Journal of Obesity & Metabolic Syndrome

Search

Article

J Obes Metab Syndr 2024; 33(2): 121-132

Published online June 30, 2024 https://doi.org/10.7570/jomes24007

Copyright © Korean Society for the Study of Obesity.

Relationship between Change in Physical Activity and Risk of Metabolic Syndrome: A Prospective Cohort Study

Doo Yong Park1, On Lee2,* , Yong Ho Lee1, Chung Gun Lee1, Yeon Soo Kim1,*

1Department of Physical Education, College of Education, Seoul National University, Seoul; 2Department of Sport Science, Korea Institute of Sport Science, Seoul, Korea

Correspondence to:
On Lee
https://orcid.org/0000-0001-9871-2310
Department of Sport Science, Korea Institute of Sport Science, 727 Hwarang-ro, Nowon-gu, Seoul 01794, Korea
Tel: +82-2-970-9559
Fax: +82-2-970-9686
E-mail: fair27@kspo.or.kr

Yeon Soo Kim
https://orcid.org/0000-0003-1447-0196
Department of Physical Education, College of Education, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Tel: +82-2-880-7794
Fax: +82-2-880-7794
E-mail: kys0101@snu.ac.kr

Received: February 14, 2023; Reviewed : April 30, 2023; Accepted: April 30, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: This study investigates the relationship between changes in physical activity levels and risk of metabolic syndrome.
Methods: This study examined 1,686 adults aged 40 to 69 years from a community-based cohort study with complete 1st to 4th follow-up data between 2011 and 2020. Changes in physical activity were evaluated through baseline and follow-up surveys using physical activity questionnaires. Metabolic syndrome was diagnosed according to the International Diabetes Federation criteria. A survival analysis was conducted using a multivariate extended Cox regression model with a significance level set at P<0.05.
Results: Participants were divided into groups according to physical activity levels. The newly inactive group (vigorous physical activity ≤150 minutes at first follow-up) had a 36% increase in the hazard ratio (HR) for metabolic syndrome compared with the consistently inactive group (≤150 minutes at both baseline and first follow-up) (HR, 1.36; 95% confidence interval [CI], 1.04 to 1.79). The newly active group (walking ≤420 minutes per week at baseline and >420 minutes per week at first follow-up) had a 25% decrease in the HR for metabolic syndrome compared with the consistently inactive group (walking ≤420 minutes per week at both baseline and first follow-up) (HR, 0.75; 95% CI, 0.57 to 0.98).
Conclusion: Changes in physical activity levels are associated with risk of metabolic syndrome. These results provide important insights for future investigations into the link between physical activity changes and disease occurrence.

Keywords: Exercise, Aged, Metabolic syndrome, Proportional hazards models, Cohort studies

Metabolic syndrome encompasses abnormalities such as hypertension, hyperglycemia, abdominal obesity, increased cholesterol, and elevated triglycerides1 and is a risk indicator for cardiovascular metabolic function, influencing the onset of cardiovascular diseases and the timing of all-cause mortality.2 The 2017 to 2018 National Health and Nutrition Examination Survey in the United States reported a 38.3% prevalence of metabolic syndrome among individuals aged 20 years and older, with prevalence rates of 38.8% for males, 37.7% for females, and 31.2% for Asians.3 Recent data from the National Health Insurance Service in Korea revealed that 14 million Koreans aged 20 years and older, 20.6% of the population, met the criteria for metabolic syndrome.4 Metabolic syndrome can arise from unhealthy dietary habits, poor sleep quality, and insufficient physical activity in modern society.5 Metabolic syndrome is widespread globally across all age groups, and identifying effective management strategies has attracted considerable interest.6

Evidence indicates that regular physical activity can reduce the risk of metabolic syndrome,7 and recognition of the importance of physical activity is increasing.8 However, evidence is needed to confirm the associations between changes in physical activity levels across domains (work and leisure) and metabolic syndrome. Previous research has shown that both high levels of work-related physical activity and engagement in leisure-related physical activity reduce risk factors for metabolic syndrome.8,9 Thus, given that most adults spend a significant amount of time in the workplace, reduced physical activity during working hours might lead to adverse health outcomes such as metabolic syndrome.10 However, previous studies highlighting the importance of increased physical activity in the workplace and during leisure time have not considered changes in physical activity levels measured through repeated assessments, hindering confirmation of the association with metabolic syndrome risk.10 Furthermore, in studies that considered only leisure-related physical activity, a distinction between the effects of work- and leisure-related physical activity differences could not be established.11 Longitudinal studies are needed to examine changes in physical activity as an independent variable associated with disease occurrence.9

In this study, we calculate ratios for the risk of metabolic syndrome according to changes in high-intensity physical activity, moderate-intensity physical activity, and walking time. Additionally, we report the association between each change in physical activity (high-intensity, moderate-intensity, and walking) and metabolic syndrome in the domains of work- and leisure-related physical activity.

Study design and population

This study used data from the Korean Genome and Epidemiology Study (KoGES) community-based cohort, a comprehensive survey for the prevention of chronic diseases, including diabetes, hypertension, osteoporosis, obesity, and metabolic syndrome. Our analysis targeted adults aged 40 to 69 years residing in Ansan City, Gyeonggi Province, Korea, who were selected for the 4th follow-up between 2019 and 2020 from a baseline survey conducted in 2011 to 2012. The following exclusion criteria were applied: lack of complete cycles 1–4 follow-up data (n=238), metabolic syndrome (n=408), or cardiovascular disease (n=9) identified during the baseline survey or missing data (n=711). Finally, 1,686 participants were included in the analysis. This study was approved by the Institutional Review Board of Korea University Ansan Hospital and the Seoul National University Bioethics Committee (IRB No. E2112/001-009). Participants provided informed, written consent after receiving and understanding information about the research purpose.

