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EPIDEMIOLOGY

Compositional Associations of Sleep and Activities within the 24-h Cycle with Cardiometabolic Health Markers in Adults

FARRAHI, VAHID1; KANGAS, MAARIT1,2; WALMSLEY, ROSEMARY3; NIEMELÄ, MAISA1; KIVINIEMI, ANTTI2,4; PUUKKA, KATRI5; COLLINGS, PAUL J.6,7; KORPELAINEN, RAIJA2,8,9; JÄMSÄ, TIMO1,2,10

Author Information
Medicine & Science in Sports & Exercise: February 2021 - Volume 53 - Issue 2 - p 324-332
doi: 10.1249/MSS.0000000000002481

Abstract

The full 24-h day is composed of four main movement behaviors, including sleep, sedentary behavior (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) (1,2). There is evidence to suggest that each movement behavior is associated with adult cardiometabolic health (3–6). However, although the evidence base for health benefits arising from recommended amounts of sleep and MVPA is reasonably strong (4,6), less is known about the health implications of sedentary time and LPA (3,5). Time-based recommendations for adults are therefore only currently available for sleep duration (7–9 h per night) and MVPA (30 min·d−1), whereas only general advice to minimize SB and perform more LPA is made (7,8). It remains unclear how time over a full 24-h cycle should be distributed between all movement behaviors for optimal cardiometabolic health in adulthood (2,9).

Historically, movement behaviors have been assumed to be independently associated with health outcomes (9,10). Previous studies have therefore primarily used traditional regression methods to examine the health implications of one movement behavior in isolation, or have typically only partially accounted for the other movement behaviors of a daily 24-h cycle (2,11). Because incomplete or improper consideration of other movement behaviors in the 24-h cycle may bias findings (11), it is important to adjust for the full range of 24-h movement behaviors using suitable analytical approaches (2,9).

Movement behaviors can be considered to be codependent and compositional, as they are mutually exclusive time-use components of a fixed period, such as the 24-h day (9–11). A change of time spent in one movement behavior necessitates an exchange of equal time for one or a combination of other movement behaviors. Compositional data analysis methods are able to accommodate codependent data that are constrained to a fixed amount of time and are therefore well suited to analyzing this type of data (10,12,13). To our knowledge, only two studies to date have used compositional data analysis to investigate associations of 24-h time use with cardiometabolic health markers in adulthood. Those studies found that MVPA was beneficially associated with markers of cardiometabolic health, but results for the other movement behaviors were inconsistent (10,14). The objective of this study was to examine how compositions of time use during the 24-h day (that is, the relative amounts of time spent asleep, in SB, LPA, and MVPA), and how time reallocations between movement behaviors, are associated with cardiometabolic health markers, including adiposity levels, blood glucose and insulin, and cholesterol profiles, in a large population-based sample of Finnish adults.

MATERIALS AND METHODS

Study Population and Design

Data for the present study were from the population-based Northern Finland Birth Cohort 1966 study (NFBC1966). NFBC1966 is a life-course study involving participants whose date of birth was expected to be in the year 1966 in northern Finland. Cohort members have been monitored prospectively on a regular basis, and data on their health, lifestyle, and socioeconomic status have been collected by questionnaires. Detailed information about the NFBC1966 study objectives, recruitment, and follow-ups (including the follow-up at age 46 yr on which this investigation is based) is available elsewhere (15–17).

Eligible participants for this cross-sectional study were those members of NFBC1966 who had participated in the latest follow-up performed at age 46 yr, and who agreed to wear an accelerometer for device-based measurement of physical activity. The 46-yr follow-up included completion of postal questionnaires, a clinical examination for the collection of fasting blood samples and anthropometric measurements, and on a separate day an oral glucose tolerance test.

Measurements

Movement behaviors

Participants were asked to wear a hip-worn accelerometer (Hookie AM20; Traxmeet Ltd., Espoo, Finland) during all waking hours except water-based activities for 14 consecutive days. Raw acceleration signals were collected and stored at 100 Hz. The accelerometer data were segmented into 6-s epochs, and mean amplitude deviation (MAD) was computed for each segment. There is excellent agreement between MAD values from Hookie and the commonly used ActiGraph GTX3 accelerometer (18). From the 6-s MAD values, monitor nonwear time was detected and removed using a method that closely resembles a validated and popular approach for count-based accelerometer data (≥90 consecutive minutes of no detected movement, allowing for short movement intervals of up to 30 s, if no other movements were detected in the 30 min either side of the current 30-min interval) (19). We changed the window size (from 2 min to 30 s) that was used for handling the artifactual acceleration as the visual speculation of signals showed that a shorter interval performs better with high-frequency raw acceleration data.

The detected wear-time intervals were then cross-referenced with self-reported sleep times (captured with two questions: “At what time do you normally go to bed?” and “At what time do you normally get out of bed?”), and all accelerometer data that overlapped with a sleep interval were discarded. The remaining 6-s epochs were classified as sedentary (sitting or lying), standing still, LPA, moderate-intensity physical activity, or vigorous-intensity physical activity on the basis of MAD values (20,21), and minutes per day in each activity was obtained by dividing time spent in each activity by the number of valid days. Further differentiation between standing still and sitting or lying was performed using a recently validated approach (20). This approach enables posture estimation from hip-based raw acceleration data on the basis of constant Earth’s gravity vector and upright walking posture, and it has shown good to excellent accuracy when compared with thigh-worn posture classification as ground truth under free-living conditions (20). Participants were required to provide four or more valid days of accelerometry, with each valid day defined as ≥10 h of monitor wear time. For the purposes of this study, LPA constituted the sum of all minutes per day spent standing still and in LPA, and MVPA was the sum of minutes per day spent in MVPA. Sleep duration was self-reported in response to the question “How many hours do you sleep on average per day?” Responses were converted to minutes per day asleep.

