The relationship between physical activity and subjective cognitive decline: Evidence from the behavioral risk factor surveillance system (BRFSS)

Xu, Peng; Zhang, Fan; Cheng, Jinqun; Huang, Yanhong; Ren, Zhiqiang; Ye, Rongrong; Fan, Jie; Li, Lixia; Gao, Yanhui

doi:10.1016/j.jad.2023.02.045

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after onset. According to the cognitive decline module of the Behavioral Risk Factor Surveillance System (BRFSS), which is the largest ongonging health survey system in the world ( 28 ). SCD was identified by the question of BRFSS, “During the past 12 months, have you experienced confusion or memory loss that is happening more often or is getting worse?” ( 29 – 31 ).…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, there were five detailed questions of aggravating confusion or memory decline mentioned in the BRFSS questionnaire, including: (1) the frequency of giving up daily household activities or common chores, (2) the frequency of requirement of assistance with these daily activities, (3) the frequency of getting help, just as you wanted, (4) the frequency of work, volunteer, or social activities disturbed by the confusion or memory disorder, and (5) whether having sought medical attention for this ( 29 , 31 ). These SCD-related outcomes evaluated by a five-point scale (Always, usually, sometimes, rarely, never) were dichotomized to determine if these outcomes were challenge (assigned as 1) vs.if they rarely or never happened (assigned as 0) ( 28 ). Consequently, the patients would better realize whether they had problems with post-stroke SCD and SCD related functional impairment through our telephone survey.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy

Sun

Liu

et al. 2023

Front. Neurol.

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Background and purposeCorpus callosum (CC) infarction is an extremely rare subtype of cerebral ischemic stroke, however, the symptoms of cognitive impairment often fail to attract early attention of patients, which seriously affects the long-term prognosis, such as high mortality, personality changes, mood disorders, psychotic reactions, financial burden and so on. This study seeks to develop and validate models for early predicting the risk of subjective cognitive decline (SCD) after CC infarction by machine learning (ML) algorithms.MethodsThis is a prospective study that enrolled 213 (only 3.7%) CC infarction patients from a nine-year cohort comprising 8,555 patients with acute ischemic stroke. Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after disease onset, and SCD was identified by Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. Based on the significant features selected by the least absolute shrinkage and selection operator (LASSO), seven ML models including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naïve Bayes (GNB), Complement Naïve Bayes (CNB), and Support vector machine (SVM) were established and their predictive performances were compared by different metrics. Importantly, the SHapley Additive exPlanations (SHAP) was also utilized to examine internal behavior of the highest-performance ML classifier.ResultsThe Logistic Regression (LR)-model performed better than other six ML-models in SCD predictability after the CC infarction, with the area under the receiver characteristic operator curve (AUC) of 77.1% in the validation set. Using LASSO and SHAP analysis, we found that infarction subregions of CC infarction, female, 3-month modified Rankin Scale (mRS) score, age, homocysteine, location of angiostenosis, neutrophil to lymphocyte ratio, pure CC infarction, and number of angiostenosis were the top-nine significant predictors in the order of importance for the output of LR-model. Meanwhile, we identified that infarction subregion of CC, female, 3-month mRS score and pure CC infarction were the factors which independently associated with the cognitive outcome.ConclusionOur study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy

Sun

Liu

et al. 2023

Front. Neurol.

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“…This greatly affects the functioning of the central nervous system (CNS), causing memory loss, difficulty concentrating and learning problems. This reduction in the individual's capacity is associated with an increased risk of dementia among other phenomena affecting health, such as different types of functional limitations and disability [4,6,[8][9][10][11][12][13]. In fact, a first stage is the onset of depression, in turn associated with various risk factors.…”

Section: Introductionmentioning

confidence: 99%

Healthy Aging in Menopause: Prevention of Cognitive Decline, Depression and Dementia through Physical Exercise

Guerrero-González,

Cueto-Ureña,

Cantón-Habas

et al. 2024

Physiologia

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The aging of the global population is a significant and complex phenomenon with far-reaching implications for healthcare systems and society. By 2030, it is projected that the number of individuals over the age of 65 will increase by nearly 1 billion, largely due to advancements in healthcare and improvements in quality of life. Aging is a multifaceted process that encompasses a wide array of changes, spanning from the cellular level to the intricate physiological systems of the human body. At the central nervous system level, aging represents a major risk factor for conditions such as depression and cognitive impairment, which are likely linked to neuroinflammatory processes and can potentially lead to more severe dementias, including Alzheimer’s disease (AD). The higher prevalence of AD in women compared to men has led to speculation that the onset of menopause and associated phenomena, particularly the decline in estrogen levels, may play a role in the development of the disease. Furthermore, research has shown that physical exercise confers both physical and mental health benefits to older adults, with women potentially experiencing the greatest advantages. Understanding the multifaceted nature of aging and its implications for health will ensure that older adults receive the support and care essential for maintaining their health and quality of life.

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Biopsychosocial risk factors for subjective cognitive decline among older adults during the COVID-19 pandemic: a population-based study

Schrempft,

Baysson,

Graindorge

et al. 2024

Public Health

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The relationship between physical activity and subjective cognitive decline: Evidence from the behavioral risk factor surveillance system (BRFSS)

Cited by 5 publications

References 46 publications

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy

Healthy Aging in Menopause: Prevention of Cognitive Decline, Depression and Dementia through Physical Exercise

Biopsychosocial risk factors for subjective cognitive decline among older adults during the COVID-19 pandemic: a population-based study

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