Metabolic syndrome

Metabolic syndrome was classified according to the presence or absence of the following criteria set by the International Diabetes Foundation: central obesity, defined as a waist circumference of 90 cm or more for male Koreans and 85 cm or more for female Koreans; elevated triglyceride levels (150 mg/dL or higher); reduced high-density lipoprotein cholesterol (HDL-C) levels (less than 40 mg/dL for males and less than 50 mg/dL for females); increased blood pressure (systolic blood pressure of 130 mmHg or higher, diastolic blood pressure of 85 mmHg or higher, or undergoing hypertension treatment); and elevated fasting blood glucose levels (fasting blood glucose of 100 mg/dL or higher or undergoing diabetes treatment). Metabolic syndrome was diagnosed when three or more of those five factors were present.12

Changes in physical activity

The International Physical Activity Questionnaire-long form (IPAQ-LF), which has 27 questions, was used as the physical activity questionnaire. This extended form facilitates a thorough assessment of physical activity across various domains, enhancing the accuracy of predicting disease prevalence compared with the IPAQ-short form.13 The IPAQ-LF categorizes physical activity into “job-related physical activity (paid jobs, farming, and any unpaid work outside),” “household-related physical activity (housework, gardening, and caring for your family),” and “leisure-related physical activity (recreation, sport, exercise, or leisure),” requiring entries for all activities lasting at least 10 minutes. Job and household (work-related physical activity) and leisure-related physical activity levels were classified based on previous research that showed an elevated risk ratio for metabolic syndrome when activity was performed for less than 150 minutes a week compared with 150 minutes or more a week.14

The baseline survey asked about time spent in work-related physical activity of high-intensity (heavy lifting, digging, and chopping wood), moderate-intensity (carrying light loads, sweeping, and washing windows), and walking during the previous 7 days. Weekly participation time (minutes/week) was categorized as <150 minutes per week and ≥150 minutes per week. To establish a total leisure-related physical activity level from the baseline survey, time spent performing high-intensity activity (aerobics, running, fast bicycling, or fast swimming), moderate-intensity activity (bicycling at a regular pace, swimming at a regular pace, or doubles tennis), and walking in the previous 7 days were combined. Weekly participation time (minutes/week) was categorized as <150 minutes per week and ≥150 minutes per week.

Changes in physical activity were analyzed by dividing the participants into the following categories: “consistently inactive,” when physical activity participation time was below the threshold at both baseline and the 1st follow-up; “newly active,” if the activity level was below the threshold at baseline but above the threshold at the 1st follow-up; “newly inactive” if the activity level was above the threshold at baseline but below the threshold at the 1st follow-up; and “consistently active,” if the activity level was above the threshold at both baseline and the 1st follow-up. Vigorous activity was categorized as <75 minutes per week or ≥75 minutes per week;15 moderate activity was categorized as <150 minutes per week or ≥150 minutes per week;15 and walking was categorized as <420 minutes per week or ≥420 minutes week, as previously described.16

Blood variable measurement

All participants fasted for more than 8 hours before blood samples were collected on the morning of each examination day. The collected blood samples were processed on-site using a centrifuge and then sent to Seoul Clinical Laboratory. Triglyceride, HDL-C, blood glucose, high-sensitivity C-reactive protein (hs-CRP), glycosylated hemoglobin (HbA1c), serum creatinine, and insulin values were calculated using an ADVIA 1800 auto analyzer (Siemens). The glomerular filtration rate (GFR) was determined using the Modification of Diet in Renal Disease formula: GFR (mL/min/1.73 m2)=175×SCr [exp(–1.154)]×age [exp(–0.203)]×(0.742 if female)×(1.21 if black).17 Additionally, the homeostasis model assessment of insulin resistance (HOMA-IR) value was calculated using the following formula: fasting insulin×fasting blood glucose/405.18

Questionnaire and other variables

The baseline survey and each follow-up examination comprised individual interviews, followed by a thorough review to improve the validity of the questionnaire through modification and clarification. Physical measurements involved a one-time assessment of height (cm) and weight (kg), and the body mass index (BMI; kg/m2) was used to analyze and determine obesity levels. Data on demographic characteristics, smoking status (non-smoker, ex-smoker, and current smoker), alcohol consumption (non-drinker, ex-drinker, and current drinker), family income levels (<1 million won, 1–2 million won, 2–3 million won, 3–4 million won, and ≥4 million won), sleep duration, medical history, and medication intake were collected using a standardized self-report questionnaire.

Changes in sitting time (ST) were detected through the IPAQ-LF. According to previous research, the STs recorded in the 2011–2012 baseline survey and the 2013–2014 1st follow-up survey were categorized into four groups.19 A ST of 7 hours or more per day at both baseline and 1st follow-up was defined as “consistently ST.” A baseline ST of less than 7 hours per day with a 1st follow-up ST of 7 hours or more per day was labeled as “newly ST.” A baseline ST of 7 hours or more per day with a 1st follow-up ST of less than 7 hours per day was labeled as “formerly ST.” A ST of less than 7 hours per day at both baseline and 1st follow-up was defined as “consistently non-ST.”

