Health Literacy, Stress and Quality of Life in Heart Failure Patients

NCT06923514 | Completed

• 1 other identifier: (1133)113A-63
• Study Type: observational
• Target: 158
• Locations: 1 country
• Sites: 1

Brief Summary

Heart failure is showing a trend of affecting younger individuals. Middle-aged heart failure patients are often the economic backbone of their families. Studies have also pointed out that approximately 38.5% of patients with acute heart failure are re-hospitalized within a year of discharge due to worsening symptoms. Patients with lower health literacy tend to have poorer health outcomes and higher re-hospitalization rates. However, there is limited research on the life and work stress, health literacy, and quality of life of middle-aged heart failure patients. Therefore, this study aims to use machine learning to analyze and predict the correlations between health literacy, stress, and quality of life in heart failure patients. This research is a cross-sectional correlational study, adopting convenience sampling. The study subjects are cardiology patients aged 18-65 diagnosed with heart failure classified as NYHA II or above by specialists at a regional teaching hospital in northern Taiwan. Data collection took place in the outpatient and inpatient departments of cardiology and cardiothoracic surgery. Structured questionnaires were used for one-on-one interviews, including basic demographic information of heart failure patients, the Chinese version of the European Health Literacy Survey Questionnaire (HLS-EU-Q47), the Chinese version of the Brief Resilience Scale (BRS), the Perceived Stress Scale (PSS), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Data will be recorded using Excel, and statistical analysis will be conducted using SPSS version 22. Descriptive statistics such as percentages, means, and standard deviations will be used to describe the demographic and variable distributions. Independent t-tests, ANOVA, and Pearson correlation coefficient will be used to analyze correlations between variables. Machine learning will be employed to analyze and predict quality of life factors in heart failure patients. It is hoped that the results of this study can provide references for nursing practice, help with clinical patient assessment, and improve the quality of care for patients.

Conditions

Heart Failure

Keywords

Machine learning, health literacy, stress, quality of life.

Outcome Measures

Primary Outcomes (1)

• Minnesota Living with Heart Failure Questionnaire

  Use the Chinese version of the Minnesota Heart Failure Quality of Life to assess quality of life. Use the Chinese version of the Minnesota Heart Failure Quality of Life Questionnaire.There are 21 questions in total, scored from 0 to 5, with a total score of 105. The higher the score, the more serious the impact of the disease on life.

  one year

Secondary Outcomes (1)

• health literacy

  one year

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Heart failure patients

You may qualify if:

• The study subjects are cardiology patients aged 18-65 diagnosed with heart failure classified as NYHA II or above by specialists at a regional teaching hospital in northern Taiwan

You may not qualify if:

• severe mental disease.2. terminal stage of other disease such as cancer.

Sponsors & Collaborators

• Cheng-Hsin General Hospital: lead

Study Sites (1)

Cheng Hsin General Hospital

Taipei, 112, Taiwan

Location

Related Publications (5)

• Kuhn TA, Gathright EC, Dolansky MA, Gunstad J, Josephson R, Hughes JW. Health Literacy, Cognitive Function, and Mortality in Patients With Heart Failure. J Cardiovasc Nurs. 2022 Jan-Feb 01;37(1):50-55. doi: 10.1097/JCN.0000000000000855.

  PMID: 34581712 RESULT

• Jiang S, Zhang X, Li X, Li Y, Yang W, Yao Y, Fu L, Zhao M, Zang X. Exploring Health Literacy Categories in Patients With Heart Failure: A Latent Class Analysis. J Cardiovasc Nurs. 2023 Jan-Feb 01;38(1):13-22. doi: 10.1097/JCN.0000000000000889. Epub 2022 Jan 13.

  PMID: 36508237 RESULT

• Heo S, Kang J, Shin MS, Lim YH, Kim SH, Kim S, An M, Kim J. Physical Symptoms, Depressive Symptoms, and Quality of Life in Patients With Heart Failure: Cluster Analysis. J Cardiovasc Nurs. 2024 Jan-Feb 01;39(1):31-37. doi: 10.1097/JCN.0000000000001043. Epub 2023 Sep 29.

  PMID: 37787730 RESULT

• Gowani AAA, Low G, Norris C, Hoben M. Internal structure validity and internal consistency reliability of the Minnesota Living with Heart Failure Questionnaire: a systematic review protocol. BMJ Open. 2023 Nov 8;13(11):e076780. doi: 10.1136/bmjopen-2023-076780.

  PMID: 37940148 RESULT

• Cajita MI, Cajita TR, Han HR. Health Literacy and Heart Failure: A Systematic Review. J Cardiovasc Nurs. 2016 Mar-Apr;31(2):121-30. doi: 10.1097/JCN.0000000000000229.

  PMID: 25569150 RESULT

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Officials

• Hei-Fen Hwang, PhD

  Natinal Taipei University of Nursing and Health Sciencs

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 18, 2025
• First Posted: April 11, 2025
• Study Start: November 20, 2024
• Primary Completion: November 3, 2025
• Study Completion: November 3, 2025
• Last Updated: March 19, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)