Abstract
Background
Depression is a prevalent disorder with significant health impacts. Physical activity is known to protect against depression, but its effects may vary in populations with disrupted sleep patterns, such as weekend catch-up sleepers, which refers to participants who sleep longer on weekends than on weekdays. This study examines the dose-response relationship between physical activity and depression in this population.
Methods
Data from 1,906 participants in the National Health and Nutrition Examination Survey (2021–2023) were analyzed. Physical activity was measured in MET-minutes per week, and depression was assessed using the PHQ-9. Multivariate linear regression, restricted cubic spline, and two-part linear regression models were employed.
Results
In the adjusted model, physical activity showed a negative trend with depression, though this association did not reach statistical significance in the fully adjusted model. Stratified analyses revealed stronger associations in women (OR = 0.86, 95% CI: 0.75, 0.99, P = 0.0329) and individuals aged 40–60 years (OR = 0.79, 95% CI: 0.65, 0.97, P = 0.0237). A threshold effect was observed, with physical activity below 2.48 MET-min/1000-wk showing a negative association with depression (OR = 0.69, 95% CI: 0.56, 0.85, P = 0.0006). Beyond this threshold, the relationship changed.
Conclusion
A nonlinear relationship between physical activity and depression was identified in weekend catch-up sleepers, with moderate activity levels (2.48 MET-min/1000-wk) offering the greatest mental health benefits, particularly in women and individuals aged 40–60 years.
Clinical trial number
Not applicable.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12888-025-07095-6.
Keywords: Depression, Physical activity, Weekend catch-up sleep, Threshold effects, NHANES
Background
Depression is a major global mental disorder characterized by persistent low mood, cognitive impairments, reduced motivation, and even suicidal tendencies [1]. Beyond its detrimental effects on individual well-being and quality of life, depression imposes a substantial burden on families and society, making it a critical public health concern [2]. According to the World Health Organization, approximately 300 million people worldwide suffer from depression, with its prevalence rising significantly in the wake of the COVID-19 pandemic [3]. Studies have shown that due to the prolonged stress, social isolation, and economic uncertainty caused by the pandemic, the prevalence of depression has significantly increased among both the general population and healthcare workers globally [4]. The uncertainty and fear associated with the COVID-19 pandemic, along with its high mortality rate, are thought to contribute to increased psychological distress, anxiety, and emotional fluctuations [5]. As one of the leading causes of disability, depression underscores the urgent need for effective predictive and preventive strategies.
Physical activity, an integral part of daily life, is widely recognized as a crucial approach to preventing and alleviating depression. Regular physical activity has been shown to regulate mood and confer significant antidepressant benefits [6]. Studies indicate that engaging in regular exercise reduces the risk of depression [7], whereas physical inactivity is associated with an increased likelihood of depressive symptoms [8]. Population-based research suggests that both low-intensity and moderate-to-vigorous physical activity effectively reduce depression incidence and symptom severity [9]. Findings from randomized controlled trials (RCTs) further demonstrate that various forms of exercise may serve as viable alternatives to pharmacological treatments for depression [9].
In contemporary society, due to work and life pressures, approximately 35% of adults experience insufficient sleep on weekdays and rely on weekend catch-up sleep to compensate for sleep deficits. Weekend compensatory sleep refers to the act of increasing sleep duration on weekends or days off to offset weekday sleep loss [10], and is typically measured as the absolute difference in sleep duration between weekdays and weekends [11]. The circadian misalignment resulting from this “social jetlag” has been linked to depressive symptoms [12]. However, existing research presents two key limitations. First, most studies examining the association between physical activity and depression focus on the general population, overlooking potential heterogeneity in individuals with disrupted sleep patterns. Specifically, individuals with weekend catch-up sleep cycles are frequently exposed to a “sleep debt-compensation” loop, which may induce characteristic alterations in hypothalamic-pituitary-adrenal (HPA) axis reactivity and neurotrophic factor expression [13], potentially modifying the antidepressant effects of physical activity. Second, current exercise intervention guidelines do not account for the impact of sleep compensation behaviors, despite evidence suggesting that circadian rhythm disruptions can alter exercise-induced brain-derived neurotrophic factor (BDNF) secretion [14].