Statistical analysis

Data analysis was performed using STATA/IC version 14.1 (STATA Corp.). A descriptive analysis, including chi-square tests and mean calculations, was conducted to explore the demographic characteristics of the study participants. Each variable is expressed as a percentage or mean with standard deviation. The incidence density of metabolic syndrome in tracked subjects throughout the entire follow-up period is reported in person-years. To determine the best analytical model for assessing the association between changes in physical activity and the risk of metabolic syndrome, a log-rank test was conducted. The proportional hazards assumption for the overall change in physical activity and the occurrence of metabolic syndrome was not met (P=0.462). Consequently, to mitigate potential distortions during data estimation, a multivariate extended Cox regression model was chosen for analysis. This model considered both time-fixed covariates (sex, sleep duration, change in sedentary time) and time-dependent covariates (age, income level, smoking status, alcohol consumption, hs-CRP, estimated GFR [eGFR], HbA1c, BMI, and HOMA-IR).20

We also used a multivariate extended Cox regression model to investigate the independent associations between changes in high-intensity physical activity, moderate-intensity physical activity, and walking time and the risk of metabolic syndrome. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The same model was used to evaluate the independent association between changes in high-intensity physical activity, moderate-intensity physical activity, and walking time across domains of physical activity (job and household, leisure) and the risk of metabolic syndrome. Multivariate extended Cox regression model analyses were adjusted for confounding variables that influence changes in physical activity and metabolic syndrome: age, sex, sleep duration, HbA1c, eGFR, hs-CRP, alcohol consumption, smoking status, income level, BMI, HOMA-IR, and sedentary behavior changes. The significance level was set at P<0.05 for all analyses.

The demographic characteristics of the study population are presented in Table 1. The group whose work-related physical activity participation exceeded 150 minutes per week had a decrease in moderate-intensity physical activity time, a higher maintenance rate of moderate-intensity activity, a decrease in walking time, a higher maintenance rate of walking time, a lower maintenance rate of daily ST, and higher eGFR levels than the group with less than 150 minutes per week of work-related activity. Additionally, the group whose work-related activity was above the threshold demonstrated a lower percentage of male participants and a lower current smoking rate than the group whose work-related activity was below the threshold. The group whose leisure-related physical activity participation exceeded 150 minutes per week was older and showed a decrease in high-intensity physical activity time, a higher maintenance rate of high-intensity activity, a decrease in walking time, a higher maintenance rate of walking time, higher eGFR, higher current alcohol consumption rate, and lower current smoking rate than the group with less than 150 minutes of leisure-related physical activity per week. No significant differences were found in sleep duration, BMI, HOMA-IR, HbA1c, or income level based on the level of physical activity participation in either domain.

Our findings about the association between changes in high-intensity physical activity, moderate-intensity physical activity, and walking time and the risk of metabolic syndrome are presented in Table 2. Regarding changes in high-intensity physical activity participation time, the incidence rate per 1,000 individuals in the newly inactive group was 42.95, which was significantly different from the 40.69 in the consistently inactive group. Following adjustments for various confounding variables, the HR for metabolic syndrome increased by 1.36 times (HR, 1.36; 95% CI, 1.04 to 1.79) in the newly inactive group compared with the consistently inactive group. Concerning changes in walking time, the incidence rate per 1,000 individuals in the newly active group was 29.53, which is significantly lower than the 41.50 in the consistently inactive group. After adjusting for various confounding variables, including age and sex, the HR for metabolic syndrome occurrence decreased by 25% (HR, 0.75; 95% CI, 0.57 to 0.98) in the newly active group. No significant association was found between changes in moderate-intensity physical activity and the risk of metabolic syndrome.

The results for the association between changes in work-related high-intensity physical activity, moderate-intensity physical activity, and walking time and the risk of developing metabolic syndrome are presented in Table 3. In the group with less than 150 minutes per week of work-related physical activity, those who were newly inactive in high-intensity physical activity showed a 1.59 times higher HR (HR, 1.59; 95% CI, 1.10 to 2.29) for metabolic syndrome than those who were consistently inactive in terms of high-intensity physical activity. In the group with 150 minutes or more per week of work-related physical activity, those who were newly active in walking showed a 40% lower HR (HR, 0.60; 95% CI, 0.38 to 0.94) for metabolic syndrome than those who were consistently inactive in walking. No significant association was found between changes in moderate-intensity work-related physical activity and the risk of metabolic syndrome.

The results for the associations between changes in leisure-related high-intensity physical activity, moderate-intensity physical activity, and walking time and the risk of metabolic syndrome are presented in Table 4. In the group with less than 150 minutes per week of overall leisure-related physical activity, those who were newly active showed a 46% lower HR (HR, 0.54; 95% CI, 0.36 to 0.79) for metabolic syndrome than those who were consistently inactive. However, no significant association was found between changes in leisure-related high-intensity physical activity, moderate-intensity physical activity, or walking time and the risk of metabolic syndrome. In the group with 150 minutes or more per week of leisure-related physical activity participation, no significant association was observed between changes in high-intensity physical activity, moderate-intensity physical activity, or walking time and the risk of metabolic syndrome.

In this study, we observed that the transition to newly inactive status from high-intensity physical activity increased the risk of metabolic syndrome; conversely, the transition to newly active status in walking was associated with a decreased risk of metabolic syndrome. Additionally, newly inactive status in high-intensity physical activity was specifically associated with an increased risk of metabolic syndrome in the group with less than 150 minutes per week of work-related physical activity. In the groups with 150 minutes or more per week of work-related physical activity or less than 150 minutes per week of leisure-related physical activity, we observed a significant association between newly active status in walking and the risk of metabolic syndrome.

