Relationship Between Types of Exercise and Quality of Life in a Korean Metabolic Syndrome Population: A Cross-Sectional Study

Abstract

Background:

Metabolic syndrome (MetS) significantly correlates with exercise. MetS also has an independent and inverse correlation to quality of life (QoL). However, few studies have examined the association between exercise and QoL in people with MetS. The aim of this study was to ascertain the relationship between exercise and QoL in a MetS population.

Methods:

This was a cross-sectional study using public data from the Sixth Korean National Health and Nutrition Examination Survey in 2014 (n = 7550). MetS was defined on the basis of the revised National Cholesterol Education Program criteria. Demographic factors, three types of exercise (resistance, flexibility, walking), five subsets of EuroQoL (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), and QoL scores (EQ-VAS), were investigated. Independent associations of each exercise on five subsets of QoL were determined using odds ratios (OR) adjusted for four demographic factors (age group, sex, weight change, and area of residence) using multivariate logistic regression analysis.

Results:

Prevalence of MetS was 26.4% and the ratio of subjects performing resistance, flexibility, or walking exercise was 17.7%, 45.8%, and 71.5% among this population, respectively. EQ-VAS of exercisers was significantly higher than that of non-exercisers in resistance, flexibility, and walking exercise. Although resistance and flexibility exercise did not correlate with any subsets of QoL, mobility and self-care were significantly associated with walking exercise (OR = 0.635, 95% CI = 0.439–0.919 and OR = 0.577, 95% CI = 0.348–0.958, respectively).

Conclusions:

All exercisers showed higher QoL scores than non-exercisers. Among QoL subsets, mobility and self-care were independently associated with walking exercise in the MetS population. Regular walking exercise was important to higher QoL in those with MetS. This is the first clinical report to indicate that QoL could be independently influenced by walking exercise.

Introduction

Metabolic syndrome (MetS) is related to diabetes and other chronic conditions.¹ It is a chronic, progressive, and complex health problem that can trigger physical, emotional, and psychosocial problems. The prevalence of MetS is increasing rapidly globally.² In Korea, the incidence of MetS has increased from 18.9% in 1997³ to 24.8% in 2008,⁴ and it is expected to continue to increase significantly. MetS has a serious impact on medical costs with the presence of MetS costing U.S. $295 more per month compared with not having MetS.⁵ Therefore, public health concerns about MetS have to be carefully studied.⁶

MetS is closely connected with physical activity and exercise. In previous studies, physical activity has been shown to ameliorate the risk of MetS. People who exercise have a lower risk of MetS, and can improve their health status by exercise.^7,8 One study also suggested that resistance exercise showed significant effects on decreasing percent body fat, and increasing lean body mass in a MetS population.⁹ In other studies, reduced flexibility was positively associated with MetS¹⁰ and there were significant improvements in metabolic factors in regular walkers.² From 1999 to 2004 National Health and Nutrition Examination Survey in U.S., MetS participants with muscular strengthening activity (engaging in ≥2 days/week) showed to have lower odds ratio (OR) of dyslipidemia, prehypertension, and impaired fasting glucose than those without the activity.¹¹

MetS is also strongly associated with quality of life (QoL). A web-based study reported that people who have MetS have poorer QoL scores than those without MetS for subscales of health-related QoL (HRQoL): role-physical, vitality, and mental health.¹² One cohort study also reported that MetS correlated with the physical domain of HRQoL.¹³ They suggested that body mass index (BMI), blood pressure, and fasting plasma glucose showed significant correlations with it among MetS components. In women, MetS has also been shown to be associated with poor physical HRQoL in both the normal and the impaired glucose regulation groups.¹⁴ In a longitudinal study, MetS has affected HRQoL. In a clinical trial¹⁵ to investigate the effects of a 12-week yoga intervention on QoL in MetS population, controlled group (non-exercise group) showed decreased QoL in both physical and mental component score during 12 weeks.

Although MetS significantly correlates with both exercise and QoL, few studies have examined the association between exercise and QoL in people with MetS.^16,17 Participants who attended 12 weeks of exercise training (Tai Chi and Qigong) showed significant improvements in four indicators of MetS (BMI, waist circumference, systolic and diastolic blood pressure), HRQoL, and psychological variables (stress and depressive symptoms).¹⁶ Furthermore, 10 weeks of resistance training could improve muscle strength, the capacity to perform ADLs, and QoL for patients with a high number of MetS risk factors.¹⁷ However, these studies had a small number of participants (n = 11 and n = 54, respectively) and they dealt with only simple and limited exercise. Moreover, Tai Chi and Qigong exercises are not well-defined programs.

