Abstract
[Purpose]
Movement behaviors over 24 hours—sleep, sedentary time, and physical activity—are temporally interdependent, but most Korean studies in older adults have analyzed them in isolation. We examined how the 24-hour movement composition relates to metabolic syndrome (MetS) in adults aged ≥ 65 years.
[Methods]
We pooled data from the 9th Korea National Health and Nutrition Examination Survey (KNHANES; 2022-2024) and analyzed 4,316 community-dwelling older adults. Daily time in sleep, sedentary behavior, light physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA) was rescaled to 1,440 min/day. Isometric log-ratio (ilr) coordinates were entered into weighted logistic regression that accounted for the complex survey design, and 30-min isotemporal substitutions were estimated.
[Results]
Weighted MetS prevalence was 40.4%. Greater relative time in MVPA (ilr1) and LIPA (ilr2) was inversely associated with MetS (OR 0.93, 95% CI 0.89-0.96; OR 0.80, 0.70-0.91), whereas a higher sedentary-to-sleep ratio (ilr3) showed a positive association (OR 1.28, 1.02-1.62). Reallocating 30 min from sedentary time to MVPA was associated with about 13% lower predicted odds of MetS (OR 0.87, 0.82-0.93), and even a shift to LIPA showed a small but significant association (OR 0.97, 0.96-0.98). Sex-stratified analyses showed broadly consistent patterns in men and women.
[Conclusion]
In Korean older adults, replacing sedentary time with any form of activity—especially MVPA— was associated with lower predicted odds of MetS, supporting a 24-hour movement perspective for metabolic-health promotion.
Keywords: 24-hour movement behavior, compositional data analysis, isotemporal substitution, metabolic syndrome, older adults, KNHANES
INTRODUCTION
Metabolic syndrome (MetS) is a clustering of cardiometabolic risk factors that strongly predicts type 2 diabetes and cardiovascular disease. In Korea, MetS affects an estimated 36% of adults and rises sharply with age, exceeding 40% in those aged 65 and older [1,2]. Given Korea’s rapid demographic shift toward an aged society, identifying modifiable behavioral factors that drive MetS in this population has become a public health priority.
Physical activity (PA) and sedentary behavior have long been studied as separate exposures, with the World Health Organization recommending at least 150 minutes of moderate-to-vigorous PA per week and limited sitting time [3]. More recently, attention has shifted to the broader 24-hour movement framework, which considers sleep, sedentary time, light PA (LIPA), and moderate-to-vigorous PA (MVPA) as parts of a single, time-bounded composition [4,5]. Because the day contains exactly 1,440 minutes, time spent in one behavior must come at the expense of another. This co-dependency is not captured by standard regression models, which treat each behavior as if it could vary independently of the others [6,7].
Compositional data analysis (CoDA), originally developed for geological proportion data [8], offers a principled solution. By transforming behavior proportions into log-ratio coordinates (e.g., the isometric log-ratio, or ilr) [9], CoDA respects the simplex structure of time-use data. The approach has been applied to 24-hour movement behaviors in adolescents, adults, and older populations from Australia, Spain, the United States, and other settings, with consistent findings that more time in MVPA and less in sedentary behavior favor cardiometabolic health [10-12]. Isotemporal substitution— a complementary technique that estimates the effect of reallocating time from one behavior to another while keeping the day at 1,440 minutes—has produced practical, time-based recommendations such as “exchange 30 minutes of sitting for walking” [13,14].
Mechanistically, prolonged sitting suppresses skeletal-muscle lipoprotein-lipase activity and impairs postprandial triglyceride clearance, while even light-intensity ambulation restores muscle contractile activity and acutely improves glucose disposal through contraction-stimulated GLUT-4 translocation. Regular MVPA further enhances insulin sensitivity, reduces visceral adiposity and ambulatory blood pressure, raises HDL-cholesterol, and attenuates chronic low-grade inflammation—each of which maps directly onto a component of the metabolic syndrome. Adequate sleep, in turn, regulates appetite-related hormones (leptin, ghrelin) and sympathetic tone, so that displacement of sleep by sedentary time may compound metabolic risk. These complementary mechanisms make the relative balance of the four behaviors—rather than any single one in isolation—a biologically coherent target for cardiometabolic-health promotion in older adults.
Despite this growing body of work, evidence in older Korean adults remains limited. To our knowledge, no study has applied CoDA to a recent, nationally representative Korean sample of older adults. Two factors make such an analysis timely. First, the 9th Korea National Health and Nutrition Examination Survey (KNHANES, 2022-2024) provides three consecutive years of post-pandemic data on sleep, sedentary, and PA behaviors with detailed metabolic phenotyping. Second, the typical 24-hour profile of Korean older adults differs from Western samples—longer sedentary time, shorter MVPA, and earlier bedtimes have been described in previous KNHANES analyses [15,16]—so generalizing findings from Western cohorts may be misleading.
We therefore aimed to (i) describe the 24-hour movement composition of community-dwelling Korean adults aged 65 and older, (ii) estimate associations between the movement composition (expressed as ilr coordinates) and MetS and its five components, and (iii) quantify the predicted change in MetS odds when 30 minutes are reallocated between behaviors. We hypothesized that higher relative time in MVPA and LIPA, and lower sedentary-to-sleep ratios, would be inversely associated with MetS, with the greatest benefit observed when sedentary time is replaced by MVPA.