Genetics, lifestyle habits, and environmental conditions are significant contributors to metabolic syndrome.21 Physical activity has beneficial effects on the progression of metabolic syndrome that extend beyond its influence on body composition, particularly through its role in energy balance regulation.21,22 Previous research indicates that moderate- to high-intensity physical activity for 120 to 180 minutes per week correlates with a decreased incidence of metabolic syndrome.23,24 In a study comparing various levels of activity among office workers, the high-activity group exhibited a 2.03 times lower risk of metabolic syndrome than the low-activity group.25 A 9-year longitudinal study investigating the correlation between changes in physical activity and metabolic syndrome found a significant association between leisure-related physical activity and metabolic syndrome, with consistently active individuals having a lower risk of metabolic syndrome than individuals who were consistently inactive.11 A previous study investigating the association between walking and metabolic syndrome found that middle-aged and older individuals who surpassed 10,000 steps per day were able to mitigate the risk of metabolic syndrome.26 Another study indicated that adults surpassing the public health recommendation for daily step count exhibited a decreased waist circumference, increased HDL-C levels, and decreased triglyceride levels.27 Longitudinal studies about changes in walking time and metabolic syndrome through adherence to step-based guidelines or an increase in the daily step count over 7 days also demonstrated improvements in various metabolic syndrome risk factors.28

We attribute the heightened risk of metabolic syndrome observed in the group newly inactive for high-intensity physical activity (Table 2) to diminished energy expenditure that results in the accumulation of adipose tissue. Engagement in high-intensity physical activity increases mitochondrial density in muscles, facilitating intracellular fatty acid oxidation,29 and high-intensity physical activity is associated with two-fold the energy consumption of moderate-intensity physical activity.30 Experimental research suggests that high-intensity interval training can mitigate abdominal obesity through increased calorie expenditure compared with maintaining a steady state.31 Those results underscore the association between participation in high-intensity, rather than moderate-intensity, physical activity and a reduced risk of metabolic syndrome.32 It has been hypothesized that a reduction in high-intensity physical activity increases the risk of metabolic syndrome more significantly than a reduction in moderate-intensity physical activity because it leads to a greater decrease in energy expenditure.32

The decreased risk of metabolic syndrome observed in the newly active group for walking (Table 2) is presumed to result from a reduction in ST and the adoption of health-promoting behaviors that lower the risk of metabolic syndrome. Consistent with previous studies, both the consistently active and newly active groups exhibited significantly higher HDL-C levels than the consistently inactive group (80.3 mg/dL vs. 64.5 mg/dL). Furthermore, a reduction in neutral fats in the newly active group but not the consistently inactive group (123.8 mg/dL vs. 158.3 mg/dL) was previously reported.28 These findings are closely associated with obesity, with increased walking time reported to prevent both obesity and metabolic syndrome.7 Moreover, the effects of increased physical activity, particularly walking, can extend beyond the effects of physical activity itself and be associated with changes in nutrition and other health habits.33 Previous studies have reported that individuals who increase their walking activity compared with that recorded 2 years earlier can reduce their risk of metabolic syndrome.7,33

The significant association between changes in high-intensity physical activity and the risk of metabolic syndrome (Table 3) was clearly evident when work-related physical activity was less than 150 minutes per week, which we attribute to the synergistic effect of oxidative stress that results from physical inactivity and increases the risk of metabolic syndrome. Previous research investigating the interactive effects of physical activity and screen time on the odds ratio (OR) for metabolic syndrome found that individuals who did not meet physical activity guidelines and had a screen time exceeding 3 hours per day had a higher risk, 2.93 times higher (OR, 2.93; 95% CI, 1.42 to 6.02) in males and 6.29 times higher (OR, 6.29; 95% CI, 1.91 to 20.6) in females, than those with high physical activity levels and screen time of less than 2 hours per day.34 Those findings support the idea that oxidative stress resulting from physical inactivity induces metabolic changes. Boyle et al.35 reported that brief exposure to physical inactivity (5 days) increased markers of cell death associated with oxidative stress. Increased oxidative stress contributes to metabolic syndrome, leading to diminished shear stress on the vascular wall due to decreased blood flow, which promotes the development of atherosclerosis and triggers inflammation or oxidative stress.36

As shown in Tables 3 and 4, the observed reduction in the risk of metabolic syndrome within the newly active group for walking was significant only when the duration of work-related physical activity was less than 150 minutes. The substantial decrease in the risk of metabolic syndrome when the duration of leisure-related physical activity was less than 150 minutes is attributed to the divergent health effects of walking in work-related versus leisure-related physical activity. Previous research has highlighted that leisure-related physical activity typically encompasses brief periods during daily life, allowing ample rest, and is characterized by voluntary engagement and self-regulation. Conversely, work-related physical activity often involves prolonged activity during daily life, adheres to a fixed schedule with obligatory participation, and might lack adequate rest intervals.37 Moreover, individuals involved in extended, high-intensity work-related physical activity can face constraints on participating in fitness training that yields health benefits.37 Therefore, it is recommended that individuals who are extensively engaged in work-related physical activity also incorporate additional leisure-time physical activity,9 which can stimulate dynamic contractions of major muscles, fostering metabolic processes or elevating cardiac output and facilitating recovery from fatigue.38 Consequently, when work-related physical activity is substantial, increased walking time might ameliorate accumulated fatigue from work activities and enhance metabolic function through dynamic contractions of lower limb muscles.38 Furthermore, when leisure-related physical activity is limited, increasing the walking time might positively influence metabolic syndrome risk factors, decreasing blood pressure and improving glucose metabolism.39,40