Therefore, we wanted to examine the relationship between three major representative exercises (resistance, flexibility, and walking) and QoL in a larger population. Herein, the aim of this study was to investigate the relationship between three types of exercise and QoL in a MetS population.

Materials and Methods

Study population

This study was based on the data obtained in 1 year of the Sixth Korean National Health and Nutrition Examination Survey (KNHANES) in 2014. KNHANES is a nationwide cross-sectional survey performed to evaluate the health and nutritional status of the general population of South Korea conducted by the Korea Centers for Disease Control and Prevention. This survey included 7550 community-dwelling people and consisted of a health and household interview, nutrition survey, physical examinations, and laboratory data.

We analyzed target subjects aged ≥20 years who had complete data on the following variables: age, sex, BMI, weight change, area of residence, EuroQoL, and EQ-5D, as well as five components of MetS (waist circumference, blood pressure, serum triglycerides, high-density lipoproteins [HDL]-C, and fasting glucose) (n = 5897). MetS was defined on the basis of the revised National Cholesterol Education Program criteria.¹⁸ A subject was diagnosed as having MetS if he or she had three or more of the following criteria: (1) waist circumference ≥90 cm in men and ≥80 cm in women using the International Obesity Task Force criteria for the Asian–Pacific population to determine waist circumference¹⁹; (2) triglycerides ≥150 mg/dL or antihyperlipidemic drug treatment; (3) HDL cholesterol <40 mg/dL in men and <50 mg/dL in women; (4) systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or antihypertensive drug treatment; and (5) fasting glucose ≥100 mg/dL or drug treatment for elevated glucose (insulin or oral agents). Because antihyperlipidemic drug use was not identified, whether it was used for treatment of elevated triglycerides or reduced HDL cholesterol in KNHANES, we included subjects who are taking the drug in elevated triglycerides to avoid duplication.

EuroQol five dimensions questionnaire (EQ-5D), which was first introduced in 1990 by the EuroQol Group,²⁰ was used to measure QoL. In the description part, health-related QoL was measured in the following five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression.²¹ In the evaluation part, participants checked their overall health status using the visual analog scale (EQ-VAS). EQ-5D was known to have good internal consistency in several studies (Cronbach's alpha = 0.70–0.78).^22
–24 The Korean version of the scale also showed high validity and reliability for cancer patients^25,26 and patients with rheumatic disease.²⁷ Therefore, EQ-5D has been widely used to evaluate QoL in many studies, and provides good sensitivity.^28,29 This study was an analysis of public data from KNHANES; therefore, ethical approval was not required.

Definitions for each variable

Subjects who performed resistance exercise were defined as persons who do exercises such as push-ups, crunches, or chin-ups for 1 day or more in the recent week.³⁰ Subjects who performed flexibility exercise were also defined as those who perform exercises (≥1 day/week) such as stretches or free gymnastics focused on flexibility. Walkers were defined as persons who walked 1 day or more (at least 10 min at a time) in the recent week.³¹

Self-reported body weight changes were classified into three types: same (−3 kg to +3 kg change), loss (>3 kg reduction), and gain (>3 kg increase) during the recent 1 year.). With respect to the residential area, subjects dwelling in “dong” (neighborhood) were defined as urban dwellers, whereas subjects dwelling in “eup” (town) or “myeon” (township) were defined as rural dwellers. BMI was calculated as the individual's body mass divided by the square of one's height (Kg/m²). In each dimension of EQ-5D, “no problem” was evaluated as normal, whereas “some” or “severe problems” were evaluated as problem.

Statistical analysis

Descriptive statistics were used to evaluate the distribution of age, sex, body weight change, area of residence, and EQ-5D. Independent T-tests were used for mean comparisons of age, BMI, and EQ-VAS between exercisers and non-exercisers in each exercise group. Comparison of age groups, sex, weight change, area of residence, and EuroQoL were conducted using the chi-squared test. Independent associations of each exercise on five subsets of QoL were determined using OR adjusted for four demographic factors (age group, sex, weight change, and area of residence) on multivariate logistic regression analysis. The adjusted model was developed through backward elimination with a significance level of 0.2 to enter and 0.05 to stay. We also evaluated possible multiple collinearities between covariates by correlation analysis and collinearity statistical tests (tolerance and variance inflation factor tests) during regression analysis. SPSS Statistics software version 21.0 (IBM Coporation, NY) was used for all analyses. P < 0.05 was considered to be statistically significant.

