leaves less time for the others), and their best balance for cognitive function in older adults is still largely unknown. This systematic review summarises the existing evidence on the associations between combinations of two or more time-use behaviours and cognitive function in older adults. Embase, Pubmed, PsycInfo, Medline and Emcare databases were searched in March 2020 and updated in May 2021, returning a total of 25,289 papers for screening. A total of 23 studies were included in the synthesis, spanning >23,000 participants (mean age 71 years). Findings support previous evidence that spending more time in physical activity and limiting sedentary behaviour is broadly associated with better cognitive outcomes in older adults. Higher proportions of moderate-vigorous physical activity in the day were most frequently associated with better cognitive function. Some evidence suggests that certain types of sedentary behaviour may be positively associated with cognitive function, such as reading or computer use. Sleep duration appears to share an inverted U-shaped relationship with cognition, as too much or too little sleep is negatively associated with cognitive function. This review highlights considerable heterogeneity in methodological and statistical approaches, and encourages a more standardised, transparent approach to capturing important daily behaviours in older adults. Investigating all three time-use behaviours together against cognitive function using suitable statistical methodology is strongly recommended to further our understanding of optimal 24-hour time-use for brain function in aging.

scholarly journals 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

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Declan John Ryan ◽  
Jorgen Antonin Wullems ◽  
Georgina Kate Stebbings ◽  
Christopher Ian Morse ◽  
Claire Elizabeth Stewart ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Behaviour ◽  
Time Use ◽  
Compositional Data ◽  
Vigorous Physical Activity ◽  
Glycated Haemoglobin ◽  
Compositional Data Analysis ◽  
Entire Study ◽  
Sample Time

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.

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