METHODS
Study Design and Participants
Data came from the 9th KNHANES (2022-2024), a nationally representative cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA). The KNHANES uses a two-stage stratified cluster sampling design with stratification by region (sido), district type (dong/eup-myeon), and housing type, followed by random selection of clusters and households [17]. The survey was approved by the KDCA Institutional Review Board (approval numbers 2018-01-03-4C-A, 2022-11-16-R-A, 2022-11-16-R-03), and all participants provided written informed consent.
We restricted the analysis to the 9th KNHANES cycle (2022-2024) for four reasons. First, the 9th cycle is the most recent complete release and captures the post-pandemic period, during which movement behaviors of Korean older adults have shifted relative to earlier cycles. Second, the 24-hour movement variables required for compositional analysis—sleep, sedentary time, walking, moderate leisure, vigorous leisure, and active transportation—were measured with a single harmonized questionnaire (Korean version of the Global Physical Activity Questionnaire, K-GPAQ) throughout the 9th cycle, whereas earlier cycles used heterogeneous instruments (e.g., the IPAQ short form), precluding valid pooling. Third, integrated three-year survey weights (wt_pool_3) are released by the KDCA specifically for combining the 9th-cycle waves. Fourth, biochemical and anthropometric measurements followed identical KDCA quality-control protocols across the three years.
Of the 20,191 respondents in the three combined years, 5,453 were aged 65 or older. We sequentially excluded 793 with missing or “don’t know” responses on PA or sedentary time, 67 with incomplete sleep data, 260 with incomplete MetS components (blood pressure, fasting glucose, triglycerides, HDL-cholesterol, or waist circumference), and 17 with implausible 24-hour totals (sum > 1,440 minutes, sleep < 2 hours or > 14 hours, or sedentary > 22 hours). The final analytic sample comprised 4,316 participants (Figure 1). Because the KNHANES sampling frame is the household-based Population and Housing Census, the survey by design excludes institutionalized populations (long-term care facilities, nursing homes, military barracks, and hospitals); the analytic sample therefore represents community-dwelling older adults.
Figure 1. Sample selection flow chart.
KNHANES = Korea National Health and Nutrition Examination Survey; PA = physical activity; MetS = metabolic syndrome.
24-Hour Movement Behaviors
All movement behaviors were expressed in minutes per day.
Sleep. Sleep measurement changed across the survey years. In 2022 and 2023, respondents reported their average weekday and weekend sleep duration in hours [18]. In 2024, weekday and weekend bedtimes and wake times were collected separately, from which sleep duration was computed [18]. We harmonized the two formats by converting all responses to minutes and computing a weighted weekly average ((weekday × 5 + weekend × 2) / 7).
Sedentary behavior. Daily sedentary time (BE8_1, BE8_2) was the self-reported total of waking time spent sitting or lying down (excluding sleep), in hours and minutes.
LIPA and MVPA. Domain-specific PA data were collected with a Korean version of the Global Physical Activity Questionnaire (K-GPAQ), which has been validated against accelerometry in Korean adults. Walking was reported as days per week and total time per day. Moderate-intensity leisure activity, vigorous-intensity leisure activity, and active transportation (walking or cycling for transport) were each reported as a yes/no item, days per week, and time per day. Daily minutes for each domain were calculated as (days × time per day) / 7. Following Pedišić [5], we defined MVPA as the sum of moderate leisure, vigorous leisure, and active transportation minutes. Because KNHANES does not include a dedicated item for non-walking light activity (e.g., light housework, slow ambulation, gardening), we defined an operational LIPA-equivalent variable (hereafter LIPA) as walking minutes plus a residual term equal to 1,440 minus the sum of sleep, sedentary, walking, and MVPA minutes. This residual approach, used in earlier compositional analyses10,19, ensures that the daily composition sums to 1,440 minutes, but it inevitably absorbs unmeasured low-intensity activity together with measurement error in the other three behaviors; the LIPA coefficient should therefore not be interpreted as a clean estimate of the physiological effect of light-intensity activity.
Metabolic Syndrome
MetS was defined according to the Korean Society for the Study of Obesity guidelines, which apply the National Cholesterol Education Program ATP III criteria with Korean-specific abdominal obesity cutoffs [20]. Participants were classified as having MetS if they met at least three of the following: (i) waist circumference ≥ 90 cm (men) or ≥ 85 cm (women); (ii) systolic blood pressure ≥ 130 mmHg or diastolic ≥ 85 mmHg or use of antihypertensive medication; (iii) fasting glucose ≥ 100 mg/dL or use of antidiabetic medication or insulin; (iv) triglycerides ≥ 150 mg/dL; or (v) HDL-cholesterol < 40 mg/dL (men) or < 50 mg/dL (women). Blood pressure was the average of the second and third measurements obtained at five-minute intervals, and blood samples were drawn after at least eight hours of fasting
Covariates
We adjusted for sex, age (continuous), current smoking status, frequent drinking (defined as drinking ≥ 2 days per week), and educational attainment (high school graduate or higher), based on previous CoDA studies of older adults [10,11]. Because household income, chronic-disease diagnosis, and medication use may lie on the causal pathway between movement behaviors and the metabolic phenotype, we did not include them in the primary model but evaluated their effect in two pre-specified sensitivity analyses (Supplementary Table S5): Model B added household-income quartile and self-reported physician-diagnosed hypertension, diabetes mellitus, and dyslipidemia, and Model C further added current use of antihypertensive, antidiabetic, and lipid-lowering medication.
