Abstract
Purpose
To test the Clustered Cardiometabolic Risk (CCMR) factor explaining the relationship between physical activity and physical quality of life (QOL).
Methods
Using the U.S. National Health and Nutrition Examination Survey 2003–2006, 2,445 adults completed the CDC Healthy Days Questionnaire for measuring QOL, wore the accelerometer for assessing physical activity pattern (PAP), and completed triglyceride, glucose, serum insulin, waist circumference, blood pressure, and HDL-cholesterol tests from which the CCMR factor was created. Physical QOL was classified as poor (≥14 days with poor physical health within past 30 days) vs. good (<14 days). We classified PAP by moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), and sedentary behavior (SB). We defined MVPA, LIPA, and SB as ≥2020 counts/minute, 100–2019 counts/minute, and ≤99 counts/minute, respectively. We further classified PAP status as unhealthy (MVPA <150 min/week & SB>LIPA) or healthy (MVPA <150 min/week & SB<LIPA, or MVPA ≥150 min/week regardless of SB>LIPA or SB<LIPA). Logistic regressions analyzed the association between unhealthy PAP and poor physical QOL, adjusting for the CCMR factor, age, sex, education, and smoking behavior.
Results
Compared with having healthy PAP, individuals having unhealthy PAP had an elevated risk of poor physical QOL (OR = 1.96; 95% CI = 1.42–2.72). However, this association was explained by higher levels of the CCMR factor (OR = 1.46; 95% CI = 1.07–1.99) through poorer serum insulin (OR = 1.35; 95% CI = 1.04–1.75) and waist circumference (OR = 1.23; 95% CI = 1.02–1.50).
Conclusion
The CCMR factor (typically insulin and waist circumference) explained the association between unhealthy physical activity and poor physical QOL.
Keywords: Cardiometabolic risk, National health and nutrition examination survey, Physical activity, Quality of life
1. Introduction
More than 70% of adult Americans perform insufficient physical activity.1 A report from the U.S. Secretary of Health and Human Services suggests that achieving recommended levels of physical activity and decreasing sedentary behavior can benefit health outcomes.2 Physiological research found positive effects of increased physical activity on cardio-related biomarkers (e.g., triglycerides, glucose, insulin, HDL-cholesterol, C-reactive protein, neutrophil levels, and homeostatic model assessment-%B or -%S).3,4 Individuals performing regular physical activity also show better quality of life (QOL).5,6
Although the association between physical activity and QOL has been examined previously, specific biological mechanisms, especially the Clustered Cardiometabolic Risk (CCMR) factor consisting of triglyceride, HDL-cholesterol, fasting plasma glucose, fasting serum insulin, waist circumference, systolic blood pressure, and diastolic blood pressure,7,8 for explaining physical activity-QOL associations have not been identified. This study tested how the CCMR factor explains the association between physical inactivity/sedentary behaviors and poor QOL in a national representative sample.
2. Methods
This cross-sectional study included 2,445 adults aged ≥18 years who partook in the U.S. National Health and Nutrition Examination Survey (NHANES)9 between 2003 and 2006 (Supplementary Fig. 1). Participants completed the CDC Healthy Days Questionnaire for measuring QOL, wore the ActiGraph accelerometer for assessing physical activity pattern (PAP), and completed laboratory tests for collecting biomarkers (triglyceride, HDL-cholesterol, fasting plasma glucose, fasting serum insulin, waist circumference, systolic blood pressure, and diastolic blood pressure) from which the CCMR factor was created.