Cardiometabolic health markers

Participants fasted overnight for 12 h and abstained from smoking and drinking coffee on the day of a clinical examination. Trained nurses measured height, weight, and waist circumference, and body mass index (BMI) was calculated. Body fat, fat mass, and visceral fat area were estimated by bioelectrical impedance analysis (InBody720; InBody, Seoul, Korea) (22). Fasting blood samples were taken and analyzed for plasma glucose, serum insulin, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides as previously described (15). The ratios of total to HDL (total/HDL cholesterol ratio) and LDL to HDL (LDL/HDL cholesterol ratio) cholesterol levels were derived as they provide a better prediction of cardiovascular disease risk than isolated lipid and lipoprotein levels (23). The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting plasma glucose and insulin levels (24). On a second fasted examination day, participants who were not using medication for diabetes underwent a 75-g oral glucose tolerance test (25), from which 2-h postload plasma glucose and insulin levels were obtained.

Covariates

Sex and birth weight were extracted from medical records. Participants self-reported their education level, employment status, marital status, and household income and further provided information about lifestyles (smoking status and alcohol consumption), health-related quality of life (26), and medication use for high blood pressure, high cholesterol, and diabetes.

Statistical Analysis

All statistical analyses were performed using R version 3.6.2 (R Core Team, Vienna, Austria). R packages “lmtest,” “robCompositions,” and “Compositions” were used to perform the compositional data analysis. The analyses were performed in accordance with recently published methods for compositional data analysis applicable to movement behaviors (10,12,13). Participant characteristics were described using standard descriptive statistics. The geometric mean is a better representation of central tendency for compositional data (10); hence, the movement behavior composition was described using compositional means, which are geometric means rescaled to collectively sum to 1440 min (24 h). The variation matrix, which provides a proper estimation of dispersion in compositional data (10), was also calculated for the movement behavior composition based on the variances of the logs of all pairwise ratios between behaviors (e.g., variance of ln [SB/LPA]).

All cardiometabolic outcome variables were log-transformed before analyses. Multiple linear regression was used to investigate associations of the 24-h movement behaviors with cardiometabolic health outcomes. The 24-h time-use composition for each participant was created by linearly rescaling the duration of all activities to sum to a total of 1440 min·d−1. Compositional explanatory variables cannot be directly included in linear regression models (27), so the movement behavior composition for each participant was expressed as ratios of its parts using isometric log-ratio (ilr) transformations before the regression analyses (10,13). The same ilr coordinate system was used to back-transform the log-ratio coordinates into proportions for interpretation as minutes per day. Associations between sleep duration and cardiometabolic outcomes may be U-shaped in adults, with both short and long durations exhibiting adverse associations with cardiometabolic outcomes (4,28). Accordingly, we examined U-shaped associations between sleep duration and cardiometabolic outcomes (29). If evidence for a U-shaped association was observed for an outcome, the analysis for that outcome was stratified by sleep duration. On the basis of the existing literature (4,28) and of sleep durations in this study sample, we stratified the analysis by ≤7.5 and >7.5 h·d−1 asleep for the outcomes displaying a U-shaped relationship. This cut point, in addition to being within the recommended level of sleep for adults (7 to 9 h per night [4,28]), was the mean sleep duration of all study participants and the approximated “breakpoint” at which associations demonstrated U-shapedness (see Figure, Supplemental Digital Content 1, Results of tests for U-shaped relationship between sleep duration and outcomes, http://links.lww.com/MSS/C98).

To assist meaningful interpretation of results, we estimated how time reallocations between movement behaviors were associated with cardiometabolic health markers (13). Specifically, results from the ilr multiple linear regression models were used to calculate estimated differences in outcomes associated with time reallocations from one movement behavior relative to all other remaining behaviors, and vice versa (13). Similarly, differences in outcomes associated with pairwise time reallocations from one movement behavior to another were estimated. Differences in outcomes were estimated for reallocations ranging from 5 to 30 min to/from MVPA and reallocations of 5–90 min to/from the other behaviors in 5-min increments and were plotted to aid interpretation.

RESULTS

Of all 12,058 NFBC1966 cohort members, 10,321 (85.5%) were alive in Finland in 2012 and were invited to the 46-yr follow-up (Fig. 1). A total of 5861 (47% of all cohort members and 57% of those who were invited) participated in the follow-up and wore the accelerometer. Of these, 3443 provided valid acceleration data, in addition to all questionnaire and clinical data that were needed for the present study. The mean (SD) values of accelerometer wear time and self-reported sleep duration for the included participants were 14.3 (1.0) and 7.5 (0.9) h·d−1, respectively. The mean age of participants was 46.6 (0.5) yr, and 55.5% were female. Full descriptive statistics of the participants included in the analysis are shown in Table 1.