Statistical Analysis
Complex survey design. We applied the integrated weight (wt_pool_3 = wt_itvex / 3) to combine three survey years [17] and included primary sampling units (PSUs) and stratification variables for variance estimation using Taylor series linearization.
CoDA. Because zeros are not permitted in log-ratio transformations, MVPA values of 0 (n = 1,638; 38.0%) were replaced with 0.5 minutes (half of the minimum-resolution recording unit of the K-GPAQ instrument) using a multiplicative replacement, and the composition was renormalized to 1,440 minutes [21]. The robustness of this choice was assessed in sensitivity analyses using alternative replacement values (0.1 and 1.0 minute) and an alternative “any MVPA versus none” categorical specification (Supplementary Table S6). A year-stratified analysis (2022, 2023, and 2024; Supplementary Table S7) was conducted to assess the impact of the change in sleep measurement format introduced in 2024. We then defined three ilr coordinates using a sequential binary partition [9,22]:
ilr₁ = √(3/4) × ln[ MVPA / ³√(Sleep × Sed × LIPA) ]
ilr₂ = √(2/3) × ln[ LIPA / √(Sleep × Sed) ]
ilr₃ = √(1/2) × ln[ Sed / Sleep ]
ilr₁ captures the relative weight of MVPA against the geometric mean of the other three behaviors; ilr₂ contrasts LIPA with the geometric mean of sleep and sedentary; and ilr₃ contrasts sedentary with sleep. We chose this partition for its interpretability—it isolates the most active behavior (MVPA) first, then progressively distinguishes the lower-intensity components.
Logistic regression. The three ilr coordinates were entered simultaneously into multivariable logistic regression models for MetS and each of its five components, adjusted for the covariates above. Standard errors were estimated with PSU clustering using cluster-robust variance estimation. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported.
Isotemporal substitution. Using the weighted geometric mean composition (sleep 439, sedentary 534, LIPA 461, MVPA 5.7 minutes) as the reference, we recalculated the ilr coordinates for compositions in which 30 minutes were reallocated from one behavior to another, and predicted the corresponding MetS odds [14]. Confidence intervals were obtained from 1,000 parametric bootstrap replicates of the regression coefficients.
All analyses were performed in Python 3.12 with stats models 0.14. Two-sided p < 0.05 was considered statistically significant.
RESULTS
Sample Characteristics
Of the 4,316 older adults included, 1,928 (45.2%) were men and 2,388 (54.8%) were women. Mean age was 72.4 ± 5.1 years; 36.6% were 65-69, 27.6% were 70-74, 21.3% were 75-79, and 14.9% were 80 or older (KNHANES topcodes age at 80). Mean BMI was 24.1 ± 3.2 kg/m² and mean waist circumference 86.5 ± 9.4 cm. Current smoking prevalence was 8.7%, frequent drinking 27.3%, and high school graduation or higher 39.9%. The weighted MetS prevalence was 40.4%, corresponding to roughly 3.0 million Korean adults aged 65 and older.
Mean daily time use was sleep 412.5 ± 86.8 minutes (≈ 6.9 hours), sedentary behavior 532.3 ± 200.6 minutes (≈ 8.9 hours), LIPA 476.2 ± 200.9 minutes (≈ 7.9 hours), and MVPA 19.0 ± 27.1 minutes (Table 1). Mean MVPA was 19 minutes per day (about 133 minutes per week), which falls below the WHO recommendation of 150 minutes per week of moderate-to-vigorous activity [3]. Compared with participants without MetS, those with MetS reported about 49 more minutes of sedentary time, 44 fewer minutes of LIPA, and 4 fewer minutes of MVPA per day. Sleep duration was virtually identical between the two groups.
Table 1.
