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ORIGINAL RESEARCH article

Front. Public Health
Sec. Aging and Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1457151
This article is part of the Research Topic Analyses on Health Status and Care Needs among Older Adults View all 29 articles

Factors related to sedentary behavior in elderly stroke patients in China: a study based on decision tree and logistic regression model

Provisionally accepted
Shuxian Liu Shuxian Liu 1Juan Li Juan Li 2*Xi Chen Xi Chen 1*Xiaowen Jiang Xiaowen Jiang 1*Rong Tang Rong Tang 1*Yumei Lv Yumei Lv 1*
  • 1 School of Nursing, Harbin Medical University (Daqing), Daqing, China
  • 2 The Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China

The final, formatted version of the article will be published soon.

    Objective: This study investigates the factors influencing sedentary behavior in elderly Chinese stroke patients using decision trees and logistic regression models. Methods: Convenience sampling method was employed to enroll 346 respondents aged ≥60 years with stroke from the Department of Neurology of three tertiary-level A hospitals in Heilongjiang province, based on the inclusion criteria. The Sedentary Behavior Questionnaire for Older Adults, the International Physical Activity Questionnaire Short Form (IPAQ-S), the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Support Scale (SSRS) were used to assess sedentary behavior, physical activity level, sleep quality, depressive symptoms, and social support, respectively. Decision tree and logistic regression models were employed to analyze the factors related to sedentary behavior in elderly stroke patients. Results: Of the 346 respondents, 233 (67.3%) had sedentary behavior. The logistic regression model showed that education level (OR = 2.843, 95%CI: 1.219-6.626), BMI (OR = 3.686, 95%CI: 1.838-7.393), longest consecutive sitting time (OR = 3.853, 95%CI: 1.867-7.953), and sleep quality (OR = 3.832, 95%CI: 1.716-8.557) were identified as risk factors for sedentary behavior in elderly stroke patients, while drink alcohol (OR = 0.386, 95%CI: 0.184-0.809) and physical activity level (OR = 0.064, 95%CI: 0.030-0.140) were identified as protective factors for sedentary behavior. Besides, the decision tree model showed that physical activity level, longest consecutive sitting time, sleep quality, BMI, depressive symptoms, and age were associated with sedentary behavior. The sensitivity and specificity of the logistic regression model were 69.9% and 93.1%, respectively, and the area under the ROC curve was 0.900 (95% CI:0.863-0.938). The sensitivity and specificity of the decision tree model were 66.4%, and 93.1% respectively, and the area under the ROC curve was 0.860 (95% CI:0.816-0.904). Conclusion: Our findings indicated that physical activity level, longest consecutive sitting time, sleep quality, and BMI were key factors associated with sedentary behavior. To achieve the purpose of improving rehabilitation effect and quality of life, this study combining decision trees with logistic regression models was of high value in studying factors influencing sedentary behavior in elderly stroke patients.

    Keywords: sedentary behavior, elderly patients, Cerebral apoplexy, logistic regression model, decision tree, Influencing factors

    Received: 30 Jun 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Liu, Li, Chen, Jiang, Tang and Lv. 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) or licensor 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.

    * Correspondence:
    Juan Li, The Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China
    Xi Chen, School of Nursing, Harbin Medical University (Daqing), Daqing, China
    Xiaowen Jiang, School of Nursing, Harbin Medical University (Daqing), Daqing, China
    Rong Tang, School of Nursing, Harbin Medical University (Daqing), Daqing, China
    Yumei Lv, School of Nursing, Harbin Medical University (Daqing), Daqing, China

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