NCT06923514Completed
Health Literacy, Stress and Quality of Life in Heart Failure Patients
Using Machine Learning to Analyze the Prediction and Correlation of Health Literacy, Stress and Quality of Life in Heart Failure Patients
Source: ClinicalTrials.gov · Last updated 19 Mar 2026
Brief summary (registry, in English)
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.
- Status (registry)
- Completed
- Study type
- Observational
- Enrollment
- 158 (actual)
- First submitted
- 18 Mar 2025
- Start date
- 20 Nov 2024
- Primary completion date
- 3 Nov 2025
- Lead sponsor
- Cheng-Hsin General Hospital
- Diseases
- Heart failure
- NCT number
- NCT06923514
What you commit to
- Study type: Observational
- Participants enrolled: 158
- From 20 Nov 2024 to the primary completion date, 3 Nov 2025
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Other illness
- 1.severe mental disease.2. terminal stage of other disease such as cancer.
Who the study is looking for
- 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
Age in the registry: 18 years to 65 years · Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Cheng Hsin General Hospital Coordinator of this site? Confirm its status | Taipei | Taiwan | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.