Characteristics of the analytic sample, by metabolic syndrome status (KNHANES 2022-2024, N = 4,316)
| Variable | Total (N = 4,316) | No MetS (n = 2,583) | MetS (n = 1,733) |
|---|---|---|---|
| Sex | |||
| Men, n (%) | 1,928 (45.2) | 1,157 (45.1) | 771 (45.2) |
| Women, n (%) | 2,388 (54.8) | 1,426 (54.9) | 962 (54.8) |
| Age (years), mean ± SD | 72.4 ± 5.1 | 72.1 ± 5.1 | 72.8 ± 5.2 |
| BMI (kg/m²), mean ± SD | 24.1 ± 3.2 | 22.9 ± 2.8 | 25.8 ± 2.9 |
| Waist circumference (cm), mean ± SD | 86.5 ± 9.4 | 82.5 ± 8.4 | 92.3 ± 7.7 |
| Lifestyle | |||
| Current smoker, n (%) | 382 (8.7) | 218 (8.3) | 164 (9.2) |
| Frequent drinker, n (%) | 1,166 (27.3) | 675 (26.7) | 491 (28.2) |
| ≥ High-school graduation, n (%) | 1,641 (39.9) | 1,061 (42.9) | 580 (35.5) |
| 24-hour movement behavior (min/day), mean ± SD | |||
| Sleep | 412.5 ± 86.8 | 412.6 ± 86.0 | 412.5 ± 88.0 |
| Sedentary | 532.3 ± 200.6 | 512.5 ± 195.8 | 561.3 ± 204.0 |
| LIPA | 476.2 ± 200.9 | 494.1 ± 200.9 | 449.9 ± 198.1 |
| MVPA | 19.0 ± 27.1 | 20.8 ± 27.8 | 16.3 ± 25.7 |
| MetS components, n (%) | |||
| Abdominal obesity | 1,988 (46.0) | 584 (22.7) | 1,404 (80.3) |
| Hypertension | 3,070 (71.6) | 1,491 (57.9) | 1,579 (91.8) |
| High fasting glucose | 2,396 (55.9) | 972 (37.6) | 1,424 (82.9) |
| Hypertriglyceridemia | 990 (23.5) | 181 (7.3) | 809 (47.2) |
| Low HDL-C | 1,026 (23.6) | 207 (7.8) | 819 (46.8) |
Note. Means and proportions are weighted using the integrated 3-year KNHANES weight. SD = standard deviation; BMI = body mass index; LIPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity; MetS = metabolic syndrome; HDL-C = high-density lipoprotein cholesterol.
Prevalence patterns mirrored the regression findings (Supplementary Table S1). The weighted prevalence of MetS decreased monotonically across quartiles of total daily physical activity (LIPA + MVPA), from 48.6% in Q1 to 33.2% in Q4 (p for trend < 0.001), and increased across quartiles of sedentary time, from 34.4% in Q1 to 48.0% in Q4 (p for trend < 0.001). Each individual component— particularly abdominal obesity and elevated triglycerides— showed the same directional gradient. Age- and sex-adjusted partial correlations between physical-activity categories (walking, MVPA, LIPA, and total PA minutes/day) and MetS components and component count are reported in Supplementary Table S4 and were consistent with the compositional results.
Movement Composition and Metabolic Syndrome
All three ilr coordinates were independently associated with MetS in the adjusted model (Table 2). A 1-unit increase in ilr₁ (relative time in MVPA) was associated with 7.4% lower odds of MetS (OR = 0.93, 95% CI 0.89-0.96, p < 0.001). The ilr₂ coordinate (relative LIPA versus sleep + sedentary) showed an even stronger inverse association (OR = 0.80, 95% CI 0.70-0.91, p = 0.001). In contrast, ilr₃ (sedentary versus sleep ratio) was positively associated with MetS (OR = 1.28, 95% CI 1.02-1.62, p = 0.036), suggesting that displacing sleep with sedentary time increases risk. Higher educational attainment was protective (OR = 0.76, 95% CI 0.66-0.88, p < 0.001), whereas sex, age, smoking, and drinking were not significant after adjustment for the movement composition.
Table 2.
Multivariable logistic regression of 24-hour movement composition (ilr coordinates) on metabolic syndrome
| Variable | OR | 95% CI | p |
|---|---|---|---|
| ilr1 (MVPA vs others) | 0.926 | (0.89, 0.96) | < 0.001 |
| ilr2 (LIPA vs sleep+sedentary) | 0.798 | (0.70, 0.91) | 0.001 |
| ilr3 (sedentary vs sleep) | 1.282 | (1.02, 1.62) | 0.036 |
| Sex (women vs men) | 0.962 | (0.82, 1.13) | 0.630 |
| Age (per 1 year) | 1.011 | (1.00, 1.03) | 0.132 |
| Current smoker | 1.127 | (0.88, 1.44) | 0.347 |
| Frequent drinker | 1.141 | (0.96, 1.36) | 0.144 |
| ≥ High-school graduation | 0.762 | (0.66, 0.88) | < 0.001 |
Note. Weighted logistic regression with PSU cluster-robust standard errors. OR = odds ratio; CI = confidence interval; ilr = isometric log-ratio. Bold rows indicate p < 0.05. ilr1 = MVPA versus the geometric mean of the other three behaviors; ilr2 = LIPA versus sleep + sedentary; ilr3 = sedentary versus sleep.
The pattern across MetS components was not uniform (Table 3). Abdominal obesity showed the most consistent associations: all three ilr coordinates were significant, with directions matching the overall MetS model. Hypertension was associated only with ilr₁ (OR = 0.95, 95% CI 0.91-0.99, p = 0.021). Hypertriglyceridemia showed the strongest association with ilr₂ (OR = 0.77, 95% CI 0.65-0.90, p = 0.002), but not with ilr₁ or ilr₃. Low HDL-cholesterol was inversely related to ilr₁ (OR = 0.93, p = 0.003). High fasting glucose was not significantly associated with any ilr coordinate, although the directions of effect were consistent with the overall pattern.
Table 3.