To date, few studies have specifically addressed whether and how weekend catch-up sleep may influence the relationship between physical activity and depression. This represents a critical gap in the literature, particularly given the prevalence of sleep compensation behaviors in modern society. Therefore, this study aims to investigate the dose-response relationship between physical activity and depression risk in individuals with weekend catch-up sleep. By elucidating the neurobiological specificity of exercise interventions under conditions of circadian misalignment, our findings will provide critical insights for developing precision-based preventive strategies tailored to contemporary lifestyle patterns.
Methods
Study population
This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES), a program conducted by the National Center for Health Statistics (NCHS) to assess the health and nutritional status of individuals in the United States. NHANES employs a complex, multistage probability sampling design to obtain a nationally representative sample of the non-institutionalized U.S. population. Participants provided demographic, socioeconomic, and health-related information through household interviews, while laboratory tests and physical examinations were conducted at mobile examination centers (MECs). All NHANES protocols were approved by the NCHS Research Ethics Review Board, and informed consent was obtained from all participants. Detailed information on the NHANES study design and data can be accessed at www.cdc.gov/nchs/NHANES/.
For this study, we used NHANES data from the 2021–2023 cycle, comprising 11,933 participants. The following exclusion criteria were applied: [1] age < 20 years (n = 4,124), [2] pregnancy (n = 41), [3] missing PHQ-9 data (n = 2,542), [4] missing physical activity (PA) data (n = 29), [5] missing sleep duration data (n = 51), [6] missing covariate data (n = 683), and [7] weekend catch-up sleep duration ≤ 0 h (n = 2,557), as these individuals did not engage in compensatory sleep behavior, which was defined in this study as a positive difference in sleep duration between weekends and weekdays. After applying these criteria, the final analytical sample included 1,906 participants (Fig. 1).
Fig. 1.
Flowchart of the sample selection from NHANES 2021–2023
Exposure variable: physical activity
Self-reported data on moderate-to-vigorous physical activity (MVPA) were obtained from the Global Physical Activity Questionnaire (GPAQ) [15]. MVPA was defined as activities that significantly increase the respiratory rate or heart rate, with intensity measured using metabolic equivalents (METs). One MET represents the energy expenditure of an individual at rest, with MET values indicating the energy cost of different activities relative to resting metabolic rate [16]. Based on standardized MET scores, PA in the study population was classified into the following categories: vigorous-intensity work-related activity (8.0 METs) and moderate-intensity work-related activity (4.0 METs) [17].
Total weekly PA MET-minutes were calculated using self-reported data from the NHANES questionnaire based on the following formula [18]:
 |
Outcome variable: depression
Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a widely used screening tool with high sensitivity and specificity for diagnosing depression [19]. The PHQ-9 consists of nine items covering symptoms such as loss of interest, low mood, sleep disturbances, fatigue, appetite changes, low self-worth, concentration difficulties, psychomotor agitation or retardation, and suicidal ideation. Each item is scored on a four-point Likert scale (0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day), resulting in a total score ranging from 0 to 27. A total PHQ-9 score of ≥ 10 was used to define depression [20].
Covariates
Based on previous literature and clinical relevance [21–25], the following covariates were included in the analysis: gender (male/female), age, education (Less than 9th grade/9–11th grade, including 12th grade without diploma/High school graduate/GED or equivalent/Some college or AA degree/College graduate or above), race (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other Race), marital status (Married/Living with partner/Widowed/Divorced/Separated/Never married), poverty-income ratio (PIR), body mass index (BMI), hypertension (yes/no), diabetes (yes/no/prediabetes), alcohol use (≥ 1 standard drink/lifetime; never consumed), and smoking status (≥ 100 cigarettes/lifetime; <100 cigarettes/lifetime).
Hypertension was defined based on at least one of the following criteria: [1] mean systolic blood pressure (SBP) ≥ 140 mmHg or mean diastolic blood pressure (DBP) ≥ 90 mmHg, [2] current use of antihypertensive medications, or [3] self-reported history of hypertension [26]. The calculation methods for average SBP (ASBP) and average DBP (ADBP) were reported in previous studies [27].
