The use of predictive modeling in determining the relationship of physical activity and mental health in older adults
The spread of dementia and depression resulting from mental health related disorders in conjunction with the non-communicable diseases (NCD) has been partly attributed to lifestyle related change. Lifestyle related changes are prominent features in many developing countries that have undergone socio-cultural transition from diet to level of activity. Critical to this understanding is the role of physical activity as a key determinant in the developmental origins of mental health and non-communicable diseases. The increase of physical inactivity in older individuals is a significant risk factor in the development of chronic diseases. Lack of adequate physical activity leads to physiologic changes that affects sleep, metabolic demands, cognitive skills, and behavioral outcomes. Measuring the level of activity serves an important function not only in determining normative values but to improve our understanding on the relationship of physical activity and mental health. Recent studies have shown the importance of physical activity in mental health, but very studies have investigated how the hidden physiological underpinning of physical activity affects the outcome of mental health using advance mathematical algorithm. This study aims to investigate the role of physical activity in the elderly population and its implication in cognitive and mental health outcomes based on neuropsychiatric assessment through the use of rating scales, while using computational algorithm as a tool to expand current knowledge on this dynamic relationship.