||Sedentary lifestyles influence the onset of many serious health problems. Healthy behavior change is an arduous task, but behavior recommenders can help their users achieve it. This novel type of recommender system can suggest its users to pair up with an exercise partner, or propose a day-by-day plan to increase their physical activeness. We quest for understanding the factors that influence well-beings of people, and develop a behavior recommender system that exploits these factors to provide suggestions for healthier lifestyles. Toward this end, we analyse various aspects of activities of daily living, such as physical exercises, work, sleep, social interactions, and daily routines. We utilize state-of-the-art timeseries analysis methods to explore trends, repetitive patterns, regularities and correlations from data such as bluetooth proximity, cell-tower locations, calorie expenditure, and other various information collected from pervasive, wearable sensors. We also show that traditional factor analysis methods can also be applied to sensor data.