Abstract
Objective
This study examined the separate and joint associations between modifiable behavioral risk factors (daily sitting time (DST), physical activity (PA) levels) and all-cause and premature mortality among individuals diagnosed with Cardiovascular Disease (CVD).
Methods
This retrospective cohort study analyzed data from 2,530 adult CVD patients in the National Health and Nutrition Examination Survey (2007–2018), and linked records to the National Death Index through December 31, 2019. Physical activity was categorized as high (≥500 MET-min/week) or low (<500 MET-min/week), while daily sitting time was classified as low (<7 hours/day) or high (≥7 hours/day). We used survey-weighted Cox regression, restricted cubic splines (RCS), and Kaplan-Meier survival analysis to evaluate relationships and conducted subgroup analyses to verify consistency.
Results
The cohort had a weighted mean age of 59.12±0.27 years (56.61% men, 68.01% non-Hispanic White), with 531 all-cause mortality (374 premature deaths). PA≥500 MET-min/week was associated with lower all-cause (HR = 0.49, 95% CI:0.37–0.64) and premature mortality (HR=0.44, 95% CI:0.32–0.61); Each 1-SD increase in PA was linked to 52% and 51% lower risks of all-cause and premature mortality. DST≥7 hours/day increased all-cause (HR=1.67, 95% CI:1.32–2.11) and premature mortality (HR=1.74, 95% CI:1.30–2.32); Each 1-SD increase in DST was linked to 31% and 33% higher risks of all-cause and premature mortality. The lowest mortality risk was among those with DST <7 hours/day and PA ≥500 MET-min/week (all-cause: HR = 0.37; premature: HR = 0.31). RCS showed linear positive associations for DST (P for nonlinearity >0.05) and nonlinear negative associations for PA (P for nonlinearity < 0.05) with mortality; subgroup analyses confirmed stable associations.
Conclusions
This study suggests that increasing physical activity and reducing daily sitting time may be associated with lower risks of all-cause mortality and premature death in patients with CVD, with more favorable effects observed when the two behaviors are combined. Promoting a combination of higher PA and lower DST may be a useful reference of routine CVD risk management.
Keywords: all-cause mortality, cardiovascular disease, daily sitting time, physical activity, premature mortality
1. Introduction
The leading global cause of death is cardiovascular disease (CVD).1,2 An estimated 17.8 million global deaths were attributable to CVD in 2017, as reported by the Global Burden of Disease (GBD) study. 3 In Europe, CVD claims over 6 million lives annually, accounting for 32% of premature mortality in men and 28% in women, with patients exhibiting significantly elevated long-term mortality risks. Beyond pharmacological interventions, lifestyle modifications, such as managing physical activity (PA) and daily sitting time (DST), play a crucial role in improving CVD outcomes.
Sedentary behavior encompasses any waking activity wherein the individual is in a sitting, reclining, or lying posture, with an energy expenditure not exceeding 1.5 METs. 4 Due to reduced mobility and rehabilitation restrictions, CVD patients typically spend more time sedentary than the general population. Conversely, regular moderate-intensity PA (e.g., brisk walking, cycling) has been proven to effectively reduce their mortality risk. Multiple large-scale cohort studies and meta-analyses indicate that engaging in over 180 minutes of PA per week, particularly maintaining moderate-to-vigorous intensity activity within a specific intensity range, is associated with the greatest reduction in all-cause and CVD mortality risk. 5
Regular PA improves cardiovascular outcomes through integrated physiological mechanisms: it enhances endothelial function and nitric oxide bioavailability, improves hemodynamic and metabolic profiles, and increases insulin sensitivity,6,7 Cohort studies demonstrate that achieving ≥500 MET-min/week of activity reduces all-cause mortality by 14% in CVD patients, 8 supporting its inclusion in the AHA’s “Life’s Essential 8” cardiovascular health metrics. 9 In contrast, sedentary behavior induces physiological detriments including impaired muscle metabolism, venous stasis, and systemic low-grade inflammation.10,11 While PA can partially mitigate these effects,12,13 prolonged sitting independently contributes to cardiovascular risk.
The safe threshold for sedentary time and its dose-response relationship with mortality risk remain unclear, and there is a lack of stratified evidence across key subgroups such as age and race/ethnicity. Study data were collected from 2007-2018 National Health and Nutrition Examination Survey (NHANES) cycles, aims to systematically address the following core questions in a cohort of CVD patients: First, quantify the strength of the association between different levels of PA and the risk of all-cause mortality and premature mortality. Second, assess the independent association between DST and the aforementioned mortality outcomes. Third, analyze the combined effect of PA and DST on mortality risk. Finally, we examine the potential effect modification of demographic factors such as age, sex, and ethnicity on these associations. If the findings confirm that the “high physical activity-low daily sitting time” behavior pattern is associated with significantly lower mortality risk, this would provide epidemiological evidence for prioritizing behavioral risk factor assessment in CVD patient management and inform the development of population-level health guidelines.