Adjusted odds ratios of MetS components for each ilr coordinate
| Outcome | Prev (%) | ilr1 OR (95% CI), p | ilr2 OR (95% CI), p | ilr3 OR (95% CI), p |
|---|---|---|---|---|
| Abdominal obesity | 46.1 | 0.934 (0.90, 0.97), < 0.001 | 0.852 (0.75, 0.97), 0.019 | 1.281 (1.03, 1.60), 0.027 |
| Hypertension | 71.1 | 0.950 (0.91, 0.99), 0.021 | 0.949 (0.81, 1.11), 0.509 | 1.145 (0.88, 1.49), 0.308 |
| High fasting glucose | 55.5 | 0.970 (0.93, 1.01), 0.146 | 0.885 (0.77, 1.02), 0.083 | 1.186 (0.95, 1.49), 0.141 |
| Hypertriglyceridemia | 22.9 | 0.966 (0.92, 1.01), 0.163 | 0.766 (0.65, 0.90), 0.002 | 1.067 (0.80, 1.43), 0.665 |
| Low HDL-C | 23.8 | 0.928 (0.88, 0.97), 0.003 | 0.878 (0.75, 1.03), 0.113 | 1.027 (0.79, 1.34), 0.844 |
Note. All models adjusted for sex, age, current smoking, frequent drinking, and education. Bold values indicate p < 0.05. Prev (%) = weighted prevalence. ilr1 = MVPA versus the geometric mean of the other three behaviors; ilr2 = LIPA versus sleep + sedentary; ilr3 = sedentary versus sleep. HDL-C = high-density lipoprotein cholesterol.
Sex-stratified compositional analyses (Supplementary Tables S2 and S3) showed broadly consistent direction of association in men and women. The inverse association of relative LIPA time with MetS was stronger in men (OR 0.78, 95% CI 0.63-0.96) than in women (OR 0.87, 95% CI 0.72-1.05); the positive association of the sedentary-to-sleep ratio reached statistical significance in women only (OR 1.59, 95% CI 1.17-2.15 versus OR 1.03, 95% CI 0.72-1.46 in men) (Supplementary Table S2). Isotemporal substitution of 30 minutes of sedentary time with MVPA was associated with approximately 11% lower predicted odds of MetS in women and 8% in men (Supplementary Table S3). Sensitivity analyses using alternative zero-MVPA replacements (0.1 and 1.0 minute; Supplementary Table S6), year-stratified models (Supplementary Table S7), and expanded covariate sets (Models B and C; Supplementary Table S5) yielded effect estimates that were essentially unchanged in direction and statistical significance.
Isotemporal Substitution
Reallocating 30 minutes between behaviors produced predicted MetS odds ratios that depended on both the donor and the receiver behavior (Table 4). The largest favorable effect occurred when 30 minutes of sedentary time were replaced with MVPA (OR = 0.87, 95% CI 0.82-0.93). Reallocations of similar magnitude were observed for sleepto-MVPA (OR = 0.89) and LIPA-to-MVPA (OR = 0.90) exchanges. A more modest but statistically significant benefit was seen when 30 minutes of sedentary time were exchanged for LIPA (OR = 0.97, 95% CI 0.96-0.98). The reverse reallocation—30 minutes of LIPA replaced with sedentary time—was associated with a 2.7% increase in MetS odds (OR = 1.03, 95% CI 1.02-1.04). Reallocations involving only sleep-sedentary or sleep-LIPA exchanges produced small, non-significant changes.
Table 4.
Predicted change in metabolic syndrome odds for 30-minute reallocations between behaviors (isotemporal substitution)
| 30-min reallocation | OR | 95% CI | Significant |
|---|---|---|---|
| Sleep → Sedentary | 1.020 | (0.998, 1.042) | — |
| Sleep → LIPA | 0.994 | (0.974, 1.013) | — |
| Sleep → MVPA | 0.890 | (0.837, 0.947) | Yes |
| Sedentary → Sleep | 0.979 | (0.960, 1.001) | — |
| Sedentary → LIPA | 0.973 | (0.964, 0.984) | Yes |
| Sedentary → MVPA | 0.871 | (0.821, 0.925) | Yes |
| LIPA → Sleep | 1.007 | (0.989, 1.027) | — |
| LIPA → Sedentary | 1.027 | (1.016, 1.037) | Yes |
| LIPA → MVPA | 0.896 | (0.845, 0.952) | Yes |
Note. Odds ratios are predicted from the multivariable logistic regression at the weighted geometric mean composition (sleep 439, sedentary 534, LIPA 461, MVPA 5.7 minutes/day) by reallocating 30 minutes between two behaviors while holding the total at 1,440 minutes/day. CIs are from 1,000 parametric bootstrap replicates. “Significant” indicates that the 95% CI does not include 1. LIPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity.
DISCUSSION
In a nationally representative sample of 4,316 community-dwelling Korean adults aged 65 and older, the composition of 24-hour movement behaviors was significantly associated with MetS prevalence and several of its components. Higher proportions of MVPA and LIPA, relative to sleep and sedentary time, were each associated with lower MetS odds. A higher sedentary-to-sleep ratio was associated with higher MetS odds. The isotemporal substitution analyses translated these compositional findings into practical, timebased estimates: replacing 30 minutes of sedentary behavior with MVPA was associated with about a 13% reduction in MetS odds, while the same reallocation to LIPA produced a smaller but still statistically significant benefit.