The outcomes of the longitudinal analysis in this study establish an independent association between changes in high-intensity physical activity, moderate-intensity physical activity, and walking time and the risk of metabolic syndrome. Nonetheless, several limitations should be noted. First, the use of a self-reported physical activity questionnaire to determine physical activity participation introduces the potential for recall bias and raises issues of questionnaire validity. Nonetheless, the exclusion of individuals with a combined ST and sleeping time exceeding 24 hours was intended to minimize errors in the collected data. The use of the IPAQ-LF, which has refined physical activity domains, was intended to enhance the accuracy of estimating disease occurrence.13 Future research should incorporate more objective physical activity measurement devices, such as accelerometers, to precisely assess changes in physical activity and the associated risk of metabolic syndrome. Second, this study focused on middle-aged individuals residing in specific regions of South Korea, potentially limiting the generalizability of the results. Subsequent research should consider a nationwide cohort study to validate the association between changes in physical activity and the risk of metabolic syndrome on a broader scale. Third, it was not feasible to adjust for dietary variables that influence metabolic syndrome in this study. However, efforts were made to augment the reliability of the study by adjusting for lifestyle habits such as smoking, alcohol consumption, and physical activity. Additionally, blood variables, including HOMA-IR, hs-CRP, eGFR, and HbA1c, were considered. Future research should investigate whether changes in physical activity are independently associated with the risk of metabolic syndrome beyond the influence of dietary habits.

This study shows that a decline in physical activity is linked to an increased risk of metabolic syndrome, whereas an increase in walking time is associated with a decreased risk of metabolic syndrome. Moreover, diminishing engagement in high-intensity physical activity, particularly in the workplace, can increase the risk of metabolic syndrome. Conversely, an increase in walking time, particularly in the context of high occupational or low leisure-related physical activity levels, correlates with a diminished risk of metabolic syndrome. These findings highlight the significance of preserving and increasing physical activity levels to prevent metabolic syndrome, particularly in occupational and leisure contexts. This research has reported important data for developing physical activity guidelines and enhancing health-related lifestyle practices.

Data in this study were from KoGES (6635-302), National Institute of Health, Korea Disease Control and Prevention Agency, Republic of Korea.

Study concept and design: DYP and YSK; acquisition of data: DYP; analysis and interpretation of data: DYP and OL; drafting of the manuscript: DYP, OL, and YSK; critical revision of the manuscript: OL, YHL, CGL, and YSK; statistical analysis: DYP and OL; administrative, technical, or material support: OL and YSK; and study supervision: OL and YSK.

Baseline characteristics of the subjects by domain of physical activity

Risk factor Work PA (n=949) Leisure PA (n=949)
<150 min/wk (n=917) ≥150 min/wk (n=665) P <150 min/wk (n=917) ≥150 min/wk (n=665) P
Age (yr) 57.23 ± 6.38 56.69 ± 5.79 0.072 56.55 ± 5.93 57.34 ± 6.26 0.009
Male sex (%) 55.01 44.53 < 0.001 47.62 52.37 0.053
Sleep time (hr/day) 6.17 ± 1.02 6.08 ± 1.04 0.056 6.10 ± 1.02 6.15 ± 1.03 0.302
BMI (kg/m2) 24.31 ± 2.67 24.23 ± 2.67 0.562 24.25 ± 2.72 24.29 ± 2.63 0.768
hs-CRP (mg/dL) 1.28 ± 2.99 1.29 ± 3.17 0.938 1.22 ± 2.44 1.34 ± 3.51 0.428
Change in vigorous PA (%) 0.075 < 0.001
Consistently inactive (low/low) 39.76 37.24 61.77 19.78
Newly active (low/high) 20.15 23.44 26.93 17.31
Newly inactive (high/low) 14.71 17.45 6.48 23.66
Consistently active (high/high) 25.38 21.88 4.76 39.25
Change in moderate PA (%) < 0.001 0.549
Consistently inactive (low/low) 55.77 8.59 35.19 33.55
Newly active (low/high) 36.93 6.90 24.21 22.47
Newly inactive (high/low) 3.16 31.64 15.74 16.45
Consistently active (high/high) 4.14 52.86 24.87 27.53
Change in walk time (%) < 0.001 < 0.001
Consistently inactive (low/low) 46.06 30.73 57.80 23.87
Newly active (low/high) 25.82 19.27 26.85 19.57
Newly inactive (high/low) 13.83 22.79 7.54 26.34
Consistently active (high/high) 14.27 27.21 7.80 30.22
Change in sitting time (%) < 0.001 0.786
Consistently non-ST (low/low) 55.23 71.22 63.49 61.72
Formerly ST (high/low) 22.00 11.59 16.27 18.06
Newly ST (low/high) 13.07 11.72 12.30 12.58
Consistently ST (high/high) 9.69 5.47 7.94 7.63
eGFR (mL/min/1.73 m2) 94.98 ± 17.94 97.09 ± 18.83 0.019 96.94 ± 18.88 95.13 ± 17.92 0.004
HOMA-IR 1.80 ± 0.85 1.77 ± 0.85 0.402 1.79 ± 0.86 1.78 ± 0.84 0.893
HbA1c (%) 5.57 ± 0.56 5.61 ± 0.64 0.221 5.56 ± 0.60 5.61 ± 0.59 0.108
Current drinking (%) 49.89 47.27 0.558 45.11 51.61 0.027
Current smoking (%) 12.53 10.16 0.001 13.49 9.78 < 0.001
Low income (%) 8.50 7.55 0.507 8.99 7.31 0.090

Values are presented as mean±standard deviation or percentage.