Results

Subjects' characteristics

Subjects with missing data about exercise (n = 139 in resistance and flexibility; n = 141 in walking) were excluded from the final statistical analyses (Fig. 1). The prevalence of MetS in all adult subjects was 26.4% (1557/5897). The basic characteristics of target participants with MetS are summarized in Table 1. The mean age of the subjects was 59.1 ± 14.0 years and 55.5% were women. Mean BMI was 25.9 ± 3.2 kg/m². Mean waist circumference, triglyceride, HDL, fasting glucose, systolic blood pressure, and diastolic blood pressure were 88.4 ± 8.2 cm, 204.0 ± 134.0 mg/dL, 45.1 ± 10.0 mg/dL, 113.9 ± 31.4 mg/dL, 126.7 ± 15.9 mmHg, and 77.9 ± 10.8 mmHg, respectively. The number of participants who have taken medications for hypertension, diabetes, and dyslipidemia was 669, 30, and 323, respectively.

FIG. 1.

Flow diagram of this study.

Table 1.

General Characteristics of Target Subjects with Metabolic Syndrome

Variables	N (%) or mean ± SD
Age	59.1 ± 14.0
Age group (years), n (%)
20–29	29 (1.9)
30–39	148 (9.5)
40–49	201 (12.9)
50–59	340 (21.8)
60–69	435 (27.9)
70–79	349 (22.4)
80–	55 (3.5)
Sex (men: women)	693: 864
Body mass index (Kg/m²)	25.9 ± 3.2
Body weight change, n (%)
Same	957 (66.2)
Loss	193 (13.4)
Gain	295 (20.4)
Area of residence, n (%)
Urban	1206 (77.5)
Rural	351 (22.5)
EQ-5D
Mobility, n (%)
Normal	1074 (75.5)
Problems	348 (24.5)
Self-care, n (%)
Normal	1319 (92.7)
Problems	104 (7.3)
Usual activities, n (%)
Normal	1198 (84.2)
Problems	225 (15.8)
Pain/discomfort, n (%)
Normal	997 (70.1)
Problems	426 (29.9)
Anxiety/depression, n (%)
Normal	1218 (85.6)
Problems	205 (14.4)
EQ-VAS	0.91 ± 0.16
MetS components
Waist circumference (cm)	88.4 ± 8.2
TG (mg/dL)	204.0 ± 134.0
HDL (mg/dL)	45.1 ± 10.0
SBP (mmHg)	126.7 ± 15.9
DBP (mmHg)	77.9 ± 10.8
Fasting glucose (mg/dL)	113.9 ± 31.4
Medication for hypertension (n)	669
Medication for diabetes (n)	303
Medication for dyslipidemia (n)	323

MetS, metabolic syndrome; TG, triglyceride; HDL, high-density lipoproteins; SBP, systolic blood pressure; and DBP, diastolic blood pressure.

The ratios of subjects who performed resistance, flexibility, or walking exercise in the MetS population were 17.7%, 45.8%, and 71.5%, respectively. All types of exercisers were younger (P < 0.001 in all) and dwelled more in urban areas (resistance; P = 0.001, flexibility and walking; P < 0.001 in both). Men performed more resistance and walking exercise than women (P < 0.001 and P = 0.010, respectively). Although there was a significant weight change (loss or gain) in the group who performed resistance exercise (P = 0.041), BMI was not different between exercisers and non-exercisers in all exercise groups.

As to EuroQoL subsets, mobility, usual activities, pain/discomfort, and anxiety/depression correlated with resistance exercise while mobility, self-care, pain/discomfort, and usual activities were associated with flexibility exercise. Walking exercise showed a significant correlation with all five subsets of EuroQoL. EQ-VAS of exercisers was significantly higher than that of non-exercisers in resistance (0.94 ± 0.12 vs. 0.90 ± 0.17, P < 0.001), flexibility (0.93 ± 0.12 vs. 0.89 ± 0.19, P < 0.001), and walking exercise (0.92 ± 0.13 vs. 0.84 ± 0.22, P < 0.001) (Table 2).

