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Recently, the “weekend warrior” pattern, characterized by meeting recommended physical activity (PA) guidelines within one or two weekly sessions, has received increasing attention. However, its association with mental health outcomes remains unclear. This study aimed to examine the relationship between PA patterns and depressive symptoms.
Methods
This study analyzed data from 26,424 adults from the Korea National Health and Nutrition Examination Survey conducted in 2014, 2016, 2018, 2020, and 2022. PA was assessed by a self-reported questionnaire and classified according to World Health Organization guidelines for moderate-to-vigorous PA. Participants were categorized as inactive (<150 min/ wk), weekend warriors (≥150 min/wk over 1–2 days), or regularly active (≥150 min/wk over ≥3 days). Depressive symptoms were defined as a Patient Health Questionnaire-9 score ≥5. Associations were examined using multivariable logistic regression, stratified by sex and age.
Results
Prevalence of depressive symptoms among participants was 19.3%. The weekend warrior PA pattern was not significantly associated with depressive symptoms. Contrarily, the regularly active PA pattern was inversely associated with depressive symptoms (odds ratio [OR], 0.72; 95% confidence interval [CI], 0.66–0.79); this association remained significant after multivariable adjustment (OR, 0.81; 95% CI, 0.74–0.89). In stratified analyses by sex and age group, the regularly active group consistently showed an inverse association with depressive symptoms, whereas no significant association was observed for the weekend warrior PA pattern.
Conclusion
Regularity of physical activity may be more important for mental health than total activity volume alone.
Depression is a mental disorder characterized by persistent sadness, loss of interest or pleasure, and functional impairment. According to the World Health Organization (WHO), approximately 322 million people were living with depression in 2015, and depressive disorders accounted for more than 50 million years lived with disability worldwide [1]. In South Korea, the 2021 Korean Mental Health Survey reported that the lifetime prevalence of major depressive disorder was 7.7% (5.7% in male and 9.8% in female) [2]. In addition, Organisation for Economic Co-operation and Development (OECD) Health Statistics 2025 reported that the age-standardized suicide mortality rate in Korea was 24.3 per 100,000 population, substantially higher than the OECD average of 10.5 per 100,000 [3].
A growing body of evidence indicates that physical activity has a protective effect against depression. Regular physical activity is associated with reduced depressive symptoms and lower incidence of major depressive disorder, potentially mediated through neurobiological changes, reduced inflammation, improved sleep, and enhanced psychosocial well-being [4-6]. The endorphin hypothesis proposes that regular physical activity increases the production of endogenous opioid peptides within the pituitary gland, leading to reduced pain perception and improvements in mood, including decreases in anxiety and depressive symptoms [7]. However, most prior studies have emphasized total activity volume, with relatively limited attention paid to the pattern in which physical activity is accumulated.
Recently, interest has emerged in the “weekend warrior” pattern of physical activity, defined as achieving recommended weekly physical activity levels through one or two concentrated exercise sessions per week [8]. Several studies have shown that weekend warriors may experience similar health benefits to regularly active individuals, including lower all-cause mortality and reduced cardiovascular risk [9,10]. Nonetheless, research exploring the mental health implications of this pattern, particularly its association with depressive symptoms, remains scarce.
Furthermore, both depression and physical activity patterns vary by sex and age. Female participants have consistently shown a higher prevalence of depression than male participants [11], and sex-specific biological and psychosocial pathways contribute to these differences [12]. Older adults also exhibit distinct physical activity behaviors and face limitations such as frailty, chronic disease, and mobility restrictions, which may modify the relationship between physical activity and depression [13]. These variations underscore the importance of stratified analyses.
Therefore, the present study aims to examine the association between physical activity patterns and depression. Analyses are further stratified by sex and age to investigate potential subgroup differences. This study seeks to provide insights into whether flexible activity patterns, such as the weekend warrior pattern, can meaningfully contribute to depression prevention across different age and sex groups.