PA, physical activity; BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; ST, sitting time; eGFR, estimated glomerular filtration rate; HOMA-IR, homeostasis model assessment of insulin resistance; HbA1c, glycosylated hemoglobin.

Incidence density and hazard ratio of metabolic syndrome according to changes in vigorous physical activity, moderate physical activity, and walk time

Risk factor MetS (n=1,686) Person-years Incidence density* (95% CI) Multivariable adjusted HR (95% CI) (n=1,686)
Change in vigorous PA
Consistently inactive (low/low) 185 4,546.03 40.69 (35.23–47.00) 1.00 (reference)
Newly active (low/high) 103 2,590.40 39.76 (32.77–48.23) 1.15 (0.89–1.48)
Newly inactive (high/low) 79 1,839.28 42.95 (34.45–53.54) 1.36 (1.04–1.79)
Consistently active (high/high) 107 2,847.80 37.57 (31.08–45.41) 1.07 (0.84–1.37)
P trend 0.391
Change in moderate PA
Consistently inactive (low/low) 170 3,992.28 42.58 (36.63–49.48) 1.00 (reference)
Newly active (low/high) 112 2,764.96 40.50 (33.56–48.60) 1.23 (0.96–1.58)
Newly inactive (high/low) 77 1,895.84 40.61 (32.48–50.77) 1.08 (0.82–1.43)
Consistently active (high/high) 115 3,158.26 36.36 (30.29–43.65) 1.02 (0.79–1.31)
P trend 0.848
Change in walk time
Consistently inactive (low/low) 190 4,577.87 41.50 (36.00–47.84) 1.00 (reference)
Newly active (low/high) 83 2,809.86 29.53 (23.82–36.62) 0.75 (0.57–0.98)
Newly inactive (high/low) 100 2,069.06 48.33 (39.72–58.79) 1.26 (0.98–1.61)
Consistently active (high/high) 101 2,366.72 42.67 (35.11–51.86) 1.08 (0.84–1.39)
P trend 0.096
Total 474 11,823.52 40.08 -

The multivariable model was adjusted for changes in vigorous PA, moderate PA, walk time, and sitting time simultaneously as well as for age, sex, income level, sleep duration, alcohol consumption, current smoking, high-sensitivity C-reactive protein, body mass index, glycosylated hemoglobin, estimated glomerular filtration rate, and homeostasis model assessment of insulin resistance.

*Incidence density=case/person-year×1,000; P<0.05.

MetS, metabolic syndrome; CI, confidence interval; HR, hazard ratio; PA, physical activity.

Hazard ratio of metabolic syndrome according to changes in vigorous physical activity, moderate physical activity, and walk time based on the baseline work physical activity level

Risk factor Total (n=1,686) Work PA, HR (95% CI)
<150 min/wk (n=918) ≥150 min/wk (n=768)
Change in vigorous PA
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 1.15 (0.89–1.48) 1.11 (0.78–1.57) 1.07 (0.74–1.53)
Newly inactive (high/low) 1.36* (1.04–1.79) 1.59* (1.10–2.29) 1.10 (0.72–1.68)
Consistently active (high/high) 1.07 (0.84–1.37) 1.15 (0.83–1.60) 0.83 (0.56–1.22)
P trend 0.391 0.216 0.408
Change in moderate PA
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 1.23 (0.96–1.58) 1.09 (0.82–1.43) 1.37 (0.71–2.63)
Newly inactive (high/low) 1.08 (0.82–1.43) 0.94 (0.41–2.16) 0.95 (0.53–1.68)
Consistently active (high/high) 1.02 (0.79–1.31) 1.04 (0.55–1.97) 0.87 (0.50–1.53)
P trend 0.848 0.848 0.233
Change in walk time
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 0.75* (0.57–0.98) 0.83 (0.59–1.17) 0.60 (0.38–0.94)
Newly inactive (high/low) 1.26 (0.98–1.61) 1.10 (0.77–1.59) 1.01 (0.70–1.46)
Consistently active (high/high) 1.08 (0.84–1.39) 1.07 (0.74–1.53) 0.82 (0.56–1.20)
P trend 0.096 0.502 0.721

Work PA is related to vigorous PA, moderate PA, and walking time, rather than being independent of them. The multivariable model was adjusted for changes in vigorous PA, moderate PA, walk time, and sitting time simultaneously as well as for age, sex, income level, sleep duration, alcohol consumption, current smoking, high-sensitivity C-reactive protein, body mass index, glycosylated hemoglobin, estimated glomerular filtration rate, and homeostasis model assessment of insulin resistance.

*P<0.05; P<0.01.

PA, physical activity; HR, hazard ratio; CI, confidence interval.

