Understanding the Relationship between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status among Older Adults in the United States: A Regression Analysis and Machine Learning Approach
Issued Date
2024-01-01
Resource Type
ISSN
08894655
eISSN
15505049
Scopus ID
2-s2.0-85206318042
Journal Title
Journal of Cardiovascular Nursing
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Cardiovascular Nursing (2024)
Suggested Citation
Ruksakulpiwat S., Thongking W., Kannan N., Wright E., Niyomyart A., Benjasirisan C., Chiaranai C., Smothers C., Aldossary H.M., Still C.H. Understanding the Relationship between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status among Older Adults in the United States: A Regression Analysis and Machine Learning Approach. Journal of Cardiovascular Nursing (2024). doi:10.1097/JCN.0000000000001150 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101693
Title
Understanding the Relationship between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status among Older Adults in the United States: A Regression Analysis and Machine Learning Approach
Author's Affiliation
Corresponding Author(s)
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Abstract
Background Nonadherence to medication among patients with heart disease poses serious risks, including worsened heart failure and increased mortality rates. Objective This study aims to explore the complex interplay between comorbidities, medication nonadherence, activities of daily living, and heart condition status in older American adults, using both traditional statistical methods and machine learning. Methods Data from 326 older adults with heart conditions, drawn from the Health and Retirement Study, were analyzed. Descriptive statistics characterized demographic profiles and comorbidities, whereas logistic regression, multiple regression analyses, and decision tree models were used to address our research inquiries. In addition, a machine learning approach, specifically decision tree models, was integrated to enhance predictive accuracy. Results Our analysis showed that factors like age, gender, hypertension, and stroke history were significantly linked to worsening heart conditions. Notably, depression emerged as a robust predictor of medication nonadherence. Further adjusted analyses underscored significant correlations between stroke and challenges in basic activities such as dressing, bathing, and eating. Depression correlated significantly with difficulties in dressing, bed mobility, and toileting, whereas lung disease was associated with bathing hindrances. Intriguingly, our decision tree model revealed that patients experiencing dressing challenges, but not toileting difficulties, were more prone to report no improvement in heart condition status over the preceding 2 years. Conclusions Blending traditional statistics with machine learning in this study reveals significant implications for crafting personalized interventions to improve patients' depression, leading to increased activities of daily living, medication adherence, reduced severity of comorbidities, and ultimately better management of heart conditions.