2. Materials and methods
2.1. Methodology and participant sample
The NHANES served as the data source for this retrospective cohort study. Employing a multistage, stratified, cluster sampling design, the survey aimed to obtain a nationally representative sample of the noninstitutionalized U.S. population. Data were collected through household interviews, physical examinations at mobile examination centers, and laboratory tests. The NHANES study secured ethical clearance from the Ethics Review Board at the National Center for Health Statistics. Each participant provided their written consent after being fully informed. The research involves a secondary look at data that’s already been anonymized and is accessible to the public. Consequently, no further application for institutional ethical review is required. We have already communicated with our organization’s ethics committee and obtained an ethics exemption. This study analyzed publicly available data from the 2007-2018 survey cycles. The initial study population comprised all adult participants (n = 113,249) from the NHANES 2007-2018 cycles. Participants meeting any of the following criteria were excluded: (1) missing cardiovascular disease information (n = 50,686); (2) without cardiovascular disease (n = 55,243); (3) missing physical activity data (n = 2,495); (4) missing daily sitting time data (n = 975); (5) missing survival information (n = 1,320). Ultimately, the analysis included 2,530 participants in total (Figure 1). All procedures adhered to the ethical principles outlined in the Declaration of Helsinki (1975; revised 2024) and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 14 All patient details have been de-identified. No formal a priori power calculation was performed to determine the sample size, as this was a retrospective cohort study utilizing existing NHANES data from the 2007–2018 cycles. Instead, all eligible participants meeting the predefined inclusion criteria were included to enhance the precision and generalizability of the findings.
Figure 1.
Participant selection flow diagram.
2.2. Definition of cardiovascular disease
The determination of cardiovascular disease cases in this investigation was based on self-disclosed medical diagnoses obtained through structured interviews. During the National Health and Nutrition Examination Survey, participants were specifically queried: “Has a healthcare provider ever diagnosed you with any of these conditions: congestive heart failure, coronary heart disease, angina, heart attack, or stroke?” Individuals responding affirmatively to any of these criteria were deemed to have cardiovascular disease for the purposes of this research.
2.3. Physical activity patterns and daily sitting time
This study uses the standardized questionnaire from the US National Health and Nutrition Examination Survey (NHANES) as the survey tool. All items in this questionnaire underwent multiple rounds of cognitive testing and field pilot testing by the US National Center for Health Statistics (NCHS) before being officially implemented nationwide. The NHANES study assessed physical activity levels and daily sitting time were using the self-administered Physical Activity Questionnaire (PAQ). Previous studies have demonstrated that PAQ has moderate to good test–retest reliability.15,16 This study assessed DST as the exposure variable. To gather information, participants were queried with a standardized inquiry: “On a standard day, what’s the total duration you spend seated or reclining? Take note of the entire DST—while you’re conscious—across environments like work, home, or educational institutions. This encompasses sitting at a desk, hanging out, being in vehicles, reading, card games, watching television, and using a computer”. 17 Following established research standards, DST was divided into two categories: low sitting time (DST < 7 hours/day) and high sitting time (DST ≥ 7 hours/day). 18 Each participant completed a PA questionnaire regarding the type, regularity, length, and intensity of their PA for the preceding 30 days. Moderate activities were characterized by light perspiration or a modest elevation in respiratory and cardiac rhythms, whereas vigorous exercises were marked by profuse sweating or a significant jump in breathing and heart rates. Based on the type and intensity, each activity was assigned a corresponding metabolic equivalent (MET) score. 19 We determined the MET-min/week for each activity over a 30-day period by multiplying its MET value by the average duration and frequency of participation during that timeframe. The total MET-minutes over the 30-day period was divided by 4.29 to obtain the average weekly physical activity volume (MET-min/week). Based on this, participants were categorized into two groups according to whether they met national recommendations: a low physical activity group (PA < 500 MET-min/week) and a high physical activity group (PA≥500 MET-min/week). 20
2.4. Covariates
The research incorporated several key variables in its analysis: demographic factors such as age and gender, alongside categorized racial and educational background (ranging from no more than secondary education to college-level education or higher), ethnic classifications (Non-Hispanic White, Mexican American, Non-Hispanic Black, Other Race-Including Multi-Racial and Other Hispanic), marital status (married/living with partner, widowed/divorced/separated, or never married), economic standing as measured by the poverty income ratio (PIR) with thresholds at <1.3, 1.3–3.5, and ≥3.5), and physical health indicators including body mass index (BMI) categorized into four groups: underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obese (≥30.0 kg/m2). Additionally, the study accounted for lifestyle choices like smoking and drinking habits, as well as pre-existing medical conditions such as diabetes, hypertension, and hyperlipidemia. The criteria for hypertension diagnosis were met through either a physician’s confirmation or blood pressure ≥ 140/90 mmHg. Diabetes was identified based on several indicators: a physician’s diagnosis, current use of insulin or oral hypoglycemic medications, HbA1c levels of 6.5% or higher, fasting blood glucose measurements of 126 mg/dL or more, or 2-hour postprandial blood glucose readings exceeding 200 mg/dL. The identification of hyperlipidemia and additional chronic diseases relied primarily on two sources: self-reported physician diagnoses and prescription medication records.
