Literature DB >> 15586132

Knowledge of symptom clusters among adults at risk for acute myocardial infarction.

Catherine J Ryan1, Julie Johnson Zerwic.   

Abstract

BACKGROUND: Individuals need to recognize acute myocardial infarction symptoms in order to seek treatment promptly. Previous acute myocardial infarction symptom studies asked subjects to identify single symptoms from a list. However, people think about illnesses or respond to symptoms by considering groups or clusters of symptoms.
OBJECTIVE: To use Q methodology to identify the cluster of symptoms that individuals at high risk for acute myocardial infarction and their significant others believe to be associated with acute myocardial infarction.
METHODS: A Q sort instrument that represented a range of symptoms was developed after analysis of 140 interviews with acute myocardial infarction survivors. Individuals with known coronary artery disease or their significant others (n = 63) sorted the resulting 49 statements describing acute myocardial infarction into "most expected" and "least expected" categories. By-person factor analysis was used.
RESULTS: Four factors were identified that described different presentations of acute myocardial infarction symptoms. Respondents loaded on the following factors: Factor 1 (traditional symptoms), Factor 2 (symptoms possibly related to gastrointestinal disorders), Factor 3 (nonspecific symptoms), and Factor 4 (a variation on traditional symptoms). This four-factor solution accounted for 36% of the total variance.
CONCLUSIONS: The Q methodology showed that people with known coronary artery disease and their significant others had varied expectations of acute myocardial infarction symptoms. New and various strategies need to be developed to help patients accurately identify acute myocardial infarction symptoms.

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Mesh:

Year:  2004        PMID: 15586132     DOI: 10.1097/00006199-200411000-00004

Source DB:  PubMed          Journal:  Nurs Res        ISSN: 0029-6562            Impact factor:   2.381


  5 in total

1.  Symptom burden clusters and their impact on psychosocial functioning following coronary artery bypass surgery.

Authors:  Amy A Abbott; Susan Barnason; Lani Zimmerman
Journal:  J Cardiovasc Nurs       Date:  2010 Jul-Aug       Impact factor: 2.083

2.  Cluster analysis of women's prodromal and acute myocardial infarction symptoms by race and other characteristics.

Authors:  Jean C McSweeney; Mario A Cleves; Weizhi Zhao; Leanne L Lefler; Shengping Yang
Journal:  J Cardiovasc Nurs       Date:  2010 Jul-Aug       Impact factor: 2.083

Review 3.  Care-seeking decisions for worsening symptoms in heart failure: a qualitative metasynthesis.

Authors:  S E Ivynian; M DiGiacomo; P J Newton
Journal:  Heart Fail Rev       Date:  2015-11       Impact factor: 4.214

4.  Feasibility analysis of the value of Q method in the classification and understanding of expert experience.

Authors:  Meng-yu Liu; Yong Li; Ai-ping Lu; Xue-jie Han
Journal:  Chin J Integr Med       Date:  2012-12-03       Impact factor: 1.978

5.  Understanding Design Tradeoffs for Health Technologies: A Mixed-Methods Approach.

Authors:  Katie O'Leary; Jordan Eschler; Logan Kendall; Lisa M Vizer; James D Ralston; Wanda Pratt
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2015-04-18
  5 in total

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