Uncovering the Impact of Productivity on Mental Health Among Older Adults: A Data-Driven Analysis

Mental health challenges such as depression and anxiety continue to affect a significant portion of the global population. As the understanding of these challenges evolves, there is an increasing need for data-driven methods to identify the factors that contribute to mental health and to strengthen resources for at-risk individuals. This study utilizes a publicly available mental health dataset from Kaggle to investigate predictors of depression in older adults. An XGBoost machine learning model was employed to estimate depression scores based on multiple psychological and behavioral variables. The analysis revealed that productivity score was the most significant predictor of depression in older adults, exhibiting a feature importance score of 0.762, substantially higher than other contributing factors. Visual analyses confirmed a strong negative correlation between productivity levels and depression severity. These findings underscore the pivotal role of productivity in late-life mental health and highlight opportunities for healthcare practitioners to develop targeted interventions aimed at improving well-being and reducing depressive symptoms among older adults.