Prediabetes was defined based on at least one of the following criteria: glycated hemoglobin (HbA1c) level between 5.7% and 6.4%; fasting plasma glucose (FPG) between 5.6 and 7.0 mmol/L; 2-hour glucose level from an oral glucose tolerance test (OGTT) between 7.8 and 11.1 mmol/L; or a clinical diagnosis of prediabetes. Diabetes was defined based on at least one of the following criteria: FPG ≥ 7.0 mmol/L, HbA1c ≥ 6.5%, random blood glucose or OGTT 2-hour glucose level ≥ 11.1 mmol/L, clinical diagnosis of diabetes, or current use of oral hypoglycemic agents or insulin [28].
Statistical analysis
Descriptive analyses were performed using means and standard deviations (SD) for continuous variables and percentages with 95% confidence intervals (CIs) for categorical variables. Baseline characteristics were compared using t-tests for continuous variables and chi-square tests for categorical variables. To assess the association between physical activity and depression, three separate multivariate logistic regression models were used: an unadjusted model, Model 1 (adjusted for gender, age, and race), and Model 2 (further adjusted for marital status, education level, PIR, BMI, hypertension, diabetes, smoking status, and alcohol use). These models were selected to progressively control for potential confounders and evaluate the robustness of the observed association. The unadjusted model provided a crude estimate of the association. Model 1 included basic demographic variables that are closely linked to both physical activity levels and depression risk. Building on this, Model 2 further adjusted for socioeconomic and health-related factors, in order to more comprehensively account for potential confounders that may influence both physical activity and depression.
The NHANES complex survey design, including sampling weights, was accounted for in all analyses. Stratified analysis was conducted using stratified multivariable logistic regression models, with stratification factors including age, gender, education, hypertension, diabetes, alcohol use, and smoking status. These factors were also considered potential effect modifiers. Interaction terms were added and assessed using likelihood ratio tests to evaluate heterogeneity in the associations across different subgroups.
Restricted cubic spline (RCS) regression was applied to investigate potential nonlinear relationships between physical activity and depression. Additionally, piecewise regression models were employed to assess potential threshold effects. Likelihood ratio tests were used to compare linear (non-segmented) and piecewise regression models to determine whether a threshold effect was present. The optimal breakpoint (K) was identified using the maximum likelihood method and a two-step recursive approach. All statistical analyses were conducted using R software (version 4.3.2) with the EmpowerStats package (version 4.2). A two-tailed p-value < 0.05 was considered statistically significant.
Results
Baseline characteristics
A total of 1,906 participants were included in the analysis, among whom 1,659 individuals did not have depression, while 247 were classified as having depression. Comparisons between the two groups revealed that individuals with depression were younger on average, had a significantly higher BMI, and had a lower poverty-to-income ratio. Additionally, the proportion of females was higher in the depression group, whereas the proportion of individuals with higher education (college degree or above) was lower. Married individuals or those living with a partner were less prevalent in the depression group, while the proportion of unmarried individuals was higher. Moreover, the proportion of smokers (≥ 100 cigarettes in a lifetime) was significantly greater among those with depression (P < 0.05). However, no significant differences were observed between the two groups regarding race, hypertension, diabetes, or alcohol use (P > 0.05). (Table 1).
Table 1.