Methods
Study design and participants
This cross-sectional study used data from the Korea National Health and Nutrition Examination Survey (KNHANES) collected in 2014, 2016, 2018, 2020, and 2022. KNHANES employs a stratified, multistage probability sampling design to provide nationally representative estimates of health and nutritional status in the Korean population. These survey cycles were selected because the Patient Health Questionnaire-9 (PHQ-9) was administered only in even-numbered survey years. Among 37,316 participants in the selected cycles, individuals with missing data on depressive symptoms or key covariates were excluded, resulting in a final analytic sample of 26,424 adults (Figure 1).
Assessment of physical activity patterns and depressive symptoms
Physical activity was categorized according to WHO guidelines for moderate-to-vigorous physical activity (MVPA) [14]. Participants reporting <150 minutes of MVPA per week were classified as inactive; those achieving ≥150 minutes per week spread over at least 3 days were considered regularly active; and those accumulating ≥150 minutes per week concentrated over 1 to 2 days were classified as weekend warriors.
Depressive symptoms were assessed using PHQ-9. Clinically significant depressive symptoms were defined as a PHQ-9 score ≥5, based on a recent validation study conducted in the general Korean population demonstrating that this cutoff provides an optimal balance of sensitivity and specificity for screening major depression [15].
Assessment of other variables
Covariates were selected based on prior evidence and their potential to confound the association between physical activity and depressive symptoms [6,16-23]. Age and sex were obtained through the health interview, and body mass index was calculated as weight in kilograms divided by height in meters squared (kg/m2) using standardized anthropometric measurements. Household income was categorized into low, middle, and high levels according to equivalized household income quartiles provided by KNHANES. Education level was classified as less than high school, high school graduate, or beyond high school. Marital status was categorized as married or unmarried, and living status was defined as living alone versus living with others. Smoking behavior was classified as current smoker, former smoker, or never smoker. Alcohol consumption was grouped into high-risk drinking, non–high-risk drinking, or non-drinking, based on frequency and quantity of alcohol intake. Clinical covariates included self-reported physician diagnoses of cerebrocardiovascular disease or cancer, and current use of medications for hypertension, dyslipidemia, or diabetes mellitus.
Statistical analysis
Descriptive analyses were first conducted to estimate prevalence, means, and standard errors of participant characteristics according to depressive symptom status. Differences in continuous variables were tested using the t-test. Categorical variables were compared using the chi-square test. To assess the association between physical activity patterns and depressive symptoms, logistic regression models were fitted, with Model 1 providing unadjusted estimates and Model 2 adjusting for all covariates described above. Analyses were stratified by sex (male vs. female) and age (<65 years vs. ≥65 years) to evaluate potential effect modification. Statistical significance was assessed using two-sided P-values, with P-value <0.05 considered statistically significant. All statistical analyses were performed using Python ver. 3.13.5 (Python Software Foundation).
KNHANES data are fully anonymized and publicly accessible; therefore, this study was exempt from Institutional Review Board approval in accordance with national ethical guidelines.
Results
General characteristics of the study population
Table 1 presents the general characteristics of the study population according to depressive symptom status. Among the 26,424 participants included in the analysis, 5,094 (19.3%) were classified as having depressive symptoms. Participants with depressive symptoms were younger than those without (49.7±17.8 years vs. 51.7±16.6 years, P<0.001). The proportion of female participants was higher in the depressive symptom group than in the nondepressive symptom group (68.2% vs. 53.9%, P<0.001).
Regarding physical activity, inactive individuals were more common among participants with depressive symptoms than among those without depressive symptoms (85.8% vs. 81.2%). The proportions of regularly active individuals and weekend warriors were lower in the depressive symptom group (P<0.001).
Socioeconomic characteristics differed significantly by depressive symptom status. Participants with depressive symptoms were more likely to have a low household income (51.7% vs. 40.6%) and lower educational attainment (less than high school, 34.0% vs. 28.1%) compared with those without depressive symptoms (all P<0.001). Unmarried status and living alone were also more prevalent in the depressive symptom group (P<0.001 for both).
Health-related behaviors varied across groups. Current smoking and high-risk alcohol consumption were more prevalent among participants with depressive symptoms, while former smoking and non–high-risk drinking were more common in the non-depressive symptom group (all P<0.001).
