Skip to main content

Advertisement

SpringerLink
Log in

Development of Measurement Model for the Value of QOL as an Influential Factor of Metabolic Syndrome

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The quality of life (QOL) has been underscored by the increase in chronic diseases like metabolic syndrome and expanded life expectancy. Studies on QOL fundamentally aims to determine what is important in every individual, and to assess the concept of subjective QOL. For these reasons, QOL has been an important factor to oneself‘s needs to be objectively measured by respondents from the various aspects of life wherein these measurements are required in evaluating the value of the QOL. In this study, a measurement model was developed to assesses health-related quality of life (HRQL) objectively using basic data, instead of indicators obtained from the subjective evaluation of patients. Data used are based on EQ-5D, which is a widely used assessment tool in measuring HRQL. The proposed tool has a very high accuracy of 90.4 %,which will contribute to improving QOL by promoting the distribution of healthcare management resources as an indicator to assess QOL uniformly based on objective information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Nesto, R. W. (2003). The relation of insulin resistance syndromes to risk of cardiovascular disease. Reviews in Cardiovascular Medicine, 4(6), S11–S18.

    Google Scholar 

  2. Evans, J. A., Bethell, H. J. N., & Turner, S. C. (2006). NSF for CHD: 3 years of 12-month follow-up audit after cardiac rehabilitation. Journal of Public Health, 28(1), 35–38.

    Article  Google Scholar 

  3. Lakka, H., Laaksonen, D. E., Lakka, T. A., Niskanen, L. K., Kumpusalo, E., Tuomilehto, J., et al. (2002). The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA, 288, 2709–16.

    Article  Google Scholar 

  4. Ford, E. S., Giles, W. H., & Dietz, W. H. (2002). Prevalence of the metabolic syndrome among US adult: Finding from the third National Health and Nutrition Examination Survey. JAMA, 287, 356–359.

    Article  Google Scholar 

  5. Yamamoto, A., Temba, H., Horibe, H., Mabuchi, H., Saito, Y., Matsuzawa, Y., et al. (2003). Life style and cardiovascular risk factors in the Japanese population-from an epidemiological survey on serum lipid levels in Japan 1990. Part 1: influence of life style and excess body weight on HDL-cholesterol and other lipid parameters in men. Journal of Atherosclerosis and Thrombosis, 10, 165–175.

    Article  Google Scholar 

  6. Reaven, G. M. (1988). Banting lecture. Role of Insulin Resistance in Human Disease Diabetes, 37, 1595–1607.

    Google Scholar 

  7. Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Fraklin, B. A., et al. (2005). Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation, 112, 2735–2752.

    Article  Google Scholar 

  8. Ford, E. S., & Chaoyang, L. I. (2008). Metabolic syndrome and health-related quality of life among US adults. Annals of Epidemiology, 18(3), 165–171.

    Article  Google Scholar 

  9. Kwak, S. (2013). Study on family strength and happiness of the pre-elderly and the elderly. Journal of Korean Home Economic Association, 51(1), 1–16.

    Google Scholar 

  10. Furlong, W. J., Feeny, D. H., Torrance, G. W., & Barr, R. D. (2001). The Health Utilities Index (HUP) system for assessing health-related quality of life in clinical studies. The finnish medical society duodecim. Annals of Medicine, 33, 375–384.

    Article  Google Scholar 

  11. Brazier, J. E., Harper, R., Jones, N. M. B., O’Cathain, A., Thomas, K. J., Usherwood, T., et al. (1992). Validating the SF-36 health survey questionnaire: New outcome measure for primary care. BMJ, 305, 18.

    Google Scholar 

  12. TE Group. (1990). EuroQol-a new facility for the measurement of health-related quality of life. Health Policy, 16, 199–208.

    Article  Google Scholar 

  13. Constitution of the World Health Organization. (1952). Handbook of basic documents (5th ed., pp. 3–20). Geneva: Palais des Nation.

    Google Scholar 

  14. Sapin, C., et al. (2004). Usefulness of EQ-5D in assessing health status in primary care patients with major depressive disorder. Health and Quality of Life Outcomes, 2(1), 20.

    Article  Google Scholar 

  15. Burström, K., Johannesson, M., & Diderichsen, F. (2001). Swedish population health-related quality of life results using the EQ-5D. Quality of Life Research, 10(7), 621–635.

    Article  Google Scholar 

  16. Schrag, A., et al. (2000). The EQ-5D–a generic quality of life measure–is a useful instrument to measure quality of life in patients with Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 69(1), 67–73.