Several findings deserve emphasis. First, the inverse association between relative LIPA time and MetS was independent of MVPA and of the sedentary-to-sleep contrast. Because the ilr coordinates encode different relative contrasts, the magnitudes of their odds ratios are not directly comparable; the behaviorally meaningful comparison is provided by the isotemporal substitution analysis (Table 4), in which exchanging 30 minutes of sedentary time for MVPA was associated with a larger predicted decrease in MetS odds than the same exchange to LIPA, although the LIPA exchange retained statistical significance. Most international PA guidelines focus on MVPA targets [3,23], which are difficult for many older adults to meet. In our sample, the average MVPA was 19 minutes per day—below the 150 minutes per week threshold—and only a small fraction of participants accumulated enough MVPA to meet this guideline. Our finding that relative LIPA time shows a measurable inverse association with MetS is consistent with several CoDA studies in older populations from Spain, Australia, and the United States [10,11,24]. For Korean older adults, in whom MVPA participation is low and the feasibility of structured exercise prescriptions can be limited by chronic conditions, the LIPA pathway may offer a more accessible behavioral target. Practical recommendations such as “stand up and move every hour” or “take a short walk after meals” may translate into meaningful population-level benefit.
Second, the positive association of the sedentary-to-sleep ratio (ilr₃) with MetS suggests that the displacement of sleep by sedentary time is harmful, beyond the absolute amount of either behavior alone. This is an underappreciated point in older-adult research: sleep duration and quality decline with age, and replacing sleep with prolonged sitting—as may occur with late-night screen time or extended TV viewing— appears unfavorable for metabolic health. The mean sleep duration in our sample (6.9 hours) was at the lower end of recommended ranges for older adults [25], and additional shortening for sedentary activities may compound metabolic risk.
Third, the substitution analyses provide concrete public health messages, although we note that isotemporal substitution estimates statistically predicted differences rather than the effects of an actual behavioral intervention. Reallocating 30 minutes of sitting to MVPA was associated with approximately 13% lower predicted odds of MetS, which is consistent in magnitude with prior isotemporal substitution work in middle-aged and older Western adults [14,26]. The smaller but statistically significant association observed for sedentary-to-LIPA exchange (OR = 0.97) is also noteworthy because LIPA can be achieved through routine activities such as light housework, gardening, or slow walking, all of which are realistic behavioral changes in older adults.
Our component-level findings parallel previous work that has reported differential responses of MetS components to movement behavior. Abdominal obesity was the most consistently associated component, suggesting that body composition mediates much of the relationship between time use and MetS in older adults [27]. Hypertriglyceridemia responded most strongly to LIPA, in line with findings that even low-intensity activity disrupts the prolonged postprandial lipemia caused by extended sitting [28]. By contrast, fasting glucose was not significantly associated with any ilr coordinate—a result that may reflect the more medication-influenced nature of glycemic control in this age group, where antidiabetic medication use is common.
The sex-stratified analyses (Supplementary Tables S2 and S3) showed that the overall pattern—greater relative MVPA and LIPA associated with lower MetS odds, and a higher sedentary-to-sleep ratio associated with higher odds— applied broadly to both men and women, but the relative emphasis differed by sex. The inverse association of relative LIPA time with MetS was clearer in men than in women, whereas the positive association of the sedentary-to-sleep ratio reached statistical significance only in women. This dissociation may reflect distinct exposure profiles: Korean older women tend to report longer sedentary time and shorter sleep than men, which may amplify the metabolic cost of sleep displacement, while Korean older men report somewhat higher walking and active-transport minutes that load onto the LIPA-equivalent variable. Sex differences in body composition (higher relative fat mass in women), insulin sensitivity, and chronic-disease prevalence may further contribute. Because the stratified analyses were exploratory and confidence intervals overlapped between sexes, these patterns should be interpreted as hypothesis-generating.
Strengths of this study include the use of a nationally representative sample, application of a CoDA methodology that respects the simplex structure of time-use data, and proper accounting for the complex KNHANES survey design through stratification, clustering, and integrated weighting. The post-pandemic data (2022-2024) also provide a contemporary snapshot following major changes in older adults’ lifestyles.
Several limitations should be acknowledged. The cross-sectional design precludes causal inference, and reverse causation cannot be ruled out—older adults with metabolic disease may be less able to engage in MVPA and may sit more. PA, sedentary time, and sleep were all assessed by self-report, which is subject to recall bias and tends to overestimate MVPA and underestimate sedentary time compared with accelerometer-derived measures [29]. The harmonization of sleep across years (averaged hours in 2022-2023 versus computed from bed/wake times in 2024) may have introduced minor measurement differences, although year-stratified sensitivity analyses showed essentially unchanged effect estimates (Supplementary Table S7), and the 2022-2023 vs. 2024 comparison showed no interaction with sleep measurement format. Our LIPA category includes both walking and a residual term for unmeasured low-intensity activity; the LIPA coefficient should therefore not be interpreted as a direct physiological estimate of light-intensity activity, which is a practical but imperfect approach [30]. The high proportion of zero MVPA values (38.0%) required a zero-replacement step for log-ratio analysis; results were robust to alternative replacement values and to a categorical “any MVPA versus none” specification (Supplementary Table S6), but the choice of replacement remains an analytic assumption. Although we adjusted for the principal sociodemographic and behavioral covariates and conducted sensitivity analyses adding household income, chronic-disease history, and medication use (Supplementary Table S5), residual confounding by unmeasured factors such as diet quality, total energy intake, occupational status, and functional disability cannot be excluded. Finally, our findings apply to community-dwelling older adults and should not be extrapolated to long-term care residents or those with severe disability.
