人工智能揭示人们坚持锻炼的动力所在

Summary：A new study with nearly 12,000 individuals' data used machine learning to find key predictors of physical activity adherence. Sitting time, gender and education level were the top predictors. Machine - learning - trained models on various data could predict exercise habits better. Findings could improve fitness plans and health policies. Key Facts：

• Top predictors: Sitting time, gender, education level.
• Study scope: 11,683 participants in a national health survey.
• Potential impact: Improve personalized plans and inform health policy. Source: University of Mississippi. A Mississippi research team used machine learning on 30,000 surveys' data (finally 11,683 relevant). They aimed to predict exercise guideline adherence. Results showed key factors like sitting time, gender, education level. Machine learning has more freedom. Some limitations exist like using subjective data. Future research could explore different factors.