In terms of clinical characteristics, participants with depressive symptoms had higher prevalences of physician-diagnosed cerebrocardiovascular disease (5.6% vs. 3.7%, P<0.001) and cancer (4.8% vs. 4.0%, P=0.014). Treatment for diabetes mellitus and dyslipidemia was also more common among those with depressive symptoms (both P<0.001).
Association between physical activity patterns and depressive symptoms
Table 2 and Figure 2 summarize the associations between physical activity patterns and depressive symptoms in the overall population and across subgroups defined by sex and age. In the overall population, compared with inactive individuals, those who were regularly active had significantly lower odds of depressive symptoms in both the unadjusted model (odds ratio [OR], 0.72; 95% confidence interval [CI], 0.66–0.79) and the multivariable-adjusted model (OR, 0.81; 95% CI, 0.74–0.89). In contrast, the weekend warrior pattern was associated with lower odds of depressive symptoms in the unadjusted analysis; however, this association was attenuated and was no longer statistically significant after multivariable adjustment (OR, 0.83; 95% CI, 0.64–1.08).
In sex-stratified analyses, regular physical activity was inversely associated with depressive symptoms in both male (OR, 0.82; 95% CI, 0.71–0.95) and female (OR, 0.81; 95% CI, 0.71–0.91). However, the weekend warrior pattern was not significantly associated with depressive symptoms in either male or female.
Age-stratified analyses showed a similar pattern. Among younger participants, regular physical activity was associated with significantly lower odds of depressive symptoms (OR, 0.82; 95% CI, 0.74–0.91), whereas the weekend warrior pattern was not significantly associated with depressive symptoms. Among older participants, neither regular physical activity nor the weekend warrior pattern was significantly associated with depressive symptoms.
As shown in Figure 2, the inverse association between regular physical activity and depressive symptoms was consistent across most subgroups, whereas estimates for the weekend warrior pattern were more heterogeneous and generally not statistically significant.
Discussion
In this study, regularly active individuals demonstrated lower odds of depressive symptoms, whereas the weekend warrior pattern showed no statistically significant associations after adjustment. These findings were consistent across sex-stratified analyses. In both male and female, regular physical activity was associated with a lower risk of depressive symptoms, but the weekend warrior pattern was not associated with a reduced risk of depression. Among adults younger than 65 years, regular physical activity was associated with a lower risk of depressive symptoms, whereas neither activity pattern was significant among older adults, highlighting potential age-related differences in physical capacity, lifestyle, and vulnerability to depressive symptoms.
Previous large-scale studies using PHQ-9 in the United States have reported that both weekend warriors and regularly active individuals experience significantly lower risks of depression [24], and a recent 2025 UK Biobank cohort study similarly reported reduced cumulative incidence of depression and anxiety in both activity patterns compared with inactivity [25]. Our study, however, did not observe a clear protective effect of the weekend warrior pattern, and this finding remained consistent after stratification by sex. A major explanation may lie in cultural and behavioral differences in physical activity patterns. Prior studies reporting strong mental-health benefits of the weekend warrior pattern were conducted predominantly in Western populations, where vigorous and structured leisure-time exercise is more common [26]. Physical activity in Korea tends to consist mainly of aerobic or walking-based activities, with relatively low participation in high-intensity or organized leisure-time exercise [27]. Such differences in activity type and intensity may attenuate the psychological benefits observed for the weekend warrior pattern in this study. Indeed, a previous study using Korean population data reported no significant association between the total volume of physical activity and depressive symptoms. Instead, a higher amount of work-related physical activity was associated with increased depressive symptoms [28]. These findings suggest that in Korea, the meaning and context of physical activity may differ from those in Western settings, and that the regularity and routinization of exercise, rather than total activity volume alone, may be more important for reducing depressive symptoms among Korean adults.
