In Japan, “Health and Productivity Management,” where companies strategically address employee health as part of their management approach, has gained significant attention amidst concerns about population decline and demands for improved labor productivity. This study collected and analyzed data on lifestyle habits and physical composition of employees at a small and medium-sized enterprise in Fukuroi City, Shizuoka Prefecture, over a three-month period using wearable devices and body composition monitors. The 30 participants (17 males, 13 females) completed surveys on physical activity, sleep, dietary habits, health awareness, and food frequency questionnaires. Clustering based on body composition data classified participants into three groups: “Balanced Fit Group,” “High Fat Group,” and “Lean Group,” according to body fat percentage and muscle mass. Additionally, principal component analysis applied to step count and sleep metrics from wearable devices categorized lifestyle behavior patterns into three types: “Low Activity,” “Medium Activity,” and “High Activity.” A relationship was observed between these behavioral patterns and physical composition, with the High Fat Group particularly showing low activity tendencies. Nutritional surveys also revealed differences in food consumption patterns across body composition clusters. This research provides fundamental insights for designing data-driven health support measures using multiple data collection methods and offers valuable implications for scientifically implementing Health and Productivity Management initiatives in corporate settings.