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Using Machine Learning to Analyze the Prediction and Correlation of Health Literacy, Stress and Quality of Life in Heart Failure Patients
Sponsor study identifier
observational
158
1 country
1
Study Start
November 20, 2024
Actual (reported)Primary Completion
November 3, 2025
Actual (reported)Study Completion
November 3, 2025
Actual (reported)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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
November 3, 2025
Actual (reported)Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
November 20, 2024
Actual (reported)March 18, 2025
Actual (reported)April 11, 2025
Actual (reported)November 3, 2025
Actual (reported)November 3, 2025
Actual (reported)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
health literacy
one year
Heart failure patients
Cheng Hsin General Hospital
Taipei, 112, Taiwan
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.
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.
Hei-Fen Hwang, PhD
Natinal Taipei University of Nursing and Health Sciencs
158 participants (actual enrollment reported by the registry).
First Submitted
March 18, 2025
First Posted
April 11, 2025
Study Start
November 20, 2024
Actual (reported)Contact the study team to confirm eligibility.
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.
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.
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.
Primary Completion
November 3, 2025
Actual (reported)Study Completion
November 3, 2025
Actual (reported)Last Updated
March 19, 2026
Record last verified: 2026-03
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