We calculated the CCMR factor based on the tests of triglyceride, HDL-cholesterol, fasting plasma glucose, fasting serum insulin, waist circumference, systolic blood pressure, and diastolic blood pressure. We normalized (log10) the participants’ triglyceride, glucose, and insulin values, and calculated the standardized z-scores (i.e., (value - mean)/standard deviation) for each variable. We inverted the HDL-cholesterol z-scores to unify the direction with other biomarkers of the CCMR factor (higher scores for higher risk), averaged the systolic and diastolic blood pressure z-scores, and averaged the z-scores of the six CCMR biomarkers for each participant.7
For physical activity measurement, participants wore the ActiGraph accelerometer for one week during waking hours except for activities in the water. A minimum of 10 h wear time per day for ≥4 days is deemed valid data for analysis.10 We classified PAP by moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), and sedentary behavior (SB). For each participant, we defined MVPA as ≥2020 counts per minute, LIPA as between 100 and 2019 counts per minute, and SB as ≤99 counts per minute.10 We further classified PAP status as unhealthy (MVPA <150 min per week & SB>LIPA) or healthy (MVPA <150 min per week & SB<LIPA, or MVPA ≥150 min per week regardless of SB>LIPA or SB<LIPA).11 For QOL, we focused on physical-related QOL, which was measured by the CDC Healthy Days Questionnaire: “How many days during the past 30 days was your physical health not good?” Based on previous studies, we classified each participant's physical QOL as poor (≥14 days) or good (<14 days) status.6
Logistic regressions were performed to analyze how the CCMR factor explains the association between unhealthy (vs. healthy) PAP and poor (vs. good) physical QOL. Four separate models were implemented to account for the influence of different covariates. In Model 1, the association between unhealthy PAP and poor physical QOL was tested without adjusting for covariates. In Model 2, the association between PAP and QOL was tested by adjusting for age, sex, educational attainment, and smoking status, which were selected based on significant bivariate associations (p < 0.1) between physical activity and QOL. Model 3 added the CCMR factor to the variables used in Model 2. Extending from Model 3, Models 4a-4f replaced the CCMR factor with the six individual biomarkers of the CCMR factors to delineate the contribution of each biomarker to the PAP-QOL associations.
SPSS Statistics 27 was used for all analyses with the consideration of 4-year sample weights.
3. Results
Table 1 shows the characteristics of participants (N = 2,445). More than 47% of participants had an unhealthy PAP and over 9% of participants had poor physical QOL.
Table 1.
Characteristics of study participants (N = 2445).
| Characteristics | N | Weighted % |
|---|---|---|
| Age at study (years) | ||
| 18–39 | 882 | 34.1 |
| 40–59 | 706 | 41.0 |
| 60–79 | 679 | 21.2 |
| ≥80 | 178 | 3.7 |
| Sex | ||
| Male | 1237 | 49.8 |
| Female | 1208 | 50.2 |
| Race/ethnicity | ||
| Non-Hispanic white | 1292 | 74.9 |
| Non-Hispanic black | 457 | 8.8 |
| Hispanic | 594 | 10.7 |
| Other | 102 | 5.6 |
| Educational attainment | ||
| Less than high school | 364 | 6.8 |
| High school graduate/general education development | 887 | 34.0 |
| Some college or associate degree | 702 | 32.6 |
| College graduate or above | 490 | 26.6 |
| Family income (poverty income ratio) | ||
| <1 | 355 | 8.3 |
| 1–2.99 | 957 | 34.8 |
| ≥3 | 1021 | 56.9 |
| Smoking status | ||
| Never smoker | 1147 | 50.2 |
| Former smoker | 676 | 27.8 |
| Current smoker | 434 | 22.0 |
| Number of chronic health conditions | ||
| 0 | 1481 | 62.1 |
| 1 | 609 | 25.1 |
| 2 | 182 | 6.3 |
| 3 | 96 | 3.9 |
| 4+ | 77 | 2.7 |
| Physical activity pattern | ||
| <150 min/week MVPA and negative LIPA-SED balance | 1250 | 47.1 |
| <150 min/week MVPA and positive LIPA-SED balance | 216 | 8.8 |
| ≥150 min/week MVPA and negative LIPA-SED balance | 631 | 29.0 |
| ≥150 min/week MVPA and positive LIPA-SED balance | 348 | 15.1 |
| Quality of life (CDC physical unhealthy days) | ||
| <14 days | 2184 | 90.7 |
| ≥14 days | 259 | 9.3 |
| Biomarkers | Mean ± SD | Weighted Mean |
| Clustered cardiometabolic risk factor | −0.01 ± 0.61 | −0.03 |
| Triglyceride (mmol/L) | 1.64 ± 1.36 | 1.65 |
| HDL-cholesterol (mmol/L) | 1.43 ± 0.41 | 1.43 |
| Fasting plasma glucose (mmol/L) | 5.79 ± 1.79 | 5.66 |
| Fasting serum insulin (pmol/L) | 67.69 ± 64.52 | 64.07 |
| Waist circumference (cm) | 97.73 ± 15.21 | 97.82 |
| Systolic blood pressure (mmHg) | 125.00 ± 20.34 | 122.87 |
| Diastolic blood pressure (mmHg) | 68.74 ± 13.93 | 70.29 |
Note, MVPA = moderate-to-vigorous physical activity; LIPA = light-intensity physical activity; SED = sedentary behavior.
