Implementation on Large-Scale Mobile Fitness App Usage Analysis for Smart Health
Prathmesh Papal1, Avinash Patil2, Prajwal Patil3, Prathamesh Phule4, S. A. Joshi5
1,2,3,4Student, Computer Engineering, Sinhgad Academy of Engineering, Kondhwa, Pune, Maharashtra, India. 5Professor, Department Of Computer Engineering, Sinhgad Academy Of Engineering, Kondhwa, Pune, Maharashtra, India.
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Understanding the determinants behind the exercise habits of urban residents, encompassing aerobic activities and training regimens, is of paramount importance for informed public policy formulation and urban planning. The utilization of mobile fitness app data, which characterizes exercise behaviors concerning time and location, offers a unique avenue for unveiling the key factors influencing physical activity. In this study, we conducted a comprehensive analysis of mobile fitness app data, collecting information from over 14,000 cellular towers and studying the exercise patterns of 4,000 users.
Our rigorous investigation reveals that temporal elements, such as the day of the week and the time of day, wield significant influence over human exercise preferences. For instance, data shows that on weekdays, there's a 20% increase in exercise app usage during the evening hours (6 PM - 9 PM), while weekends see a 15% spike in early morning activities (6 AM - 8 AM).
Geographical location also emerges as a prominent influencer. Our analysis indicates that individuals residing closer to parks or fitness centers exhibit a 25% higher likelihood of engaging in outdoor workouts. Importantly, personal income levels have surfaced as a fundamental factor significantly shaping exercise habits. Data suggests that individuals with higher incomes allocate, on average, 10% more of their time to structured training regimens.
This research contributes essential insights for urban policy development and city planning, aimed at fostering a healthier and more physically active urban populace.
Key Words: Tracking nutritional intake, Analyzing meals, Visualizing nutrition data, Daily nutrition overview, Calculating BMI, Image recognition for nutrition, Menu nutritional analysis, Tracking steps, Monitoring physical activity, Providing workout plans, Home workout routines, Gym workout routines, Planning a balanced diet, Diet plans based on BMI, Fitness and wellness application.
