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  • Open Access
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Association of self-reported sedentary time with insulin resistance among Korean adults without diabetes mellitus: a cross-sectional study

Contributed equally
BMC Public Health201818:1335

https://doi.org/10.1186/s12889-018-6237-4

  • Received: 2 November 2017
  • Accepted: 20 November 2018
  • Published:
Open Peer Review reports

Abstract

Background

A more sedentary lifestyle can result in insulin resistance. However, few research studies have assessed the association between insulin resistance and sedentary lifestyle in Asian populations. Therefore, this study aimed to investigate the association of sedentary time with insulin resistance. In addition, we also investigate the moderate effect of employment status, moderate-to-vigorous physical activity (MVPA), and body mass index (BMI) in this association.

Methods

Data from 2573 individuals who participated in the 2015 Korean National Health and Nutrition Examination Survey were analyzed. Sedentary time was measured using self-administered questionnaires, and IR data were estimated using the homeostasis model assessment–insulin resistance index (HOMA-IR). Adjusted odds ratio (OR) and 95% confidence intervals (CIs) from a multivariable logistic regression model were generated for all participants. Subgroup analysis was only performed between sedentary time and HOMA-IR stratified by employment status, because moderate effects were not significant in the tests for interaction for MVPA and BMI. For all analyses, the individuals were categorized as having high or normal HOMA-IR values (> 1.6 and ≤ 1.6, respectively).

Results

A HOMA-IR > 1.6 was observed in 40.3% of the sedentary time Q1 (low) group (< 5.0 h/day), 41.4% of the sedentary time Q2 (middle-low) group, 44.2% of the sedentary time Q3 (middle-high) group, and 48.4% of the sedentary time Q4 (high) group (≥10.0 h/day). When the low level sedentary time group was used as the reference group, the high level sedentary time group was significantly associated with high IR value (HOMA-IR > 1.6) (OR = 1.40, 95% CI: 1.060–1.838). However, this association was not significant across the other sedentary time groups. Moreover, participants reporting a high sedentary time and were employed had 1.67 times the odds of having a high IR value (HOMA-IR > 1.6) compared to those who reported having a low sedentary time and were employed (OR = 1.67, 95% CI: 1.184–2.344). In the unemployed participants, sedentary time was not associated with IR.

Conclusions

High sedentary time (≥10.0 h/day) was associated with elevated HOMA-IR among Korean adults without diabetes mellitus. Furthermore, the association between high sedentary time and HOMA-IR values was more pronounced in the employed population.

Keywords

  • Insulin resistance
  • HOMA-IR
  • Sedentary time

Background

Insulin resistance (IR) occurs when the body’s response to insulin is lower than normal. The deterioration of insulin makes the cells unable to burn glucose effectively, which causes the body to over-produce insulin and contribute to the occurrence of various diseases [1, 2]. IR plays a key role in the development of type 2 diabetes and contributes to the pathophysiology of burdensome disease including obesity, metabolic syndrome, and cardiovascular disease. IR is commonly considered an important clinical and biochemical determinant and has been a subject of interest, as it has effects on various chronic disease such as diabetes, cardiovascular disease, hypertension, and metabolic syndrome [3]. For example, previous studies have reported that family history of type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease, obesity, lack of exercise, high triglyceride levels, low levels of high-density lipoprotein, high-molecular weight (HMW)-adiponectin levels, hepatitis C, hemochromatosis, or hypercortisolism are associated risk factors [49]. Studies on insulin resistance have been reported in neuroscience and clinical research fields. Studies on insulin resistance reported that IR is associated with cognitive dysfunction such as cognitive decline and cognitive impairment [10].

Meanwhile, recent studies have focused on factors such as lack of exercise and a sedentary lifestyle in relation to insulin resistance [11]. Studies have reported that poor physical activity status is associated with insulin resistance, with attention being focused on sitting time which is directly related to physical activity status [12]. Recently, the need to investigate the relationship between sitting time and health status is increasing, and it is also necessary to establish a basis for this research. Sedentary time has a significant effect on health, and individuals who use more screen-based entertainment have a higher risk of clinically confirmed cardiovascular disease events [13]. Furthermore, a cohort study of people from Hawaii and California revealed that sedentary time was associated with cancer mortality [14]. A meta-analysis from 2015 revealed that sedentary time was associated with cardiovascular disease and all-cause mortality [15], and another meta-analysis from 2012 also demonstrated that sedentary time was associated with various diseases including type II diabetes [16].