Weighted baseline characteristics of included participants
| Characteristic | Total (n = 1906) | Without depression (n = 1659) | With depression (n = 247) | P-value |
|---|---|---|---|---|
| Age (years) | 48.18 ± 15.67 | 48.83 ± 15.55 | 43.87 ± 15.77 | < 0.001* |
| BMI (kg/m 2 ) | 29.83 ± 7.48 | 29.66 ± 7.17 | 31.02 ± 9.23 | 0.007* |
| Income-poverty ratio | 3.10 ± 1.65 | 3.19 ± 1.63 | 2.52 ± 1.64 | < 0.001* |
| PHQ-9 score | 4.22 ± 4.72 | 2.76 ± 2.61 | 14.02 ± 3.99 | < 0.001* |
| PA (MET-min/1000-wk) | 1.24 ± 1.57 | 1.26 ± 1.55 | 1.07 ± 1.66 | 0.079 |
| Gender (n %) | 0.012* | |||
| male | 828 (43.44%) | 739 (44.54%) | 89(36.03%) | |
| female | 1078 (56.56%) | 920 (55.46%) | 158 (63.97%) | |
| Race (n %) | 0.183 | |||
| Mexican American | 152 (7.97%) | 130 (7.84%) | 22 (8.91%) | |
| Other Hispanic | 218 (11.44%) | 190 (11.45%) | 28 (11.34%) | |
| Non-Hispanic White | 1100 (57.71%) | 968 (58.35%) | 132 (53.44%) | |
| Non-Hispanic Black | 224 (11.75%) | 184 (11.09%) | 40 (16.19%) | |
| Other Race - Including Multi-Racial | 212 (11.12%) | 187 (11.27%) | 25 (10.12%) | |
| Education (n %) | < 0.001* | |||
| Less than 9th grade | 59 (3.10%) | 49 (2.95%) | 10 (4.05%) | |
| 9-11th grade (Includes 12th grade with no diploma) | 125 (6.56%) | 105 (6.33%) | 20 (8.10%) | |
| High school graduate/GED or equivalent | 373 (19.57%) | 316 (19.05%) | 57 (23.08%) | |
| Some college or AA degree | 590 (30.95%) | 497 (29.96%) | 93 (37.65%) | |
| College graduate or above | 759 (39.82%) | 692 (41.71%) | 67 (27.13%) | |
| Marital status (n %) | < 0.001* | |||
| Married/Living with partner | 1070 (56.14%) | 977 (58.89%) | 93 (37.65%) | |
| Widowed/Divorced/Separated | 399 (20.93%) | 331 (19.95%) | 68 (27.53%) | |
| Never married | 437 (22.93%) | 351 (21.16%) | 86 (34.82%) | |
| Hypertension (n %) | 0.856 | |||
| yes | 700 (36.73%) | 608 (36.65%) | 92 (37.25%) | |
| no | 1206 (63.27%) | 1051 (63.35%) | 155 (62.75%) | |
| Diabetes Mellitus (n %) | 0.332 | |||
| yes | 259 (13.59%) | 219 (13.20%) | 40 (16.19%) | |
| no | 1026 (53.83%) | 902 (54.37%) | 124(50.20%) | |
| prediabetes | 621 (32.58%) | 538 (32.43%) | 83 (33.60%) | |
| Alcohol Use (n %) | 0.472 | |||
| ≥ 1 standard drink/life | 1758 (92.24%) | 1533 (92.41%) | 225 (91.09%) | |
| never consumed | 148 (7.76%) | 126 (7.59%) | 22 (8.91%) | |
| Smoke (n %) | 0.001* | |||
| ≥ 100 cigarettes /life | 703 (36.88%) | 589 (35.50%) | 114 (46.15%) | |
| < 100 cigarettes /life | 1203 (63.12%) | 1070 (64.50%) | 133 (53.85%) |
Median ± standard deviation for continuous; n (%) for categorical
Abbreviations: BMI, body mass index; PHQ-9, Patient Health Questionnaire-9; PA, physical activity
Significant values (P < 0.05) are in bold
Association between physical activity and depression in weekend catch-up sleepers
In the unadjusted model, the association between PA (MET-min/1000-wk) and depression was not statistically significant (OR = 0.92, 95% CI: 0.84–1.01, P = 0.0801). In Model 1, after adjusting for gender, age, and race, a significant negative association was observed (OR = 0.90, 95% CI: 0.81–0.99, P = 0.0303), suggesting that higher PA (MET-min/1000-wk) was associated with a lower risk of depression. In the fully adjusted model, although the association remained negative in direction, it did not reach statistical significance. This non-significant result may reflect a nonlinear relationship between the two variables, which could have attenuated the overall effect estimate in the fully adjusted model. (Table 2)
Table 2.
