Influence of Lifestyles on Mild Cognitive Impairment: A Decision Tree Model Study

Clin Interv Aging. 2020 Oct 28:15:2009-2017. doi: 10.2147/CIA.S265839. eCollection 2020.

Abstract

Objective: To explore the effects of different lifestyle choices on mild cognitive impairment (MCI) and to establish a decision tree model to analyse their predictive significance on the incidence of MCI.

Methods: Study participants were recruited from geriatric and physical examination centres from October 2015 to October 2019: 330 MCI patients and 295 normal cognitive (NC) patients. Cognitive function was evaluated by the Mini-Mental State Examination Scale (MMSE) and Clinical Dementia Scale (CDR), while the Barthel Index (BI) was used to evaluate life ability. Statistical analysis included the χ 2 test, logistic regression, and decision tree. The ROC curve was drawn to evaluate the predictive ability of the decision tree model.

Results: Logistic regression analysis showed that low education, living alone, smoking, and a high-fat diet were risk factors for MCI, while young age, tea drinking, afternoon naps, social engagement, and hobbies were protective factors for MCI. Social engagement, a high-fat diet, hobbies, living condition, tea drinking, and smoking entered all nodes of the decision tree model, with social engagement as the root node variable. The importance of predictive variables in the decision tree model showed social engagement, a high-fat diet, tea drinking, hobbies, living condition, and smoking as 33.57%, 27.74%, 22.14%, 11.94%, 4.61%, and 0%, respectively. The area under the ROC curve predicted by the decision tree model was 0.827 (95% CI: 0.795~0.856).

Conclusion: The decision tree model has good predictive ability. MCI was closely related to lifestyle; social engagement was the most important factor in predicting the occurrence of MCI.

Keywords: behaviours habit; decision tree model; influencing factors; lifestyle; mild cognitive impairment.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Cognition*
  • Cognitive Dysfunction / diagnosis*
  • Decision Trees
  • Dementia / diagnosis
  • Female
  • Geriatric Assessment / methods*
  • Humans
  • Life Style*
  • Logistic Models
  • Male
  • Middle Aged
  • ROC Curve
  • Risk Assessment
  • Risk Factors

Grants and funding

This study was supported by the Shandong Science and Technology Development Plan Project (2011YD18045), Natural Science Foundation of Shandong Province (ZR2012HM049), Fund supported Project of Qingdao Science and Technology Bureau (09-1-1-33-nsh; 15-9-2-74-nsh), and Fund Project of Science and Technology Bureau of Huangdao District of Qingdao (2014-1-73).