FIGURE 1
FIGURE 1:
The selection of the study population from the NFBC1966.
TABLE 1 - Characteristics of the study population overall and by sleep duration categories.
Mean (SD) or Count (%)
Variable Full Sample (N = 3443) Sleep Duration, ≤7.5 h·d−1 (n = 1939) Sleep Duration, >7.5 h·d−1 (n = 1504)
Demographics
 Age, yr 46.6 (0.5) 46.6 (0.5) 46.6 (0.5)
 Sex
  Male 1532 (44.5%) 968 (49.9%) 564 (37.5%)
  Female 1911 (55.5%) 971 (50.1%) 940 (62.5%)
 Education
  Comprehensive school 206 (6%) 135 (7%) 71 (4.7%)
  Vocational/college level   education 2164 (62.9%) 1248 (64.4%) 916 (60.9%)
  Polytechnic/university degree 1073 (31.2%) 556 (28.7%) 517 (34.4%)
 Employment status
  Employed 3112 (90.4%) 1792 (92.4%) 1320 (87.8%)
  Unemployed 178 (5.2%) 92 (4.7%) 86 (5.7%)
  Other (e.g.,   student, homemaker) 153 (4.4%) 55 (2.9%) 98 (6.5%)
 Marital status
  Married/cohabiting 2748 (79.8%) 1544 (79.6%) 1204 (80.1%)
  Divorced/widowed 376 (11%) 189 (9.8%) 144 (9.5%)
  Unmarried 319 (9.2%) 206 (10.6%) 156 (10.4%)
 Household income (€ per year)
  ≤50,000 1404 (40.8%) 789 (40.7%) 615 (40.9%)
  50,001 to 100,000 1622 (47.1%) 926 (47.8%) 696 (46.3%)
  >100,000 417 (12.1%) 224 (11.6%) 193 (12.8%)
 Birth weight, kg 3.5 (0.5) 3.4 (0.5) 3.5 (0.5)
Lifestyle factors, medication use, and health-related quality of life
Alcohol consumption, g·d−1 10.7 (16.4) 11.4 (16.9) 9.6 (4.1)
Health-related quality of life score 0.92 (0.06) 0.92 (0.06) 0.92 (0.06)
Smoking status
 Nonsmoker 1870 (54.3%) 987 (50.9%) 883 (58.7%)
 Former smoker 968 (28.1%) 378 (19.5%) 227 (15.1%)
 Current smoker 605 (17.8%) 574 (29.6%) 394 (26.2%)
Diabetes, cholesterol, and/or hypertension medication
 Yes 580 (16.8%) 334 (17.2%) 246 (16.4%)
 No 2863 (83.2%) 1605 (82.8%) 1258 (83.6%)
Cardiometabolic biomarkers
Fasting insulin, pmol·L−1 9.4 (8.1) 9.5 (8.7) 9.4 (7.4)
2-h insulin, pmol·L−1 59.2 (57.2) 59.5 (59.9) 58.7 (53.6)
Fasting glucose, mmol·L−1 5.5 (0.8) 5.5 (0.8) 5.5 (0.8)
2-h glucose, mmol·L−1 5.8 (1.6) 5.9 (1.6) 5.7 (1.6)
HOMA-IR 2.4 (3.2) 2.5 (3.8) 2.4 (2.3)
Triglycerides, mmol·L−1 1.2 (0.8) 1.2 (0.8) 1.3 (0.9)
Total/HDL cholesterol ratio 1.6 (0.2) 1.6 (0.3) 1.6 (0.3)
LDL/HDL cholesterol ratio 2.3 (0.9) 2.4 (0.9) 2.4 (0.9)
Adiposity measures
Body fat, % 28.3 (9) 27.8 (9.1) 29.1 (8.8)
Fat mass, kg 22.5 (10.3) 22.4 (10.6) 22.7 (10)
Visceral fat area, cm2 97.5 (40.4) 102.1 (41.2) 103.0 (39.2)
BMI, kg·m−2 26.6 (4.7) 26.7 (4.7) 26.5 (4.7)
Waist circumference, cm 91.1 (13.2) 91.8 (13.4) 90.1 (13)

The results of tests for U-shaped associations between sleep duration and cardiometabolic outcomes are shown in the Supplementary File 1 (see Figure, Supplemental Digital Content 1, Results of tests for U-shaped relationship between sleep duration and outcomes, http://links.lww.com/MSS/C98). Evidence for U-shaped relationships (slopes with opposite signs with P < 0.10) was seen for fasting serum insulin, 2-h glucose, HOMA-IR, triglycerides, visceral fat area, and BMI. The compositional means of movement behaviors, overall and stratified by sleep duration, are shown in Table 2. Compared with the compositional means of participants who slept >7.5 h·d−1, participants who slept ≤7.5 h·d−1 had a larger compositional mean for SB, LPA, and MVPA. The variation matrix of the included sample, overall and stratified by sleep duration, is described in Supplementary File 2 [see Table, Supplemental Digital Content 2, Variation matrix of time spent in sleep, sedentary behavior (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) by sleep duration categories, http://links.lww.com/MSS/C99]. Overall, the largest log-ratio variances all included MVPA, which indicates that MVPA was least dependent on the other movement behaviors. The lowest log-ratio variance was between sleep and SB (0.063), which indicates more consistent proportionality (codependency) between these behaviors. The same pattern of codependency was observed when the sample was stratified by participants who slept ≤7.5 and >7.5 h·d−1.