Table 2.

Characteristics of Subjects by Three Types of Exercises

	Resistance (N = 1418)			Flexibility (N = 1418)			Walking (N = 1416)
	Exerciser (n = 276)	Non-exerciser (n = 1142)	P	Exerciser (n = 713)	Non-exerciser (n = 705)	P	Exerciser (n = 1114)	Non-exerciser (n = 302)	P
Age	56.4 ± 14.9	60.1 ± 13.3	<0.001 ^a	57.9 ± 13.3	60.9 ± 13.9	<0.001 ^a	58.6 ± 13.5	62.5 ± 13.7	<0.001 ^a
Age group (years), n (%)
20–29	12 (48.0)	13 (52,0)	<0.001 ^a	13 (52.0)	12 (48.0)	<0.001 ^a	23 (92.0)	2 (8.0)	<0.001 ^a
30–39	34 (27.6)	89 (72.4)		66 (53.7)	57 (46.3)		100 (81.3)	23 (18.7)
40–49	36 (20.1)	143 (79.9)		109 (60.9)	70 (39.1)		155 (86.6)	24 (13.4)
50–59	64 (20.6)	247 (79.4)		164 (52.7)	147 (47.3)		254 (81.7)	57 (18.3)
60–69	77 (18.7)	335 (81.3)		214 (51.9)	198 (48.1)		319 (77.4)	93 (22.6)
70–79	48 (14.8)	276 (85.2)		136 (42.0)	188 (58.0)		238 (73.9)	84 (26.1)
80–	5 (11.4)	39 (88.6)		11 (25.0)	33 (75.0)		25 (56.8)	19 (43.2)
Sex, n (%)
Men	183 (29.6)	435 (70.4)	<0.001 ^a	329 (51.8)	298 (48.2)	0.321	506 (81.9)	112 (18.1)	0.010 ^b
Women	93 (11.6)	707 (88.4)		393 (49.1)	407 (50.9)		608 (76.2)	190 (23.8)
Body mass index (Kg/m²)	26.2 ± 3.4	25.9 ± 3.2	0.172	25.9 ± 3.1	26.0 ± 3.3	0.692	25.9 ± 3.2	25.9 ± 3.2	0.975
Body weight change, n (%)
Same	162 (17.7)	754 (82.3)	0.041 ^b	466 (50.9)	450 (49.1)	0.900	712 (78.0)	201 (22.0)	0.092
Loss	45 (23.6)	146 (76.4)		94 (49.2)	97 (50.8)		144 (75.4)	47 (24.6)
Gain	67 (23.2)	222 (76.8)		148 (51.2)	141 (48.8)		240 (83.0)	49 (17.0)
Area of residence, n (%)
Urban	237 (21.4)	872 (78.6)	0.001 ^a	598 (53.9)	511 (46.1)	<0.001 ^a	908 (81.9)	201 (18.1)	<0.001 ^a
Rural	39 (12.6)	270 (87.4)		115 (37.2)	194 (62.8)		206 (67.1)	101 (32.9)
EQ-5D
Mobility, n (%)
Normal	227 (21.2)	844 (78.8)	0.004 ^a	562 (52.5)	509 (47.5)	0.004 ^a	884 (82.6	186 (17.4)	<0.001 ^a
Problems	49 (14.2)	297 (85.8)		151 (43.6)	195 (56.4)		229 (66.4)	116 (33.6)
Self-care, n (%)
Normal	260 (19.8)	1054 (80.2)	0.275	678 (51.6)	636 (48.4)	<0.001 ^a	1055 (80.4)	258 (19.6)	0.001 ^a
Problems	16 (15.4)	88 (84.6)		35 (33.7)	69 (66.3)		59 (57.3)	44 (42.7)
Usual activities, n (%)
Normal	244 (20.4)	950 (79.6)	0.033 ^b	628 (52.6)	566 (47.4)	<0.001 ^a	973 (81.6)	220 (18.4)	<0.001 ^a
Problems	32 (14.3)	192 (85.7)		85 (37.9)	139 (62.1)		141 (63.2)	82 (36.8)
Pain/discomfort, n (%)
Normal	219 (22.0)	776 (78.0)	<0.001 ^a	518 (52.1)	477 (47.9)	0.040 ^b	814 (81.9)	180 (18.1)	<0.001 ^a
Problems	57 (13.5)	366 (86.5)		195 (46.1)	228 (53.9)		300 (71.1)	122 (28.9)
Anxiety/depression, n (%)
Normal	252 (20.7)	963 (79.3)	0.003 ^a	622 (51.2)	593 (48.8)	0.093	970 (79.9)	244 (20.1)	0.006 ^a
Problems	24 (11.8)	179 (88.2)		91 (44.8)	112 (55.2)		144 (71.3)	58 (28.7)
EQ-VAS	0.94 ± 0.12	0.90 ± 0.17	<0.001 ^a	0.93 ± 0.12	0.89 ± 0.19	<0.001 ^a	0.92 ± 0.13	0.84 ± 0.22	<0.001 ^a