    Article  Google Scholar 

  17. Jansson, K. Å., et al. (2009). Health-related quality of life (EQ-5D) before and one year after surgery for lumbar spinal stenosis. Journal of Bone and Joint Surgery, British, 91(2), 210–216.

    Article  Google Scholar 

  18. Pickard, A. S., et al. (2007). Health utilities using the EQ-5D in studies of cancer. Pharmacoeconomics, 25(5), 365–384.

    Article  Google Scholar 

  19. Han SH (2008) Health-related quality of life in patients with chronic diseases visiting in rehabilitation outpatient clinic. Unpublished doctor’s thesis. Graduate school of medicine, Hanyang University, Seoul

  20. Berry, M. J. A., & Linoff, G. (1997). Data mining techniques: For marketing, sales, and customer support. New York: Wiley.

    Google Scholar 

  21. Cadavid, S., Abdel-Mottaleb, M., & Helal, A. (2012). Exploiting visual quasi-periodicity for real-time chewing event detection using active appearance models and support vector machines. Personal and Ubiquitous Computing, 16(6), 729–739.

    Article  Google Scholar 

  22. Muñoz, A., et al. (2011). Design and evaluation of an ambient assisted living system based on an argumentative multi-agent system. Personal and Ubiquitous Computing, 15(4), 377–387.

    Article  Google Scholar 

  23. Lee, K. D., Nam, M. Y., Chung, K. Y., Lee, Y. H., & Kang, U. G. (2013). Context and profile based cascade classifier for efficient people detection and safety care system. Multimedia Tools and Applications, 63(1), 27–44.

    Article  Google Scholar 

  24. Jung, E. Y., Kim, J. H., Chung, K. Y., & Park, D. K. (2013). Home health gateway based healthcare services through U-health platform. Wireless Personal Communications, 73(2), 207–218.

    Article  Google Scholar 

  25. Kang, S. K., Chung, K. Y., Ryu, J. K., Rim, K. W., & Lee, J. H. (2013). Bio-interactive healthcare service system using lifelog based context computing. Wireless Personal Communications, 73(2), 341–351.

    Article  Google Scholar 

  26. Kim JH, Chung KY (2013) Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications. doi:10.1007/s11042-011-0919-6

  27. Jung H, Chung KY (2013) Mining based associative image filtering using harmonic mean. Cluster Computing. doi:10.1007/s10586-013-0318-z

  28. Varkey, J. P., Pompili, D., & Walls, T. A. (2012). Human motion recognition using a wireless sensor-based wearable system. Personal and Ubiquitous Computing, 16(7), 897–910.

    Article  Google Scholar 

  29. Yang, J. G., Kim, J. K., Kang, U. G., & Lee, Y. H. (2013). Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis. Personal and Ubiquitous Computing, 17(7), 1315–1572.

    Article  Google Scholar 

  30. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data, engineering, pp 3–14

  31. Witten, I. H., & Eibe, F. (2000). Data mining: Practical machine learning tools and techniques (pp. 84–88). Los Altos: Morgan Kaufmann.

    Google Scholar 

  32. Jung TH (2006) Harmful web document filtering using Naive Bayesian classifier, 2006. XXX.

  33. Jung YG, Kang HG, Park WI (2002) Dependency analysis of infertility patient data through machine learning, methods, 2002 XXX.

  34. Korea Centers for Disease Control and Prevention (2010). 5th Korean National Health and Nutrition Examinations Survey (KNHANES V-1). Centers for Disease Control and Prevention.

  35. Expert Panel on Detection. (2001). Evaluation and treatment of high blood cholesterol in adults. Execute summary of the third report of the national cholesterol education p program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA, 285, 2486–2497.

    Article  Google Scholar 

  36. Oh, J. Y., Yang, Y. J., Kim, B. S., & JH Kang, J. H. (2007). Validity and reliability of Korean version of International physical activity questionnaire (IPAQ) Short Form. Journal of the Korean Academy of Family Medicine, 28, 532–541.

    Google Scholar 

  37. Baecke, J. A., Burema, J., & JE Frijters, J. E. (1982). A short questionnaire for the measurements of habitual physical activity in epidemiological studies. The American Journal of Clinical Nutrition, 36, 936–942.

    Google Scholar 

  38. Ainsworth, B. E., Jacobs, D. R., & Leon, A. S. (1993). Validity and realiability of self-reported physical activity status: The Lipid Research Clinics questionnaire. Medicine and Science in Sports and Exercise, 25, 92–98.