2.5. Outcome ascertainment
Mortality status was determined by linking NHANES participant data with the National Death Index (NDI), with data tracked up to December 31, 2019. The primary outcome indicators are premature mortality and all-cause mortality. Premature mortality was considered to be any death occurring before the age of 75 years. This age threshold was based on the approximate average age at death for U.S. adults during the study period. 21 All-cause mortality included deaths from any and every cause. 22
2.6. Statistical analysis
The analysis adhered to NHANES protocols using R software (v 4.5.1). To guarantee the findings accurately reflected the U.S. adult population, the study included primary sampling units, stratification criteria, and weighting variables to account for the discrepancies in selection probabilities and any oversampling of particular groups due to the intricate sampling scheme. Continuous data are shown as weighted averages ± SE, and categorical data are reported as weighted counts (proportions). Intergroup comparisons employed weighted Rao-Scott χ2 tests for categorical variables and weighted t-tests for continuous variables, both tailored for complex sampling designs. To tackle any missing covariate data, multiple imputation techniques were applied. Missing covariate data were imputed using multivariate imputation by chained equations (MICE) with predictive mean matching (PMM). Specifically, continuous variables were imputed using linear regression with PMM, and categorical variables were imputed using logistic regression. The imputation model included all covariates and outcomes. Five imputed datasets were generated, and estimates were pooled using Rubin’s rules. The proportion of missing data for each covariate is detailed in Supplementary Table 1. The association between exercise, extended periods of sedentary behavior, and the likelihood of death was evaluated by fitting weighted Cox proportional hazards models, presenting findings as hazard ratios with corresponding 95% CI. In addition to categorical analyses, PA and DST were also modeled as continuous variables to estimate the hazard ratio per 1-standard-deviation (SD) increase. For PA, the SD was calculated within the analytic cohort; for DST, the same approach was applied. Four sequentially adjusted nested models were established as follows: Model 1 was the unadjusted base model; Model 2 considered basic demographic factors such as gender and age; Model 3 added socioeconomic and behavioral factors, like BMI, education, race/ethnicity, drinking history, marital status, smoking, along with the PIR, to Model 2; and Model 4 further incorporated clinical comorbidities such as hypertension, diabetes, and hyperlipidemia into Model 3. The potential nonlinear associations were assessed utilizing restricted cubic spline (RCS) curves. Kaplan-Meier (KM) curves were used to compare survival patterns between PA and DST in the CVD cohort. Subgroup analyses stratified by each covariate were conducted to explore effect heterogeneity, and interaction terms were examined via likelihood ratio tests. The robustness of the primary results was examined through the following sensitivity analyses: First, participants who died within one year of follow-up were excluded. Second, participants with incomplete data were excluded. Statistical significance was determined using two-tailed tests (P<0.05).
3. Results
3.1. Baseline characteristics
This study included 2,530 CVD patients from NHANES 2007-2018. The cohort had a weighted mean age of 59.12±0.27 years, was predominantly male (56.61%), and Non-Hispanic White (68.01%). During follow-up, 531 deaths occurred, including 374 premature deaths.
Baseline characteristics stratified by exposure are shown in Tables 1 and 2. Compared with those with lower DST (<7 hours/day, n=1,506), patients with high DST (≥7 hours/day, n=1,024) had a significantly higher prevalence of Non-Hispanic White ethnicity, higher BMI, and differed in alcohol intake and diabetes status (all P < 0.05, Table 1). Similarly, the high PA group (≥500 MET-min/week, n=1,210) was significantly more likely to comprise younger individuals, males, Non-Hispanic Whites, and those with higher education, PIR, and BMI, while having lower rates of hypertension and diabetes, compared to the low PA group (n=1,320, Table 2). No significant differences were found for smoking, alcohol use, or hyperlipidemia by PA level.
Table 1.
Characteristics of the study population according to the levels of daily sitting time.
| Variable | Total (n = 2530) | <7(n=1506) | ≥7(n=1024) | Statistic | P |
|---|---|---|---|---|---|
| Age, Mean (SE) | 59.12 (0.27) | 58.69 (0.37) | 59.67 (0.45) | t=1.63 | 0.107 |
| Sex, n(%) | χ2=0.71 | 0.463 | |||
| Male | 1422 (56.61) | 845 (55.87) | 577 (57.54) | | |
| Female | 1108 (43.39) | 661 (44.13) | 447 (42.46) | | |
| Race and ethnicity, n(%) | χ2=38.91 | <.001 | |||
| Mexican American | 288 (5.29) | 219 (7.16) | 69 (2.90) | | |
| Other Hispanic | 246 (4.34) | 171 (5.37) | 75 (3.03) | | |
| Non-Hispanic White | 1077 (68.01) | 580 (64.15) | 497 (72.93) | | |
| Non-Hispanic Black | 688 (14.08) | 390 (14.23) | 298 (13.89) | | |