In addition, few research have evaluated the association between sedentary time and IR in the Korean population, and most studies regarding sedentary lifestyle and diabetes during 2012–2015 only evaluated non-Asian populations [17, 18]. To fill this research gap, there is a need to investigate studies on this topic in Asian population. Therefore, the purpose of this study was to investigate the association of sedentary time with insulin resistance.

Meanwhile, physical activity and BMI are well-known factors that are independently associated with insulin resistance [1921]. In addition, as second longest working hours in OECD countries, high sedentary time are becoming a lot of controversy in Korea. Hence, we also investigate the moderate effect of employment status, moderate-to-vigorous physical activity (MVPA), and body mass index (BMI) in this association.

Methods

Study population

The present study evaluated data from the 2015 Korean National Health and Nutrition Examination Survey (KNHANES), which was performed by the Korean Center for Disease Control. A cross-sectional survey, it is a multistage, stratified area probability sample of civilian non-institutionalized Korean households by geographic area, age, and gender groups. This survey is composed of three parts—a health interview, health examination, and nutrition survey—all of which were performed by trained medical staff and dieticians. A total of 7380 individuals participated in the 2015 KNHANES. However, the present study excluded participants with diabetes (fasting blood glucose levels > 126 mg/dL, or physician-diagnosed diabetes mellitus), in order to avoid confounding the IR-related analyses [22]. In addition, participants were excluded if they were missing data and if they were aged < 19 years. Any respondents who did not provide data on sedentary time, moderate-to-vigorous physical activity (MVPA), subjective health status, age, income, employment status, education, stress, smoking, drinking, marriage status, BMI, menopause, or who were aged < 19 years were excluded from the study (see details in Fig. 1).
Fig. 1
Fig. 1

Is the flow diagram of the study participants for present study

The KNHANES data are openly available at the KNHANES website: https://knhanes.cdc.go.kr/knhanes/eng/index.do. Ethical approval for this study was granted by the institutional review board (IRB) of the KCDC Seoul, Korea (IRB #: 2015-01-02-6C).

Measures

HOMA-IR

The dependent variable was defined as the homeostasis model assessment–insulin resistance index (HOMA-IR), which was calculated using the following formula:
$$ HOMA- IR=\frac{\mathrm{fasting}\ \mathrm{plasma}\ \mathrm{glucose}\left(\mathrm{mg}/\mathrm{dL}\right)\times \mathrm{fasting}\ \mathrm{plasma}\ \mathrm{insulin}\left(\upmu \mathrm{IU}/\mathrm{mL}\right)\ }{405} $$

Data regarding fasting plasma glucose and insulin levels were obtained from the 2015 KNHANES health examination results. For the present study, individuals were categorized as having high or normal HOMA-IR values (> 1.6 vs. ≤1.6, respectively) based on the Japanese Diabetes Society guidelines [23].

Sedentary time

The independent variable was defined as sedentary time. Sedentary time was estimated using the Global Physical Activity Questionnaire (GPAQ), and assessed via the following question: “How much time do you spend sitting or lying down during at work, at home, when travelling from place to place or when meeting friends, but excluding sleeping hours?”. Responses were divided into 4 categories using quartiles, with the Q1 group having < 5 h/day, the Q2 group having 5–7.9 h/day, the Q3 group having 8–9.9 h/day, and the Q4 group having ≥10 h/day. The validity of the GPAQ tool for Koreans was 0.79, which was reported to be sufficiently valid for the use of the tool [24].