TABLE 2 - Compositional means (percentage of a 24-h day) for sleep, SB, LPA, and MVPA by sleep duration categories, in minutes per day.
Compositional Mean (%)
Movement Behavior Full Sample (N = 3443) Sleep Duration, ≤7.5 h·d−1 (n = 1939) Sleep Duration, >7.5 h·d−1 (n = 1504)
Sleep 515.7 (35.8%) 483.8 (33.6%) 558.2 (38.8%)
SB 496.9 (34.5%) 512.9 (35.6%) 475.6 (33%)
LPA 381.8 (26.5%) 395.9 (27.5%) 363.2 (25.2%)
MVPA 45.6 (3.2%) 47.4 (3.3%) 43 (3%)

The results of ilr compositional data analysis regression models, for cardiometabolic outcomes that were characterized by linear and U-shaped associations with sleep duration, are displayed in Tables 3 and 4, respectively. The composition of movement behaviors across the 24-h day was significantly associated with each of the cardiometabolic outcomes (model P value <0.001 for all). As shown in Tables 3 and 4, regardless of the shape of association with sleep duration, relative to all other behaviors, more daily time in both MVPA and LPA was consistently beneficially associated with cardiometabolic outcomes (e.g., 2-h insulin: MVPA, β = −0.28; LPA, β = −0.30). For outcomes with a linear relationship with sleep duration (Table 3), relative to all other behaviors, more time asleep and SB were both detrimentally associated with outcomes (e.g., total/HDL cholesterol ratio: sleep, β = 0.13; SB, β = 0.05); the only exceptions were that time in SB was not associated with fasting plasma glucose and time in LPA was not significantly associated with waist circumference (although the association bordered significance, P = 0.091). For outcomes that showed a U-shaped relationship with sleep duration (Table 4), generally more daily SB relative to the all other behaviors was detrimentally associated with outcomes. More sleep was detrimentally associated with BMI (β = 0.11) and triglycerides (β = 0.41) in individuals with longer sleeping durations. In addition, in longer sleepers, although the associations were not statistically significant, there was some evidence to indicate that more daily time asleep was adversely associated with HOMA-IR and visceral fat area (P = 0.065 and P = 0.094, respectively).

TABLE 3 - Compositional multiple linear regression estimates for cardiometabolic outcomes that displayed a linear relationship with sleep duration.
Sleep SB LPA MVPA
Measures n Model R 2 Model P β (95% CI) P β (95% CI) P β (95% CI) P β (95% CI) P
Cardiometabolic biomarkers
2-h insulin 3006 0.13 <0.001 0.36 (0.27 to 0.46) <0.001 0.22 (0.15 to 0.28) 0.001 −0.30 (−0.36 to −0.24) <0.001 −0.28 (−0.30 to −0.20) <0.001
Fasting plasma glucose 3380 0.16 <0.001 0.03 (0.01 to 0.04) 0.05 0.01 (0 to 0.02) 0.178 −0.03 (−0.04 to −0.02) 0.001 −0.01 (−0.02 to −0.10) 0.002
Total/HDL cholesterol ratio 3427 0.27 <0.001 0.13 (0.10 to 0.16) <0.001 0.05 (0.03 to 0.07) 0.006 −0.11 (−0.13 to −0.09) <0.001 −0.08 (−0.09 to −0.07) <0.001
LDL/HDL cholesterol ratio 3429 0.23 <0.001 0.17 (0.12 to 0.21) <0.001 0.09 (0.06 to 0.12) 0.003 −0.14 (−0.17 to −0.11) <0.001 −0.11 (−0.13 to −0.10) <0.001
Adiposity measures
Body fat 3381 0.39 <0.001 0.01 (0.06 to 0.13) 0.004 0.16 (0.14 to 0.19) <0.001 −0.15 (−0.17 to −0.13) <0.001 −0.11 (−0.12 to −0.10) <0.001
Fat mass 3381 0.21 <0.001 0.13 (0.08 to 0.18) 0.009 0.26 (0.23 to 0.30) <0.001 −0.24 (−0.27 to −0.21) <0.001 −0.15 (−0.17 to −0.14) <0.001
Waist circumference 3424 0.33 <0.001 0.03 (0.01 to 0.04) 0.091 0.07 (0.06 to 0.08) <0.001 −0.05 (−0.06 to −0.04) <0.001 −0.04 (−0.05 to −0.03) <0.001
Only the regression coefficients corresponding to the first ilr coordinate are shown because the first ilr coordinates contain all the information relative to the remaining movement behaviors. All models have been adjusted for age, sex, birth weight, education level, employment status, marital status, household income, health-related quality of life, lifestyle factors (smoking status and alcohol consumption), and medication (for blood pressure, cholesterol, and/or diabetes). Significant associations are shown in bold.