| Other Race-Including Multi-Racial | 231 (8.28) | 146 (9.09) | 85 (7.25) | | |
| Smoking status, n(%) | χ2=0.44 | 0.624 | |||
| No | 921 (35.89) | 563 (36.45) | 358 (35.17) | | |
| Yes | 1609 (64.11) | 943 (63.55) | 666 (64.83) | | |
| Educational level, n(%) | χ2=8.25 | 0.066 | |||
| Less than high school | 815 (21.71) | 523 (23.50) | 292 (19.43) | | |
| High school or equivalent | 665 (28.12) | 384 (28.58) | 281 (27.52) | | |
| College or higher | 1050 (50.17) | 599 (47.92) | 451 (53.04) | | |
| Alcohol use, n(%) | χ2=11.06 | 0.006 | |||
| No | 704 (23.34) | 444 (25.82) | 260 (20.18) | | |
| Yes | 1826 (76.66) | 1062 (74.18) | 764 (79.82) | | |
| Marital status, n(%) | χ2=6.24 | 0.193 | |||
| Married or living with a partner | 1396 (61.42) | 850 (63.29) | 546 (59.04) | | |
| Single (widowed/divorced/separated) | 855 (28.65) | 489 (26.68) | 366 (31.16) | | |
| Never married | 279 (9.92) | 167 (10.02) | 112 (9.79) | | |
| All-cause mortality, n(%) | χ2=20.46 | <.001 | |||
| No | 1999 (82.75) | 1234 (85.76) | 765 (78.92) | | |
| Yes | 531 (17.25) | 272 (14.24) | 259 (21.08) | | |
| Premature mortality, n(%) | χ2=25.11 | <.001 | |||
| No | 2156 (87.85) | 1324 (90.74) | 832 (84.18) | | |
| Yes | 374 (12.15) | 182 (9.26) | 192 (15.82) | | |
| Hypertension, n(%) | χ2=0.05 | 0.873 | |||
| No | 580 (26.70) | 347 (26.88) | 233 (26.48) | | |
| Yes | 1950 (73.30) | 1159 (73.12) | 791 (73.52) | | |
| Diabetes, n(%) | χ2=24.58 | <.001 | |||
| No | 1384 (59.80) | 857 (64.09) | 527 (54.35) | | |
| Yes | 1146 (40.20) | 649 (35.91) | 497 (45.65) | | |
| Hyperlipemia, n(%) | χ2=3.71 | 0.122 | |||
| No | 377 (12.35) | 234 (13.47) | 143 (10.93) | | |
| Yes | 2153 (87.65) | 1272 (86.53) | 881 (89.07) | | |
| PIR, n(%) | χ2=8.32 | 0.184 | |||
| <1.3 | 1143 (32.90) | 697 (33.91) | 446 (31.62) | | |
| [1.3,3.5) | 895 (37.84) | 549 (39.14) | 346 (36.18) | | |
| ≥3.5 | 492 (29.26) | 260 (26.94) | 232 (32.20) | | |
| BMI, n(%) | χ2=41.54 | <.001 | |||
| <25 | 460 (17.16) | 287 (19.13) | 173 (14.66) | | |
| [25,30) | 686 (26.85) | 456 (30.51) | 230 (22.19) | | |
| ≥30 | 1384 (55.98) | 763 (50.36) | 621 (63.15) | | |
SE: Standard Error; t: t-test, χ2: Chi-square test. PIR, poverty income ratio; BMI, body mass index.
Table 2.
Characteristics of the Study Population According to the Levels of Physical activity.
| Variable | Total (n = 2530) | <500 (n=1320) | ≥500 (n=1210) | Statistic | P |
|---|---|---|---|---|---|
| Age, Mean (SE) | 59.12 (0.27) | 60.06 (0.36) | 58.28 (0.44) | t=-3.05 | 0.003 |
| Sex, n(%) | χ2=22.57 | <.001 | |||
| Male | 1422 (56.61) | 669 (51.67) | 753 (61.05) | | |
| Female | 1108 (43.39) | 651 (48.33) | 457 (38.95) | | |
| Race and ethnicity, n(%) | χ2=13.31 | 0.017 | |||
| Mexican American | 288 (5.29) | 141 (4.92) | 147 (5.62) | | |
| Other Hispanic | 246 (4.34) | 136 (4.65) | 110 (4.07) | | |
| Non-Hispanic White | 1077 (68.01) | 552 (66.00) | 525 (69.81) | | |
| Non-Hispanic Black | 688 (14.08) | 395 (16.60) | 293 (11.80) | | |
| Other Race-Including Multi-Racial | 231 (8.28) | 96 (7.82) | 135 (8.70) | | |
| Smoking status, n(%) | χ2=3.13 | 0.235 | |||
| No | 921 (35.89) | 465 (34.11) | 456 (37.49) | | |
| Yes | 1609 (64.11) | 855 (65.89) | 754 (62.51) | | |
| Educational level, n(%) | χ2=50.33 | <.001 | |||
| Less than high school | 815 (21.71) | 509 (27.10) | 306 (16.86) | | |
| High school or equivalent | 665 (28.12) | 337 (29.19) | 328 (27.15) | | |
| College or higher | 1050 (50.17) | 474 (43.71) | 576 (55.99) | | |
| Alcohol use, n(%) | χ2=4.80 | 0.081 | |||
| No | 704 (23.34) | 412 (25.28) | 292 (21.59) | | |
| Yes | 1826 (76.66) | 908 (74.72) | 918 (78.41) | | |
| Marital status, n(%) | χ2=39.13 | <.001 | |||
| Married or living with a partner | 1396 (61.42) | 671 (55.91) | 725 (66.38) | | |
| Single (widowed/divorced/separated) | 855 (28.65) | 500 (34.54) | 355 (23.35) | | |
| Never married | 279 (9.92) | 149 (9.55) | 130 (10.26) | | |
| All-cause mortality, n(%) | χ2=106.27 | <.001 | |||
| No | 1999 (82.75) | 941 (74.59) | 1058 (90.10) | | |
| Yes | 531 (17.25) | 379 (25.41) | 152 (9.90) | | |
| Premature mortality, n(%) | χ2=80.25 | <.001 | |||
| No | 2156 (87.85) | 1056 (81.72) | 1100 (93.37) | | |
| Yes | 374 (12.15) | 264 (18.28) | 110 (6.63) | | |
| Hypertension, n(%) | χ2=22.60 | 0.001 | |||
| No | 580 (26.70) | 268 (22.30) | 312 (30.67) | | |
| Yes | 1950 (73.30) | 1052 (77.70) | 898 (69.33) | | |
| Diabetes, n(%) | χ2=51.86 | <.001 | |||
| No | 1384 (59.80) | 653 (52.40) | 731 (66.46) | | |
| Yes | 1146 (40.20) | 667 (47.60) | 479 (33.54) | | |
| Hyperlipemia, n(%) | χ2=4.68 | 0.078 | |||
| No | 377 (12.35) | 181 (10.86) | 196 (13.69) | | |
| Yes | 2153 (87.65) | 1139 (89.14) | 1014 (86.31) | | |
| PIR, n(%) | χ2=86.57 | <.001 | |||
| <1.3 | 1143 (32.90) | 658 (38.41) | 485 (27.94) | | |
| [1.3,3.5) | 895 (37.84) | 473 (41.06) | 422 (34.95) | | |
| ≥3.5 | 492 (29.26) | 189 (20.52) | 303 (37.11) | | |
| BMI, n (%) | χ2=44.17 | <.001 | |||
| <25 | 460 (17.16) | 219 (14.31) | 241 (19.74) | | |
| [25,30) | 686 (26.85) | 327 (22.80) | 359 (30.50) | | |
| ≥30 | 1384 (55.98) | 774 (62.90) | 610 (49.77) | | |
SE: Standard Error; t: t-test, χ2: Chi-square test. PIR, poverty income ratio; BMI, body mass index.