Table 2 shows the association between unhealthy PAP and poor physical QOL with and without adjusting for the CCMR factor and covariates. Models 1 and 2 suggest that compared with participants having healthy PAP, those having unhealthy PAP had an elevated risk of poor physical QOL (OR = 1.96; 95% CI = 1.42, 2.72 in Model 1; OR = 1.49; 95% CI = 1.03, 2.17 in Model 2). However, Model 3 suggests that the association between unhealthy PAP and poor physical QOL was explained after adding the CCMR factor and other covariates. Specifically, there was a significant association between the CCMR factor and poor physical QOL (OR = 1.46; 95% CI = 1.07, 1.99), whereas the association between unhealthy PAP and poor physical QOL became not significant (OR = 1.36; 95% CI = 0.93, 2.00).
Table 2.
Associations between PAP and QOL adjusting for the CCMR, six individual CCMR biomarkers, and other covariates.
| Model 1 |
Model 2 |
Model 3 |
Model 4a |
Model 4b |
Model 4c |
Model 4d |
Model 4e |
Model 4f |
|
|---|---|---|---|---|---|---|---|---|---|
| Factors | OR |
OR |
OR |
OR |
OR |
OR |
OR |
OR |
OR |
| (95% CI) | (95% CI) | (95% CI) | (95% CI) | (95% CI) | (95% CI) | (95% CI) | (95% CI) | (95% CI) | |
| Physical activity pattern | |||||||||
| Healthy pattern | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
| Unhealthy pattern | 1.96∗∗∗ | 1.49∗ | 1.36 | 1.47∗ | 1.46∗ | 1.46 | 1.33 | 1.38 | 1.49∗ |
|
(1.42, 2.72) |
(1.03, 2.17) |
(0.93, 2.00) |
(1.01, 2.12) |
(1.00, 2.13) |
(0.99, 2.15) |
(0.91, 1.94) |
(0.94, 2.02) |
(1.02, 2.18) |
|
| Age at study (years) | 1.02∗∗∗ | 1.02∗∗ | 1.02∗∗ | 1.02∗∗∗ | 1.02∗∗ | 1.02∗∗∗ | 1.02∗∗ | 1.02∗∗∗ | |
| (1.01, 1.03) | (1.01, 1.03) | (1.01, 1.03) | (1.01, 1.03) | (1.01, 1.03) | (1.02, 1.04) | (1.01, 1.03) | (1.01, 1.03) | ||
| Sex | |||||||||
| Male | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Female | 0.95 | 1.09 | 0.96 | 1.02 | 0.97 | 1.02 | 1.07 | 0.95 | |
| (0.61, 1.48) | (0.68, 1.74) | (0.61, 1.51) | (0.63, 1.64) | (0.62, 1.51) | (0.65, 1.62) | (0.68, 1.68) | (0.60, 1.50) | ||
| Educational attainment | |||||||||
| College graduate or above | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Some college or AA | 1.65 | 1.56 | 1.64 | 1.63 | 1.61 | 1.58 | 1.59 | 1.65 | |
| (0.95, 2.85) | (0.91, 2.68) | (0.95, 2.83) | (0.95, 2.80) | (0.93, 2.79) | (0.92, 2.72) | (0.92, 2.74) | (0.95, 2.85) | ||
| High school graduate/GED | 1.83∗ | 1.67 | 1.80∗ | 1.80∗ | 1.75 | 1.68 | 1.73 | 1.83∗ | |