In summary, among Korean adults aged 65 and older, greater relative time in MVPA or LIPA—and less displacement of sleep by sedentary time—was associated with lower predicted odds of MetS. Reallocating 30 minutes of sedentary time to even light activity such as walking was associated with a favorable predicted difference in MetS odds. In a population where current MVPA guidelines are met by very few, encouraging incremental shifts toward any form of activity is a plausible direction for public-health policy and clinical practice in older Koreans; confirmation in prospective and interventional studies is warranted.
Footnotes
ACKNOWLEDGMENTS
This study used data from the 9th Korea National Health and Nutrition Examination Survey (2022-2024), provided by the Korea Disease Control and Prevention Agency. The authors thank the survey participants and field staff for their contributions.
The authors declare no conflict of interest.
FUNDING
No external funding was received for this study.
Supplementary Material
Weighted prevalence (%) of metabolic syndrome and its components by quartile of 24-hour movement behaviors, KNHANES 2022–2024 (N = 4,317)
Sex-stratified weighted logistic regression of the 24-hour movement composition on metabolic syndrome
Sex-stratified isotemporal substitution: predicted odds ratios for 30-minute reallocations between behaviors
Age- and sex-adjusted partial correlation coefficients (Pearson) between physical-activity categories (minutes/day) and metabolic-syndrome– related variables
Sensitivity analysis: effect of expanded covariate adjustment on ilr coefficients for metabolic syndrome
Sensitivity analysis: alternative zero-MVPA replacement values and an “any MVPA vs none” categorical specification
Sensitivity analysis: year-stratified weighted logistic regression of 24-hour movement composition on metabolic syndrome (testing the impact of the 2024 sleep-measurement format change)
REFERENCES
- 1.Huh JH, Kang DR, Jang JY, Shin JH, Kim JY, Choi S, Cho EJ, Park JS, Sohn IS, Jo SH, Kim H, Park CG, Bae JH, Cho JH, Hong KS, Cho JY, Park KH, Choi HC, Choi DJ, Lim S, Lee EJ. Metabolic syndrome epidemic among Korean adults: Korean survey of cardiometabolic syndrome (2018) Atherosclerosis. 2018;277:47–52. doi: 10.1016/j.atherosclerosis.2018.08.003. [DOI] [PubMed] [Google Scholar]
- 2.Lim S, Shin H, Song JH, Kwak SH, Kang SM, Won Yoon J, Choi SH, Cho SI, Park KS, Lee HK, Jang HC, Koh KK. Increasing prevalence of metabolic syndrome in Korea: the Korean National Health and Nutrition Examination Survey for 1998-2007. Diabetes Care. 2011;34(6):1323–8. doi: 10.2337/dc10-2109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62. doi: 10.1136/bjsports-2020-102955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Tremblay MS, Carson V, Chaput JP, Connor Gorber S, Dinh T, Duggan M, Faulkner G, Gray CE, Gruber R, Janson K, Janssen I, Katzmarzyk PT, Kho ME, Latimer-Cheung AE, LeBlanc C, Okely AD, Olds T, Pate RR, Phillips A, Poitras VJ, Rodenburg S, Sampson M, Saunders TJ, Stone JA, Stratton G, Weiss SK, Zehr L. Canadian 24-hour movement guidelines for children and youth: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S311–27. doi: 10.1139/apnm-2016-0151. [DOI] [PubMed] [Google Scholar]
- 5.Pedišić Ž, Dumuid D, Olds T. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology. 2017;49(2):252–69. [Google Scholar]
- 6.Chastin SFM, 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. doi: 10.1371/journal.pone.0139984. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Dumuid D, Stanford TE, Martín-Fernández JA, Pedišić Ž, Maher CA, Lewis LK, Hron K, Katzmarzyk PT, Chaput JP, Fogelholm M, Hu G, Lambert EV, Maia J, Sarmiento OL, Standage M, Barreira TV, Broyles ST, Tudor-Locke C, Tremblay MS, Olds T. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38. doi: 10.1177/0962280217710835. [DOI] [PubMed] [Google Scholar]
- 8.Aitchison J. The statistical analysis of compositional data. London: Chapman and Hall; 1986. [Google Scholar]
- 9.Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C. Isometric logratio transformations for compositional data analysis. Math Geol. 2003;35(3):279–300. [Google Scholar]
- 10.McGregor DE, Palarea-Albaladejo J, Dall PM, Hron K, Chastin SFM. Cox regression survival analysis with compositional covariates: application to modelling mortality risk from 24-h physical activity patterns. Stat Methods Med Res. 2020;29(5):1447–65. doi: 10.1177/0962280219864125. [DOI] [PubMed] [Google Scholar]
- 11.del Pozo Cruz B, Biddle SJH, Gardiner PA, Ding D. Light-intensity physical activity and life expectancy: National Health and Nutrition Survey. Am J Prev Med. 2021;61(3):428–33. doi: 10.1016/j.amepre.2021.02.012. [DOI] [PubMed] [Google Scholar]
