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

BackgroundStudies 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.MethodNinety-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.ResultsTriglyceride 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.ConclusionOlder adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile.

The whole day matters: Understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan

“…Of the 51 studies included in this review, 31 studies 27 , 28 , 29 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 examined adherence (meeting vs. not meeting) to 24-h movement guidelines, and 20 studies 16 , 18 , 19 , 20 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 used compositional analyses to explore the 24-h time-use composition of movement behaviors. Studies were conducted in 20 different countries but were most commonly conducted in Canada ( n = 12), 19 , 20 , 27 , 28 , 29 , 35 , 43 , 49 , 55 , 57 ...…”

“…Studies were conducted in 20 different countries but were most commonly conducted in Canada ( n = 12), 19 , 20 , 27 , 28 , 29 , 35 , 43 , 49 , 55 , 57 , 62 , 75 the United States ( n = 10), 16 , 40 , 41 , 44 , 45 , 46 , 47 , 48 , 61 , 72 Australia ( n = 6), 38 , 42 , 59 , 63 , 65 , 71 and the United Kingdom ( n = 4), 54 , 66 , 67 , 74 with 3 or fewer studies conducted in each other country. 34 , 36 , 37 , 39 , 50 , 51 , 52 , 53 , 54 , 66 , 67 , 68 , 69 , 70 , 73 , 74 , 76 , 77 A total of 5 studies were multinational in nature and were conducted with participants from 4 or more different countries. 18 , 56 , 58 , 60 , 64 A total of 46 studies employed a cross-sectional design, and 2 71,72 were prospective...…”

“… 34 , 53 , 77 Timelines for prospective studies ranged from 4 months 71 to 5 years. 72 In terms of age groups, 1 study 42 was conducted with infants, 2 studies 49 , 59 with toddlers, 11 studies 28 , 34 , 35 , 37 , 38 , 39 , 46 , 51 , 53 , 62 , 77 with preschoolers, 14 studies 18 , 36 , 40 , 48 , 52 , 56 , 58 , 60 , 61 , 64 , 65 , 66 , 75 , 76 with children, 4 studies 45 , 47 , 50 , 54 with youth, 8 studies 19 , 27 , 29 , 41 , 43 , 44 , 55 , 57 with both children and youth, 5 studies 16 , 67 , 68 , 69 , 70 with adults, 4 studies 63 , 71 , 73 , 74 with older adults, and 2 studies 20 , 72 with both adults and older adults. The sample sizes of the included studies ranged fr...…”

“…The average quality score for included studies was 74%. Of the 20 publications, 1 study 74 had a score between 50% and 59%, 4 studies 68 , 69 , 70 , 76 had scores between 60% and 69%, 11 studies 16 , 18 , 19 , 20 , 62 , 64 , 65 , 66 , 67 , 71 , 77 had scores between 70% and 79%, and 4 studies 63 , 72 , 73 , 75 had scores between 80% and 89%. The average score of the included studies for the quality subscales of reporting, external validity, internal validity—bias, internal validity—confounding, and power were 96%, 35%, 78%, 68%, and 13%, respectively.…”

Differing Context-Specific Sedentary Behaviors in Australian Adults with Higher and Lower Diabetes Risk

“…Treating time spent in context-speci c sedentary behaviors as being bound within a xed composition (as distinct from treating them as absolute or stand-alone attributes) allows information to be expressed relative to each behavior within the set time frame. This accounts for inter-dependence between the different sedentary behaviors -changing time in one behavior necessitates change in one or more other behavior (13)(14)(15)(16)(17). In the context of diabetes risk, compositional methods can identify how variations in the speci c sedentary behaviors that make up overall sitting time might differ between those who are at higher or lower diabetes risk.…”