Independent T-tests were used for mean comparisons of age, body mass index, and EQ-VAS between exercisers and non-exercisers for each type of exerciser. Comparisons of age groups, sex, weight change, area of residence, and EQ-5D were conducted using the chi-squared test.

Values with P < 0.05 are in bold.

P < 0.01.

P < 0.05.

Independent associations of each variable on exercise

On multivariate logistic regression, no significant collinearity was identified for any of the covariates in statistical tests of collinearity. Adjusted regression analyses showed that urban dwellers performed more of all types of exercise. Younger subjects (less than 30 years old) performed resistance exercise more frequently than 40s, 60s, and 70s (P = 0.016, P = 0.040, and P = 0.031, respectively). Women were less frequent exercisers than men only in resistance exercise (OR = 0.333, 95% CI = 0.251–0.442). Subjects with current body weight gain performed more resistance exercise than those without weight change (OR = 1.470, 95% CI = 1.051–2.055). Urban dwelling was an independent variable of all types of exercise. There were no significant associations between all subsets of EuroQoL and resistance or flexibility exercise. However, subjects with abnormal mobility and self-care did less walking exercise than those without abnormal mobility and self-care (OR = 0.635, 95% CI = 0.4391–0.919 and OR = 0.577, 95% CI = 0.348–0.958, respectively) (Table 3).

Table 3.

Adjusted Odds Ratios of Three Types of Exercises

	Resistance		Flexibility		Walking
	Adjusted OR (95% CI)	P	Adjusted OR (95% CI)	P	Adjusted OR (95% CI)	P
Age group (years)
20–29 (n = 29)	1.000		1.000		1.000
30–39 (n = 148)	0.412 (0.164–1.038)	0.060	1.080 (0.454–2.570)	0.862	0.380 (0.082–1.754)	0.215
40–49 (n = 201)	0.324 (0.130–0.810)	0.016 ^b	1.497 (0.642–3.491)	0.350	0.635 (0.138–2.925)	0.560
50–59 (n = 340)	0.418 (0.172–1.015)	0.054	1.089 (0.479–2.476)	0.840	0.491 (0.110–2.193)	0.352
60–69 (n = 435)	0.396 (0.164–0.958)	0.040 ^b	1.155 (0.511–2.612)	0.729	0.427 (0.096–1.893)	0.263
70–79 (n = 349)	0.365 (0.147–0.910)	0.031 ^b	0.829 (0.363–1.891)	0.655	0.429 (0.096–1.925)	0.269
80–(n = 55)	0.309 (0.084–1.139)	0.078	0.396 (0.135–1.167)	0.093	0.258 (0.051–1.305)	0.101
Sex
Men (n = 693)	1.000		1.000		1.000
Women (n = 864)	0.333 (0.251–0.442)	<0.001 ^a	1.022 (0.816–1.282)	0.847	0.815 (0.618–1.076)	0.148
Body weight change
Same (n = 957)	1.000		1.000		1.000
Loss (n = 193)	1.431 (0.969–2.112)	0.072	0.971 (0.704–1.340)	0.858	0.894 (0.612–1.305)	0.560
Gain (n = 295)	1.470 (1.051–2.055)	0.024 ^b	0.917 (0.692–1.216)	0.549	1.347 (0.944–1.923)	0.101
Area of residence
Urban (n = 1206)	1.000		1.000		1.000
Rural (n = 351)	0.534 (0.366–0.780)	0.001 ^a	0.526 (0.403–0.686)	<0.001 ^a	0.485 (0.362–0.651)	<0.001 ^a
EQ-5D
Mobility
Normal (n = 1074)	1.000		1.000		1.000
Problems (n = 348)	1.118 (0.691–1.809)	0.650	1.190 (0.854–1.659)	0.303	0.635 (0.439–0.919)	0.016 ^b
Self-care
Normal (n = 1319)	1.000		1.000		1.000
Problems (n = 104)	1.119 (0.562–2.229)	0.748	0.722 (0.438–1.1911)	0.202	0.577 (0.348–0.958)	0.033 ^b
Usual activities
Normal (n = 1198)	1.000		1.000		1.000
Problems (n = 225)	1.253 (0.759–2.068)	0.378	0.750 (0.526–1.069)	0.112	0.673 (0.434–1.044)	0.077
Pain/Discomfort
Normal (n = 997)	1.000		1.000		1.000
Problems (n = 426)	0.736 (0.520–1.042)	0.084	1.028 (0.764–1.383)	0.856	0.875 (0.615–1.244)	0.456
Anxiety/Depression
Normal (n = 1218)	1.000		1.000		1.000
Problems (n = 205)	0.651 (0.397–1.066)	0.088	0.943 (0.668–1.332)	0.739	0.922 (0.618–1.377)	0.692