3.2. DST and PA: Individual and collective impacts on mortality risk
After adjusting for all confounding factors using a multivariate Cox regression model, analysis revealed that high daily sedentary time (DST ≥ 7 hours/day) and low physical activity levels (PA < 500 MET-minutes/week) were independent risk factors for mortality in patients (see Table 3). Specifically, high DST was associated with a 67% increased risk of all-cause mortality (HR = 1.67, CI: 1.32–2.11) and a 74% increased risk of premature mortality (HR = 1.74, 95% CI: 1.30–2.32). Conversely, high PA levels were associated with a 51% reduction in all-cause mortality risk (HR = 0.49, 95% CI: 0.37–0.64) and a 56% reduction in premature mortality risk (HR = 0.44, 95% CI: 0.32–0.61). When modeled continuously, each 1-SD increment in PA was associated with a 52% lower risk of all-cause mortality (HR = 0.48, 95% CI: 0.35–0.67) and a 51% lower risk of premature mortality (HR = 0.49, 95% CI: 0.36–0.68). Conversely, each 1-SD increment in DST was associated with a 31% higher risk of all-cause mortality (HR = 1.31, 95% CI: 1.17–1.47) and a 33% higher risk of premature mortality (HR = 1.33, 95% CI: 1.17–1.51).
Table 3.
Link between daily sitting time, physical activity, and fatality rates (overall and early death) in cardiovascular patients.
| Variables | Mode1 | Model2 | Model3 | Model4 | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | |
| Premature mortality | ||||||||
| DST | 1.37 (1.19 - 1.57) | <.001 | 1.36 (1.18 - 1.56) | <.001 | 1.36 (1.20 - 1.54) | <.001 | 1.33 (1.17 - 1.51) | <.001 |
| DST category | ||||||||
| <7h/d | Reference | | Reference | | Reference | | Reference | |
| ≥7h/d | 1.82 (1.33 - 2.50) | <.001 | 1.80 (1.30 - 2.50) | <.001 | 1.80 (1.35 - 2.41) | <.001 | 1.74 (1.30 - 2.32) | <.001 |
| PA | 0.38 (0.26 - 0.55) | <.001 | 0.38 (0.25 - 0.57) | <.001 | 0.47 (0.34 - 0.65) | <.001 | 0.49 (0.36 - 0.68) | <.001 |
| PA category | ||||||||
| <500 MET-min/week | Reference | | Reference | | Reference | | Reference | |
| ≥500 MET-min/week | 0.35 (0.26 - 0.47) | <.001 | 0.34 (0.25 - 0.48) | <.001 | 0.42 (0.31 - 0.57) | <.001 | 0.44 (0.32 - 0.61) | <.001 |
| Joint group | ||||||||
| PA<500+ DST≥7h/d | Reference | | Reference | | Reference | | Reference | |
| PA≥500+ DST≥7h/d | 0.38 (0.23 - 0.62) | <.001 | 0.37 (0.22 - 0.63) | <.001 | 0.48 (0.29 - 0.79) | 0.004 | 0.49 (0.30 - 0.82) | 0.006 |
| PA<500+ DST<7h/d | 0.66 (0.46 - 0.95) | 0.026 | 0.68 (0.46 - 0.99) | 0.046 | 0.65 (0.46 - 0.93) | 0.017 | 0.67 (0.48 - 0.93) | 0.018 |
| PA≥500+ DST<7h/d | 0.25 (0.17 - 0.37) | <.001 | 0.25 (0.17 - 0.38) | <.001 | 0.30 (0.21 - 0.43) | <.001 | 0.31 (0.21 - 0.46) | <.001 |
| All-cause mortality | ||||||||
| DST | 1.33 (1.19 - 1.50) | <.001 | 1.32 (1.17 - 1.49) | <.001 | 1.32 (1.18 - 1.48) | <.001 | 1.31 (1.17 - 1.47) | <.001 |
| DST category | ||||||||
| <7h/d | Reference | | Reference | | Reference | | Reference | |
| ≥7h/d | 1.68 (1.32 - 2.13) | <.001 | 1.68 (1.30 - 2.16) | <.001 | 1.71 (1.35 - 2.16) | <.001 | 1.67 (1.32 - 2.11) | <.001 |
| PA | 0.36 (0.25 - 0.52) | <.001 | 0.37 (0.25 - 0.55) | <.001 | 0.46 (0.32 - 0.64) | <.001 | 0.48 (0.35 - 0.67) | <.001 |
| PA category | ||||||||
| <500 MET-min/week | Reference | | Reference | | Reference | | Reference | |
| ≥500 MET-min/week | 0.38 (0.29 - 0.49) | <.001 | 0.38 (0.29 - 0.51) | <.001 | 0.47 (0.36 - 0.61) | <.001 | 0.49 (0.37 - 0.64) | <.001 |
| Joint group | ||||||||
| PA<500+ DST≥7h/d | Reference | | Reference | | Reference | | Reference | |
| PA≥500+ DST≥7h/d | 0.37 (0.23 - 0.58) | <.001 | 0.38 (0.24 - 0.61) | <.001 | 0.48 (0.30 - 0.76) | 0.002 | 0.49 (0.31 - 0.77) | 0.002 |
| PA<500+ DST<7h/d | 0.67 (0.50 - 0.91) | 0.009 | 0.69 (0.50 - 0.95) | 0.021 | 0.65 (0.48 - 0.88) | 0.006 | 0.65 (0.48 - 0.88) | 0.005 |
| PA≥500+ DST<7h/d | 0.30 (0.22 - 0.41) | <.001 | 0.30 (0.22 - 0.42) | <.001 | 0.35 (0.26 - 0.48) | <.001 | 0.37 (0.26 - 0.51) | <.001 |
HR: Hazard Ratio, CI: Confidence Interval. Model1: crude model. Model2: adjusted for age and sex. Model 3: adjusted for age, sex, race and ethnicity, educational level, smoking status, alcohol use, marital status, BMI, and PIR. Model4: additionally adjusted for hypertension, diabetes, and hyperlipemia. DST, daily sitting time; PA, physical activity; BMI, body mass index; PIR, poverty income ratio.