| (1.02, 3.30) | (0.94, 2.97) | (1.00, 3.25) | (1.01, 3.22) | (0.97, 3.16) | (0.95, 3.00) | (0.98, 3.08) | (1.02, 3.30) | ||
| Less than high school | 2.32∗ | 2.12∗ | 2.28∗ | 2.25∗ | 2.18∗ | 2.17∗ | 2.28∗ | 2.32∗ | |
| (1.23, 4.38) | (1.17, 3.86) | (1.22, 4.25) | (1.23, 4.13) | (1.18, 4.03) | (1.17, 4.04) | (1.21, 4.29) | (1.22, 4.39) | ||
| Smoking status | |||||||||
| Never smoker | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Former smoker | 0.97 | 0.98 | 0.97 | 0.98 | 0.98 | 0.97 | 0.97 | 0.97 | |
| (0.64, 1.47) | (0.65, 1.47) | (0.64, 1.46) | (0.65, 1.48) | (0.65, 1.47) | (0.66, 1.45) | (0.64, 1.47) | (0.64, 1.48) | ||
| Current smoker | 1.24 | 1.27 | 1.23 | 1.23 | 1.25 | 1.32 | 1.28 | 1.24 | |
| (0.85, 1.80) | (0.87, 1.86) | (0.85, 1.77) | (0.85, 1.79) | (0.86, 1.83) | (0.91, 1.92) | (0.88, 1.86) | (0.86, 1.78) | ||
| CCMR factor | 1.46∗ | ||||||||
|
(1.07, 1.99) |
|||||||||
| Six biomarkers of CCMR factor | |||||||||
| Triglyceride (mmol/L) | 1.07 | ||||||||
| (0.91, 1.27) | |||||||||
| HDL-cholesterol (mmol/L) | 1.10 | ||||||||
| (0.92, 1.32) | |||||||||
| Fasting plasma glucose (mmol/L) | 1.15 | ||||||||
| (0.98, 1.35) | |||||||||
| Fasting serum insulin (pmol/L) | 1.35∗ | ||||||||
| (1.04, 1.75) | |||||||||
| Waist circumference (cm) | 1.23∗ | ||||||||
| (1.02, 1.50) | |||||||||
| Average blood pressure (mmHg) | 1.00 | ||||||||
| (0.80, 1.25) |
|||||||||
| Model fit | |||||||||
| −2 Log likelihood (-2LL) | 1628.34 (1) | 1515.49 (8) | 1503.14 (9) | 1513.85 (9) | 1513.34 (9) | 1506.62 (9) | 1505.13 (9) | 1500.09 (9) | 1515.19 (9) |
| -2LL change (reference: Model 1)a | NA | −112.85 (7) | −125.20 (8) | −114.49 (8) | −115.00 (8) | −121.72 (8) | −123.21 (8) | −128.25 (8) | −113.15 (8) |
| X2-statstic (p-value) | NA | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
| -2LL change (reference: Model 2)a | NA | NA | −12.35 (1) | −1.64 (1) | −2.15 (1) | −8.87 (1) | −10.36 (1) | −15.40 (1) | −0.30 (1) |
| X2-statstic (p-value) |
NA | NA | <0.001 | 0.200 | 0.143 | 0.003 | 0.001 | <0.001 | 0.584 |
Note, CCMR = clustered cardiometabolic risk; NA = not applicable; PAP = physical activity pattern; QOL = quality of life.
∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Compared to Model 1 (the reference model), model fits were significantly improved for Model 2 and Model 3, as suggested by the significant X2-statistic. The magnitude of improvement was more salient for Model 3 compared to Model 2.
