||We quest for understanding the factors that influence well-beings of people. 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. Recent results show that activity regularity, social interaction regularity and certain daily routines have strong influences on life satisfaction and well-being. This project is part of a larger framework of lifestyle recommenders research, and proceeds in parallel with “visual analytics for pervasive sensor data” project.