Methods and apparatus for analyzing and providing feedback of training effects, primary exercise benefits, training status, balance between training intensities and an automatic feedback system and apparatus for guiding future training

1. A method for determining a training load distribution of a user using a device with a processor, memory and software, the method comprising:

determining and storing a value depicting a fitness level of user and a value depicting a maximum heart rate of the user:

continuously measuring heart rate by a heart sensor and recording selected number of measured values;

obtaining intensity values depicting exercise intensity, alternatively by calculating continuously an exercise intensity using a measured heart rate value and recording selected number of calculated intensity values and/or by measuring, external workload;

calculating an aerobic training load and an aerobic training effect using said intensity values;

determining, each high intensity period from the recorded values of exercise intensity values and determining characteristics of an interval including:

at least one derivative of intensity; and

at least one of:

an amount of intensity increase during the high intensity period;

a duration of the high intensity period; and

an external workload during the high intensity period;

calculating an anaerobic training load and an anaerobic training effect using the determined characteristics;

dividing the aerobic training effect into intensity categories of: Aerobic Low and Aerobic High, based on a cumulative time at predetermined zones of speed, average power and/or heart rate each scaled based on the fitness level, and dependent on the calculated anaerobic training effect and the calculated aerobic training effect, wherein an activity class (AC)-based target values for monthly training load (MTL) are determined for each intensity category;

storing the calculated training load into three intensity categories of Aerobic Low, Aerobic High and Anaerobic, in order to provide a training load distribution between the three intensity categories; and

outputting the training load distribution to an output device;

wherein the category Aerobic High involves a higher heart rate than the category Aerobic Low and does not belong in the category of Anaerobic.

2. The method according to claim 1 , wherein one or more of the intensity categories are further divided into sub-categories.

3. The method according to claim 2 , wherein

the Aerobic Low category is further divided into sub-categories of Recovery and Base; and/or

the Aerobic High category is dived into two or more sub-categories of Tempo, Lactate threshold and VO2 max; and/or

the Anaerobic category is divided into Anaerobic capacity and speed.

4. The method according to claim 1 , comprising classifying a workout into two or more categories is based on the aerobic training effect, anaerobic training effect and time spent at different intensity zones during the workout, wherein the categories depict the physiological benefit of the workout.

5. The method according to the claim 1 , wherein

the intensity is measured as a relative intensity,

wherein the relative intensity is benchmarked against one or more of: lactate threshold heart rate (LTHR), lactate threshold speed (LT speed), functional threshold power (FTP), VO2 max and speed/power corresponding to VO2 max of the user.

6. The method according to claim 1 , wherein the distributed training load comprises summing all of the accumulated training loads of each intensity category.

7. The method according to the claim 1 , wherein the training effect is a scaled value of a training load, which is measurable using cumulative scores of excess post-exercise oxygen consumption (EPOC) or training impulse (TRIMP).

8. The method according to claim 1 , wherein the training load distribution is outputted as a daily calendar, a weekly (7-day) illustration, a monthly (28-day) illustration, a custom date as specified by the user, or a yearly (365-day) distribution.

9. The method according to claim 1 , wherein the category Aerobic Low corresponds to a low-intensity aerobic training, optionally an aerobic exercise at a heart rate below 90% of the user's lactate threshold heart rate (LTHR).

10. The method according to claim 1 , comprising calculating the training load sums for each intensity category and the training load target areas for each intensity category.

11. The method according to claim 1 , comprising an activity class (AC) of the user, which refers to the user's fitness level, activity history or training history; and evaluating the present activity class (AC) using background information, a fitness level, a maximum oxygen consumption value and/or training history data; and wherein each activity class (AC) has its own specific training load target, which preferably comprises a range, more preferably the training load target comprises a lowest limit for a training load of the activity class (AC) and an upper limit for a training load of the activity class (AC).

12. The method according to claim 11 , comprising an average activity class (AC), which is a maximum of a VO2 max-based value, a monthly (28-day) training load-based value, and an activity class constant, wherein the monthly training load is calculated as a weighted sum, optionally the monthly training sum is calculated by computing the daily average training load of the user as a weighted average, and scaling the daily average training load to an average monthly training load.

13. The method according the claim 1 , comprising

determining primary and secondary benefits of exercise using said aerobic training effect, anaerobic training effect and time at different intensity zones, wherein the primary and secondary benefits are selected from a group of: Recovery, Base, Tempo, Lactate Threshold, VO2 max, Anaerobic capacity and speed; and

determining magnitude of the primary and secondary benefits based on the accumulated aerobic load and the accumulated anaerobic load.

14. The method according to claim 1 , wherein the high intensity intervals are detected by analyzing the derivatives of heart rate with regard to time, i.e. the degree of heart rate changes.

15. The method according to claim 1 , comprising providing guidance based on the sums of the aerobic and anaerobic loads, and respective personal targets, said targets being based on the fitness level and total accumulated training load in history.

16. The method according to claim 1 , wherein the characteristics of each interval comprises at least:

starting % HRmax or % VO2 max-value,

final peak % HRmax or % VO2 max-value, and

a value depicting the end of the interval,

and timestamps of the characteristics.