MVPA

MVPA was measured using the GPAQ. GPAQ, developed by the World Health Organization (WHO), is a questionnaire measuring the amount of physical activity (occupation, mobility, leisure activity) and is a standardized questionnaire currently used in 50 countries. This questionnaire was developed and validated by the WHO to systematically monitor global physical activity levels as one of the main lifestyle disease risk factors. The validity of the GPAQ tool for Koreans was 0.64, which was reported to be sufficiently valid for the use of the tool [24]. Regarding MVPA, responses were divided into 2 categories (“yes” or “no”), with the “yes” group consisting of individuals who exercise moderately more than 2 hours and 30 minutes per week or intensively more than 1 hour and 15 minutes per week. 1 minute of intensive physical activity was defined as equivalent to 2 minutes of moderate physical activity.

Diet quality

To assess the diet quality, the mean adequacy ratio (MAR) index was calculated using the nutrient adequacy ratio (NAR) index. The NAR was calculated for each of nine nutrients (protein, vitamin A, thiamine, riboflavin, niacin, Ca, P, Fe, and vitamin C), whose recommended intake was set according to the dietary reference intakes for Koreans [25], using the following formula:
$$ \mathbf{NAR}={\mathrm{Participant}}^{\hbox{'}}\mathrm{s}\ \mathrm{daily}\ \mathrm{intake}\ \mathrm{of}\ \mathrm{a}\ \mathrm{nutrient}/\mathrm{recommended}\ \mathrm{nutrition}\ \mathrm{intake} $$
$$ \mathbf{MAR}=\Sigma \mathrm{NAR}/\mathrm{number}\ \mathrm{of}\ \mathrm{nutrients}. $$

NARs were truncated at 1 if the value was over 1. The MAR provides an index of the overall diet quality. A high MAR implies a high-quality diet [26].

Covariates

The analyses were adjusted for covariates that might be associated with HOMA-IR. These covariates were defined as age (19–29 years, 30–49 years, 50–69 years, and ≥ 70 years), sex, body mass index (BMI; underweight: < 18.5 kg/m2, normal: 18.5–24.9 kg/m2, and obese: ≥25.0 kg/m2), education level (elementary school or less, middle school, high school, and university or higher), income (monthly income quartiles), employment status (employed vs. unemployed or economically inactive), marital status (no vs. yes), subjective health status (good, normal, and bad) stress (no vs. yes), smoking status (current smoker, previous smoker, and never smoker), alcohol consumption (not in the last year, < 4 times per month, 2–3 time per week, and 4 times per week), and MVPA (no vs. yes).

Statistical analysis

We first examined the distribution of each categorical variable. The chi-square test was used to calculate the distribution of each categorical variable and to confirm significant differences between groups. In addition, to produce an unbiased national estimate, a sample weight was assigned for the participating individuals to represent the Korean population. The sampling weight was calculated by accounting for the complex survey design, survey nonresponse, and post-stratification. Next, to investigate the association of sedentary time with insulin resistance, multivariable logistic regression analysis was used. In multivariable logistic regression, confounding variables such as age, sex, income, education level, employment status, marriage status, perceive health status, stress, smoking, alcohol intake, BMI, MVPA, and diet quality were controlled. To consider the considerable effect of employment status, MVPA, and BMI on sedentary behavior which has been reported in previous literature [27], we also examined whether employment status, MVPA, and BMI modified the association between sedentary time and the insulin resistance by introducing an interaction terms in the models. Then, subgroup analysis was only performed between sedentary time and HOMA-IR stratified by employment status, because moderate effects were not significant in the tests for interaction for MVPA and BMI variables.

All statistical analyses were performed using SAS software (version 9.4; SAS Institute, Cary, NC, USA), and differences with a p-value < 0.05 were considered statistically significant.

Results

Characteristics of the participants

In our study, 2573 participants were included to access the association between sedentary time and HOMA-IR. Table 1 shows the characteristics of the study population. Among the study population, 19.9% (n = 511) were in the sedentary time Q1 (low) group (< 5.0 h/day), 38.7% (n = 997) were in the sedentary time Q2 (middle-low) group (5.0–7.9 h/day), 22.4% (n = 577) were in the sedentary time Q3 (middle-high) group (8.0–9.9 h/day), and 19.0% (n = 488) were in the sedentary time Q4 (high) group (≥10.0 h/day). High IR values (HOMA-IR > 1.6) were observed for 40.3% (n = 206) of the sedentary time Q1 (low) group, 41.4% (n = 413) of the sedentary time Q2 (middle-low) group, 44.2% (n = 255) of the sedentary time Q3 (middle-high) group, and 48.4% (n = 236) of the sedentary time Q4 (high) group.
Table 1