Association between PA (MET-min/1000-wk) and depression among weekend Catch-Up sleepers in NHANES 2021–2023
| Exposure | Crude | Model 1 | Model 2 |
|---|---|---|---|
| PA (MET-min/1000-wk) | 0.92 (0.84, 1.01) 0.0801 | 0.90 (0.81, 0.99) 0.0303* | 0.91 (0.83, 1.01) 0.0639 |
Note: Crude: Unadjusted
Model 1: Adjusted for gender, race, and age
Model 2: Adjusted for gender, race, age, BMI, marital status, income-poverty ratio, education, smoking status, alcohol use, hypertension and diabetes mellitus
Stratified analysis
Stratified analyses demonstrated that the inverse association between PA and depression was more pronounced in specific subgroups. The association was strongest among individuals aged 40–60 years (OR = 0.79, P = 0.0237), suggesting that midlife adults may be particularly responsive to the mood-regulating effects of PA. A significant association was also observed in females (OR = 0.86, P = 0.0329). Although no statistically significant associations were found among males or other age groups, the direction of the relationship remained negative across all subgroups. These subgroup differences may reflect heterogeneity in physiological responses, stress coping mechanisms, or activity engagement patterns. Interaction tests indicated that the association between PA and depression remained robust across different subgroups (P for interaction > 0.05). (Table 3; Fig. 2)
Table 3.
The effect size of PA (MET-min/1000-wk) on depression in prespecified and exploratory subgroups
| Subgroups | Total | OR (95%CI) P-value | P for interaction |
|---|---|---|---|
| Age (years) | 0.1636 | ||
| < 40 | 642 | 0.98 (0.86, 1.11) 0.7491 | |
| ≥ 40, < 60 | 692 | 0.79 (0.65, 0.97) 0.0237* | |
| ≥ 60 | 572 | 0.86 (0.66, 1.12) 0.2606 | |
| Gender (n %) | 0.1593 | ||
| male | 828 | 0.99 (0.87, 1.13) 0.8794 | |
| female | 1078 | 0.86 (0.75, 0.99) 0.0329* | |
| Education (n %) | 0.5687 | ||
| Less than 9th grade | 59 | 1.30 (0.65, 2.62) 0.4613 | |
| 9-11th grade (Includes 12th grade with no diploma) | 125 | 1.08 (0.81, 1.44) 0.5910 | |
| High school graduate/GED or equivalent | 373 | 0.91 (0.75, 1.10) 0.3311 | |
| Some college or AA degree | 590 | 0.87 (0.73, 1.03) 0.0955 | |
| College graduate or above | 759 | 0.86 (0.69, 1.08) 0.1960 | |
| Marital Status (n %) | 0.2108 | ||
| Married/Living with partner | 1070 | 1.01 (0.88, 1.16) 0.8746 | |
| Widowed/Divorced/Separated | 399 | 0.84 (0.68, 1.03) 0.0957 | |
| Never married | 437 | 0.87 (0.73, 1.03) 0.0986 | |
| Hypertension (n %) | 0.4088 | ||
| yes | 700 | 0.84 (0.70, 1.02) 0.0827 | |
| no | 1206 | 0.93 (0.84, 1.04) 0.2300 | |
| Diabetes Mellitus (n %) | 0.8641 | ||
| yes | 259 | 0.84 (0.63, 1.12) 0.2412 | |
| no | 1026 | 0.93 (0.82,1.05) 0.2633 | |
| prediabetes | 621 | 0.92 (0.77, 1.09) 0.3261 | |
| Alcohol Use (n %) | 0.1130 | ||
| ≥ 1 standard drink/life | 1758 | 0.93 (0.85, 1.03) 0.1655 | |
| never consumed | 148 | 0.60 (0.31, 1.17) 0.1335 | |
| Smoke (n %) | 0.9797 | ||
| ≥100 cigarettes /life | 703 | 0.92 (0.81, 1.05) 0.2310 | |
| <100 cigarettes /life | 1203 | 0.93 (0.81, 1.06) 0.2700 |
All presented covariates were fully adjusted (as Model 2)
Significant values (P < 0.05) are in bold
Fig. 2.