Adjusted OR by multivariate logistic regression analysis (adjusted for four demographic factors: age group, sex, weight change, and area of residence).

Values with P < 0.05 are in bold.

P < 0.01.

P < 0.05.

OR, odds ratio.

Discussion

The most important findings of this study were that the prevalence of MetS was 26.4% and subjects with MetS were more likely to perform walking exercise, followed by flexibility, and then resistance exercise. All exercisers showed higher QoL scores (EQ-5D) than non-exercisers. Younger age, male sex, body weight gain, and urban dwelling were independent variables of resistance exercise. Although resistance and flexibility exercise did not associate with any subsets of QoL, usual mobility and self-care were significantly associated with walking exercise.

In our study, MetS subjects who were young or dwelled in urban areas tended to do more exercise than those who were relatively old or lived in rural areas. Because young urban dwellers have more demands and opportunities to exercise, this may be why they show significant correlations with exercise. Bustos et al. also reported that subjects in rural areas or less developed parts of the country had a higher risk of MetS after comparing two socioeconomic Latin American settings.³² Thus, rural dwellers with MetS must be an important target group for therapeutic exercise education to prevent and manage MetS. In addition, women performed significantly less resistance exercise than men (OR = 0.333) in our study. However, Xiao et al. reported that the metabolic effect of resistance exercise was verified only in women. Only female patients with MetS were associated with a decreased risk of several MetS components of between 20% and 45% after self-reported physical activity.² This sex difference might result in the metabolic effects of exercise to be higher in women, although men tend to do more exercise than women. Therefore, female MetS patients who do little exercise should be recommended for active exercise.

In our study, all types of exercisers showed higher QoL scores (EQ-5D) than non-exercisers. However, there was no correlation of any subset of QoL with resistance or flexibility exercise, whereas mobility and self-care were specifically associated with walking exercise. Liu et al. reported that Tai Chi and Qigong exercise training for 12 weeks improved four indicators of MetS and the SF-36 health summary with subscales for general health, mental health, and vitality.¹⁶ However, in this study, the number of participants was too small (n = 11) and Tai Chi and Qigong exercises are not well-defined programs. One study also suggested that individuals with a high number (≥2) of metabolic risk factors improved QoL after 10 weeks of resistance training, whereas those with a lower number (≤1) of metabolic risk factors did not.¹⁷ Because MetS is defined by three or more metabolic risk factors, resistance training might have a beneficial effect on QoL in the MetS population as inferred by the study. However, in our study, there was no association between any subset of QoL and resistance exercise in a MetS population. Although QoL itself did not correlate with the behavioral pattern of resistance exercise in our cross-sectional study, further studies that investigate the effectiveness of resistance exercise intervention on QoL in MetS population are needed.

Walking was the only exercise that was independently correlated with QoL in our study. Walking 4–5 km everyday has been known to improve mood, QoL, and self-confidence, as well as metabolic risk.³³ Yasunaga et al. also reported that subjects who did aerobic exercises (5000 steps/day or 7.5 min/day at >3 METs) had better psychosocial health, including a greater HRQoL.³⁴ The Italian Diabetes and Exercise Study suggested that an intensive exercise intervention (150 min/week in two divided sessions of aerobic and resistance exercise supervised by a trainer) showed significant benefits to QoL and the QoL improved systematically in relation to the attained physical activity volume.³⁵ Therefore, walking is a simple and accessible aerobic form of activity that should be encouraged by healthcare professionals in those with MetS.