In the subgroup of adults under 65 years, only regular physical activity was associated with a lower risk of depression, whereas among those aged 65 years or older, neither regular activity nor the weekend warrior pattern showed significant associations. In older adults, the burden of chronic diseases, functional limitations, social isolation, disability, and bereavement often plays a major role in shaping depressive symptoms. These factors may outweigh or obscure any potential mental-health benefit of physical activity alone. For example, a recent study showed that, in older adults, the number of chronic diseases strongly correlates with depressive symptoms, and difficulties in activities of daily living and financial burden related to disease mediate much of this association [29]. Moreover, self-reported physical activity in older age may be less accurate (due to recall bias, misclassification, or over-/under-estimation), which could attenuate the observed associations in this group. Although some Korean studies have reported a protective association between physical activity and depressive symptoms, these studies primarily compared physically inactive individuals with those who were physically active, producing a clearer contrast in exposure [30,31]. Contrarily, our study focused on differentiating specific activity accumulation patterns (regular physical activity vs. weekend warrior vs. insufficient activity), and the difference in total activity volume between these groups may be relatively small in older adults. Moreover, older adults often engage in lower-intensity or sporadic activity that may meet the guideline threshold but may not reach the level associated with measurable mental-health benefits [32,33], possibly explaining why even regular physical activity did not show a statistically significant association. This suggests that in older populations, whether individuals engage in physical activity may be more important for mental health than the specific pattern by which activity is accumulated, with activity regularity or concentration playing a relatively limited role.
Strengths and limitations
This study has several strengths. First, it utilized a nationally representative sample, enhancing the generalizability of the findings to the broader population. In addition, unlike many previous studies, we directly compared regularly active, weekend warrior, and inactive groups within the same analytical framework, allowing for a clearer evaluation of differences across physical activity patterns.
However, several limitations should also be acknowledged. The cross-sectional design prevents causal inference. The use of self-reported physical activity may have introduced recall bias or misclassification. Furthermore, physical activity was not objectively measured using devices such as accelerometers, possibly limiting the accuracy of activity intensity and duration assessment. Importantly, the questionnaire-based measure used in this study did not capture the specific domains, contexts, or detailed intensity patterns of physical activity, which are essential for distinguishing activity accumulation patterns such as regular activity and weekend warrior behavior. This limited granularity may have reduced our ability to detect meaningful differences between activity patterns and may partly explain the null finding for the weekend warrior group. Moreover, despite adjustment for multiple covariates, the possibility of residual confounding cannot be fully excluded. Finally, the nonsignificant associations observed in our study may reflect limited statistical power rather than a true absence of effect, as the sample size and exposure distribution may not have been sufficient to detect modest associations. Thus, the null findings should be interpreted with caution. Moreover, because the analyses were based on nationally representative data from Korean adults, the findings may not be directly generalizable to populations with different cultural, behavioral, or demographic characteristics. The results should, therefore, not be interpreted as evidence of patterns unique to Koreans, and replication in other populations is warranted.
Conclusion
Regular physical activity was consistently associated with lower odds of depressive symptoms, whereas the weekend warrior pattern did not demonstrate a significant benefit. Among adults aged 65 years or older, neither activity pattern was associated with lower odds of depressive symptoms, suggesting that factors other than physical activity may play a more prominent role in shaping depressive symptoms in older populations. From a clinical perspective in Korea, these findings underscore the importance of emphasizing the regularity and consistency of physical activity, rather than total volume alone, when recommending exercise as a strategy to improve depressive symptoms.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
The authors used ChatGPT (GPT-5.2, OpenAI) solely for English language editing and stylistic refinement. All content was reviewed and approved by the authors, who take full responsibility for the manuscript.
Funding
None.
Data availability
The data used in this study are publicly available from the KNHANES, conducted by the Korea Disease Control and Prevention Agency (KDCA). The datasets can be accessed through the KNHANES website (https://knhanes.kdca.go.kr/) upon submission of a data use request and approval in accordance with KDCA data access policies.
Author contribution
Conceptualization: SK, EK. Data curation: SK. Formal analysis: SK. Methodology: SK, EK. Investigation: SK, EK. Validation: SK, EK. Visualization: SK. Supervision: SIC. Writing–original draft: SK, EK. Writing–review & editing: all authors. Final approval of the manuscript: all authors.
Figure. 1.
Flow diagram of study participant selection. Flow diagram illustrating the selection of study participants. Participants with missing values for variables of interest were excluded. The final analytic sample was derived after applying all inclusion and exclusion criteria. PHQ-9, Patient Health Questionnaire-9.
Figure. 2.
