The Validity of Ultra-Short-Term Heart Rate Variability during Physical Activity and Cognitive Load

As mixed reality (MR) environments gain popularity for enhancing human performance, there is a growing need for real-time monitoring to personalize training programs and to allow human-machine interaction. Heart rate variability (HRV) is a valuable metric for assessing physical and cognitive load, but traditional short-term (ST) HRV analysis requires prolonged measurement periods, limiting its practicality in dynamic MR scenarios. To support real-time adaptation in MR environment, it is crucial to assess whether ultra-short-term (UST) HRV analysis can reliably capture physiological responses during mixed physical and cognitive tasks. ECG signals were collected at baseline (squats), and dual-task (squats combined with cognitive load) conditions from seven young and healthy subjects. Sixteen time domain variables were extracted from HRV excerpts of 300, 240, 180, 120, 60, and 30 seconds. Significant differences between baseline and dual-task conditions were investigated using analysis of variance (ANOVA). Additionally, Pearson’s correlation, Bland-Altman, and trends analysis were used to assess the robustness of a subset of UST HRV variables as reliable alternatives to ST ones. Six out of sixteen UST HRV variables (MeanNN, CVSD, MedianNN, Prc20NN, Prc80NN, and pNN50) were identified as reliable variables across all window lengths and conditions. Three additional HRV variables (RMSSD, SDSD, and SDRMSSD) showed a close to significant trend across conditions. This preliminary analysis shows that HRV time-domain UST variables can be potentially used as a reliable alternative of standard ST ones, even in the presence of physical activity combined with cognitive load.