    Article  Google Scholar 

  39. Salonen, J. T., & Lakka, T. (1987). Assessment of physcial activity in population studies-validity and consistency of the methods in the Kuopio ischemic heart disease risk factor study. Scandinavian Journal of Medicine and Science in Sports, 9, 89–95.

    Google Scholar 

  40. Kohl, H. W., Blair, S. N., Paffenbarger, R. S., Macera, C. A., & Kronenfeld, J. J. (1988). A mail survey of physical activity habits as related to measured physical fitness. American Journal of Epidemiology, 127, 1228–1239.

    Google Scholar 

  41. Lee, G. H., Kim, C. H., Shin, H. C., Park, Y. W., & Sung, E. J. (2007). The relation of physical activity to health related qualtiy of life. Jounal of Korean Academic Family Medicine, 28, 451–459.

    Google Scholar 

  42. Craig, C. L., Marshall, A. L., Sjostrom, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., et al. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise, 35(8), 1381–1395.

    Article  Google Scholar 

  43. IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)—short and long forms. Revised Nov 2005. URL:http://www.ipaq.ki.se/scoring.htm.

  44. Sjostrom, M., Ekelund, U., Prrtvliet, E., Hurtig-Wennlof, A., & Yngve, A. (2000). Assessment of physical activity using IPAQ (version 4) and activity monitors (CSA). Measurement in Physical Education and Exercise Sciences, 4, 263–264.

    Google Scholar 

  45. Booth, M., et al. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise, 195(9131/03), 1381–3508.

    Google Scholar 

  46. IPAQ Research Committee (2005) Guidelines for data processing and analysis of the international physical activity questionnaire (IPAQ)–Short and long forms. Retrieved September 17, 2008.

  47. Ahn, S. H., Park, B. W., Noh, D. Y., Nam, S. J., Lee, E. S., Lee, M. K., et al. (2007). Health-related quality of life in disease-free survivors of breast cancer with the general population. Annals of Oncology, 18(1), 173–182.

    Article  Google Scholar 

  48. Picavet, H. S., & Hoeymans, N. (2004). Health related quality of life in multiple musculoskeletal diseases: SF-36 and EQ-5D in the DMC3 study. [Research Support, Non-U.S. Gov’t]. Annals of the Rheumatic Diseases, 63(6), 723–729.

    Article  Google Scholar 

  49. van der Zanden, B. P., Dijkgraaf, M. G. W., Blanken, P., de Borgie, C. A. J. M., van Ree, J. M., & van den Brink, W. (2006). Validity of the EQ-5D as a generic health outcome instrument in a heroin-dependent population. Drug and Alcohol Dependence, 82(2), 111–118.

    Article  Google Scholar 

  50. Joung, K. H. (2011). Health-related quality of Life among home-dwelling people with arthritis in Korea: Comparative study of osteoarthritis and rheumatoid arthritis. Journal of the Korean Data and Information Science Society, 22(3), 555–563.

    Google Scholar 

  51. Stromgren, A. S., Goldschmidt, D., Groenvold, M., Petersen, M. A., Jensen, P. T., Pedersen, L., et al. (2002). Self-assessment in cancer patients referred to palliative care—A study of feasibility and symptom epidemiology. Cancer, 94(2), 512–520.

    Article  Google Scholar 

  52. Ahn, S. H., Park, B. W., Noh, D. Y., Nam, S. J., Lee, E. S., Lee, M. K., et al. (2007). Health-related quality of life in disease-free survivors of breast cancer with the general population. Annals of Oncology, 18(1), 173–182.

    Article  Google Scholar 

  53. Bayes, T (1984) An essay toward solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London 53.

  54. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2–3), 103–130.

    Article  MATH  Google Scholar 

  55. Zhu, W., Zeng, N,, & Wang, N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS \({\textregistered }\) Implementations, NESUG proceedings: Health Care and Life Sciences. Maryland: Baltimore.

Download references

Acknowledgments

This work was supported by the Gachon University Research Fund of 2013 (GCU-2013-R353).

Author information

Authors and Affiliations

  1. Department of Computer Science, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea

    Junggi Yang

  2. IT Department, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea

    Youngho Lee

Authors
  1. Junggi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youngho Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youngho Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, J., Lee, Y. Development of Measurement Model for the Value of QOL as an Influential Factor of Metabolic Syndrome. Wireless Pers Commun 79, 2639–2654 (2014). https://doi.org/10.1007/s11277-014-1843-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1843-7

Keywords

Search

Navigation