Stratified analysis for the association between physical activity and depression in weekend catch-up sleepers. Figure legends: This figure presents the association between physical activity and depression stratified by age group, gender, education, marital status, hypertension, diabetes mellitus, alcohol use and smoke. Results are expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance (P < 0.05) is indicated by an asterisk (*). An OR less than 1 indicates that higher levels of physical activity are associated with a reduced risk of depression. The associations were more pronounced among women and individuals aged 40–60 years
Threshold effect analysis of physical activity on depression
To further investigate the potential nonlinear relationship between PA (MET-min/1000-wk) and depression, we conducted a threshold effect analysis. In the linear model, PA (MET-min/1000-wk) exhibited a negative association with depression (OR = 0.91, P = 0.0639), though it did not reach statistical significance. However, in the piecewise regression model, a threshold (K) of 2.48 was identified, suggesting a nonlinear relationship between PA and depression. Below this threshold, PA was significantly associated with reduced odds of depression (OR = 0.69, P = 0.0006), indicating a strong protective effect. In contrast, above the threshold, the association weakened and became non-significant (OR = 1.17, P = 0.0881), suggesting a potential plateau effect. The likelihood ratio test yielded a P-value of 0.003, indicating that the piecewise regression model provided a better fit than the linear model. These findings highlight the importance of moderate levels of physical activity, which may represent the optimal range for depression prevention in this population. (Table 4; Fig. 3)
Table 4.
Threshold effect analysis of PA (MET-min/1000-wk) on depression
| Outcome | Depression |
|---|---|
| Fitting by the standard linear Model | 0.91 (0.83, 1.01) 0.0639 |
| Fitting by the two-piecewise linear Model | |
| Inflection point (K) | 2.48 |
| < K-segment effect | 0.69 (0.56, 0.85) 0.0006* |
| > K-segment effect | 1.17 (0.98, 1.39) 0.0881 |
| Log likelihood ratio | 0.003* |
Fig. 3.
Restricted cubic spline model of the association between physical activity and depression in weekend catch-up sleepers. Figure legends: This figure illustrates the nonlinear relationship between physical activity (MET-min/1000-wk) and odds of depression using a restricted cubic spline model. The solid curve represents the estimated odds ratio, and the shaded area indicates the 95% confidence interval. A threshold effect is observed at 2.48 MET-min/1000-wk, with a statistically significant protective effect on depression below this threshold (P < 0.05)
Significant values (P < 0.05) are in bold.
Discussion
In this population-based study, we observed a nonlinear relationship between physical activity and depression risk among weekend catch-up sleepers. Our findings suggest the presence of a threshold effect in this population, where the risk of depression significantly decreases with increasing physical activity intensity, reaching its lowest at 2.48 MET-min/1000-wk. This indicates a nonlinear relationship between physical activity and depression risk. Notably, low-to-moderate-intensity physical activity appears beneficial for mental health, which is particularly relevant in modern society, as such activity levels are more attainable for the general population [29, 30].
Our study focuses on the increasingly busy work schedules in contemporary society and explores the potential of an active lifestyle in mitigating depression risk. While early studies predominantly reported a significant negative association between physical activity and depression, more recent research has proposed a nonlinear relationship. Despite the growing prevalence of sleep compensation behaviors, limited studies have examined the dose-response relationship between physical activity and depression risk specifically in weekend catch-up sleepers. To the best of our knowledge, this study is the first to explore this association in this specific population.
From a clinical and public health perspective, our findings support the development of personalized interventions. For individuals who experience insufficient sleep on weekdays due to work or life stress and rely on weekend catch-up sleep for recovery, we recommend engaging in regular, moderate-intensity physical activity—such as brisk walking, cycling, or light resistance training—to promote mental health, rather than focusing solely on high-intensity exercise. Given that this population often faces time constraints, sleep deprivation, and psychological stress simultaneously, promoting sustainable, low-barrier forms of physical activity may improve both adherence and intervention effectiveness.
