Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: The application presents providing next workout recommendation(s) for a user. The current training load for the next workout recommendation is determined based on a present activity class, a phase of the micro-cycle of successive training days and the sum of the training load values for a number of past trainings. The present activity class of the user corresponds to a present fitness level of the user. A total target training load is determined based on the present activity class and the training goal, which relates to maintaining, increasing or increasing fast the fitness level of the user. The phase of the micro-cycle of successive training days for the next workout recommendation is determined by the realized training load value(s) for daily training session(s) of at least one previous day from training history data, as a percentage of the sum of the training load values for a number of past trainings.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method and apparatus for determining an individualized need for sleep and real-time indication of sleep pressure from physiological data, including at least heart rate data. The method may first involve collecting certain demographic information about the user, such as age data. A sleep need value may then be calculated for the user, based on their expected need for sleep, based on factors such as high chronic stress, acute and chronic training load (for the individual and as an absolute matter), and any other factors such as altitude or alcohol consumption. A sleep pressure value may then be calculated throughout the day, based on the user's activity level and their sleep need (including, for example, sleep debt information), which may then be used to provide feedback to the user as to when they should go to bed.

Abstract: The application presents providing next workout recommendation(s) for a user. The current training load for the next workout recommendation is determined based on a present activity class, a phase of the micro-cycle of successive training days and the sum of the training load values for a number of past trainings. The present activity class of the user corresponds to a present fitness level of the user. A total target training load is determined based on the present activity class and the training goal, which relates to maintaining, increasing or increasing fast the fitness level of the user. The phase of the micro-cycle of successive training days for the next workout recommendation is determined by the realized training load value(s) for daily training session(s) of at least one previous day from training history data, as a percentage of the sum of the training load values for a number of past trainings.