Stress usually gets measured by asking questions, drawing blood, or using bulky clinical equipment. This study asks whether a wrist wearable can detect stressful events passively, simply by tracking heart rate and heart rate variability. Stress has been implicated as a contributing factor to numerous diseases, including cardiovascular disease, depression, and certain types of cancers.

The number of stressful events, the degree to which someone responds to a stressful event, and the time it takes to recover from a stressful event have been associated with mortality and poor mental and physical health. Despite the links between stress and disease, few methods exist that allow people to objectively quantify the amount of stress caused by an event.

As a result, individuals lack the knowledge to avoid or properly manage stressful events. To appropriately manage stress, methods that quantify the impact and duration of stressful events in real time need to be developed. In controlled laboratory settings, stress is typically gauged with surveys, cardiovascular measures, or blood and saliva tests.

Surveys require conscientious feedback, cardiovascular measures can require bulky equipment and specialized knowledge, and blood or saliva tests require specialized equipment and trained technicians. Translating these laboratory-grade stress examinations into tests that are easier to perform and interpret may promote greater adoption of stress tracking.

Certain wearable devices continuously measure cardiovascular parameters using photoplethysmography and provide an opportunity to investigate the magnitude of stressful events in real time. Heart rate and heart rate variability have the potential to be surrogate markers of stress, because both can vary in response to the degree of sympathetic or parasympathetic input.

The continuous monitoring provided by the WHOOP strap was used to determine the magnitude of stressful events on cardiovascular metrics in free-living conditions. A wrist-worn WHOOP version three point zero or four point zero device continuously collects heart rate, three-axis accelerometer, temperature, and three-axis gyroscope data.

Running and high-stress work activities are logged through participant manual input or auto-detection by the WHOOP analytics platform, while sleep and wake onset are autodetected. For the analysis, a user needed at least twenty-five days of data over a twenty-eight-day span, and no more than twenty-eight days of data were used for each participant.

Heart rate and heart rate variability were calculated during periods that lacked motion detected by the accelerometer. Moving-block sub-sampling used a thirty-second step size and a five-minute block of photoplethysmography-derived beat-to-beat intervals and accelerometry data.

Heart rate variability was calculated as the root-mean-square of successive differences. Heart rate and heart rate variability derived from the WHOOP device have been validated against gold-standard measures from electrocardiograms.

Heart rate and heart rate variability were aggregated over the activity or over thirty-minute intervals before or following an activity. The aggregated values were subtracted by the median hourly heart rate and heart rate variability for that time of day for a given user.

The post-activity normalized values were then subtracted from the pre-activity normalized values. Cardiovascular parameters were not calculated during running because motion artifacts prevent reliable acquisition of the photoplethysmography data.

Within a given clock hour, motionless heart rate decreases early, increases from time point four point five to thirteen point five, and decreases later in the day. Heart rate variability increases from time point zero point five to eleven point five, then decreases from time point eleven point five to twenty-three point five.

The sleep-aligned panels show the sharpest transition around the dashed sleep-onset line: both curves fall toward onset, then recover gradually afterward. Around wake onset, the motionless-heart-rate curve in panel E jumps abruptly from below the baseline to above it, while panel F shows a similarly steep rise in variability followed by a longer, gradual decline.

Motionless heart rate and heart rate variability exhibit a circadian rhythm that aligns with the time a person falls asleep or wakes up. Using clock time, prior research indicates that heart rate is elevated during the daytime and decreases leading up to and throughout the night.

Prior research indicates that heart rate variability increases throughout sleep, peaks in the early morning, and subsequently decreases throughout the day. Measures that rely on heart rate and heart rate variability may need to be corrected for the time of day when the measure was taken.

The effects of a physical stressor were examined from thirty minutes before to five hours after twenty-three thousand six hundred sixty-five running activities. Runs were grouped into light, moderate, and vigorous loads based on time spent in cardiovascular zones to test for a dose effect.

Relative to the period before the run, motionless heart rate remained elevated after a moderate or vigorous run for at least one hundred eighty to two hundred ten minutes, while it was unchanged or decreased after a light run. Heart rate variability remained depressed after a moderate or vigorous run for at least two hundred seventy to three hundred minutes, while it was unchanged after a light run.

In panel c, the table shows light running changes ranging from minus three point six to zero point nine RMSSD, while moderate running ranges from minus twenty-four to minus eleven, and vigorous running from minus twenty-seven to minus nineteen. In panel d, the corresponding area-under-the-curve bars are approximately minus two hundred, minus three thousand two hundred, and minus six thousand RMSSD-minutes for light, moderate, and vigorous running.

The effects of psychological stress were examined from thirty minutes before to three hundred minutes after eight thousand nine hundred twenty-eight high-stress work periods. Motionless heart rate was elevated during and for up to thirty minutes after high-stress work, then was either unchanged or depressed.

Heart rate variability tended to be lower during high-stress work and was significantly lower ninety to three hundred minutes after the event. These results suggest that motionless heart rate and heart rate variability from wearables may be used as surrogate markers of physical or psychologically stressful events.

In panel B, the bottom table shows the largest reported reduction in HRV in the two hundred ten to two hundred forty minute block, with a ΔHRV of negative six point three and a P-value of one times ten to the minus six. The plotted confidence intervals also show substantial variability across these thirty-minute periods.

Motionless heart rate and heart rate variability exhibit a circadian rhythm that can be used as a baseline to detect deviations caused by stressful events. The results demonstrate a method of normalization for detecting deviations in stress-related metrics and provide evidence that stress can be detected passively outside clinical settings.

These findings may be useful in detecting stressful events, increasing interoception, and improving responses to stressors. Wearable-derived heart rate and heart rate variability changed after both strenuous runs and high-stress work, against a time-of-day baseline.

That suggests a possible passive way to quantify stress outside clinical settings.