A significant proportion of the UK population, particularly elderly individuals, are affected by sleep disorders. These are characterized by irregular sleep patterns that significantly impact their quality of life and social functioning. Common sleep disorders like insomnia, sleep apnea, hypersomnia, and attenuated REM sleep are more common in the elderly since they are already more prone to chronic health problems. Wearable technology has made it possible to continuously monitor bio-signals, such as heart rate, skin temperature, and activity levels, at an inexpensive price. This has the potential to provide useful insights on how users sleep. In particular, older people dealing with chronic health conditions can benefit from the real-time health assessments made possible by these wearable devices. Using secondary data, a quantitative analysis was carried out to investigate the impact of several health parameters on overall sleep. An interactive visualization was created to find patterns, correlations, and trends among the four stages of sleep (Total, Shallow, Awake, and Deep Sleep) in relation to factors like gender, BMI, marital status, step count, and fall incidence after the data had been pre-processed for suitability. The primary findings indicate that men tend to sleep more than women, who are more prone to insomnia and males with higher BMIs sleep more, while women with higher BMIs have less sleep. Physical inactivity caused by conditions such as osteoarthritis, fractures, and urinary tract infections negatively impacts the quality of sleep. Furthermore, emotional stability in women is a stronger predictor of excellent sleep regardless of physical activity levels, while higher step counts are associated with better sleep for males. The findings from the data analysis could be further help in developing a data-driven framework for monitoring sleep conditions of elderly population with sleep disorders with integration of AI and Mobile health technology.