Key result
Personalized machine learning using HRV features successfully detects everyday psychological stress episodes.
Why the study?
Developing automatic methods to measure psychological stress in everyday life has become an important research challenge.
Does a personalized mobile system using HRV indices accurately detect self-reported psychological stress episodes in everyday life?
Population
15 participants monitored longitudinally with a total of 561 ECG analyzed
Design
Experience-sampling study
Authors
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May enable real-time ambulatory stress detection; hypothesis-generating and requires larger validation before clinical adoption.
Observational (n=15)
Does a personalized mobile system using HRV indices accurately detect self-reported psychological stress episodes in everyday life?
Effect estimate: Beta -0.53 (95% CI -0.96 to -0.11)
p-value: p=0.01
A personalized mobile system utilizing HRV indices and self-reported psychological data can be trained to automatically detect stress events in naturalistic settings.
Cipresso et al. (2021) conducted an observational in Psychological stress (n=15). Heart rate variability (HRV) monitoring vs. Self-reported stress levels was evaluated on Global perceived stress predicted by RMSSD (Beta -0.53, 95% CI -0.96 to -0.11, p=0.01). A personalized machine learning system utilizing heart rate variability features, including RMSSD (Beta -0.53), successfully detected and classified self-reported psychological stress episodes in everyday settings.
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