When examining the joint associations of PA and DST, patients with low DST combined with high PA had the lowest mortality risk when compared to the “high DST/low PA” group, showing a 63% reduction in all-cause mortality risk (HR = 0.37, 95% CI: 0.26–0.51) and a 69% reduction in premature mortality risk (HR = 0.31, 95% CI: 0.21–0.46). Notably, even among patients with high DST, maintaining high PA levels reduced mortality risk by 51%. Sensitivity analysis results further supported the robustness of these findings (see Supplementary Tables 2 and 3).
3.3. Dose-response association between DST and PA with mortality risk
RCS analysis showed a notable direct correlation between DST and the likelihood of overall and early death (P for nonlinearity > 0.05), indicating a continuous increase in mortality risk with longer sedentary time (Figure 2). In contrast, PA showed a significant nonlinear negative association with the risks of all-cause and premature mortality (P for nonlinearity < 0.05). Specifically, mortality risk decreased rapidly with increasing activity levels at the lower end of the physical activity range, while the risk reduction plateaued at higher activity levels. (Figure 3).
Figure 2.
RCS curves for the association of the daily sitting time and mortality.(a) shows the curve of DST with the risk of all-cause mortality; (b) shows the curve of DST with the risk of premature death.
Figure 3.
RCS curves for the association of the physical activity and mortality. (a) Shows the curve of PA with the risk of all-cause mortality; (b) shows the curve of PA with the risk of premature death.
3.4. DST and PA impact on patient survival, singular and combined
Kaplan-Meier curves demonstrated significant associations of DST and PA with survival outcomes. Patients with high DST (≥7 hours/day) exhibited lower cumulative survival probabilities for both all-cause and premature mortality compared to the low DST group (Figure 4). Conversely, high PA (≥500 MET-min/week) was associated with significantly superior survival probabilities (Figure 5). In the combined analysis, patients with the high DST/low PA profile showed the poorest survival, whereas those with the low DST/high PA profile had the most favorable outcomes (Figure 6). These results confirm that high PA and low DST are independent and combined protective factors for survival in CVD patients.
Figure 4.
Kaplan-Meier survival curves stratified by daily sitting time (<7 hours/day vs. ≥7 hours/day). Panel A shows the association with all-cause mortality; Panel B shows the association with premature death.
Figure 5.
Kaplan-Meier survival curves stratified by physical activity level (<500 MET-min/week vs. ≥500 MET-min/week). Panel A shows the association with all-cause mortality; Panel B shows the association with premature death.
Figure 6.
Kaplan-Meier survival curves stratified by the combination of daily sitting time and physical activity (high DST/low PA, high DST/high PA, low DST/low PA, and low DST/high PA). Panel A shows the association with all-cause mortality; Panel B shows the association with premature death.
3.5. Subgroup analysis
Subgroup analyses demonstrated the consistent protective effect of the “high PA and low DST” lifestyle pattern against all-cause and premature mortality across all predefined subgroups, including those stratified by gender, age, ethnicity, socioeconomic status, and clinical comorbidities (all interaction P > 0.05; Figures 7 and 8). In the overall cohort, The pattern correlated to a 28% diminished likelihood of overall mortality (HR = 0.72, 95% CI: 0.64–0.80) and a 32% reduced chance of early death (HR = 0.68, 95% CI: 0.59–0.78). The association was statistically significant (all P < 0.05) and directionally consistent in all subgroups, indicating no evidence of effect modification.
Figure 7.
Subgroup evaluation—All-cause mortality.
Figure 8.
Subgroup evaluation—Premature Mortality.
