Back

Daily Stress and Heart Rate Variability Among Mindfulness Meditation Practitioners: An mHealth Observational Study

Takezawa, J.; Geng, S.; Fujino, M.; Miyake, M.; Sasahara, K.; Yatani, K.; Niida, A.

2025-08-19 health informatics
10.1101/2025.08.16.25333623 medRxiv
Show abstract

BackgroundMindfulness meditation has been reported to reduce stress and enhance well-being. However, its effects on heart rate variability (HRV) - a physiological marker of stress - remain underexplored. ObjectiveTo examine how meditation practice is associated with subjective stress, HRV, and their interaction, using mobile health (mHealth) technologies. MethodsThis three-week observational study included 90 participants: 19 meditation practitioners, 32 recreational runners, and 39 individuals without regular meditation or exercise habits. HRV was continuously recorded using Garmin smartwatches. Subjective stress and activity were assessed three times daily via a smartphone-based experience sampling method (ESM), yielding 4,557 responses; 632 meditation sessions were time-logged; questionnaires were administered at the end of the study. ResultsStress (PSS) was lower in meditation and running groups vs. controls (Kruskal-Wallis P = .024; adjusted Wilcoxon P = .048 and .040). RMSSD was higher in runners vs. controls (P < .001) but not different between meditation and controls. Higher perceived stress was associated with concurrent RMSSD reduction of -2.24 ms (95% CI -3.97 to -0.26), steeper in runners (-3.94 ms; 95% CI -7.04 to -0.74). During meditation, RMSSD increased by +4.68 ms (95% CI 2.96 to 6.38) and remained elevated for at least 30 minutes post-practice. ConclusionsAlthough daily-life HRV among meditation practitioners was not elevated overall, their ability to increase HRV at will with a prolonged residual effect may relate to stress reduction and warrants further investigation. Trial RegistrationNot applicable.

Published in Journal of Medical Internet Research (predicted rank #1) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.