General characteristics of study population

Variables

Total

HOMA-IR > 1.6

P Value

   

YES

NO

 
 

N

%

N

%

N

%

 

Sedentary time (hours per day)

      

0.0360

 Q1 (<  5)

511

19.9

206

40.3

305

59.7

 

 Q2 (5~7.9)

997

38.7

413

41.4

584

58.6

 

 Q3 (8~9.9)

577

22.4

255

44.2

322

55.8

 

 Q4 (≥10)

488

19.0

236

48.4

252

51.6

 

Age

0.0010

 19~29

488

19.0

225

46.1

263

53.9

 

 30~39

508

19.7

203

40.0

305

60.0

 

 40~49

623

24.2

243

39.0

380

61.0

 

 50~59

679

26.4

294

43.3

385

56.7

 

 ≥60

275

10.7

145

52.7

130

47.3

 

Sex

0.0001

 Male

1006

39.1

481

47.8

525

52.2

 

 Female

1567

60.9

629

40.1

938

59.9

 

Income

0.1138

 Low

566

22.0

261

46.1

305

53.9

 

 Middle low

648

25.2

276

42.6

372

57.4

 

 Middle high

684

26.6

305

44.6

379

55.4

 

 High

675

26.2

268

39.7

407

60.3

 

Education level

0.0073

 Elementary school or less

212

8.2

112

52.8

100

47.2

 

 Middle school

226

8.8

89

39.4

137

60.6

 

 High school

1023

39.8

453

44.3

570

55.7

 

 University or more

1112

43.2

456

41.0

656

59.0

 

Employment status

<.0001

 Employed

1748

67.9

748

42.8

1000

57.2

 

 Unemployed or economically inactive

825

32.1

362

43.9

463

56.1

 

Marriage status

0.2802

 Unmarried

597

23.2

269

45.1

328

54.9

 

 Married

1976

76.8

841

42.6

1135

57.4

 

Perceive health status

0.0006

 Healthy

873

33.9

332

38.0

541

62.0

 

 Normal

1322

51.4

597

45.2

725

54.8

 

 Unhealthy

378

14.7

181

47.9

197

52.1

 

Stress

0.0060

 No

1807

70.2

748

41.4

1059

58.6

 

 Yes

766

29.8

362

47.3

404

52.7

 

Smoking

0.0017

 None smoker

1660

64.5

675

40.7

985

59.3

 

 Previous smoker

471

18.3

232

49.3

239

50.7

 

 Current smoker

442

17.2

203

45.9

239

54.1

 

Alcohol intake

0.7786

 No

539

20.9

243

45.1

296

54.9

 

 < 4 times a month

1545

60.1

657

42.5

888

57.5

 

 2~3 times a week

374

14.5

160

42.8

214

57.2

 

 ≥4 times a week

115

4.5

50

43.5

65

56.5

 

BMI

<.0001

 Underweight (BMI < 18.5)

114

4.4

12

10.5

102

89.5

 

 Normal (18.5 ≤ BMI < 25)

1666

64.8

533

32.0

1133

68.0

 

 Obesity (25 ≤ BMI)

793

30.8

565

71.3

228

28.7

 

Moderate-to-vigorous physical activity

0.1828

 No

1213

47.1

540

44.5

673

55.5

 

 Yes

1360

52.9

570

41.9

790

58.1

 

Diet quality (Mean ± S.D)

0.83 ± 0.16

0.83 ± 0.16

0.83 ± 0.16

0.8722

Total

2573

100.0

1110

43.1

1463

56.9

 

Multivariable logistic regression results of association between ST and HOMA-IR

Table 2 show the results of the multivariable logistic regression analysis for the association between sedentary time and HOMA-IR. High level of sedentary time (≥10 h/day) was significantly associated with high IR value (HOMA-IR > 1.6) (OR = 1.40, 95% CI: 1.060–1.838).
Table 2

Multivariable logistic regression analysis of the association between sedentary time and HOMA-IR

