The HRV calculation method displayed in Apple Health is SDNN (Standard Deviation of Normal-to-Normal intervals). SDNN refers to the standard deviation of all normal heart rate intervals (NN intervals). It reflects the overall level of heart rate variability, including the influence of both the sympathetic and parasympathetic nervous systems. A larger SDNN value typically indicates higher heart rate variability, which means that the heart has a stronger adaptability to external and internal stimuli. SDNN values can be influenced by various factors, such as age, gender, and health status, and thus need to be interpreted in the context of individual backgrounds.

In contrast, the HRV values displayed in Era are calculated using RMSSD (Root Mean Square of Successive Differences). RMSSD refers to the square root of the average of the sum of the squares of the differences between consecutive heart rate intervals. It primarily reflects the activity level of the parasympathetic nervous system (i.e., the background recovery system). A higher RMSSD value usually indicates stronger parasympathetic nervous system activity, which is associated with better recovery and cardiovascular health.

In summary, both SDNN and RMSSD are important parameters for measuring HRV, but they focus on different aspects.

Differences:

• SDNN reflects overall HRV, including long-term and short-term heart rate variability, while RMSSD only reflects short-term heart rate variability.
• The calculation of SDNN includes all variations in heart rate intervals, while RMSSD only calculates variations in adjacent heart rate intervals.

Advantages:

• SDNN is suitable for assessing the overall regulatory level of the cardiac autonomic nervous system and is more sensitive to HRV changes over longer time scales.
• RMSSD is suitable for assessing the short-term regulatory level of the cardiac autonomic nervous system and is more sensitive to HRV changes over shorter time scales.

Era chooses to use RMSSD as its calculation method because it is more sensitive than SDNN and is better suited to capturing subtle changes in the body, making it an appropriate indicator for short-term stress status reminders.