Diet and lifestyle behaviours simultaneously act on frailty: it is time to move the threshold of frailty prevention and control forward

• Main Authors: Shan-lan Yang (Author), Lei Wu (Author), He-lang Huang (Author), Lang-lang Zhang (Author), Yi-xin Chen (Author), Sheng Zhou (Author), Xiu-xiu Chen (Author), Jiao-feng Wang (Author), Chao-bao Zhang (Author), Zhi-jun Bao (Author)
• Format: Book
• Published: BMC, 2024-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-024-18639-y; 1471-2458
• Keywords: Frail; Diet; Life behavior; Decision tree; Prevalence study; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 24, Iss 1, Pp 1-14 (2024)

Abstract Background To analyse the association among the simultaneous effects of dietary intake, daily life behavioural factors, and frailty outcomes in older Chinese women, we predicted the probability of maintaining physical robustness under a combination of different variables. Methods The Fried frailty criterion was used to determine the three groups of "frailty", "pre-frailty", and "robust", and a national epidemiological survey was performed. The three-classification decision tree model was fitted, and the comprehensive performance of the model was evaluated to predict the probability of occurrence of different outcomes. Results Among the 1,044 participants, 15.9% were frailty and 50.29% were pre-frailty; the overall prevalence first increased and then decreased with age, reaching a peak at 70-74 years of age. Through univariate analysis, filtering, and embedded screening, eight significant variables were identified: staple food, spices, exercise (frequency, intensity, and time), work frequency, self-feeling, and family emotions. In the three-classification decision tree, the values of each evaluation index of Model 3 were relatively average; the accuracy, recall, specificity, precision, and F1 score range were between 75% and 84%, and the AUC was also greater than 0.800, indicating excellent performance and the best interpretability of the results. Model 3 takes exercise time as the root node and contains 6 variables and 10 types, suggesting the impact of the comprehensive effect of these variables on robust and non-robust populations (the predicted probability range is 6.67-93.33%). Conclusion The combined effect of these factors (no exercise or less than 0.5 h of exercise per day, occasional exercise, exercise at low intensity, feeling more tired at work, and eating too many staple foods (> 450 g per day) are more detrimental to maintaining robustness.