Forest plot of the association between physical activity patterns and depressive symptoms. Forest plot showing multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for depressive symptoms according to physical activity patterns, with the inactive group as the reference. Estimates are derived from Model 2. They are presented for the overall population, stratified by sex (male, female) and age group (younger, <65 years; older, ≥65 years). Circles indicate point estimates, horizontal lines represent 95% CIs, and the vertical dashed line denotes an OR of 1.0.
Table 1.
General characteristics of the study population
Characteristic
Overall (n=26,424)
Normal (n=21,330)
Depressive (n=5,094)
P-value
Age (y)
51.3±16.8
51.7±16.6
49.7±17.8
<0.001
Sex
<0.001
Male
11,461 (43.4)
9,839 (46.1)
1,622 (31.8)
Female
14,963 (56.6)
11,491 (53.9)
3,472 (68.2)
Body mass index (kg/m2)
24.0±3.6
24.0±3.5
23.9±4.0
0.074
Physical activity group
<0.001
Inactive
21,687 (82.1)
17,315 (81.2)
4,372 (85.8)
Regularly active
4,263 (16.1)
3,608 (16.9)
655 (12.9)
Weekend warrior
474 (1.8)
407 (1.9)
67 (1.3)
Household income
<0.001
Low
11,288 (42.7)
8,653 (40.6)
2,635 (51.7)
Middle
7,387 (28.0)
6,076 (28.5)
1,311 (25.7)
High
7,749 (29.3)
6,601 (30.9)
1,148 (22.5)
Education
<0.001
Less than high school
7,732 (29.3)
6,001 (28.1)
1,731 (34.0)
High school graduate
8,850 (33.5)
7,133 (33.4)
1,717 (33.7)
Beyond high school
9,842 (37.2)
8,196 (38.4)
1,646 (32.3)
Marital status
<0.001
Married
21,710 (82.2)
17,821 (83.5)
3,889 (76.3)
Unmarried
4,714 (17.8)
3,509 (16.5)
1,205 (23.7)
Living status
<0.001
Alone
3,255 (12.3)
2,357 (11.1)
898 (17.6)
Others
23,169 (87.7)
18,973 (88.9)
4,196 (82.4)
Smoking
<0.001
Current smoker
4,662 (17.6)
3,594 (16.8)
1,068 (21.0)
Former smoker
5,778 (21.9)
4,883 (22.9)
895 (17.6)
Never smoker
15,984 (60.5)
12,853 (60.3)
3,131 (61.5)
Alcohol consumption
<0.001
High-risk drinking
3,020 (11.4)
2,360 (11.1)
660 (13.0)
Non–high-risk drinking
10,923 (41.3)
8,997 (42.2)
1,926 (37.8)
Non-drinking
12,481 (47.2)
9,973 (46.8)
2,508 (49.2)
Underlying disease
Diagnosis of cerebrocardiovascular disease
1,066 (4.0)
780 (3.7)
286 (5.6)
<0.001
Diagnosis of cancer
1,104 (4.2)
859 (4.0)
245 (4.8)
0.014
Treatment of hypertension
5,988 (22.7)
4,825 (22.6)
1,163 (22.8)
0.762
Treatment of diabetes
2,464 (9.3)
1,903 (8.9)
561 (11.0)
<0.001
Treatment of dyslipidemia
3,781 (14.3)
2,962 (13.9)
819 (16.1)
<0.001
Values are presented as mean±standard deviation for continuous variables and number (%) for categorical variables. P-values were obtained using the t-test for continuous variables and the chi-square test for categorical variables. Household income was categorized into low, middle, and high levels according to equivalized household income quartiles provided by the Korea National Health and Nutrition Examination Survey.
Table 2.
