โก Quick Summary
This study assessed treatment adherence among elderly patients with multimorbid type 2 diabetes mellitus (T2DM), revealing an average adherence score of 45.30. Key factors influencing adherence included elevated HbA1c, depression, self-care, and income.
๐ Key Details
- ๐ Sample Size: Elderly patients with multimorbid T2DM from six community health centers
- ๐งฉ Data Collected: Treatment adherence, self-care activities, social support, cognitive function, and depression
- โ๏ธ Methodology: Lasso-Logistic regression, random forest, and multiple linear regression
- ๐ Key Findings: Average adherence score of 45.30 (SD = 5.99)
๐ Key Takeaways
- ๐ Elevated HbA1c significantly reduces treatment adherence (ฮฒ = -4.417, P < 0.01).
- ๐ Depression is a critical barrier to adherence (ฮฒ = -1.207, P < 0.01).
- ๐ช Improved self-care enhances adherence (ฮฒ = 0.081, P < 0.01).
- ๐ฐ Higher income positively influences treatment compliance (ฮฒ = 0.589, P < 0.01).
- ๐ Multi-method approach validated predictors through both linear and non-linear modeling.
- ๐ Focused interventions should target glycemic control and depression management.
- ๐ Economic support and tailored guidance are essential for improving adherence.
๐ Background
Type 2 diabetes mellitus (T2DM) is a prevalent condition among the elderly, often accompanied by multiple comorbidities. Treatment adherence is crucial for managing this complex health issue, yet many elderly patients struggle with compliance due to various factors, including cognitive decline and socioeconomic challenges. Understanding these factors is essential for developing effective interventions.
๐๏ธ Study
Conducted between May and July 2024, this cross-sectional study involved elderly patients with multimorbid T2DM seeking treatment at community health service centers in Shanghai. Researchers employed a combination of machine learning techniques, including Lasso-Logistic regression, to identify key determinants of treatment adherence.
๐ Results
The study revealed an average treatment adherence score of 45.30 among participants. The integrated machine learning approach identified four significant predictors of adherence: elevated HbA1c and depression were associated with lower adherence, while improved self-care and higher income correlated with better compliance. These findings highlight the complex interplay of medical and psychosocial factors affecting treatment adherence.
๐ Impact and Implications
The insights gained from this study have profound implications for diabetes care in the elderly. By focusing on modifiable factors such as glycemic control and mental health, healthcare providers can tailor interventions to enhance treatment adherence. This approach not only aims to improve health outcomes but also seeks to minimize morbidity in this vulnerable population.
๐ฎ Conclusion
This study underscores the importance of understanding the multifaceted nature of treatment adherence in elderly patients with multimorbid T2DM. By leveraging advanced modeling techniques, we can identify critical factors that influence adherence and develop targeted interventions. The future of diabetes management lies in personalized care strategies that address both medical and psychosocial needs.
๐ฌ Your comments
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Assessment of Factors Associated with Treatment Adherence Among Elderly Patients with Multimorbid Type 2 Diabetes Mellitus Using Lasso-Logistic Regression.
Abstract
AIMS AND OBJECTIVES: This study aimed to investigate treatment adherence among elderly patients with multimorbid type 2 diabetes mellitus (T2DM), and analyze the influencing factors.
DESIGN: A single-centre, cross-sectional study design.
METHODS: In this study, convenience sampling was used to examine elderly patients with multimorbid T2DM seeking treatment at six community health service centers within the Jinqiao Medical Alliance in the Pudong New Area of Shanghai between May and July 2024. Demographic and disease-related data were collected including treatment adherence, self-care activities, social support, cognitive function, and depression. Factors influencing treatment adherence were investigated through three machine learning approaches: random forest algorithm for detecting non-linear patterns, multiple linear regression for linear relationship analysis, and Lasso-Logistic regression with L1 regularization to optimize feature selection while controlling multicollinearity. This tripartite methodology synergistically combines ensemble learning, parametric modeling, and sparse logistic regression to ensure robust predictor identification.
RESULTS: This study found that the average treatment adherence score for elderly patients with multimorbid T2DM was 45.30 (SD = 5.99). Integrated machine learning (random forest, Lasso-Logistic regression, and linear regression) identified four key determinants: elevated HbA1c (ฮฒ = -4.417, P < 0.01) and depression (ฮฒ = -1.207, P < 0.01) significantly reduced adherence, whereas improved self-care (ฮฒ = 0.081, P < 0.01) and higher income (ฮฒ = 0.589, P < 0.01) enhanced compliance. This multi-method approach validated predictors through both linear and non-linear modeling frameworks.
CONCLUSION: This study quantifies adherence in elderly T2DM patients (Mean=45.30) and identifies four modifiable predictors through advanced modeling. Prioritized interventions should focus on enhancing glycemic control through intensified HbA1c monitoring for upward trends and integrating depression management into diabetes care plans, while leveraging self-care capacity and economic support as foundational enhancers through tailored guidance and support programs to improve treatment adherence, optimize health outcomes, and minimize morbidity in this population.
Author: [‘Ma R’, ‘Zhou B’, ‘Liu T’, ‘Wang Y’]
Journal: Patient Prefer Adherence
Citation: Ma R, et al. Assessment of Factors Associated with Treatment Adherence Among Elderly Patients with Multimorbid Type 2 Diabetes Mellitus Using Lasso-Logistic Regression. Assessment of Factors Associated with Treatment Adherence Among Elderly Patients with Multimorbid Type 2 Diabetes Mellitus Using Lasso-Logistic Regression. 2026; 20:506859. doi: 10.2147/PPA.S506859