4. Discussion
This study systematically examined the independent and combined effects of two modifiable behavioral risk factors—PA and DST on all-cause mortality and premature mortality using a nationally representative CVD cohort. Analysis suggested that DST≥7 hours/day and PA<500 MET-min/week are both independent risk factors for all-cause mortality and premature mortality. Compared with the PA<500 MET-min/week and DST≥7 hours/day group, the PA≥500 MET-min/week and DST < 7 hours/day group showed the greatest reduction in risks of all-cause and premature mortality, with a notably reduced hazard ratio (HR=0.31, 95% CI: 0.21-0.46) for premature mortality.
These associations are supported by plausible physiological mechanisms. Regular exercise improves endothelial function via the PI3K-AKT pathway, enhances insulin sensitivity through metabolic adaptations, and provides cardioprotective effects via autonomic regulation6,7,23; whereas DST≥7 hours/day behavior leads to muscular metabolic dysfunction, blood stasis, and low-grade inflammation.10,11 Consequently, reducing DST may establish a physiological foundation for enhancing the benefits of PA, while increased PA can mitigate the adverse effects of DST behavior. These complementary physiological pathways provide a rational mechanism for the reduced mortality risk observed among individuals adhering to the “PA≥500 MET-min/week and DST < 7 hours/day” behavioral pattern. Our findings support and corroborate prior evidence reported in the literature, collectively revealing a nonlinear dose-response relationship between PA and mortality: risk initially demonstrates a steep inverse association with increasing activity levels, followed by a plateau phase. Our results corroborate the findings reported by Feng and colleagues, which indicated the most pronounced benefits within the 0–150 minutes/week activity range. 24 Physical inactivity, a major public health concern, is associated with 5–10% of premature mortality globally, 25 and its protective effects have also been demonstrated in populations with chronic diseases such as type 1 diabetes. 26 Building on this, our study further clarifies that achieving≥500 MET-min/week in patients with CVD is associated with a 56% reduction in the risk of premature mortality. This finding supports the conclusion that PA is crucial for delaying mortality in patients with CVD. 27 Separately, sedentary behavior is an established risk factor for CVD and premature mortality in the general population. 28 Our study validated this association in patients with CVD, observing that sedentary behavior was linked to a 74% escalation in the likelihood of premature mortality. Although research in this population is limited and often focuses on all-cause mortality outcomes, 29 our study refined the risk trajectory by incorporating a broader disease spectrum. We identified a threshold for significantly elevated all-cause mortality risk at 7 hours per day (HR 1.67). Compared with studies in stroke patients, which reported a threshold of 8 hours (HR 1.50), 30 this difference suggests potential nuances in susceptibility to sedentary behavior across different CVD subgroups. Nevertheless, all evidence converges on the same core conclusion: sedentary behavior is a significant driver of mortality risk.
Cardiovascular disease is influenced by a complex interplay of traditional and emerging risk factors. Traditional risk factors, including hypertension, diabetes, hyperlipidemia, smoking, and obesity, have long been established as major contributors to CVD morbidity and mortality. 31 These factors were adjusted for in our multivariable models to isolate the independent effects of PA and DST. Beyond these conventional determinants, recent evidence has highlighted the growing importance of non-traditional risk factors, including psychosocial stress, social life and environment, inflammatory biomarkers, novel biomarkers, chronic conditions, infections, and sex-specific condition.32,33 While these emerging factors may interact with lifestyle behaviors to modulate cardiovascular risk, their assessment is often not feasible in large epidemiological surveys such as NHANES. In this context, our study focused on two modifiable behavioral factors, PA and DST, that are both feasible to assess in routine clinical practice and amenable to behavioral modification, providing pragmatic evidence for risk stratification in CVD patients.
Among patients with CVD, the combination of PA ≥ 500 MET-min/week and DST < 7 hours/day constitutes a potent protective factor against all-cause mortality and premature mortality. In this study, patients who were PA ≥ 500 MET-min/week and DST < 7 hours/day exhibited the lowest risks of all-cause and premature mortality, indicating that PA and reduced DST jointly exert favorable protective effects, consistent with some prior research. For example, Research on stroke patients demonstrated that those maintaining activity and sitting fewer than 8 hours per day faced substantially lower overall mortality rates. 30 This study extends this finding to a broader spectrum of CVD populations. Furthermore, the study observed that even among sedentary patients with CVD, higher levels of PA were associated with reduced risks of all-cause and premature mortality. This differs from a forward-looking cohort analysis indicating that in subjects with DST durations of ≥8 hours, achieving recommended PA levels failed to significantly reduce mortality. 34 This discrepancy may stem from several factors: First, the harmful effects of sedentary behavior may exhibit a “threshold effect,” where risks intensify beyond a higher threshold of ≥8 hours of DST, making it difficult for conventional doses of PA to fully offset these harms. Second, the longer exposure period assessed in this study (30 days) compared to previous studies may more accurately reflect the cumulative effects of habitual activity levels.
Unlike previous studies that primarily focused on all-cause mortality,35–37 this research centers on the critical outcome of “death before age 75,” providing evidence for developing personalised exercise prescriptions in clinical practice. The study further assessed the stability of the protective effect of the combined behavioral pattern of “PA≥500 MET-min/week and DST<7 hours/day” across different subgroups, revealing consistent protective benefits against premature mortality across all subgroups. Notably, the protective effect was particularly pronounced among high-income individuals with a PIR≥3.5, potentially reflecting greater access to exercise resources and health guidance in this demographic. This finding provides crucial insights for developing inclusive strategies to prevent premature mortality.
