IDEAS home Printed from https://ideas.repec.org/a/sae/clnure/v28y2019i2p165-181.html
   My bibliography  Save this article

Exploring Symptom Clusters in People With Heart Failure

Author

Listed:
  • Jumin Park
  • Debra K. Moser
  • Kathleen Griffith
  • Jeffrey R. Harring
  • Meg Johantgen

Abstract

Patients with heart failure (HF) experience multiple symptoms or symptom clusters. The purposes of this study were to (a) determine if distinct latent classes of HF symptoms could be identified, and (b) explore whether sociodemographic and clinical characteristics influenced symptom cluster membership. A total of 4,011 HF patients recruited from outpatient setting completed the Minnesota Living With Heart Failure Questionnaire (MLHFQ), including five physical symptoms (edema, shortness of breath, fatigue-increased need to rest, fatigue-low energy, and sleep difficulties) and three psychological symptoms (worrying, feeling depressed, and cognitive problems). Four distinct classes using latent class profile analysis were identified: low distress (Class 1), physical distress (Class 2), psychological distress (Class 3), and high distress (Class 4). Significant differences among the four latent classes were found for age, education level, and comorbidities. Symptom clusters are useful for recognition of HF symptoms, allowing for the development of strategies that target symptom groups.

Suggested Citation

  • Jumin Park & Debra K. Moser & Kathleen Griffith & Jeffrey R. Harring & Meg Johantgen, 2019. "Exploring Symptom Clusters in People With Heart Failure," Clinical Nursing Research, , vol. 28(2), pages 165-181, February.
    • Handle: RePEc:sae:clnure:v:28:y:2019:i:2:p:165-181
    DOI: 10.1177/1054773817729606
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1054773817729606
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1054773817729606?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    2. Cutler, David M. & Lleras-Muney, Adriana, 2010. "Understanding differences in health behaviors by education," Journal of Health Economics, Elsevier, vol. 29(1), pages 1-28, January.
    3. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    2. Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
    3. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    4. Roberta Adorni & Andrea Greco & Marco D’Addario & Francesco Zanatta & Francesco Fattirolli & Cristina Franzelli & Alessandro Maloberti & Cristina Giannattasio & Patrizia Steca, 2022. "Sense of Coherence Predicts Physical Activity Maintenance and Health-Related Quality of Life: A 3-Year Longitudinal Study on Cardiovascular Patients," IJERPH, MDPI, vol. 19(8), pages 1-14, April.
    5. Kiero Guerra-Peña & Zoilo Emilio García-Batista & Sarah Depaoli & Luis Eduardo Garrido, 2020. "Class enumeration false positive in skew-t family of continuous growth mixture models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    6. Anindita Chakravarty & Rajdeep Grewal & V. Sambamurthy, 2013. "Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles," Information Systems Research, INFORMS, vol. 24(4), pages 976-997, December.
    7. Heike Heidemeier & Anja Göritz, 2013. "Individual Differences in How Work and Nonwork Life Domains Contribute to Life Satisfaction: Using Factor Mixture Modeling for Classification," Journal of Happiness Studies, Springer, vol. 14(6), pages 1765-1788, December.
    8. Bartolucci Francesco & Murphy Thomas Brendan, 2015. "A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 193-203, December.
    9. Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.
    10. Zachary K. Collier & Haobai Zhang & Bridgette Johnson, 2021. "Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches," Evaluation Review, , vol. 45(6), pages 309-333, December.
    11. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    12. Joseph L Ward & Russell M Viner, 2016. "Secondary Education and Health Outcomes in Young People from the Cape Area Panel Study (CAPS)," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-14, June.
    13. Julian Aichholzer & Sylvia Kritzinger & Carolina Plescia, 2021. "National identity profiles and support for the European Union," European Union Politics, , vol. 22(2), pages 293-315, June.
    14. G. Miller & Yuriy Pylypchuk, 2014. "Marital Status, Spousal Characteristics, and the Use of Preventive Care," Journal of Family and Economic Issues, Springer, vol. 35(3), pages 323-338, September.
    15. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    16. Bergvall, Sanna & Fernström, Clara & Ranehill, Eva & Sandberg, Anna, 2024. "The Impact of PhD Studies on Mental Health—A Longitudinal Population Study," Working Papers in Economics 846, University of Gothenburg, Department of Economics.
    17. Michael Geruso & Dean Spears, 2018. "Neighborhood Sanitation and Infant Mortality," American Economic Journal: Applied Economics, American Economic Association, vol. 10(2), pages 125-162, April.
    18. Resul Cesur & Naci H. Mocan, 2013. "Does Secular Education Impact Religiosity, Electoral Participation and the Propensity to Vote for Islamic Parties? Evidence from an Education Reform in a Muslim Country," NBER Working Papers 19769, National Bureau of Economic Research, Inc.
    19. Olof Östergren & Pekka Martikainen & Olle Lundberg, 2018. "The contribution of alcohol consumption and smoking to educational inequalities in life expectancy among Swedish men and women during 1991–2008," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(1), pages 41-48, January.
    20. Dalton, Patricio S. & Nhung, Nguyen & Rüschenpöhler, Julius, 2020. "Worries of the poor: The impact of financial burden on the risk attitudes of micro-entrepreneurs," Journal of Economic Psychology, Elsevier, vol. 79(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:clnure:v:28:y:2019:i:2:p:165-181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.