Skip to main content

Advertisement

SpringerLink
Log in

Trajectories of perceived social support in acute coronary syndrome

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

Perceived social support is known to be an important predictor of health outcomes in patients with acute coronary syndrome (ACS). This study investigates patterns of longitudinal trajectories of patient-reported perceived social support in individuals with ACS.

Methods

Data are from 3013 patients from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry who had their first cardiac catheterization between 2004 and 2011. Perceived social support was assessed using the 19-item Medical Outcomes Study Social Support Survey (MOS) 2 weeks, 1 year, and 3 years post catheterization. Group-based trajectory analysis based on longitudinal multiple imputation model was used to identify distinct subgroups of trajectories of perceived social support over a 3-year follow-up period.

Results

Three distinct social support trajectory subgroups were identified, namely: “High” social support group (60%), “Intermediate” social support group (30%), and “Low” social support subgroup (10%). Being female (OR = 1.67; 95% CI = [1.18–2.36]), depression (OR = 8.10; 95% CI = [4.27–15.36]) and smoking (OR = 1.70; 95% CI = [1.23–2.35]) were predictors of the differences among these trajectory subgroups.

Conclusion

Although the majority of ACS patients showed increased or fairly stable trajectories of social support, about 10% of the cohort reported declining social support. These findings can inform targeted psycho-social interventions to improve their perceived social support and health outcomes.

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. Strike, P. C., & Steptoe, A. (2005). Behavioral and emotional triggers of acute coronary syndromes: A systematic review and critique. Psychosomatic Medicine, 67(2), 179–186.

    Article  PubMed  Google Scholar 

  2. Chung, M. C., Berger, Z., & Rudd, H. (2008). Coping with posttraumatic stress disorder and comorbidity after myocardial infarction. Comprehensive Psychiatry, 49(1), 55–64.

    Article  PubMed  Google Scholar 

  3. Kumar, A., & Cannon, C. P. (2009). Acute coronary syndromes: Diagnosis and management, part I. Mayo Clinic Proceedings, 84(10), 917–938, https://doi.org/10.1016/S0025-6196(11)60509-0.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Graven, L. J., & Grant, J. S. (2014). Social support and self-care behaviors in individuals with heart failure: An integrative review. International Journal of Nursing Studies, 51(2), 320–333. https://doi.org/10.1016/j.ijnurstu.2013.06.013.

    Article  PubMed  Google Scholar 

  5. Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social Relationships and mortality risk: A meta-analytic review. PLOS Medicine, 7(7), e1000316.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Dekker, R. L., Peden, A. R., Lennie, T. A., Schooler, M. P., & Moser, D. K. (2009). Living with depressive symptoms: Patients with heart failure. American journal of critical care: An Official Publication. American Association of Critical-Care Nurses, 18(4), 310–318. https://doi.org/10.4037/ajcc2009672.

    Article  Google Scholar 

  7. Valtorta, N. K., Kanaan, M., Gilbody, S., Ronzi, S., & Hanratty, B. (2016). Loneliness and social isolation as risk factors for coronary heart disease and stroke: Systematic review and meta-analysis of longitudinal observational studies. Heart, 102(13), 1009–1016. https://doi.org/10.1136/heartjnl-2015-308790.

    Article  CAS  PubMed  Google Scholar 

  8. Hakulinen, C., Pulkki-Raback, L., Virtanen, M., Jokela, M., Kivimaki, M., & Elovainio, M. (2018). Social isolation and loneliness as risk factors for myocardial infarction, stroke and mortality: UK Biobank cohort study of 479 054 men and women. Heart, 104(18), 1536–1542. https://doi.org/10.1136/heartjnl-2017-312663.

    Article  PubMed  Google Scholar 

  9. White, A. M., Philogene, G. S., Fine, L., & Sinha, S. (2009). Social support and self-reported health status of older adults in the United States. American Journal of Public Health, 99(10), 1872–1878. https://doi.org/10.2105/AJPH.2008.146894.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Simms, A. D., Batin, P. D., Kurian, J., Durham, N., & Gale, C. P. (2012). Acute coronary syndromes: An old age problem. Journal of Geriatric Cardiology: JGC, 9(2), 192–196. https://doi.org/10.3724/SP.J.1263.2012.01312.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Holden, L., Lee, C., Hockey, R., Ware, R. S., & Dobson, A. J. (2015). Longitudinal analysis of relationships between social support and general health in an Australian population cohort of young women. Quality of Life Research, 24(2), 485–492. https://doi.org/10.1007/s11136-014-0774-9.

    Article  PubMed  Google Scholar 

  12. Lett, H. S., Blumenthal, J. A., Babyak, M. A., Strauman, T. J., Robins, C., & Sherwood, A. (2005). Social support and coronary heart disease: Epidemiologic evidence and implications for treatment. Psychosomatic Medicine, 67(6), 869–878.

    Article  PubMed  Google Scholar 

  13. Bosworth, H. B., Siegler, I. C., Olsen, M. K., Brummett, B. H., Barefoot, J. C., Williams, R. B., et al. (2000). Social support and quality of life in patients with coronary artery disease. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 9(7), 829–839.

