New West Virginia University research has shown that heart rate variability — the time between heartbeats — reported on smartwatches and rings is different from what would be recorded in clinical settings.

“Consumer wearables are reporting heart rate variability data and metrics that previously you would only get in the hospital and a lab setting, but they’re recording them differently,” said Matt Tenan, program director for Human Performance Research and Data Science at the WVU Rockefeller Neuroscience Institute.

Wearables operate with photoplethysmography, or PPG, a technology that shines a light into the skin and produces a reflection of the blood moving just below the finger or wrist to record heart rate. In a hospital, an electrocardiography — ECG or EKG — machine measures the heart’s electrical activity through electrodes placed on the body.

“You’re looking at two things, one is blood flow and the other is the electrical signal of the heart,” Tenan said.

Tenan and colleagues from the WVU Rockefeller Neuroscience Institute and WVU School of Medicine Department of Exercise Physiology conducted a study to determine the validity of the measures wearables report for heart rate variability. Their work was published in the journal Sports Medicine in a paper entitled Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study.

Since the wearables measure the pulse instead of the heart’s electrical signals, Tenan and his team used data from previous research to conduct a simulation analysis of the process involved when blood moves from the heart to the finger or wrist.

“There’s a lot going on between when the heart beats and when the blood gets all the way to the arm or wrist, what’s called pulse arrival time,” Tenan explained. “By simulating that and knowing what’s happening at the heart, we determined what’s being measured with the consumer wearable is different. It’s not to say the consumer wearable is bad or not useful, it’s just not the same.”

To conduct the study, researchers collaborated with the cardiovascular lab at the University of Waterloo in Ontario, Canada.

The study also found not all wearables are created equally when it comes to the calculations they use to record health measurements.

Researchers found the method Apple uses, standard deviation of normal-to-normal, or SDNN, is the most accurate. Other brands implement a system called root mean square standard deviation, RMSSD, which Tenan said produces a wider range of error in the measurement.

Tenan hopes the study encourages manufacturers of wearables to consider switching their calculations to SDNN.

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