Future public health strategies should pay greater attention to the unique challenges faced by individuals with irregular daily routines. Incorporating physical activity interventions into comprehensive health management frameworks—alongside workplace wellness initiatives—could provide this high-risk group with practical, tailored recommendations and behavioral support. Such efforts may contribute to broader improvements in mental health and more effective prevention of related disorders.
Furthermore, our subgroup analyses revealed a more pronounced negative association between physical activity and depression among women and individuals aged 40–60 years. Similar studies have suggested that the protective effect of physical activity against depression is more prominent in women [11, 31, 32], potentially due to psychological factors, as women may derive greater social benefits from physical activity than men [33]. Compared to women, although men also showed a negative trend, the association did not reach statistical significance. This difference may be related to gender-specific ways of coping with emotional stress—for example, men may be more likely to internalize emotions or have lower emotional sensitivity to sleep restriction [34, 35], which could make the psychological regulatory effects of weekend catch-up sleep less pronounced in men.
The stronger association observed in the 40–60 age group may be explained by age-related declines in brain-derived neurotrophic factor (BDNF), which exercise has been shown to upregulate [36], thereby improving emotional regulation. Additionally, chronic stress in middle-aged individuals may lead to hypothalamic-pituitary-adrenal (HPA) axis dysregulation and physical activity has been found to lower resting cortisol levels [37] while promoting circadian rhythm stability, ultimately benefiting emotional and physical health. In contrast, although younger individuals also exhibited a negative trend, the effect may be less pronounced due to their relatively robust physiological regulatory functions and lower dependence on sleep and physical activity for emotional well-being [38, 39]. Older adults, on the other hand, may experience a weaker association between weekend catch-up sleep and mood improvement because of the cumulative burden of chronic illnesses, physical decline, and reduced responsiveness to interventions [40].
Multiple potential mechanisms may explain the observed nonlinear relationship between physical activity and depressive symptoms among individuals with weekend catch-up sleep (WCS). In this population, chronic sleep restriction during weekdays may lead to emotional dysregulation, impaired neuroplasticity, and heightened inflammatory and stress-related responses. Regular physical activity may counteract these adverse effects through various pathways. First, it enhances neuroplasticity and brain function, particularly in regions critical for emotional regulation, such as the prefrontal cortex and hippocampus [41], which are especially vulnerable to sleep deprivation. Second, physical activity increases levels of endorphins and monoamine neurotransmitters while modulating the hypothalamic–pituitary–adrenal (HPA) axis, thereby promoting emotional stability [37] and potentially alleviating psychological stress associated with irregular sleep patterns. Third, it upregulates the expression of brain-derived neurotrophic factor (BDNF) and insulin-like growth factor-1 (IGF-1) [36, 42], supporting synaptic function and mitochondrial metabolism—both of which are often disrupted under chronic sleep debt [43]. Fourth, physical activity improves cerebral blood flow and elevates vascular endothelial growth factor (VEGF), facilitating neurogenesis and recovery from sleep loss–induced neural dysfunction [44–46]. Fifth, it helps reduce systemic inflammation—exacerbated by insufficient sleep—by lowering pro-inflammatory markers and promoting the release of anti-inflammatory cytokines [46]. Finally, physical activity fosters psychosocial well-being by enhancing self-esteem, self-efficacy, and social interaction, which may be especially beneficial for individuals whose weekday sleep deprivation undermines social engagement and psychological resilience [47]. Therefore, physical activity may serve a compensatory role in WCS populations, helping to restore physiological and psychological balance.
However, this relationship may not be linear. Although physical activity generally exerts positive effects on mental health, a threshold effect appears to exist, with its protective impact on depression risk plateauing beyond 2.48 MET-min/1000-wk. In the context of ongoing sleep debt, moderate levels of physical activity may effectively alleviate depressive symptoms through mechanisms such as regulation of the hypothalamic–pituitary–adrenal (HPA) axis [37], enhancement of neuroplasticity [41], and improvement of inflammatory status [46]. However, once activity levels exceed a certain threshold, these protective mechanisms may become saturated, and further increases in activity may offer no additional benefit. Moreover, intense physical activity without adequate recovery could elevate oxidative stress and metabolic burden, potentially counteracting some of the positive effects [48].