TABLE 4 - Compositional multiple linear regression estimates for cardiometabolic outcomes that displayed a U-shaped relationship with sleep duration.
Sleep SB LPA MVPA
Measures n Model R 2 Model P β (95% CI) P β (95% CI) P β (95% CI) P β (95% CI) P
Cardiometabolic biomarkers
Fasting serum insulin
 Sleep duration ≤7.5 h·d−1 1904 0.17 <0.001 0.17 (0.06 to 0.29) 0.134 0.25 (0.17 to 0.32) 0.001 −0.24 (−0.30 to −0.18) <0.001 −0.18 (−0.21 to −0.15) <0.001
 Sleep duration >7.5 h·d−1 1476 0.20 <0.001 0.24 (0.08 to 0.40) 0.131 0.20 (0.11 to 0.30) 0.036 −0.30 (−0.38 to −0.21) 0.001 −0.15 (−0.18 to −0.12) <0.001
2-h glucose
 Sleep duration ≤7.5 h·d−1 1674 0.07 <0.001 −0.02 (−0.08 to 0.04) 0.725 0.09 (0.06 to 0.13) 0.006 −0.01 (−0.04 to 0.02) 0.695 −0.06 (−0.08 to −0.05) <0.001
 Sleep duration >7.5 h·d−1 1326 0.10 <0.001 0.12 (0.04 to 0.13) 0.141 −0.02 (−0.06 to 0.03) 0.721 −0.06 (−0.10 to −0.02) 0.151 −0.04 (−0.06 to −0.03) 0.004
HOMA-IR
 Sleep duration ≤7.5 h·d−1 1885 0.19 <0.001 0.18 (0.06 to 0.31) 0.152 0.26 (0.18 to 0.34) 0.001 −0.26 (−0.34 to −0.19) <0.001 −0.18 (−0.21 to −0.15) <0.001
 Sleep duration >7.5 h·d−1 1460 0.22 <0.001 0.32 (0.15 to 0.50) 0.065 0.2 (0.10 to 0.31) 0.057 −0.36 (−0.45 to −0.27) <0.001 −0.16 (−0.20 to −0.13) <0.001
Triglycerides
 Sleep duration ≤7.5 h·d−1 1934 0.20 <0.001 0.13 (0.04 to 0.22) 0.152 0.19 (0.13 to 0.25) 0.001 −0.23 (−0.28 to −0.18) <0.001 −0.09 (−0.11 to −0.07) <0.001
 Sleep duration >7.5 h·d−1 1495 0.23 <0.001 0.41 (0.27 to 0.54) 0.003 −0.02 (−0.10 to 0.06) 0.785 −0.26 (−0.33 to −0.19) <0.001 −0.13 (−0.15 to −0.10) <0.001
Adiposity measures
Visceral fat area
 Sleep duration ≤7.5 h·d−1 1900 0.19 <0.001 0.12 (0.04 to 0.21) 0.141 0.23 (0.18 to 0.28) <0.001 −0.17 (−0.22 to −0.13) <0.001 −0.18 (−0.20 to −0.16) <0.001
 Sleep duration >7.5 h·d−1 1481 0.18 <0.001 0.19 (0.08 to 0.30) 0.094 0.16 (0.09 to 0.23) 0.021 −0.11 (−0.25 to −0.14) 0.001 −0.15 (−0.17 to −0.13) <0.001
BMI
 Sleep duration ≤7.5 h·d−1 1938 0.17 <0.001 0.04 (0.01 to 0.07) 0.188 0.07 (0.05 to 0.09) <0.001 −0.06 (−0.08 to −0.05) <0.001 −0.05 (−0.06 to −0.04) <0.001
 Sleep duration >7.5 h·d−1 1503 0.20 <0.001 0.11 (0.06 to 0.15) 0.022 0.04 (0.01 to 0.07) 0.13 −0.10 (−0.13 to −0.08) <0.001 −0.05 (−0.06 to −0.04) <0.001
Only the regression coefficients corresponding to the first ilr coordinate are shown because the first ilr coordinates contain all the information relative to the remaining movement behaviors. All models have been adjusted for age, sex, birth weight, education level, employment status, marital status, household income, health-related quality of life, lifestyle factors (smoking status and alcohol consumption), and medication (for blood pressure, cholesterol and/or diabetes). Significant associations are shown in bold.

The results for time reallocations between movement behaviors with all cardiometabolic health outcomes are presented in Supplementary File 3 (see Appendix, Supplemental Digital Content 3, Figures of results of time reallocations between movement behaviors with all cardiometabolic health outcomes, http://links.lww.com/MSS/C100). From the estimates (percent change), it was apparent that more time in MVPA at the expense of all other behaviors was associated with favorable changes in outcomes. For instance, as shown in Figure 2 by way of an example, 30 min·d−1 more MVPA relative to the remaining behaviors was significantly associated with lower 2-h insulin (−11.8%, 95% confidence interval [CI] = −13.9 to −9.6). Reallocating 30 min·d−1 from sleep, SB, and LPA to MVPA was consistently associated with lower 2-h insulin (−13%, 95% CI = −15.3 to −10.7; −12.4%, 95% CI = −14.5 to −10.3; and −9.4%, 95% CI = −11.8 to −7.0, respectively). In general, reallocating time from SB or sleep to LPA was favorably associated with outcomes but to a lesser extent compared with MVPA. Again, using Figure 2 as an example, 60 min·d−1 more LPA at the expense of SB and sleep was associated with lower 2-h insulin (−6.1%, 95% CI = −7.7 to −4.4, and −7.4%, 95% CI = −10.3 to −4.5, respectively). Conversely, opposite time reallocations, including adding time to any other behavior from MVPA or generally adding time to sleep or SB from LPA, were associated with unfavorable changes in outcomes. As shown in Figure 2, reallocating 30 min·d−1 from MVPA to sleep, SB, and LPA was associated with higher 2-h insulin (31.2%, 95% CI = 24.6–38.1; 30.3%, 95% CI = 24.0–36.9; and 26.3%, 95% CI = 20.0–33.0, respectively). Reallocating 60 min·d−1 from LPA to SB and sleep was also associated with higher 2-h insulin (6.9%, 95% CI = 4.8–8.9, and 8.3%, 95% CI = 4.9–11.7, respectively). See Supplementary File 3 for a full description of results for all outcomes (see Appendix, Supplemental Digital Content 3, Figures of results of time reallocations between movement behaviors with all cardiometabolic health outcomes, http://links.lww.com/MSS/C100).

FIGURE 2
FIGURE 2:
Systematically altered movement behavior compositions and percent change in 2-h post–glucose load insulin levels. (A) Percent change for reallocation of time from one movement behavior relative to the remaining movement behaviors (e.g., in the second column [SB], 60 min·d−1 more SB is associated with percent change of 3.5, compared with 2-h insulin at the mean composition). (B) Percent change for pairwise reallocation of time from one movement behavior (rows) to another movement behaviors (columns) (e.g., row two, column three indicates the estimated difference in 2-h insulin by reallocating time from SB to LPA, compared with 2-h insulin at the mean composition).