Several limitations of this study should be acknowledged. First, PA, DST, and CVD status were all derived from self-report, which is susceptible to recall and social desirability bias. Second, the final analytic cohort comprised only 2.2% of the initial NHANES adult population due to exclusions for incomplete CVD data. This high exclusion rate may introduce selection bias, as included participants were more likely to be community-dwelling and functionally capable, limiting generalizability to institutionalized or severely impaired CVD patients. This study mainly reflects people with cardiovascular disease who live in regular households in the U.S, so its findings may not fully apply to patients living in nursing homes, hospitals, or care facilities, or to patients with severe disabilities. So, we should be cautious when trying to apply the results to the broader CVD population. Third, although we excluded deaths within the first year to mitigate reverse causation, the lack of objective measures of disease severity (e.g., ejection fraction, NYHA class) precludes us from fully ruling out the possibility that functional limitations, rather than behavioral factors per se, drove the observed associations. Fourth, as a secondary analysis of existing data, no formal sample size calculation was performed, and certain subgroup analyses may have limited statistical power. Fifth, PA and DST were measured only at baseline, so changes over time were not captured. Residual confounding from unmeasured factors (e.g., sleep quality, diet, medication adherence) may also persist. Finally, this is an observational study, so causal inferences cannot be drawn.
Despite these limitations, the study’s strengths include a nationally representative sample, rigorous adjustment for comprehensive confounders, and objective mortality linkage via the National Death Index. The consistent findings across sensitivity and subgroup analyses further support the robustness of our conclusions.
We recommend individualised lifestyles for patients with CVD, emphasizing the concurrent achievement of < 7 hours of DST and ≥500 MET-min/week of PA. Clinicians should assess each patient’s functional status and create a personalized physical activity plan based on that, starting with low-intensity activities like walking and gradually increasing to reach a goal of ≥500 MET-minutes/week, which aligns with standard cardiac rehab programs. For key populations such as the elderly, those with multiple comorbidities, or individuals with low socioeconomic status, accessible exercise resources should be provided while adhering to the principle of gradual progression. Intervention studies have demonstrated that structured exercise programs can effectively increase physical activity levels and reduce sedentary time in CVD patients, 38 supporting the feasibility of implementing the behavioral pattern identified in our study. Future studies will still need proactive interventions to further confirm the causal relationships,explore the impact of behavioral dynamics on risk, and develop risk prediction models integrating behavioral and clinical factors to achieve precision prevention and control for high-risk patients.
5. Conclusion
This nationwide cohort study suggests that higher PA and lower DST were associated with lower hazards of all-cause and premature mortality in patients with CVD. Notably, the pattern combining PA ≥ 500 MET-min/week and DST < 7 hours/day was associated with the lowest mortality risk, with mortality risk reduced by more than 60% compared with the reference group. These associations were stable across subgroups stratified by gender, age, ethnicity, socioeconomic status and comorbidity, supporting the use of these behavioral factors in routine cardiovascular risk evaluation.
Supplemental material
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Acknowledgements
The authors acknowledge the NHANES survey teams and study participants for their valuable contributions to this research. During the preparation of this work, the authors used ChatGPT (OpenAI) for language refinement. The authors have reviewed and revised the output and take full responsibility for the content of the published article.
Appendix.
Abbreviations
- DST
Daily sitting time
- PA
Physical activity
- CVD
Cardiovascular Disease
- NHANES
National Health and Nutrition Examination Survey
- MET
Min/week metabolic equivalent of task-minutes per week
- RCS
Restricted cubic spline
- K-M
Kaplan-Meier
- CI
Confidence interval
- HR
Hazard ratio
- PIR
Poverty income ratio
- BMI
Body mass index
Author contributions: Y.Y: was responsible for designing the study, analyzing data, interpreting the data, drafting the manuscript, revising the manuscript, and approving the final version. H.N: participated in the design of analyses, data analysis, and approval of the final version. Y.C: interpretation of the data and the approval of the final version. D.W: interpretation of the data and the approval of the final version. X.C: participated in formulating the research question, design of analyses, data analysis, interpretation of the data, and the approval of the final version. All authors read and approved the final version of the manuscript. All authors contributed equally to the manuscript and read and approved the final version of the manuscript.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by a grant from Army Logistics Autonomous Research Program (ZLJ22J027).
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental material: Supplemental material for this article is available online.
ORCID iDs
Yuqing Yuan https://orcid.org/0009-0006-6550-5896
Xuebin Cao https://orcid.org/0000-0003-1492-0775
Ethical considerations
The NHANES survey protocol was approved by the NCHS Research Ethics Review Board. The participants provided their written informed consent to participate in this study. As this study constitutes a secondary analysis of publicly available, de-identified data, it was exempt from additional ethical review by our institutional review board. Already communicated with our organization’s ethics committee and got an ethics exemption.
Data Availability Statement
The datasets generated or analyzed during this study are available in the NHANES repository. https://www.cdc.gov/nchs/nhanes/index.htm.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Supplemental material for Association of modifiable behavioral risk factors with premature and all-cause mortality in patients with cardiovascular disease: A retrospective cohort study by Yuqing Yuan, Huiru Niu, Yingkai Cui, Dongying Wang and Xuebin Cao in Science Progress.
Data Availability Statement
The datasets generated or analyzed during this study are available in the NHANES repository. https://www.cdc.gov/nchs/nhanes/index.htm.