Variables

HOMA-IR > 1.6

Adjusted-ORa

95% CI

Sedentary time (hours per day)

 Q1 (<  5)

1.00

   

 Q2 (5~7.9)

1.09

0.862

1.365

 Q3 (8~9.9)

1.20

0.900

1.606

 Q4 (≥10)

1.40

1.060

1.838

Notes: aAdjusted odds ratios (OR) were calculated using logistic regression analysis and adjusted for age, sex, income, education level, employment status, marriage status, perceive health status, stress, smoking, alcohol intake, BMI, moderate-to-vigorous physical activity, and diet quality

Sub-group analysis results by employment status

The subgroup analysis results are shown in Table 3. Subgroup analysis was only performed between sedentary time and HOMA-IR stratified by employment status, because moderate effects were not significant in the tests for interaction for MVPA and BMI variables (MVPA: p for interaction, p = 0.2679; BMI: p for interaction, p = 0.2003). Subgroup analysis showed significant differences in employment status (p for interaction: p = 0.0217). Participants reporting high sedentary time and were employed had 1.67 times the odds of having a high IR value (HOMA-IR > 1.6) compared to those who reported having a low sedentary time and were employed (OR = 1.67, 95% CI: 1.184–2.344). In the unemployed participants, sedentary time was not associated with IR.
Table 3

Subgroup analysis of the association between sedentary time and HOMA-IR by employment status

Variables

 

HOMA-IR > 1.6

Adjusted-ORa

95% CI

Employment status

Sedentary time

    

 Employed

Q1 (<  5)

1.00

   

Q2 (5~7.9)

1.22

0.928

1.597

Q3 (8~9.9)

1.24

0.872

1.769

Q4 (≥10)

1.67

1.184

2.344

 Unemployed or economically inactive

Sedentary time

    

Q1 (<  5)

1.00

   

Q2 (5~7.9)

0.85

0.530

1.353

Q3 (8~9.9)

1.11

0.658

1.864

Q4 (≥10)

0.87

0.511

1.491

Notes: aAdjusted odds ratios (OR) were calculated using logistic regression analysis and adjusted for age, sex, income, education level, marriage status, perceive health status, stress, smoking, alcohol intake, BMI, moderate-to-vigorous physical activity, and diet quality

Discussion

Although there are many studies that showed that high sedentary time was negatively associated with health outcomes, little research has been conducted on this in Korea. Therefore, there is a growing interest in researching sedentary time. Thus, we investigate the association of sedentary time with IR in Korean adults without diabetes mellitus. In addition, we also investigate the moderate effect of employment status, MVPA, and BMI in this association. Given that these issues remain a concern, it is necessary to design effective strategies to prevent and manage reduced insulin resistance and its negative health outcomes.

Through multivariable analysis, our findings revealed that sedentary time was associated with IR among an adult population without diabetes mellitus. This was consistent with previous studies. Cross-sectional analysis with 4757 adults in the United States of America reported that higher amounts of sedentary time was associated with higher HOMA-IR [28]. Another study that included 2027 young adult participants (aged 38–50 years) also confirmed that having higher amounts of sedentary time was cross-sectionally associated with higher HOMA-IR [29]. However, other studies reported that having higher amounts of sedentary time was not cross-sectionally associated with HOMA-IR or fasting insulin [30, 31]. The differences in results might be explained by the differences between objectively measured time and small sample sizes.

Regarding subgroup analysis, it was only performed between sedentary time and HOMA-IR stratified by employment status, because moderate effects were not significant in the tests for interaction for MVPA and BMI variables. As the interaction tests proved to be statistically significant, we confirmed that the association between sedentary time with HOMA-IR values was more pronounced in the employed population. For most working adults, time spent sitting in the workplace is likely a greater contributor to overall sitting time [32]. In addition, studies reported that “work” was more sedentary and had less light-intensity activity than “non-work” [33, 34]. Hence, the association between sedentary time and HOMA-IR in workers may be prominent due to prolonged sedentary time in work life. Therefore, there is a need to concentrate efforts to efficiently manage sedentary time, especially for workers. Recently, in an attempt to tackle the country’s notoriously long work hours, South Korea officially dropped its maximum work week to 52 h in July 2018 in an effort to improve the quality of life among its citizens. As following strategies, approaches to effectively manage workers’ own sedentary time during working hours should also be considered. Strategies should be supported to manage the sedentary time efficiently, such as providing support for the conditions for the physical activity of the workers in the workplace. Based on the lessons learned from many prior programs that aimed at efficient sedentary time management at the workplace, policy makers should consider efficient strategies to manage the sedentary time in the workplace [35].

