Cardiovascular disease is the leading cause of death in the western world. Frequent or chronic reductions in heart rate variability (HRV) are a powerful predictor of cardiovascular disease. Psychological stress has been suggested to be an important factor in the development of reduced HRV. Recently, Verkuil et al. (2016) introduced a laboratory-based method to measure additional HRV reduction in everyday life, and reductions in HRV related to psychological stress. In the current paper, we discuss alternative methods to detect additional HRV reductions, in real life data sets without the necessity of laboratory-based calibration, and even in existing data sets. All of these methods use a subset of 24h\' worth of HRV and movement data to do so: either the first 10min of every hour, the full 24h, a combination of 10min from three consecutive hours, or a classification of level of movement. We also present a method to visualize HRV and movement data to be able to detect episodes of reduced additional HRV optically. The method that used the full 24h\' worth of data detected the largest percentage of episodes of reduced additional HRV that actually match with self-reported stress levels, making this method the most promising.