    Article  CAS  Google Scholar 

  14. Norris, C. M., Spertus, J. A., Jensen, L., Johnson, J., Hegadoren, K. M., Ghali, W. A., et al. (2008). Sex and gender discrepancies in health-related quality of life outcomes among patients with established coronary artery disease. Circulation: Cardiovascular Quality and Outcomes, 1(2), 123–130. https://doi.org/10.1161/CIRCOUTCOMES.108.793448

    Article  Google Scholar 

  15. Staniute, M., Brozaitiene, J., & Bunevicius, R. (2013). Effects of social support and stressful life events on health-related quality of life in coronary artery disease patients. The Journal of Cardiovascular Nursing, 28(1), 83–89. https://doi.org/10.1097/JCN.0b013e318233e69d

    Article  PubMed  Google Scholar 

  16. Leifheit-Limson, E. C., Reid, K. J., Kasl, S. V., Lin, H., Jones, P. G., Buchanan, D. M., et al. (2010). The role of social support in health status and depressive symptoms after acute myocardial infarction: Evidence for a stronger relationship among women. Circulation: Cardiovascular Quality and Outcomes, 3(2), 143–150. https://doi.org/10.1161/CIRCOUTCOMES.109.899815.

    Article  Google Scholar 

  17. Ghali, W. A., Knudtson, M. L., & on behalf of the APPROACH Investigators. (2000). Overview of the alberta provincial project for outcome assessment in coronary heart disease. The Canadian Journal of Cardiology, 16(10), 1225–1230.

    CAS  PubMed  Google Scholar 

  18. Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science & Medicine (1982), 32(6), 705–714.

    Article  CAS  Google Scholar 

  19. Gjesfjeld, C. D., Greeno, C. G., & Kim, K. H. (2008). A confirmatory factor analysis of an abbreviated social support instrument: The MOS-SSS. Research on Social Work Practice, 18(3), 231–237. https://doi.org/10.1177/1049731507309830.

    Article  Google Scholar 

  20. Robitaille, A., Orpana, H., & McIntosh, C. N. (2011). Psychometric properties, factorial structure, and measurement invariance of the English and French versions of the Medical Outcomes Study social support scale. Health Reports, 22(2), 33–40.

    PubMed  Google Scholar 

  21. Stafford, L., Berk, M., & Jackson, H. J. (2007). Validity of the Hospital Anxiety and Depression Scale and Patient Health Questionnaire-9 to screen for depression in patients with coronary artery disease. General Hospital Psychiatry, 29(5), 417–424.

    Article  PubMed  Google Scholar 

  22. Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370.

    Article  CAS  PubMed  Google Scholar 

  23. Stern, A. F. (2014). The hospital anxiety and depression scale. Occupational Medicine (Oxford, England), 64(5), 393–394. https://doi.org/10.1093/occmed/kqu024.

    Article  Google Scholar 

  24. Harel, O., Mitchell, E. M., Perkins, N. J., Cole, S. R., Tchetgen Tchetgen, E. J., Sun, B., et al. (2018). Multiple imputation for incomplete data in epidemiologic studies. American Journal of Epidemiology, 187(3), 576–584. https://doi.org/10.1093/aje/kwx349.

    Article  PubMed  Google Scholar 

  25. Ma, J., Raina, P., Beyene, J., & Thabane, L. (2012). Comparing the performance of different multiple imputation strategies for missing binary outcomes in cluster randomized trials: A simulation study. Journal of Open Access Medical Statistics, 2, 93–103.

    Google Scholar 

  26. Faris, P. D., Ghali, W. A., Brant, R., Norris, C. M., Galbraith, P. D., Knudtson, M. L., et al. (2002). Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. Journal of Clinical Epidemiology, 55(2), 184–191.

    Article  PubMed  Google Scholar 

  27. Little, R. J., & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). New Yorkk: Wiley.

    Book  Google Scholar 

  28. Nagin, D. S. (2005). Group-based modelling of development. London: Harvard University Press.

    Book  Google Scholar 

  29. Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating development trajectories. Sociological Methods & Research, 29(3), 374–393.

    Article  Google Scholar 

  30. Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology, 6, 109–138. https://doi.org/10.1146/annurev.clinpsy.121208.131413.

    Article  PubMed  Google Scholar 

  31. Nagin, D. S. (2014). Group-based trajectory modeling: An overview. Annals of Nutrition & Metabolism, 65(2–3), 205–210. https://doi.org/10.1159/000360229.

    Article  CAS  Google Scholar 

  32. Andruff, H., Carraro, N., Thompson, A., & Gaudreau, P. (2009). Latent class growth modelling: A tutorial. Tutorials Quantitative Methods for Psychology, 5, 11–24.

    Article  Google Scholar 

  33. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.

