Objectives: 

As known, regression to mean (RTM) is a statistical phenomenon that can make natural variation in repeated data look like real change. On the other hand, typical investigation of real life stress contains repeat measurement of physiological parameters. We have assessed the extent to which RTM influences on the difference between baseline and stress levels of heart rate variability (HRV) parameters.

Methods: 

We compared HRV parameters and heart rate (HR) of students at rest and shortly before real life stress. 165 healthy female students with a mean age of 21.13±0.08 years volunteered for the experiment. To study the role of probabilistic forecasting of results in the development of the real life stress affect on the heart rate control, each student was asked to predict her mark. Groups were formed based on the difference between the expectations and the actual mark (group 1: expectation = actual mark; group 2: expectation > actual mark; group 3: expectation < actual mark). Graphs and linear regression method were used to identify RTM. RTM was adjusted with ANCOVA.

Results: 

We find significant differences between groups during control day (group 2/3 HR, Mann-Whitney (U)=265.50; p =0.03; group 2/3 and 1/3 LF, p< 0.05). We observed significant increase of HR and LF/HF in stress day for all groups, decrease of HF in groups 2 and 3 (p< 0.05). At the same time, there were no significant differences between groups in crude value of change (follow-up minus baseline measurements). Adjusted for RTM effects, differences in the values of change HR, LH, HF, LF/HF between groups were significant.

Conclusion: 

Assessing change in HRV from rest to real life stress may underestimate or overestimate response, due to the impact of RTM and individual psychological features.

Supported by the Ministry of Education and Science of Russia (grant 2.2.3.3/2028).