There are several limitations of this study. First, it is difficult to establish a causal relationship between QoL and exercise at present because of the genuine limitations of cross-sectional studies. Thus, it is impossible to discriminate whether subjects with a high QoL do more exercise or if subjects who do more exercise obtain a high QoL. Second, there can be reservations about how representative the three types of exercise were. Aerobic exercise such as bicycling, running, or ball games could not be investigated in the current study. Because we have used open-access big data from government, we only had data for these three types of exercise. Third, exercise duration and frequency were not quantified. Because each type of exercise was defined as doing the exercise on 1 day or more (at least 10 min at a time) in the recent week, this might be the minimum of the standard for “doing exercise” and dose–response relationships between exercise and QoL cannot be revealed in this study.

Conclusions

All types of exercisers showed higher QoL scores than non-exercisers. Among QoL subsets, mobility and self-care were independently associated with walking exercise in a MetS population. Resistance and flexibility exercise did not correlate with any subset of QoL. Regular walking exercise was important to higher QoL in those with MetS. Although it is a cross-sectional study, this is the first clinical report to indicate that QoL could be independently influenced by walking exercise. Further longitudinal or controlled trials are needed to reveal the causal relationship of this phenomenon.

Footnotes

Acknowledgments

The KNHANES has been financially supported by the Health Promotion Fund with administrative support by the Ministry of Health and Welfare.

Author Disclosure Statement

No competing financial interests exist.

References

Ford

, Schulze

, Pischon

, et al. Metabolic syndrome and risk of incident diabetes: Findings from the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. Cardiovasc Diabetol, 2008; 7:35.

Xiao

, Wu

, Xu

, et al. Association of physical activity with risk of metabolic syndrome: Findings from a cross-sectional study conducted in rural area, Nantong, China. J Sports Sci, 2016; 34:1839–1848.

Hwang

, Jee

, Oh

, et al. Metabolic syndrome as a predictor of cardiovascular diseases and type 2 diabetes in Koreans. Int J Cardiol, 2009; 134:313–321.

Lim

, Ko

, Ban

. Prevalence and risk factors of metabolic syndrome in the Korean population—Korean National Health Insurance Corporation Survey 2008. J Adv Nurs, 2013; 69:1549–1561.

Fitch

, Pyenson

, Iwasaki

. Metabolic syndrome and employer sponsored medical benefits: An actuarial analysis. Value Health, 2007; 10:S21–S8.

Vancampfort

, Stubbs

. Physical activity and metabolic disease among people with affective disorders: Prevention, management and implementation. J Affect Disord [Epub ahead of print]; DOI: 10.1016/j.jad.2016.07.042.

Kallwitz

, Loy

, Mettu

, et al. Physical activity and metabolic syndrome in liver transplant recipients. Liver Transpl, 2013; 19:1125–1131.

Kuwahara

, Honda

, Nakagawa

, et al. Leisure-time exercise, physical activity during work and commuting, and risk of metabolic syndrome. Endocrine, 2016; 53:710–721.

South

, Layne

, Stuart

, et al. Effects of short-term free-weight and semiblock periodization resistance training on metabolic syndrome. J Strength Cond Res, 2016; 30:2682–2696.

10.

Chang

, Chen

, Chien

, et al. Effectiveness of community-based exercise intervention programme in obese adults with metabolic syndrome. J Clin Nurs, 2016; 25:2579–2589.

11.

Churilla

, Magyari

, Ford

, et al. Muscular strengthening activity patterns and metabolic health risk among US adults. J Diabetes, 2012; 4:77–84.

12.

Jahangiry

, Shojaeezadeh

, Montazeri

, et al. Health-related quality of life among people participating in a metabolic syndrome E-screening program: A web-based study. Int J Prev Med, 2016; 7:27.

13.

Corica

, Corsonello

, Apolone

, et al. Metabolic syndrome, psychological status and quality of life in obesity: The QUOVADIS Study. Int J Obes (Lond), 2008; 32:185–191.

14.

Hatami

, Deihim

, Amiri

, et al. Association between metabolic syndrome and health-related quality of life among individuals with normal and impaired glucose regulation: Findings from Tehran Lipid and Glucose Study. Arch Iran Med, 2016; 19:577–583.