DISCUSSION

In this study, a compositional data analysis approach was used to examine the codependent relationships of movement behaviors over the 24-h day with markers of cardiometabolic health, in a large population-based sample of Finnish adults. Relative to time spent in other behaviors, more daily LPA and MVPA were both beneficially associated with cardiometabolic health outcomes. Conversely, more daily SB and sleep were both detrimentally associated with multiple cardiometabolic outcomes. The estimates collectively suggest that more daily time in either MVPA at the expense of any other movement behavior or LPA at the expense of sleep or SB is favorably associated with cardiometabolic health.

This is the first study to use a compositional data analysis approach to model the cardiometabolic health associations of movement behaviors in midadulthood, while simultaneously accounting for possible U-shaped relations between sleep duration and cardiometabolic outcomes. In line with the existing literature (4,30), we identified that the nature of relationships varied by risk markers, with evidence for U-shaped associations with fasting serum insulin, 2-h glucose, HOMA-IR, triglycerides, visceral fat area, and BMI, but no evidence against linearity with all other outcomes. Accommodating U-shaped associations with sleep may explain why, compared with previous compositional studies (10,14,31,32), we found more consistent associations between movement behaviors with cardiometabolic outcomes. Two earlier studies of adults examined associations of 24-h movement behavior compositions with a subset of cardiometabolic outcomes similar to those examined here (10,14). The results of those studies were mixed for all movement behaviors except MVPA, which was consistently associated with lower adiposity measures and beneficially associated with certain cardiometabolic biomarkers (10,14). Our more consistent results might also in part be due to the longer measurement protocol that we used for accelerometer-based measurement of activities (i.e., 14 vs 7 consecutive days). This likely conferred more accurate estimation of habitual physical activity and sedentary time and, in turn, more precise estimates of associations with health outcomes. It is also conceivable that the classification of movement behaviors across the whole continuum was more accurate in this study. Previous studies have used cut point–based methods for assessing physical activities (10,14), which are relatively inaccurate in the estimation of activities of lower intensity under free-living settings compared with activities with higher intensities such as MVPA (33). By contrast, the method used here for the estimation of physical activity has shown good to excellent performance against thigh-worn accelerometry under free-living settings (20).

The main finding of this study is that relative to all other behaviors, more daily time in both LPA and MVPA was beneficially associated with multiple cardiometabolic health markers. The health benefits of MVPA have been well documented in both compositional (10,14,34–36) and noncompositional studies (2,6), but the health-enhancing potential of LPA has received less research interest (3). Few adults meet the recommended 30 min·d−1 of MVPA (37). This highlights the necessity for enhanced understanding of the health implications of LPA, which is a more feasible intensity of movement that is accessible regardless of physical fitness, inclination, and opportunity to be higher physically active. Evidence has started to emerge that more time spent in LPA (3,38), even after accounting for MVPA (39), could improve cardiometabolic outcomes. Furthermore, compositional studies have also reported that more LPA could be beneficial for reduced mortality risk, even after accounting for other activity intensities (40) and sleep (41). Our findings, although supporting the established physical activity recommendations that encourage MVPA for better cardiometabolic health (2), also confirm the findings of recent studies suggesting that LPA may also confer meaningful cardiometabolic health benefits in adults (3).

With regard to the estimates of time reallocations for LPA and MVPA, there are at least two things of note. First, differences in the 24-h time-use budget that were beneficially associated with cardiometabolic health outcomes included more MVPA at the expense of all other behaviors, or to a lesser extent more LPA at the expense of SB or sleep. Second, lower MVPA and LPA (if not reallocated to MVPA) are adversely associated with cardiometabolic outcomes. Although the estimated differences might not be directly comparable with previous studies in terms of effect size due to the differences in study design and measurement of movement behaviors, these results partly agree with the results of previous compositional data analysis studies. Previous studies have also consistently reported that changing time in MVPA is most potently associated with cardiometabolic outcomes, with the association being nonlinear and asymmetrical (10,32,36,42,43). Given that individuals may find adding time to their daily amount of LPA more feasible than performing more MVPA (3), a possible implication of our results is that to achieve favorable changes in cardiometabolic outcomes, practitioners may consider advising middle-age adults to perform more LPA, while also stressing the importance of maintaining daily levels of MVPA.

More daily SB and sleep beyond 7.5 h·d−1 (both relative to all other behaviors) were both unfavorably associated with cardiometabolic health markers. Importantly, more sleep in individuals who slept up to but not more than 7.5 h·d−1 was not associated with any of the cardiometabolic health outcomes that displayed U-shaped associations with sleep. Our results for SB are in line with previous studies which have reported that more sedentary time is associated with poorer cardiometabolic health, although most of those studies failed to account for sleep (44). Currently, little is known in the literature about the mechanisms by which longer sleep duration is related to cardiometabolic health and even less about the interrelationship between sleep duration and sedentary and the combined effects of these behaviors on cardiometabolic health (2,7,45). However, in accordance with the present results, a recent systematic review concluded that sleeping more than 7 to 8 h per night was associated with a higher degree of cardiovascular disease risk (and also mortality) compared with sleeping less than 7 to 8 h per night (46).

It was apparent from the estimates that generally favorable differences in cardiometabolic outcomes could be achieved by reallocation of time in SB to LPA or MVPA or, to a comparable extent, by reallocation of time spent asleep to LPA or MVPA. Of note is that, for the outcomes that displayed U-shaped relationships with sleep duration, more daily time in SB was detrimentally associated with outcomes irrespective of whether participants slept more or less than 7.5 h·d−1. This suggests that reduced sedentary time may be beneficial for cardiometabolic health regardless of sleep duration, which is in line with the findings of a previous study using an isotemporal substitution approach (47).