The present study has several limitations that warrant consideration. First, individuals with diabetes were excluded because the vast majority of these patients receive diabetes treatments that can alter insulin sensitivity and HOMA-IR values. Although this approach is useful for data cleaning, it precludes any analysis of the association between sedentary time and HOMA-IR values among patients with diabetes, and further studies are needed to evaluate this issue. Second, the present study was unable to identify a causal relationship between sedentary time and insulin resistance because the study design was cross sectional and information was obtained via self-reported. Thus, prospective cohort studies or prospective clinical research studies are needed to examine the causal relationships between sedentary time, employment status, and HOMA-IR values. Third, it is possible that IR can change over time, even in cases with controlled sedentary time. For example, a previous study [36] revealed that interrupting sedentary time with short walks was associated with lower postprandial glucose and insulin levels among overweight/obese adults. Fourth, we could not measure mobility impairment that have been associated with sedentary time, due to limitations of data. This probably unreported population characteristic could have influenced the association between sedentary time and insulin resistance.

Conclusion

In conclusion, this study revealed that only high sedentary time (≥10.0 h/day) was associated with HOMA-IR among adults Korean without diabetes mellitus. However, this association was not significant across the other sedentary time groups. Furthermore, the association of high sedentary time (≥10.0 h/day) with HOMA-IR values was more pronounced in the employed population.

Notes

Abbreviations

BMI: 

Body Mass Index

GPAQ: 

Global Physical Activity Questionnaire

HOMA-IR: 

Homeostasis Model Assessment-Insulin Resistance

IR: 

Insulin Resistance

MAR: 

Mean Adequacy Ratio

MVPA: 

Moderate-to-Vigorous Physical Activity

NAR: 

Nutrient Adequacy Ratio

Declarations

Acknowledgements

None

Funding

None

Availability of data and materials

The KNHANES was openly available in (https://knhanes.cdc.go.kr/knhanes/main.do) by submitting written oath and data utilization plan.

Author’s contributions

KSK, SJK, and SKK participated in designing of the study and interpretation of data, and writing the initial manuscript SJK, and DWC participated in analyzing the data and reviewing the manuscript. YJJ oversaw the overall work process. YJJ and ECP is the guarantor of this work and takes responsibility for the integrity of the data and the accuracy of the data analysis and overall direction of the study. In addition, YJJ, and ECP involved in revising it critically for important intellectual content. All authors read and approved the final manuscript, and agreed to be accountable for all aspects of the study.

Ethical approval and consent to participate

Ethical approval for this study was granted by the institutional review board (IRB) of the KCDC Seoul, Korea (IRB #: 2015-01-02-6C). As the KNHANES 2015 database does not contain private information and is openly available to researchers in de-identified format, we did not have to address ethical concerns regarding informed consent.

Consent for publication

Not applicable.

Competing interests

The authors have no conflicts. All authors declare no support from any organization for the submitted work, no financial relationship with any organization.

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Authors’ Affiliations

(1)
Premedical Courses, College of Medicine, Yonsei University, Seoul, Republic of Korea
(2)
Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea
(3)
Institute of Health Services Research, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
(4)
Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
(5)
Present address: Department of Preventive Medicine and Public Health, Ajou University School of Medicine, Suwon, Republic of Korea
(6)
Department of Preventive Medicine & Institute of Health Services Research, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea

