The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses - Original Research Published 06 February 2025

Q: What dataset and participant characteristics are used in the study?

The analysis uses the 2018 China Health and Retirement Longitudinal Study (CHARLS) baseline data, including 10,136 participants aged 45 and above.

Q: How are activities of daily living and speech impediments measured?

Activities of daily living are assessed using the Barthel Index (BI). Speech impediments are derived from CHARLS records labeled “Speech impediments.”

Q: What do the statistical and machine learning results show about the ADL–speech impediment relationship?

Statistical analyses identify a significant negative association between ADL and speech impediments, and hierarchical regression confirms robustness across models. Machine learning validation supports ADL’s predictive ability, with model performance reflected by AUC values.

Aug 4, 2026

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Speech impediments among middle-aged and older adults require early identification to support public health interventions. This study evaluates Activities of Daily Living (ADL) as a potential predictive marker for speech impediments using CHARLS 2018 data (10,136 participants aged 45+). ADL is measured via the Barthel Index, and speech impediments are analyzed through statistical testing and hierarchical regression. Machine learning models (SVM, decision tree, logistic regression) validate the association and quantify predictive performance, supporting ADL as an early indicator of risk.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Background

Methods

Data and Measures

Statistical Analyses

Machine Learning Models

Results

Demographic Characteristics and Correlation

Regression Findings

Predictive Validation (AUC)

Conclusion

TYPE Original Research PUBLISHED 06 February 2025 DOI 10.3389/fpubh.2025.1491527 OPEN ACCESS EDITED BY Jaiteg Singh, Chitkara University, India REVIEWED BY Xiaoming Tian, Xi’an University of Posts and Telecommunications, China Orlando Chaves, University of the Valley, Colombia *CORRESPONDENCE Dan Wu [16877866@qq.com](16877866@qq.com) RECEIVED 05 September 2024 ACCEPTED 29 January 2025 PUBLISHED 06 February 2025 CITATION Jun L, Li H, Mao Y, Hu L and Wu D (2025) The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses. Front. Public Health 13:1491527. doi: 10.3389/fpubh.2025.1491527 COPYRIGHT © 2025 Jun, Li, Mao, Hu and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses Liu Jun, Hongguo Li, Yu Mao, Lan Hu and Dan Wu * Traditional Chinese Medicine Department, The Fourth Hospital of Changsha, Changsha, Hunan, China Introduction: Speech impediments (SIs) are increasingly prevalent among middle-aged and older adults, raising concerns within public health. Early detection of potential SI in this demographic is critical. This study investigates the potential of Activities of Daily Living (ADL) as a predictive marker for SI, utilizing data from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which includes 10,136 individuals aged 45 and above. The Barthel Index (BI) was used to assess ADL, and the correlation between ADL and SI was examined through statistical analyses. Machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression) were employed to validate the findings and elucidate the underlying relationship between ADL and SI. Background: SI poses significant challenges to the health and quality of life of middle-aged and older adults, increasing the demands on community-based and home care services. In the context of global aging, it is crucial to investigate the factors contributing to SI. While the role of ADL as a potential biomarker for SI remains unclear, this study aims to provide new evidence supporting ADLas an early predictor of SI through statistical analysis and machine learning validation. Methods: Data were derived from the 2018 CHARLS national baseline survey, comprising 10,136 participants aged 45 and above. ADL was evaluated using the BI, and SI was assessed based on the CHARLS records of “Speech impediments.”Statistical analyses, including independent sample t-tests, chi-square tests, Pearson and Spearman correlation tests, and hierarchical multiple linear regression, were conducted using SPSS 25.0. Machine learning algorithms, specifically Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented in Python 3.10.2. Results: Analysis of demographic characteristics revealed that the average BI score in the “With Speech impediments” group was 49.46, significantly lower than the average score of 85.11 in the “Without Speech impediments” group. Pearson correlation analysis indicated a significant negative correlation between ADL and SI (r = −0 . 205, p < 0.001) . Hierarchical multiple linear regression confirmed the robustness of this negative correlation across three models (B = −0 .001, β = −0 . 168, t = −16 . 16, 95% CI = −0.001 to −0 .001, p = 0.000) . Machine learning algorithms validated the statistical findings, confirming the predictive accuracy of ADL for S

FAQs

What dataset and participant characteristics are used in the study?

The analysis uses the 2018 China Health and Retirement Longitudinal Study (CHARLS) baseline data, including 10,136 participants aged 45 and above.

How are activities of daily living and speech impediments measured?

Activities of daily living are assessed using the Barthel Index (BI). Speech impediments are derived from CHARLS records labeled “Speech impediments.”

What do the statistical and machine learning results show about the ADL–speech impediment relationship?

Statistical analyses identify a significant negative association between ADL and speech impediments, and hierarchical regression confirms robustness across models. Machine learning validation supports ADL’s predictive ability, with model performance reflected by AUC values.

Aug 4, 2026

Upload by Levi