The strengths of this study include the relatively large population-based sample of Finnish adults and the wide range of cardiometabolic health markers that were investigated. In addition, device-based measurement of daily activities was captured over a relatively long timeframe and with raw accelerometry, from which movement behaviors were estimated using a robust analytical approach (20). Testing for possible U-shaped relationships between sleep duration and outcomes, and when necessary stratifying analyses by short and long sleep, is another strength. Limitations include that, because of its observational and cross-sectional design, inference about the temporality of associations is limited and causality cannot be determined. For instance, it is conceivable that poorer cardiometabolic health might lead to longer sleep duration, rather than vice versa. Furthermore, our results are based on theoretical time reallocations. Future studies, perhaps a series of experimental and longitudinal cohort studies, are needed to more realistically understand the true effects of different time substitutions on cardiometabolic outcomes (43). The study sample was homogenous in terms of age and ethnicity. Although beneficial with respect to reducing the potential for confounding of the observed associations, this limits the generalizability of the results to more diverse populations. Sleep duration was self-reported and therefore was probably measured with less accuracy than the other movement behaviors, including sedentary time and physical activity, which were estimated by accelerometry. However, although self-reported sleep durations are often higher than accelerometer-estimated sleep durations in middle-age adults, the differences between them are small (48). It is therefore unlikely that the result of associations would have been different or changed with accelerometer-estimated sleep durations compared with the results that were found here with self-reported sleep durations. Hence, the point at which the U-shaped relationships were found (7.5 h·d−1) could be slightly different with accelerometer-estimated sleep durations. Studies with device-based estimates of sleep duration are needed to verify the optimal amount of sleep for better cardiometabolic health in adults.

CONCLUSIONS

More daily time in MVPA at the expense of any other behavior could be the most time-efficient change in the movement behavior composition for improving cardiometabolic health in midadulthood. Alternatively, more daily time in LPA at the expense of sleep or SB could also be beneficial for cardiometabolic health, but to a lesser extent compared with more time in MVPA. Conversely, reduced daily time in MVPA or LPA seemed to be detrimental for cardiometabolic health, which suggests that daily levels of MVPA and LPA, if not increased, should at least be maintained to prevent deterioration of cardiometabolic health.

The authors thank all cohort members and researchers who participated in the 46 yrs study. They acknowledge the NFBC project center for their contribution in managing the NFBC1966 study and the UKK Institute for providing the accelerometers for the study.

NFBC1966 received financial support from the University of Oulu (grant no. 24000692), the Oulu University Hospital (grant no. 24301140), and the European Regional Development Fund (grant no. 539/2010 A31592). The present study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska–Curie grant agreement no. 713645, the Ministry of Education and Culture in Finland (grant nos. OKM/86/626/2014, OKM/43/626/2015, OKM/17/626/2016, and OKM/54/626/2019), and the Northern Ostrobothnia Hospital District. These funders also supported VF’s placement at Bradford Institute for Health Research where the data analysis was performed. PJC is funded by a British Heart Foundation (BHF) Immediate Postdoctoral Basic Science Research Fellowship (FS/17/37/32937). RW is supported by a Medical Research Council Industrial Strategy Studentship (grant no. MR/S502509/1). AMK is funded by the Finnish Foundation for Cardiovascular Research (Helsinki, Finland). The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

The authors declare no conflicts of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