    Book  Google Scholar 

  34. SAS Institute Inc. (2013). Base SAS® 9.4 procedures guide: Statistical procedures. Cary: SAS Institute Inc.

    Google Scholar 

  35. Powers, S. M., Bisconti, T. L., & Bergeman, C. S. (2014). Trajectories of social support and well-being across the first two years of widowhood. Death Studies, 38(8), 499–509. https://doi.org/10.1080/07481187.2013.846436.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Dean, A., Matt, G. E., & Wood, P. (1992). The effects of widowhood on social support from significant others. Journal of Community Psychology, 20(4), 309–325. https://doi.org/10.1002/1520-6629(199210)20:43.0.CO;2-V.

    Article  Google Scholar 

  37. Shankar, A., Mcmunn, A., Banks, J., & Steptoe, A. (2011). Loneliness, social isolation, and behavioral and biological health indicators in older adults. Health Psychology, 30(4), 377–385. https://doi.org/10.1037/a0022826.

    Article  PubMed  Google Scholar 

  38. Kristofferzon, M. L., Lofmark, R., & Carlsson, M. (2003). Myocardial infarction: Gender differences in coping and social support. Journal of Advanced Nursing, 44(4), 360–374.

    Article  PubMed  Google Scholar 

  39. Ikeda, A., Iso, H., Kawachi, I., Yamagishi, K., Inoue, M., & Tsugane, S. (2008). Social support and stroke and coronary heart disease. Stroke, 39(3), 768–775. https://doi.org/10.1161/STROKEAHA.107.496695.

    Article  PubMed  Google Scholar 

  40. Garnefski, N., Kraaij, V., Schroevers, M. J., Aarnink, J., van der Heijden, D. J., van Es, S. M., et al. (2009). Cognitive coping and goal adjustment after first-time myocardial infarction: Relationships with symptoms of depression. Behavioral Medicine (Washington, D. C.), 35(3), 79–86, https://doi.org/10.1080/08964280903232068.

    Article  Google Scholar 

  41. Son, H., Friedmann, E., Thomas, S. A., & Son, Y. J. (2016). Biopsychosocial predictors of coping strategies of patients postmyocardial infarction. International Journal of Nursing Practice, 22(5), 493–502. https://doi.org/10.1111/ijn.12465.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Helgeson, V. S. (2003). Social support and quality of life. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 12(Suppl 1), 25–31.

    Article  Google Scholar 

  43. McConnell, T. R., Trevino, K. M., & Klinger, T. A. (2011). Demographic differences in religious coping after a first-time cardiac event. Journal of Cardiopulmonary Rehabilitation and Prevention, 31(5), 298–302. https://doi.org/10.1097/HCR.0b013e31821c41f0.

    Article  PubMed  Google Scholar 

Download references

Funding

This research was supported by the University of Calgary O’Brien Institute of Public Health.

Author information

Authors and Affiliations

  1. Department of Community Health Sciences & O’Brien Institute for Public Health, University of Calgary, Calgary, Canada

    Meng Wang, Maria Santana, Zhiying Liang, Danielle A. Southern, Matthew T. James, Hude Quan, William Ghali & Tolulope T. Sajobi

  2. Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada

    Colleen M. Norris & Michelle M. Graham

  3. Faculty of Nursing, University of Alberta, Edmonton, Canada

    Colleen M. Norris

  4. Faculty of Health Sciences, University of Lethbridge, Lethbridge, Canada

    Oluwagbohunmi Awosoga

  5. Department of Medicine, University of Calgary, Calgary, Canada

    Matthew T. James, Stephen B. Wilton, William Ghali & Merril Knudtson

  6. Department of Cardiac Sciences, University of Calgary, Calgary, Canada

    Stephen B. Wilton & Merril Knudtson

  7. Department of Economics, University of Calgary, Calgary, Canada

    Mingshan Lu

Authors
  1. Meng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Colleen M. Norris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michelle M. Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria Santana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiying Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oluwagbohunmi Awosoga
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Danielle A. Southern
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew T. James
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephen B. Wilton
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hude Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mingshan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. William Ghali
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Merril Knudtson
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tolulope T. Sajobi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tolulope T. Sajobi.

Ethics declarations

Conflict of interest

The authors declare that there’s no conflict of interest.

Ethical approval

Ethics approval was obtained from the University of Calgary Conjoint Health Research Ethics Board (REB14-1320).

Informed consent

Informed consent was obtained from all subjects included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 4, 5, 6 and 7 and Fig. 2.

Table 4 Comparisons of included and excluded patients’ characteristics
Full size table
Table 5 Characteristics of study participants by MOS subgroup—based on original data without imputation
Full size table
Table 6 Associations (odds ratio, 95% CI) between patients’ characteristics and MOS trajectory subgroup membership for patients survived—based on original data without imputation
Full size table
Table 7 Bayesian information criterion (BIC) and Akaike information criterion (AIC) during model selection—group-based trajectory modeling
Full size table
Fig. 2
figure 2

Longitudinal social support trajectories based on MOS scores—based on original data without imputation

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, M., Norris, C.M., Graham, M.M. et al. Trajectories of perceived social support in acute coronary syndrome. Qual Life Res 28, 1365–1376 (2019). https://doi.org/10.1007/s11136-018-02095-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-018-02095-4

Keywords

Search

Navigation