In Model 4, separate analyses of the six individual biomarkers of the CCMR factor reveals that a poorer status of fasting serum insulin (OR = 1.35; 95% CI = 1.04, 1.75) and waist circumference (OR = 1.23; 95% CI = 1.02, 1.50), instead of unhealthy PAP, was significantly associated with poor physical QOL.
4. Discussion
This study demonstrates that unhealthy physical activity behaviors were associated with poor physical QOL through the influences of cardiometabolic risks measured by the CCMR factor. Among these cardiometabolic factors, interestingly, serum insulin and waist circumference, instead of triglyceride, glucose, blood pressure, and HDL-cholesterol, significantly explained the association between physical activity and QOL. From a statistical viewpoint, serum insulin and waist circumference were moderately correlated (a correlation coefficient 0.54 among study participants), and the magnitude was higher than with triglyceride, glucose, blood pressure, and HDL-cholesterol (coefficients <0.35). From a clinical viewpoint, physically inactive individuals often have higher insulin concentrations and unhealthy waist size than other cardiometabolic factors,12,13 both of which are associated with a higher burden of chronic conditions and poorer QOL.14, 15, 16
The evidence of the CCMR factor in explaining the association between unhealthy PAP and poor physical QOL paves the foundation for future clinical practice and research to improve QOL of general populations through health promotion interventions targeting the increase of physical activity to meet the recommended guidelines, especially using a scalable, longitudinal design. Emerging evidence suggests that increased physical activity over time can lead to decreased cardiovascular risks,17 and the replacment of sedentary time with a moderate-to-vigorous intensity of physical activity is associated with improved cardiovascular health over a 10-year observation.18 Future studies can extend our design by considering novel biomarkers (e.g., circulating angiogenetic factors, and antiatherogenic adaptations in vascular function and structure) to elucidate complex cardiometabolic pathways delineating physical activity and QOL associations.19
The strengths of this study include the use of a representative national sample, an objective measure of physical activity using the Actigraph accelerometer, and the incorporation of several important biomarkers. However, this study has several limitations. First, the use of a cross-sectional design cannot elucidate the causal relationship between physical activity, cardiometabolic biomarkers, and physical QOL. Additionally, we focused only on the CDC Healthy Days measure for assessing physical QOL. Longitudinal studies are warranted to replicate our findings using other QOL measures (e.g., the SF-36 or PROMIS).
In conclusion, cardiometabolic biomarkers are significant biological factors explaining the relationship between unhealthy physical activity behaviors and poor physical QOL.
Author contributions
Concept and design: Huang IC.
Administrative support: Liu JH.
Provision of study materials: NHAMES website (publicly available).
Assembly of data: Huang FH, Liu JH.
Data analysis and interpretation: Huang FH, Liu JH, Huang IC.
Manuscript writing: Huang FH, Huang IC.
Editing and final approval of manuscript: all authors.
Funding
None.
