|Keywords:||heart rate dynamics, heart rate, heart rate variability, burnout, maslach burnout inventory|
|Full text PDF:||http://dspace.library.uu.nl:8080/handle/1874/302264|
The purpose of this study was to detect a change in the level of burnout in a person through Heart Rate (HR) dynamics, such as HR and Heart Rate Variability (HRV) measures. Research performed so far includes detection through questionnaires and, more recently, through biomarkers such as cortisol, respiration and blood values. However, biomarker findings are inconsistent and hard to compare, likely because research has focused on single HR measurements between different people, which by themselves already vary. This thesis describes a study continuing previous work done at Philips Research, which aspires to differentiate itself from previous burnout research by combining a longitudinal study with nightly and breathing exercise intra-personal photoplethysmogram (PPG) signal measurements obtained from individuals recovering from burnout through therapy. It was hypothesised that the HR and HRV before and after recovery from burnout would have changed, while similar measurements from healthy people would show little to no difference. This study investigated the correlation of the HR dynamics and the Maslach Burnout Inventory (MBI) scores of a group of 24 healthy participants. As expected, we found no significant difference between the two measurement weeks in the values for the HR at night, the HRV during paced breathing and regular breathing or the MBI scores. We also confirmed the existence of a small HR dip between falling asleep and waking up, and a larger HR dip between the maximum and minimum HR of the night, but we could not find any relation between the extent of this dip and the level of burnout. Furthermore, we showed that the paced breathing exercise HRV feature values were significantly different from the HRV feature values obtained during the regular breathing measurement, but again we found no correlation between the change in any of the paced breathing HRV feature values and the change in burnout level. From the exploratory analysis we conclude that the root mean square of successive differences between adjacent interbeat intervals (RMSSD), high frequency (HF) power and low frequency (LF) power may be interesting to look at throughout the night for data gathered from revalidating burnout participants in future research.