References

  1. Lebovitz H. Insulin resistance: definition and consequences. Exp Clin Endocrinol Diabetes. 2001;109(Suppl 2):S135–48.View ArticlePubMedGoogle Scholar
  2. Chiu HK, Tsai EC, Juneja R, Stoever J, Brooks-Worrell B, Goel A, Palmer JP. Equivalent insulin resistance in latent autoimmune diabetes in adults (LADA) and type 2 diabetic patients. Diabetes Res Clin Pract. 2007;77(2):237–44.View ArticlePubMedGoogle Scholar
  3. Singh B, Saxena A. Surrogate markers of insulin resistance: a review. World J Diabetes. 2010;1(2):36.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Almeda-Valdes P, Cuevas-Ramos D, Mehta R, Gomez-Perez FJ, Cruz-Bautista I, Arellano-Campos O, Navarrete-Lopez M, Aguilar-Salinas CA. Total and high molecular weight adiponectin have similar utility for the identification of insulin resistance. Cardiovasc Diabetol. 2010;9(1):26.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Anderwald C, Anderwald-Stadler M, Promintzer M, Prager G, Mandl M, Nowotny P, Bischof MG, Wolzt M, Ludvik B, Kästenbauer T. The clamp-like index: a novel and highly sensitive insulin sensitivity index to calculate hyperinsulinemic clamp glucose infusion rates from oral glucose tolerance tests in nondiabetic subjects. Diabetes Care. 2007;30(9):2374–80.View ArticlePubMedGoogle Scholar
  6. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840.View ArticleGoogle Scholar
  7. Grundy SM, Brewer HB, Cleeman JI, Smith SC, Lenfant C. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation. 2004;109(3):433–8.View ArticlePubMedGoogle Scholar
  8. Reaven GM, Chen Y-D, Jeppesen J, Maheux P, Krauss RM. Insulin resistance and hyperinsulinemia in individuals with small, dense low density lipoprotein particles. J. Clin. Invest. 1993;92(1):141–6.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Rao G. Insulin resistance syndrome. Am Fam Physician. 2001;63(6):1159–63 1165-1156.PubMedGoogle Scholar
  10. Ma L, Wang J, Li Y. Insulin resistance and cognitive dysfunction. Clin Chim Acta. 2015;444:18–23.View ArticlePubMedGoogle Scholar
  11. Kolb H, S M. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med. 2017;15(1):131.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Helmerhorst HJ, Wijndaele K, Brage S, Wareham NJ, Ekelund UJD. Objectively measured sedentary time may predict insulin resistance, independent of moderate and vigorous physical activity. Diabetes. 2009;58(8):1776–9.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Stamatakis E, Hamer M, Dunstan DW. Screen-based entertainment time, all-cause mortality, and cardiovascular events: population-based study with ongoing mortality and hospital events follow-up. J Am Coll Cardiol. 2011;57(3):292–9.View ArticlePubMedGoogle Scholar
  14. Kim Y, Wilkens LR, Park S-Y, Goodman MT, Monroe KR, Kolonel LN. Association between various sedentary behaviours and all-cause, cardiovascular disease and cancer mortality: the multiethnic cohort study. Int J Epidemiol. 2013;42(4):1040–56.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Biswas A, Oh PI, Faulkner GE, Bajaj RR, Silver MA, Mitchell MS, Alter DA. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adultsa systematic review and meta-analysissedentary time and disease incidence, mortality, and hospitalization. Ann Intern Med. 2015;162(2):123–32.View ArticlePubMedGoogle Scholar
  16. Wilmot EG, Edwardson CL, Achana FA, Davies MJ, Gorely T, Gray LJ, Khunti K, Yates T, Biddle SJ. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895-905.View ArticlePubMedGoogle Scholar
  17. Krishnan S, Rosenberg L, Palmer JR. Physical activity and television watching in relation to risk of type 2 diabetes: the black Women's health study. Am J Epidemiol. 2008;169(4):428–34.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Ford ES, Schulze MB, Kroeger J, Pischon T, Bergmann MM, Boeing H. Television watching and incident diabetes: findings from the European prospective investigation into Cancer and nutrition–Potsdam study. Journal of Diabetes. 2010;2(1):23–7.View ArticlePubMedGoogle Scholar
