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

Active, not recruiting

• Conditions: Heart Failure

• Registration Number: NCT06923514
• Lead Sponsor: Cheng-Hsin General Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-11-20
• Study First Submitted: 2025-03-18
• Status Verified: 2025-04
• Study First Submitted QC: 2025-04-10
• Last Update Submitted: 2025-04-10
• Study First Posted: 2025-04-11
• Last Update Posted: 2025-04-11
• Primary Completion: 2025-11-03
• Study Completion: 2025-11-03

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 158

Inclusion Criteria

• 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

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Minnesota Living with Heart Failure Questionnaire	one year	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.

Secondary Outcome Measures

Name	Time	Method
health literacy	one year	Use the Chinese version of European Health Literacy Survey Questionnaire (HLS-EU-Q) to assess health literacy .; Use the Chinese version of HLS-EU-Q.There are 47 questions in total. Based on a four-point Likert scale, Total score 0-50. 0-25 means Inadequate,26-33 means Problematic,34-42 means Sufficient,43-50 means Excellent.

Trial Locations

Locations (1)

Cheng Hsin General Hospital; 🇨🇳 Taipei, Taiwan