- 12.Powell C, Browne LD, Carson BP, Dowd KP, Perry IJ, Kearney PM, Harrington JM, Donnelly AE. 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. 2020;50(1):205–17. doi: 10.1007/s40279-019-01153-2. [DOI] [PubMed] [Google Scholar]
- 13.Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–27. doi: 10.1093/aje/kwp163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Stamatakis E, Rogers K, Ding D, Berrigan D, Chau J, Hamer M, Bauman A. All-cause mortality effects of replacing sedentary time with physical activity and sleeping using an isotemporal substitution model: a prospective study of 201,129 mid-aged and older adults. Int J Behav Nutr Phys Act. 2015;12:121. doi: 10.1186/s12966-015-0280-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Park S, Lee S, Kim Y, Lee Y, Kang MW, Kim K, Kim YC, Han SS, Lee H, Lee JP, Joo KW, Lim CS, Kim YS, Kim DK. Causal effects of relative fat, protein, and carbohydrate intake on chronic kidney disease: a Mendelian randomization study. Am J Clin Nutr. 2021;113(4):1023–31. doi: 10.1093/ajcn/nqaa379. [DOI] [PubMed] [Google Scholar]
- 16.Kim H, Choi HJ. Association between sleep duration and metabolic syndrome in Korean adults: a cross-sectional analysis. Sleep Breath. 2020;24(2):673–82. [Google Scholar]
- 17.Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES) Int J Epidemiol. 2014;43(1):69–77. doi: 10.1093/ije/dyt228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Korea Disease Control and Prevention Agency . Guidelines for the use of the 9th Korea National Health and Nutrition Examination Survey raw data (2022-2024) Cheongju: KDCA; 2026. [Google Scholar]
- 19.Foley B, Engelen L, Gale J, Bauman A, Mackey M. Sedentary behavior and musculoskeletal health: changes after a workplace intervention. Med Sci Sports Exerc. 2018;50(8):1731–9. [Google Scholar]
- 20.Korean Society for the Study of Obesity . Clinical practice guidelines for obesity 2022. 9th ed. Seoul: KSSO; 2022. [Google Scholar]
- 21.Martín-Fernández JA, Barceló-Vidal C, Pawlowsky-Glahn V. Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Math Geol. 2003;35(3):253–78. [Google Scholar]
- 22.Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. Modeling and analysis of compositional data. Chichester: John Wiley & Sons; 2015. [Google Scholar]
- 23.Korea Centers for Disease Control and Prevention . Korean physical activity guideline. 2nd ed. Cheongju: KCDC; 2020. [Google Scholar]
- 24.Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020;9(6):493–510. doi: 10.1016/j.jshs.2020.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N, Herman J, Katz ES, Kheirandish-Gozal L, Neubauer DN, O’Donnell AE, Ohayon M, Peever J, Rawding R, Sachdeva RC, Setters B, Vitiello MV, Ware JC, Adams Hillard PJ. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3. doi: 10.1016/j.sleh.2014.12.010. [DOI] [PubMed] [Google Scholar]
- 26.Chastin SFM, De Craemer M, De Cocker K, Powell L, Van Cauwenberg J, Dall P, Hamer M, Stamatakis E. 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. doi: 10.1136/bjsports-2017-097563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Healy GN, Matthews CE, Dunstan DW, Winkler EAH, Owen N. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003-06. Eur Heart J. 2011;32(5):590–7. doi: 10.1093/eurheartj/ehq451. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, Shaw JE, Bertovic DA, Zimmet PZ, Salmon J, Owen N. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012;35(5):976–83. doi: 10.2337/dc11-1931. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Skender S, Ose J, Chang-Claude J, Paskow M, Brühmann B, Siegel EM, Steindorf K, Ulrich CM. Accelerometry and physical activity questionnaires—a systematic review. BMC Public Health. 2016;16:515. doi: 10.1186/s12889-016-3172-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, Lewis LK, Olds T. 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. doi: 10.1177/0962280217737805. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Weighted prevalence (%) of metabolic syndrome and its components by quartile of 24-hour movement behaviors, KNHANES 2022–2024 (N = 4,317)
Sex-stratified weighted logistic regression of the 24-hour movement composition on metabolic syndrome
Sex-stratified isotemporal substitution: predicted odds ratios for 30-minute reallocations between behaviors
Age- and sex-adjusted partial correlation coefficients (Pearson) between physical-activity categories (minutes/day) and metabolic-syndrome– related variables
Sensitivity analysis: effect of expanded covariate adjustment on ilr coefficients for metabolic syndrome
Sensitivity analysis: alternative zero-MVPA replacement values and an “any MVPA vs none” categorical specification
Sensitivity analysis: year-stratified weighted logistic regression of 24-hour movement composition on metabolic syndrome (testing the impact of the 2024 sleep-measurement format change)