Association between physical activity patterns and depressive symptoms
Group
Model 1
Model 2
OR (95% CI)
P-value
OR (95% CI)
P-value
Overall
Inactive
1 (Reference)
1 (Reference)
Regularly active
0.72 (0.66–0.79)
<0.001
0.81 (0.74–0.89)
<0.001
Weekend warrior
0.65 (0.50–0.85)
0.001
0.83 (0.64–1.08)
0.186
Male
Inactive
-
1 (Reference)
Regularly active
-
0.82 (0.71–0.95)
0.007
Weekend warrior
-
0.78 (0.55–1.10)
0.159
Female
Inactive
-
1 (Reference)
Regularly active
-
0.81 (0.71–0.91)
0.001
Weekend warrior
-
0.95 (0.62–1.45)
0.810
Younger
Inactive
-
1 (Reference)
Regularly active
-
0.82 (0.74–0.91)
<0.001
Weekend warrior
-
0.88 (0.67–1.16)
0.356
Older
Inactive
-
1 (Reference)
Regularly active
-
0.81 (0.62–1.05)
0.114
Weekend warrior
-
0.30 (0.07–1.29)
0.106
ORs and P-values for depressive symptoms according to physical activity patterns, with the inactive group as the reference category. Model 1 presents unadjusted estimates, and Model 2 presents multivariable-adjusted estimates controlling for potential confounders. Results are shown for the overall population and stratified by sex (male, female) and age group (younger, <65 years; older, ≥65 years).
OR, odds ratio; CI, confidence interval.
References
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2. Ministry of Health and Welfare. 2021 National Mental Health Survey. Ministry of Health and Welfare; 2022
5. Pearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R, et al. Association between physical activity and risk of depression: a systematic review and meta-analysis. JAMA Psychiatry 2022;79:550-9.
8. Shiroma EJ, Lee IM, Schepps MA, Kamada M, Harris TB. Physical activity patterns and mortality: the weekend warrior and activity bouts. Med Sci Sports Exerc 2019;51:35-40.
9. O'Donovan G, Lee IM, Hamer M, Stamatakis E. Association of “weekend warrior” and other leisure time physical activity patterns with risks for all-cause, cardiovascular disease, and cancer mortality. JAMA Intern Med 2017;177:335-42.
10. Dos Santos M, Ferrari G, Lee DH, Rey-Lopez JP, Aune D, Liao B, et al. Association of the “weekend warrior” and other leisure-time physical activity patterns with all-cause and cause-specific mortality: a nationwide cohort study. JAMA Intern Med 2022;182:840-8.
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16. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010;67:220-9.
17. Lorant V, Deliege D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol 2003;157:98-112.
19. Fluharty M, Taylor AE, Grabski M, Munafo MR. The association of cigarette smoking with depression and anxiety: a systematic review. Nicotine Tob Res 2017;19:3-13.
23. Badescu SV, Tataru C, Kobylinska L, Georgescu EL, Zahiu DM, Zagrean AM, et al. The association between diabetes mellitus and depression. J Med Life 2016;9:120-5.
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Association between physical activity patterns and depressive symptoms in Korean adults: a nationwide crosssectional study
Figure. 1. Flow diagram of study participant selection. Flow diagram illustrating the selection of study participants. Participants with missing values for variables of interest were excluded. The final analytic sample was derived after applying all inclusion and exclusion criteria. PHQ-9, Patient Health Questionnaire-9.
Figure. 2. Forest plot of the association between physical activity patterns and depressive symptoms. Forest plot showing multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for depressive symptoms according to physical activity patterns, with the inactive group as the reference. Estimates are derived from Model 2. They are presented for the overall population, stratified by sex (male, female) and age group (younger, <65 years; older, ≥65 years). Circles indicate point estimates, horizontal lines represent 95% CIs, and the vertical dashed line denotes an OR of 1.0.
Graphical abstract
Figure. 1.
Figure. 2.