Declaration of competing interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jesf.2022.07.005.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Piercy K.L., Troiano R.P., Ballard R.M., et al. The physical activity guidelines for Americans. JAMA. 2018;320(19):2020–2028. doi: 10.1001/jama.2018.14854. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.2018 Physical Activity Guidelines Advisory Committee . 2018. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington D.C.: U.S. Department of Health and Human Services. [Google Scholar]
- 3.Healy G.N., Matthews C.E., Dunstan D.W., Winkler E.A.H., Owen N. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003-06. Eur Heart J. 2011;32(5):590–597. doi: 10.1093/eurheartj/ehq451. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Loprinzi P.D., Sheffield J., Tyo B.M., Fittipaldi-Wert J. Accelerometer-determined physical activity, mobility disability, and health. Disabil Health J. 2014;7(4):419–425. doi: 10.1016/j.dhjo.2014.05.005. [DOI] [PubMed] [Google Scholar]
- 5.Anokye N.K., Trueman P., Green C., Pavey T.G., Taylor R.S. Physical activity and health related quality of life. BMC Publ Health. 2012;12:624. doi: 10.1186/1471-2458-12-624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kim J., Im J.S., Choi Y.H. Objectively measured sedentary behavior and moderate-to-vigorous physical activity on the health-related quality of life in US adults: the National Health and Nutrition Examination Survey 2003-2006. Qual Life Res Int J Qual Life Asp Treat Care Rehabil. 2017;26(5):1315–1326. doi: 10.1007/s11136-016-1451-y. [DOI] [PubMed] [Google Scholar]
- 7.Wijndaele K., Orrow G., Ekelund U., et al. Increasing objectively measured sedentary time increases clustered cardiometabolic risk: a 6 year analysis of the ProActive study. Diabetologia. 2014;57(2):305–312. doi: 10.1007/s00125-013-3102-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Lamb M.J.E., Westgate K., Brage S., et al. Prospective associations between sedentary time, physical activity, fitness and cardiometabolic risk factors in people with type 2 diabetes. Diabetologia. 2016;59(1):110–120. doi: 10.1007/s00125-015-3756-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Us Centers for Disease Control and Prevention NHANES questionnaires, datasets, and related documentation. https://wwwn.cdc.gov/nchs/nhanes/Default.aspx November 7, 2021.
- 10.Troiano R.P., Berrigan D., Dodd K.W., Mâsse L.C., Tilert T., McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–188. doi: 10.1249/mss.0b013e31815a51b3. [DOI] [PubMed] [Google Scholar]
- 11.Loprinzi P.D., Lee H., Cardinal B.J. Daily movement patterns and biological markers among adults in the United States. Prev Med. 2014;60:128–130. doi: 10.1016/j.ypmed.2013.12.017. [DOI] [PubMed] [Google Scholar]
- 12.Kriska A.M., Pereira M.A., Hanson R.L., et al. Association of physical activity and serum insulin concentrations in two populations at high risk for type 2 diabetes but differing by BMI. Diabetes Care. 2001;24(7):1175–1180. doi: 10.2337/diacare.24.7.1175. [DOI] [PubMed] [Google Scholar]
- 13.Hamasaki H., Noda M., Moriyama S., et al. Daily physical activity assessed by a triaxial accelerometer is beneficially associated with waist circumference, serum triglycerides, and insulin resistance in Japanese patients with prediabetes or untreated early type 2 diabetes. J Diabetes Res. 2015;2015:526201. doi: 10.1155/2015/526201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Rahman M.S., Hossain K.S., Das S., et al. Role of insulin in health and disease: an update. Int J Mol Sci. 2021;22(12):6403. doi: 10.3390/ijms22126403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ross R., Neeland I.J., Yamashita S., et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR working group on visceral obesity. Nat Rev Endocrinol. 2020;16(3):177–189. doi: 10.1038/s41574-019-0310-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Alonso J., Ferrer M., Gandek B., et al. Health-related quality of life associated with chronic conditions in eight countries: results from the International Quality of Life Assessment (IQOLA) Project. Qual Life Res. 2004;13(2):283–298. doi: 10.1023/b:qure.0000018472.46236.05. [DOI] [PubMed] [Google Scholar]
- 17.Leskinen T., Stenholm S., Heinonen O.J., et al. Change in physical activity and accumulation of cardiometabolic risk factors. Prev Med. 2018;112:31–37. doi: 10.1016/j.ypmed.2018.03.020. [DOI] [PubMed] [Google Scholar]
- 18.Whitaker K.M., Pettee Gabriel K., Buman M.P., et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: the CARDIA study. J Am Heart Assoc. 2019;8(1) doi: 10.1161/JAHA.118.010212. e010212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Fiuza-Luces C., Santos-Lozano A., Joyner M., et al. Exercise benefits in cardiovascular disease: beyond attenuation of traditional risk factors. Nat Rev Cardiol. 2018;15(12):731–743. doi: 10.1038/s41569-018-0065-1. [DOI] [PubMed] [Google Scholar]
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