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

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Publication Details

Author listSuebsarn Ruksakulpiwat, Witchuda Thongking, Naveen Kannan, Ellis Wright, Atsadaporn Niyomyart, Chitchanok Benjasirisan, Chantira Chiaranai, Christine Smothers, Heba M. Aldossary, Carolyn Harmon Still

PublisherLippincott, Williams & Wilkins

Publication year2024

Journal acronymJ. Cardiovasc. Nurs.

Start page1

End page10

Number of pages10

ISSN0889-4655

eISSN1550-5049

URLhttps://journals.lww.com/jcnjournal/fulltext/9900/understanding_the_relationship_between.224.aspx

LanguagesEnglish-United States (EN-US)


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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.


Keywords

activities of daily livingheart conditionMachine Learningmedication nonadherenceolder adults


Last updated on 2025-20-02 at 00:00