Hazard ratio of metabolic syndrome according to changes in vigorous physical activity, moderate physical activity, and walk time based on the baseline leisure physical activity level

Risk factor Total (n=1,686) Leisure PA, HR (95% CI)
<150 min/wk (n=756) ≥150 min/wk (n=930)
Change in vigorous PA
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 1.15 (0.89–1.48) 1.03 (0.72–1.46) 1.19 (0.82–1.74)
Newly inactive (high/low) 1.36* (1.04–1.79) 0.98 (0.54–1.77) 1.45 (0.96–2.19)
Consistently active (high/high) 1.07 (0.84–1.37) 1.28 (0.66–2.49) 1.07 (0.73–1.55)
P trend 0.391 0.567 0.919
Change in moderate PA
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 1.23 (0.96–1.58) 1.47 (0.99–2.18) 1.00 (0.71–1.39)
Newly inactive (high/low) 1.08 (0.82–1.43) 1.49 (0.97–2.31) 0.77 (0.53–1.12)
Consistently active (high/high) 1.02 (0.79–1.31) 1.27 (0.84–1.93) 0.77 (0.56–1.07)
P trend 0.848 0.324 0.068
Change in walk time
Consistently inactive (low/low) 1.00 (reference) 1.00 (reference) 1.00 (reference)
Newly active (low/high) 0.75* (0.57–0.98) 0.54 (0.36–0.79) 1.09 (0.73–1.63)
Newly inactive (high/low) 1.26 (0.98–1.61) 0.83 (0.49–1.39) 1.42 (0.96–2.10)
Consistently active (high/high) 1.08 (0.84–1.39) 0.59 (0.31–1.10) 1.31 (0.89–1.93)
P trend 0.096 0.059 0.185

Leisure PA is related to vigorous PA, moderate PA, and walking time rather than being independent of them. The multivariable model was adjusted for change in vigorous PA, moderate PA, walk time, and sitting time simultaneously as well as for age, sex, income level, sleep duration, alcohol consumption, current smoking, high-sensitivity C-reactive protein, body mass index, glycosylated hemoglobin, estimated glomerular filtration rate, and homeostasis model assessment of insulin resistance.

*P<0.05; P<0.01.

PA, physical activity; HR, hazard ratio; CI, confidence interval.

