The importance of sleep in people’s lives and concern about their quality have both been gaining an increasing relevance and awareness. Nowadays, the number of people who use mobile and wearable devices to monitor their sleep and day activity is quite revealing. But while some of those recent devices may even provide reliable measurements of some of the sleep parameters and sleep structure itself, they do not provide yet a justification for what has led to that situation and to those values. With the present study, we intend to verify and establish relationships between some environmental factors, such as temperature, humidity, luminosity, noise or air quality and between sleep performance and activity during the day. Several time series machine learning models were used to predict sleep stages and to estimate the level of day activity of a person.