College Students’ Perception of Current and Projected 30-Year Cardiovascular Disease Risk Using Cluster Analysis with Internal Validation

  • Dieu-My T. TranEmail author
Original Paper


Cardiovascular risk factors in young adults at a national level are less than ideal specifically for hypercholesterolemia, hypertension, and diabetes. Explore college students’ perception of their 30-year cardiovascular disease (CVD) risk using cluster analysis technique with internal validation. This is a descriptive and cross-sectional study. A total of 133 college students, aged 20–36 with no known history of CVD, were recruited and used to perform cluster analysis with internal validation. The mean age of the sample was 24.85 and predominately female (59.5%). The mean score for perception of cardiovascular risk factors was 21.20 ranging from 12 to 34 points on a Likert scale. The mean score for the 30-year CVD risk assessment was 5.23 ranging from 1 to 22%. Five clusters emerged from the cluster analysis. However, two of the clusters, the highest risk with moderate perception and low risk and lowest perception, were identified as areas for potential intervention as there were discrepancies between subjects’ perceived risk and their actual 30-year risk. The national data and literature has indicated a lack of awareness of CVD risk among this population which our study also concurred. Identifying the discrepancies between the perceived and projected CVD risk are useful for researchers and clinicians such as nurses to take the initiative to focus on and begin to intervene in this population to reduce potential adverse events of CVD.


Cluster analysis Students Cardiovascular risk Perception 


Compliance with Ethical Standards

Conflict of interest

The author declared no conflicts of interest related to this manuscript.


  1. 1.
    Akhtar, P. C., Haw, S. J., Currie, D. B., Zachary, R., & Currie, C. E. (2009). Smoking restrictions in the home and secondhand smoke exposure among primary schoolchildren before and after introduction of the scottish smoke-free legislation. Tobacco Control, 18(5), 409–415. Scholar
  2. 2.
    Arts, J., Fernandez, M. L., & Lofgren, I. E. (2014). Coronary heart disease risk factors in college students. Advances in Nutrition, 5(2), 177–187. Scholar
  3. 3.
    Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., et al. (2017). Heart disease and stroke statistics-2017 update: A report from the american heart association. Circulation, 135(10), e146–e603. Scholar
  4. 4.
    Breckenridge, J. N. (2000). Validating cluster analysis: Consistent replication and symmetry. Multivariate Behavioral Research, 35(2), 261–285.CrossRefGoogle Scholar
  5. 5.
    Bucholz, E. M., Gooding, H. C., & de Ferranti, S. D. (2018). Awareness of cardiovascular risk factors in US young adults aged 18–39 years. American Journal of Preventive Medicine, 54(4), e67–e77.CrossRefGoogle Scholar
  6. 6.
    Collins, K. M., Dantico, M., Shearer, N. B. C., & Mossman, K. L. (2004). Heart disease awareness among college students. Journal of Community Health, 29(5), 405–420.CrossRefGoogle Scholar
  7. 7.
    Davidson, P. M., Salamonson, Y., Rolley, J., Everett, B., Fernandez, R., Andrew, S., et al. (2011). Perception of cardiovascular risk following a percutaneous coronary intervention: A cross sectional study. International Journal of Nursing Studies, 48(8), 973–978. Scholar
  8. 8.
    Deborah, L. J., Baskaran, R., & Kannan, A. (2010). A survey on internal validity measure for cluster validation. International Journal of Computer Science & Engineering Survey, 1(2), 85–102.CrossRefGoogle Scholar
  9. 9.
    Gooding, H. C., McGinty, S., Richmond, T. K., Gillman, M. W., & Field, A. E. (2014). Hypertension awareness and control among young adults in the national longitudinal study of adolescent health. Journal of General Internal Medicine, 29(8), 1098–1104.CrossRefGoogle Scholar
  10. 10.
    Greene, G. W., Schembre, S. M., White, A. A., Hoerr, S. L., Lohse, B., Shoff, S., et al. (2011). Identifying clusters of college students at elevated health risk based on eating and exercise behaviors and psychosocial determinants of body weight. Journal of the American Dietetic Association, 111(3), 394–400.CrossRefGoogle Scholar
  11. 11.
    Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2–3), 107–145.CrossRefGoogle Scholar
  12. 12.
    Huang, T. T., Kempf, A. M., Strother, M. L., Li, C., Lee, R. E., Harris, K. J., et al. (2004). Overweight and components of the metabolic syndrome in college students. Diabetes Care, 27(12), 3000–3001.CrossRefGoogle Scholar
  13. 13.
    Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions. Neural Computation, 16(6), 1299–1323.CrossRefGoogle Scholar
  14. 14.
    Liu, Y., Li, Z., Xiong, H., Gao, X., & Wu, J. (2010). Understanding of internal clustering validation measures. In Paper presented at IEEE 10th international conference on data mining (ICDM), pp. 911–916.Google Scholar
  15. 15.
    Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., & Wu, S. (2013). Understanding and enhancement of internal clustering validation measures. IEEE Transactions on Cybernetics, 43(3), 982–994.CrossRefGoogle Scholar
  16. 16.
    Pencina, M. J., D’Agostino, R. B., Larson, M. G., Massaro, J. M., & Vasan, R. S. (2009). Predicting the 30-year risk of cardiovascular disease the framingham heart study. Circulation, 119(24), 3078–3084.CrossRefGoogle Scholar
  17. 17.
    Quatromoni, P. A., Copenhafer, D. L., Demissie, S., D’Agostino, R. B., O’Horo, C. E., Nam, B., et al. (2002). The internal validity of a dietary pattern analysis. the framingham nutrition studies. Journal of Epidemiology and Community Health, 56(5), 381.CrossRefGoogle Scholar
  18. 18.
    Racette, S. B., Deusinger, S. S., Strube, M. J., Highstein, G. R., & Deusinger, R. H. (2005). Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. Journal of American College Health, 53(6), 245–251.CrossRefGoogle Scholar
  19. 19.
    Tovar, E. G., Rayens, M. K., Clark, M., & Nguyen, H. (2010). Development and psychometric testing of the health beliefs related to cardiovascular disease scale: Preliminary findings. Journal of Advanced Nursing, 66(12), 2772–2784. Scholar
  20. 20.
    Tran, D. M., Zimmerman, L. M., & Kupzyk, K. A. (2016). Validation of the knowledge and perception of cardiovascular risk factors questionnaires for college students. Journal of Nursing Measurement, 24(2), 202–214. Scholar
  21. 21.
    Vella-Zarb, R. A., & Elgar, F. J. (2009). The ‘freshman 5’: A meta-analysis of weight gain in the freshman year of college. Journal of American College Health, 58(2), 161–166.CrossRefGoogle Scholar
  22. 22.
    Wang, C. J., & Biddle, S. J. (2001). Young people’s motivational profiles in physical activity: A cluster analysis. Journal of Sport and Exercise Psychology, 23(1), 1–22.CrossRefGoogle Scholar
  23. 23.
    Yim, O., & Ramdeen, K. T. (2015). Hierarchical cluster analysis: Comparison of three linkage measures and application to psychological data. The Quantitative Methods for Psychology, 11(1), 8–21.CrossRefGoogle Scholar
  24. 24.
    Zaki, S. M., Ajabnoor, M. A., Aziz, M. A., & Hassan, R. M. (2013). Relation of body composition to dietary habits and lifestyles among male college students with possible blood pressure affection: A cross section study. Wulfenia Journal, 20(4), 231–244.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of NursingUniversity of Nevada, Las VegasLas VegasUSA

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