  1. O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev 2015;16:1-12.
    Pubmed CrossRef
  2. Isomaa B, Almgren P, Tuomi T, Forsén B, Lahti K, Nissén M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 2001;24:683-9.
    Pubmed CrossRef
  3. Liang X, Or B, Tsoi MF, Cheung CL, Cheung BM. Prevalence of metabolic syndrome in the United States National Health and Nutrition Examination Survey 2011-18. Postgrad Med J 2023;99:985-92.
    Pubmed CrossRef
  4. Health Insurance Review and Assessment Service. 2021 National Health Insurance statistical yearbook [Internet]. HIRA; 2023 [cited 2024 Jun 5]. Available from: https://www.hira.or.kr/bbsDummy.do?pgmid=HIRAJ030000007001&brdScnBltNo=4&brdBltNo=6&pageIndex=1&pageIndex2=1
  5. Macías N, Espinosa-Montero J, Monterrubio-Flores E, Hernández-Barrera L, Medina-Garcia C, Gallegos-Carrillo K, et al. Screen-based sedentary behaviors and their association with metabolic syndrome components among adults in Mexico. Prev Chronic Dis 2021;18:E95.
    Pubmed KoreaMed CrossRef
  6. Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, et al. Metabolic syndrome: updates on pathophysiology and management in 2021. Int J Mol Sci 2022;23:786.
    Pubmed KoreaMed CrossRef
  7. Amirfaiz S, Shahril MR. Objectively measured physical activity, sedentary behavior, and metabolic syndrome in adults: systematic review of observational evidence. Metab Syndr Relat Disord 2019;17:1-21.
    Pubmed CrossRef
  8. Yeo Y, Cho IY, Sim MS, Song HG, Song YM. Relationship between daily sedentary behaviors and metabolic syndrome in middle-aged adults: results from a health survey in Taean-Gun, Republic of Korea. Metab Syndr Relat Disord 2021;19:48-55.
    Pubmed CrossRef
  9. Holtermann A, Hansen JV, Burr H, Søgaard K, Sjøgaard G. The health paradox of occupational and leisure-time physical activity. Br J Sports Med 2012;46:291-5.
    Pubmed CrossRef
  10. Nam JY, Kim J, Cho KH, Choi Y, Choi J, Shin J, et al. Associations of sitting time and occupation with metabolic syndrome in South Korean adults: a cross-sectional study. BMC Public Health 2016;16:943.
    Pubmed KoreaMed CrossRef
  11. Yang X, Telama R, Hirvensalo M, Mattsson N, Viikari JS, Raitakari OT. The longitudinal effects of physical activity history on metabolic syndrome. Med Sci Sports Exerc 2008;40:1424-31.
    Pubmed CrossRef
  12. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5.
    Pubmed CrossRef
  13. IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. IPAQ Research Committee; 2005.
  14. Cho JH, Ko J, Lim ST. Relationship between metabolic syndrome and moderate-to-vigorous physical activity among adults 18 years old and over. PLoS One 2021;16:e0258097.
    Pubmed KoreaMed CrossRef
  15. World Health Organization. Global recommendations on physical activity for health [Internet]. WHO; 2010 [cited 2024 Jun 5]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK305057
  16. Gorczyca AM, Eaton CB, LaMonte MJ, Manson JE, Johnston JD, Bidulescu A, et al. Change in physical activity and sitting time after myocardial infarction and mortality among postmenopausal women in the Women's Health Initiative-Observational Study. J Am Heart Assoc 2017;6:e005354.
  17. Bowlby W, Zelnick LR, Henry C, Himmelfarb J, Kahn SE, Kestenbaum B, et al. Physical activity and metabolic health in chronic kidney disease: a cross-sectional study. BMC Nephrol 2016;17:187.
    Pubmed KoreaMed CrossRef
  18. Parker K, Tucker LA. The role of physical activity in the relationship between sitting time and insulin resistance. Int J Exerc Sci Conf Proc 2021;14:47.
  19. Cabanas-Sánchez V, Guallar-Castillón P, Higueras-Fresnillo S, Rodríguez-Artalejo F, Martínez-Gómez D. Changes in sitting time and cardiovascular mortality in older adults. Am J Prev Med 2018;54:419-22.
    Pubmed CrossRef
  20. Baik SH, Fung KW, McDonald CJ. The mortality risk of proton pump inhibitors in 1.9 million US seniors: an extended cox survival analysis. Clin Gastroenterol Hepatol 2022;20:e671-81.
    Pubmed CrossRef
  21. Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, Wilmot EG, et al. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS One 2012;7:e34916.
    Pubmed KoreaMed CrossRef
  22. Sardinha LB, Magalhães JP, Santos DA, Hetherington-Rauth M. Intensity matters: impact of physical activity energy expenditure at moderate and vigorous intensity on total and abdominal obesity in children. Eur J Clin Nutr 2023;77:546-50.
    Pubmed CrossRef
  23. Barengo NC, Kastarinen M, Lakka T, Nissinen A, Tuomilehto J. Different forms of physical activity and cardiovascular risk factors among 24-64-year-old men and women in Finland. Eur J Cardiovasc Prev Rehabil 2006;13:51-9.
    Pubmed CrossRef
  24. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. J Phys Act Health 2005;2:366-83.
    CrossRef
  25. Ko KJ, Kim EH, Baek UH, Gang Z, Kang SJ. The relationship between physical activity levels and metabolic syndrome in male white-collar workers. J Phys Ther Sci 2016;28:3041-6.
    Pubmed KoreaMed CrossRef
  26. Cocate PG, de Oliveira A, Hermsdorff HH, Alfenas Rde C, Amorim PR, Longo GZ, et al. Benefits and relationship of steps walked per day to cardiometabolic risk factor in Brazilian middle-aged men. J Sci Med Sport 2014;17:283-7.
    Pubmed CrossRef
  27. Sisson SB, Camhi SM, Church TS, Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps/day and metabolic syndrome. Am J Prev Med 2010;38:575-82.
    Pubmed CrossRef
  28. Zając-Gawlak I, Pelclová J, Groffik D, Přidalová M, Nawrat-Szołtysik A, Kroemeke A, et al. Does physical activity lower the risk for metabolic syndrome: a longitudinal study of physically active older women. BMC Geriatr 2021;21:11.
    Pubmed KoreaMed CrossRef
  29. Muscella A, Stefàno E, Lunetti P, Capobianco L, Marsigliante S. The regulation of fat metabolism during aerobic exercise. Biomolecules 2020;10:1699.
    Pubmed KoreaMed CrossRef
  30. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults' participation in physical activity: review and update. Med Sci Sports Exerc 2002;34:1996-2001.
    Pubmed CrossRef
  31. Trapp EG, Chisholm DJ, Freund J, Boutcher SH. The effects of high-intensity intermittent exercise training on fat loss and fasting insulin levels of young women. Int J Obes (Lond) 2008;32:684-91.
    Pubmed CrossRef
  32. Janssen I, Ross R. Vigorous intensity physical activity is related to the metabolic syndrome independent of the physical activity dose. Int J Epidemiol 2012;41:1132-40.
    Pubmed KoreaMed CrossRef
  33. Byberg L, Zethelius B, McKeigue PM, Lithell HO. Changes in physical activity are associated with changes in metabolic cardiovascular risk factors. Diabetologia 2001;44:2134-9.
    Pubmed CrossRef
  34. Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, Hercberg S, Oppert JM. Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obes Res 2005;13:936-44.
    Pubmed CrossRef
  35. Boyle LJ, Credeur DP, Jenkins NT, Padilla J, Leidy HJ, Thyfault JP, et al. Impact of reduced daily physical activity on conduit artery flow-mediated dilation and circulating endothelial microparticles. J Appl Physiol (1985) 2013;115:1519-25.
    Pubmed KoreaMed CrossRef
  36. Johnson BD, Mather KJ, Wallace JP. Mechanotransduction of shear in the endothelium: basic studies and clinical implications. Vasc Med 2011;16:365-77.
    Pubmed CrossRef
  37. Krause N, Brand RJ, Arah OA, Kauhanen J. Occupational physical activity and 20-year incidence of acute myocardial infarction: results from the Kuopio Ischemic Heart Disease Risk Factor Study. Scand J Work Environ Health 2015;41:124-39.
    Pubmed CrossRef
  38. American College of Sports Medicine Position Stand. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med Sci Sports Exerc 1998;30:975-91.
    CrossRef
  39. Moreau KL, Degarmo R, Langley J, McMahon C, Howley ET, Bassett DR, et al. Increasing daily walking lowers blood pressure in postmenopausal women. Med Sci Sports Exerc 2001;33:1825-31.
    Pubmed CrossRef
  40. Swartz AM, Strath SJ, Bassett DR, Moore JB, Redwine BA, Groër M, et al. Increasing daily walking improves glucose tolerance in overweight women. Prev Med 2003;37:356-62.
    Pubmed CrossRef