15.

Lau

, Yu

, Woo

. Effects of a 12-week hatha yoga intervention on metabolic risk and quality of life in Hong Kong Chinese adults with and without metabolic syndrome. PLoS One, 2015; 10:e0130731.

16.

Liu

, Miller

, Burton

, et al. A preliminary study of the effects of Tai Chi and Qigong medical exercise on indicators of metabolic syndrome, glycaemic control, health-related quality of life, and psychological health in adults with elevated blood glucose. Br J Sports Med, 2010; 44:704–709.

17.

Levinger

, Goodman

, Hare

, et al. The effect of resistance training on functional capacity and quality of life in individuals with high and low numbers of metabolic risk factors. Diabetes Care, 2007; 30:2205–2210.

18.

Grundy

, Cleeman

, Daniels

, et al. Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation, 2005; 112:2735–2752.

19.

Coates

, Uhler

, Hall

, et al. Risk of breast cancer in young women in relation to body size and weight gain in adolescence and early adulthood. Br J Cancer, 1999; 81:167–174.

20.

EuroQol

. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 1990; 16:199–208.

21.

Hatswell

, Vegter

. Measuring quality of life in opioid-induced constipation: Mapping EQ-5D-3L and PAC-QOL. Health Econ Rev, 2016; 6:14.

22.

Savoia

, Fantini

, Pandolfi

, et al. Assessing the construct validity of the Italian version of the EQ-5D: Preliminary results from a cross-sectional study in North Italy. Health Qual Life Outcomes, 2006; 4:47.

23.

King

Jr. , Tsevat

, Roberts

. Measuring preference-based quality of life using the EuroQol EQ-5D in patients with cerebral aneurysms. Neurosurgery, 2009; 65:565–572.

24.

Cheung

, Wong

, Samartzis

, et al. Psychometric validation of the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) in Chinese patients with adolescent idiopathic scoliosis. Scoliosis Spinal Disord, 2016; 11:19.

25.

Kim

, Hwang

, Kim

, et al. Validity and reliability of the EQ-5D for cancer patients in Korea. Support Care Cancer, 2012; 20:3155–3160.

26.

Kim

, Jo

, Lee

, et al. Validity and reliability of EQ-5D-3 L for breast cancer patients in Korea. Health Qual Life Outcomes, 2015; 13:203.

27.

Kim

, Cho

, Uhm

, et al. Cross-cultural adaptation and validation of the Korean version of the EQ-5D in patients with rheumatic diseases. Qual Life Res, 2005; 14:1401–1406.

28.

Eaglehouse

, Schafer

, Arena

, et al. Impact of a community-based lifestyle intervention program on health-related quality of life. Qual Life Res, 2016; 25:1903–1912.

29.

Huppertz-Hauss

, Aas

, Lie Hoivik

, et al. Comparison of the multiattribute utility instruments EQ-5D and SF-6D in a Europe-Wide population-based cohort of patients with inflammatory bowel disease 10 years after diagnosis. Gastroenterol Res Pract, 2016; 2016:5023973.

30.

Schoenfeld

, Ogborn

, Krieger

. Effects of resistance training frequency on measures of muscle hypertrophy: A systematic review and meta-analysis. Sports Med, 2016; 46:1689–1697.

31.

Heesch

, van Gellecum

, Burton

, et al. Physical activity and quality of life in older women with a history of depressive symptoms. Prev Med, 2016; 91:299–305.

32.

Bustos

, da Silva

, Amigo

, et al. Metabolic syndrome in young adults from two socioeconomic Latin American settings. Nutr Metab Cardiovasc Dis, 2007; 17:581–589.

33.

Montesi

, Moscatiello

, Malavolti

, et al. Physical activity for the prevention and treatment of metabolic disorders. Intern Emerg Med, 2013; 8:655–666.

34.

Yasunaga

, Togo

, Watanabe

, et al. Yearlong physical activity and health-related quality of life in older Japanese adults: The Nakanojo Study. J Aging Phys Act, 2006; 14:288–301.

35.

Balducci

, Zanuso

, Nicolucci

, et al. Effect of an intensive exercise intervention strategy on modifiable cardiovascular risk factors in subjects with type 2 diabetes mellitus: a randomized controlled trial: The Italian Diabetes and Exercise Study (IDES). Arch Intern Med, 2010; 170:1794–1803.