This study has several strengths. First, it utilizes a large, nationally representative sample from the NHANES database. Second, three distinct models were employed to adjust for potential confounders, enhancing the robustness of our findings. Third, subgroup analyses were conducted to assess the stability of the relationship between physical activity and depression risk among weekend catch-up sleepers. Finally, our study provides an evidence-based “optimal exercise threshold” for weekend catch-up sleepers, offering targeted recommendations to maximize mental health benefits.
However, our findings should be interpreted with caution due to several limitations. First, both physical activity and sleep data were based on self-reported information, which may be subject to recall bias and reporting bias. For instance, participants may have overestimated or underestimated their activity levels and sleep duration. Second, given the cross-sectional design of this study, causal relationships between physical activity and depression cannot be established. While physical activity may influence the risk of depression, the possibility of reverse causality—where depressive symptoms influence physical activity levels—cannot be ruled out. In addition, the absence of objective measures (such as accelerometers or sleep diaries) limited the precision of our assessment of physical activity and sleep duration. Although our models adjusted for several known confounding factors, residual confounding may still exist—particularly from unmeasured but potentially important variables that could be associated with both physical activity and depression. These may include genetic predisposition, social support networks, occupational stress, and life event–related stress [49].
Therefore, future studies should aim to more comprehensively account for these potential confounders in their design to validate the robustness of current findings. Moreover, longitudinal designs incorporating objective measures of physical activity and sleep are warranted to better elucidate potential causal mechanisms. Finally, future research should further explore the multidimensional characteristics of physical activity and their associations with depression to gain a more comprehensive understanding.
Conclusion
A nonlinear relationship between physical activity and depression was identified in weekend catch-up sleepers, with moderate activity levels (2.48 MET-min/1000-wk) offering the greatest mental health benefits, particularly in women and individuals aged 40–60 years.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We extend our gratitude to all NHANES participants and staff for their invaluable contributions.
Abbreviations
- ADBP
Average diastolic blood pressure
- ASBP
Average systolic blood pressure
- BDNF
Brain-derived neurotrophic factor
- BMI
Body mass index
- CDC
Centers for Disease Control and Prevention
- CI
Confidence intervals
- DBP
Diastolic blood pressure
- FPG
Fasting plasma glucose
- GPAQ
Global Physical Activity Questionnaire
- HbA1c
Glycated hemoglobin
- HPA
Hypothalamic-pituitary-adrenal
- IGF-1
Insulin-like growth factor-1
- MECs
Mobile examination centers
- MET
Metabolic equivalent
- MVPA
Moderate-to-vigorous physical activity
- NCHS
The National Center for Health Statistics
- NHANES
The National Health and Nutrition Examination Survey
- OGTT
Oral glucose tolerance test
- OR
Odds ratio
- PA
Physical activity
- PHQ-9
Patient Health Questionnaire-9
- PIR
Poverty-to-income ratio
- RCS
Restricted cubic spline
- SBP
Systolic blood pressure
- SD
Standard deviation
- VEGF
Vascular endothelial growth factor
- WCS
Weekend catch-up sleep
Author contributions
Kunyu Qiu: Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yilei Liu: Writing – original draft, Visualization, Resources. Yan Zhang: Writing – original draft, Resources. Jie Gu: Writing – original draft, Resources.Yanyan Huang: Writing – review & editing, Supervision, Resources, Project administration, Conceptualization.
Funding
No specific funding for this research was provided by the government, business, or nonprofit sectors.
Data availability
Data is provided within the manuscript or supplementary information files.
Declarations
Ethics and consent to participate declarations
The National Center for Health Statistics Ethics Review Board approved this study’s human subjects components, which followed the Declaration of Helsinki. For every subject, written informed permission was acquired. As NHANES data are publicly available and fully de-identified, their use for secondary data analysis is considered exempt from further IRB review under most institutional guidelines.
Consent for publication
All participants gave informed consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
Data is provided within the manuscript or supplementary information files.