Graphical abstract
Association between physical activity patterns and depressive symptoms in Korean adults: a nationwide crosssectional study
Characteristic
Overall (n=26,424)
Normal (n=21,330)
Depressive (n=5,094)
P-value
Age (y)
51.3±16.8
51.7±16.6
49.7±17.8
<0.001
Sex
<0.001
Male
11,461 (43.4)
9,839 (46.1)
1,622 (31.8)
Female
14,963 (56.6)
11,491 (53.9)
3,472 (68.2)
Body mass index (kg/m2)
24.0±3.6
24.0±3.5
23.9±4.0
0.074
Physical activity group
<0.001
Inactive
21,687 (82.1)
17,315 (81.2)
4,372 (85.8)
Regularly active
4,263 (16.1)
3,608 (16.9)
655 (12.9)
Weekend warrior
474 (1.8)
407 (1.9)
67 (1.3)
Household income
<0.001
Low
11,288 (42.7)
8,653 (40.6)
2,635 (51.7)
Middle
7,387 (28.0)
6,076 (28.5)
1,311 (25.7)
High
7,749 (29.3)
6,601 (30.9)
1,148 (22.5)
Education
<0.001
Less than high school
7,732 (29.3)
6,001 (28.1)
1,731 (34.0)
High school graduate
8,850 (33.5)
7,133 (33.4)
1,717 (33.7)
Beyond high school
9,842 (37.2)
8,196 (38.4)
1,646 (32.3)
Marital status
<0.001
Married
21,710 (82.2)
17,821 (83.5)
3,889 (76.3)
Unmarried
4,714 (17.8)
3,509 (16.5)
1,205 (23.7)
Living status
<0.001
Alone
3,255 (12.3)
2,357 (11.1)
898 (17.6)
Others
23,169 (87.7)
18,973 (88.9)
4,196 (82.4)
Smoking
<0.001
Current smoker
4,662 (17.6)
3,594 (16.8)
1,068 (21.0)
Former smoker
5,778 (21.9)
4,883 (22.9)
895 (17.6)
Never smoker
15,984 (60.5)
12,853 (60.3)
3,131 (61.5)
Alcohol consumption
<0.001
High-risk drinking
3,020 (11.4)
2,360 (11.1)
660 (13.0)
Non–high-risk drinking
10,923 (41.3)
8,997 (42.2)
1,926 (37.8)
Non-drinking
12,481 (47.2)
9,973 (46.8)
2,508 (49.2)
Underlying disease
Diagnosis of cerebrocardiovascular disease
1,066 (4.0)
780 (3.7)
286 (5.6)
<0.001
Diagnosis of cancer
1,104 (4.2)
859 (4.0)
245 (4.8)
0.014
Treatment of hypertension
5,988 (22.7)
4,825 (22.6)
1,163 (22.8)
0.762
Treatment of diabetes
2,464 (9.3)
1,903 (8.9)
561 (11.0)
<0.001
Treatment of dyslipidemia
3,781 (14.3)
2,962 (13.9)
819 (16.1)
<0.001
Group
Model 1
Model 2
OR (95% CI)
P-value
OR (95% CI)
P-value
Overall
Inactive
1 (Reference)
1 (Reference)
Regularly active
0.72 (0.66–0.79)
<0.001
0.81 (0.74–0.89)
<0.001
Weekend warrior
0.65 (0.50–0.85)
0.001
0.83 (0.64–1.08)
0.186
Male
Inactive
-
1 (Reference)
Regularly active
-
0.82 (0.71–0.95)
0.007
Weekend warrior
-
0.78 (0.55–1.10)
0.159
Female
Inactive
-
1 (Reference)
Regularly active
-
0.81 (0.71–0.91)
0.001
Weekend warrior
-
0.95 (0.62–1.45)
0.810
Younger
Inactive
-
1 (Reference)
Regularly active
-
0.82 (0.74–0.91)
<0.001
Weekend warrior
-
0.88 (0.67–1.16)
0.356
Older
Inactive
-
1 (Reference)
Regularly active
-
0.81 (0.62–1.05)
0.114
Weekend warrior
-
0.30 (0.07–1.29)
0.106
Table 1. General characteristics of the study population
Values are presented as mean±standard deviation for continuous variables and number (%) for categorical variables. P-values were obtained using the t-test for continuous variables and the chi-square test for categorical variables. Household income was categorized into low, middle, and high levels according to equivalized household income quartiles provided by the Korea National Health and Nutrition Examination Survey.
Table 2. Association between physical activity patterns and depressive symptoms
ORs and P-values for depressive symptoms according to physical activity patterns, with the inactive group as the reference category. Model 1 presents unadjusted estimates, and Model 2 presents multivariable-adjusted estimates controlling for potential confounders. Results are shown for the overall population and stratified by sex (male, female) and age group (younger, <65 years; older, ≥65 years).