REFERENCES

1. Grgic J, Dumuid D, Bengoechea EG, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15(1):69.
2. Rosenberger ME, Fulton JE, Buman MP, et al. The 24-hour activity cycle: a new paradigm for physical activity. Med Sci Sports Exerc. 2019;51(3):454–64.
3. Chastin SFM, De Craemer M, De Cocker K, et al. How does light-intensity physical activity associate with adult cardiometabolic health and mortality? Systematic review with meta-analysis of experimental and observational studies. Br J Sports Med. 2019;53(6):370–6.
4. Knutson KL. Sleep duration and cardiometabolic risk: a review of the epidemiologic evidence. Best Pract Res Clin Endocrinol Metab. 2010;24(5):731–43.
5. Brocklebank LA, Falconer CL, Page AS, Perry R, Cooper AR. Accelerometer-measured sedentary time and cardiometabolic biomarkers: a systematic review. Prev Med. 2015;76:92–102.
6. Lee I-M, Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29.
7. Watson NF, Badr MS, Belenky G, et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Sleep. 2015;38(6):843–4.
8. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1423–34.
9. Chaput J-P, Carson V, Gray C, Tremblay M. Importance of all movement behaviors in a 24 hour period for overall health. Int J Environ Res Public Health. 2014;11(12):12575–81.
10. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. 2015;10(10):e0139984.
11. Pedišić Ž. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research—the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;46(1):135–46.
12. Dumuid D, Pedišić Ž, Stanford TE, et al. 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. Stat Methods Med Res. 2019;28(3):846–57.
13. Dumuid D, Stanford TE, Martin-Fernández JA, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38.
14. McGregor D, Carson V, Palarea-Albaladejo J, Dall P, Tremblay M, Chastin S. Compositional analysis of the associations between 24-h movement behaviours and health indicators among adults and older adults from the Canadian health measure survey. Int J Environ Res Public Health. 2018;15(8):1779.
15. Kiviniemi AM, Perkiömäki N, Auvinen J, et al. Fitness, fatness, physical activity, and autonomic function in midlife. Med Sci Sport Exerc. 2017;49(12):2459–68.
16. University of Oulu Web site [Internet]. NFBC 1966 data collection. [cited 2020 Feb 10]. Available from: https://www.oulu.fi/nfbc/node/19663.
17. University of Oulu Web site [Internet]. 46-year follow-up study. [cited 2020 Feb 10]. Available from: https://www.oulu.fi/nfbc/node/26627.
18. Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila A-M, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7(18).
19. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64.
20. Vähä-Ypyä H, Husu P, Suni J, Vasankari T, Sievänen H. Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer. Scand J Med Sci Sports. 2018;28(3):1092–102.
21. Vähä-Ypyä H, Vasankari T, Husu P, et al. Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD). PLoS One. 2015;10(8):e0134813.
22. Jensky-Squires NE, Dieli-Conwright CM, Rossuello A, Erceg DN, McCauley S, Schroeder ET. Validity and reliability of body composition analysers in children and adults. Br J Nutr. 2008;100(4):859–65.
23. Millán J, Pintó X, Muñoz A, et al. Lipoprotein ratios: physiological significance and clinical usefulness in cardiovascular prevention. Vasc Health Risk Manag. 2009;5:757–65.
24. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487–95.
25. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.
26. Sintonen H. The 15D instrument of health-related quality of life: properties and applications. Ann Med. 2001;33(5):328–36.
27. Hron K, Filzmoser P, Thompson K. Linear regression with compositional explanatory variables. J Appl Stat. 2012;39(5):1115–28.
28. Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3.
29. Simonsohn U. Two lines: a valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Adv Methods Pract Psychol Sci. 2018;1(4):538–55.
30. Full KM, Gallo LC, Malhotra A, et al. Modeling the cardiometabolic benefits of sleep in older women: exploring the 24-hour day. Sleep. 2020;43(1):zsz205.
31. McGregor DE, Palarea-Albaladejo J, Dall PM, Stamatakis E, Chastin SFM. Differences in physical activity time-use composition associated with cardiometabolic risks. Prev Med Rep. 2018;13:23–9.
32. Dumuid D, Lewis LK, Olds TS, Maher C, Bondarenko C, Norton L. Relationships between older adults’ use of time and cardio-respiratory fitness, obesity and cardio-metabolic risk: a compositional isotemporal substitution analysis. Maturitas. 2018;110:104–10.
33. Calabró MA, Lee J-M, Saint-Maurice PF, Yoo H, Welk GJ. Validity of physical activity monitors for assessing lower intensity activity in adults. Int J Behav Nutr Phys Act. 2014;11:119.
34. Talarico R, Janssen I. Compositional associations of time spent in sleep, sedentary behavior and physical activity with obesity measures in children. Int J Obes (Lond). 2018;42(8):1508–14.
35. Dumuid D, Wake M, Clifford S, et al. The association of the body composition of children with 24-hour activity composition. J Pediatr. 2019;208:43–49.e9.
36. Gupta N, Dumuid D, Korshøj M, Jørgensen MB, Søgaard K, Holtermann A. Is daily composition of movement behaviors related to blood pressure in working adults?Med Sci Sports Exerc. 2018;50(10):2150–5.
37. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Heal. 2018;6(10):e1077–86.
38. Füzéki E, Engeroff T, Banzer W. Health benefits of light-intensity physical activity: a systematic review of accelerometer data of the National Health and Nutrition Examination Survey (NHANES). Sport Med. 2017;47(9):1769–93.
39. Amagasa S, Machida M, Fukushima N, et al. Is objectively measured light-intensity physical activity associated with health outcomes after adjustment for moderate-to-vigorous physical activity in adults? A systematic review. Int J Behav Nutr Phys Act. 2018;15(1):65.
40. von Rosen P, Dohrn I-M, Hagströmer M. Association between physical activity and all-cause mortality: a 15-year follow-up using a compositional data analysis. Scand J Med Sci Sports. 2020;30(1):100–7.
41. McGregor DE, Palarea-Albaladejo J, Dall PM, del Pozo Cruz B, Chastin SF. Compositional analysis of the association between mortality and 24-hour movement behaviour from NHANES. Eur J Prev Cardiol. 2019;204748731986778. doi:10.1177/2047487319867783.
42. Gupta N, Korshøj M, Dumuid D, Coenen P, Allesøe K, Holtermann A. Daily domain-specific time-use composition of physical behaviors and blood pressure. Int J Behav Nutr Phys Act. 2019;16(4).
43. Powell C, Browne LD, Carson BP, et al. Use of compositional data analysis to show estimated changes in cardiometabolic health by reallocating time to light-intensity physical activity in older adults. Sports Med. 2019;50(1):205–17.
44. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. Am J Prev Med. 2011;41(2):207–15.
45. Lakerveld J, Mackenbach JD, Horvath E, et al. The relation between sleep duration and sedentary behaviours in European adults. Obes Rev. 2016;17(1 Suppl):62–7.
46. Kwok CS, Kontopantelis E, Kuligowski G, et al. Self-reported sleep duration and quality and cardiovascular disease and mortality: a dose-response meta-analysis. J Am Heart Assoc. 2018;7(15):e008552.
47. Buman MP, Winkler EA, Kurka JM, et al. Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol. 2014;179(3):323–34.
48. Lauderdale DS, Knutson KL, Yan LL, et al. Objectively measured sleep characteristics among early-middle-aged adults: the CARDIA study. Am J Epidemiol. 2006;164(1):5–16.
Keywords:

ISOTEMPORAL SUBSTITUTION; METABOLIC DISEASES; ADIPOSITY; INSULIN RESISTANCE; DYSLIPIDEMIAS; PHYSICAL ACTIVITY

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