Objectives

This study is a proof of concept pilot to assess the level of heart-rate coherence/synchronization between healers and participant healees in a remote group healing session

Hypotheses

•  Coherence healing induces detectable synchronization or correlation through BBI, heart rate, and HRV analyses among healers and between healers/healees
• Specific frequencies or cyclic patterns are likely observed during synchronization
• Certain individuals act as “strong attractors,” influencing group synchronization over time
• Unique “fingerprint” signatures indicate conducive states for healing in healers and healees.
• Higher synchronization correlates with greater group healing efficacy

Participants

• 3-8 instrumented healers in healing sessions every two weeks, totaling 7 sessions over months
• 7 remote video participants with validated chronic pain conditions

Outcomes

Employing numerous pair-wise synchronization techniques: correlation between multiple HRV measures (standard time-domain, frequency domain), Pearson and Spearman Correlation Coefficient and Dynamic Time Warping (both also with time lags), Wavelet Analyses (Cross-Spectral and Cross-Coherence), Empirical Mode Decomposition with Phase Synchronization, and Causal Decomposition Analysis:

• Similarity patterns were found between healer and healee, with the healer leading by about one second on average, suggesting temporal coherence dynamics warranting further exploration (PCC analysis)
• No significant differences across all healings but when only “successful” healings were evaluated, a trend toward lower HRV differences was found between healer-healee pairs compared to healer-healer pairs, which may suggest that based on certain autonomic nervous system dynamics there is slightly more healer-healee coherence compared to healer-healer coherence (HRV analysis)
• No significant differences in cross-spectral and cross-coherence analyses across all healings but when only “successful” healings were evaluated, the number of times when cross-coherence was elevated was greater when healer-healee pairwise comparisons were done vs. healer-healer comparisons (Wavelet analysis)
• Greater phase synchronization of IMF4 (and less IMF3), suggesting frequency-specific coherence dynamics between healers and healees during healing sessions. IMF4 was in the 10-12 second cycle range and IMF3 was typically in the 5 second cycle range (EMD analysis)
• Quantifying interactive dynamics during the healing by focusing on IMF4’s causal relationships offered insight into the dynamic nature of the coherence relationships between each of the individuals involved in the group healing. With a focus only on “successful” healings, no single person (healer or healee) acted as the “strong attractor” to guide HR dynamics for the rest of the group. Rather, causality maps revealed a complex exchange of causality, switching from one individual to another over the span of the healing sessions. Though purely a speculative hypothesis based on the visual causality map, it appeared that healers may “support” another healer in influencing at healee (CDA analysis)
• “Successful” healing sessions appeared to have more prominent respiratory sinus arrhythmia (RSA) and very low frequency (VLF) fluctuations in the healees (and healers), indicative of relaxation and coherence responses. However, the strength/magnitude of these did not correlate with healer “experience level.” The “breakthrough” moment in healing may be associated with a transient rise in heart rate, though this is speculation since the exact moment of breakthrough was not recorded

Learnings

• In future research, obtain baseline “control” to assess whether healer-healee coherence/synchronization patterns are observed while not in a healing session
• Record “breakthrough” moments during sessions to ascertain whether any unique HR dynamic features are observed during these qualitative moments
• Improve BBI data quality, particularly with RRI data rather than equally-spaced, interpolated BBI times series used here. This would allow for more complex analytical approaches
• Consider collecting additional physiological measures (EEG, EDA), beyond BBI data