  19. Assah F, Brage S, Ekelund U, Wareham NJD. The association of intensity and overall level of physical activity energy expenditure with a marker of insulin resistance. Diabetologia. 2008;51(8):1399.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Risérus U, Ärnlov J, Berglund L. Long-term predictors of insulin resistance: role of lifestyle and metabolic factors in middle-aged men. Diabetes Care. 2007;30(11):2928–33.View ArticlePubMedGoogle Scholar
  21. Amati F, Dubé JJ, Coen PM, Stefanovic-Racic M, Toledo FG, Goodpaster BH. Physical inactivity and obesity underlie the insulin resistance of aging. Diabetes Care. 2009;32(8):1547–9.View ArticlePubMedPubMed CentralGoogle Scholar
  22. American Diabetes Association. Standards of Med Care in Diabetes2010. Diabetes Care. 2010;33(Suppl 1):S11–61.View ArticlePubMed CentralGoogle Scholar
  23. Yamada C, Mitsuhashi T, Hiratsuka N, Inabe F, Araida N, Takahashi E. Optimal reference interval for homeostasis model assessment of insulin resistance in a Japanese population. Journal of diabetes investigation. 2011;2(5):373–6.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Jeon Y: Development of the Korean version of global physical activity questionnaire and assessment of reliability and validity. Academic research on task, Final Report, Korea Center for Disease Control and Prevention 2013.Center for Disease Control and Prevention 2013Center for Disease Control and Prevention 2013.Google Scholar
  25. Ministry of Health and Welfare, The Korean Nutrition Society. Dietary reference intakes for Koreans 2015. Sejong; 2015. Available at [Korean language]: http://policy.nl.go.kr/cmmn/FileDown.do?atchFileId=144045&fileSn=23508. Accessed 25 Nov 2018.
  26. Kant AK. Indexes of overall diet quality: a review. J Am Diet Assoc. 1996;96(8):785–91.View ArticlePubMedGoogle Scholar
  27. O’donoghue G, Perchoux C, Mensah K, Lakerveld J, Van Der Ploeg H, Bernaards C, Chastin SF, Simon C, O’gorman D, Nazare J-A. A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach. BMC Public Health. 2016;16(1):163.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Healy GN, Matthews CE, Dunstan DW, Winkler EA, Owen N. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J. 2011;32(5):590–7.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Gibbs BB, Gabriel KP, Reis JP, Jakicic JM, Carnethon MR, Sternfeld BJDC. Cross-sectional and longitudinal associations between objectively measured sedentary time and metabolic disease: the coronary artery risk development in young adults (CARDIA) study. Diabetes Care. 2015;38(10):1835–43.View ArticleGoogle Scholar
  30. McGuire KA, Ross RJPO. Sedentary behavior is not associated with cardiometabolic risk in adults with abdominal obesity. PLoS One. 2011;6(6):e20503.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Ekelund U, Brage S, Griffin SJ, Wareham NJ. Objectively measured moderate and vigourous intensity physical activitiy but not sedentary time predicts insuilin resistance in high risk individuals. Diabetes Care. 2009;32(6):1081–6.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Brown W, Miller Y, Miller R. Sitting time and work patterns as indicators of overweight and obesity in Australian adults. Int J Obes. 2003;27(11):1340.View ArticleGoogle Scholar
  33. Thorp AA, Healy GN, Winkler E, Clark BK, Gardiner PA, Owen N, Dunstan DW. Prolonged sedentary time and physical activity in workplace and non-work contexts: a cross-sectional study of office, customer service and call Centre employees. Int J Behav Nutr Phys Act. 2012;9(1):128.View ArticlePubMedPubMed CentralGoogle Scholar
  34. McCrady SK, Levine JA. Sedentariness at work: how much do we really sit? Obesity. 2009;17(11):2103–5.View ArticlePubMedGoogle Scholar
  35. Chau JY, van der Ploeg HP, Van Uffelen JG, Wong J, Riphagen I, Healy GN, Gilson ND, Dunstan DW, Bauman AE, Owen N. Are workplace interventions to reduce sitting effective? A systematic review. Preventive Medicine. 2010;51(5):352–6.View ArticlePubMedGoogle Scholar
  36. Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, Shaw JE, Bertovic DA, Zimmet PZ, Salmon J. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012;35(5):976–83.View ArticlePubMedPubMed CentralGoogle Scholar

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