Dispositional Flow and Related Psychological Measures Associated with Heart Rate Diurnal Rhythm

Omid Jamalipournokandeh, Junichi Hori, Kiyoshi Asakawa, Kazuo Yana
Vol. 12 (2023) p. 9-20

This study examined whether six dispositional psychological measures; Frequency of Flow ExperienceResilienceSelf-EsteemSelf-EfficacyWill for Meaningful Life, and Trait-Anxiety are associated with heart rate diurnal rhythm parameters. The associations of four physiological parameters—24-hour and 12-hour periodic component amplitudes, diurnal heart rate range amplitude, and autonomic switching rate—with the six dispositional psychological measures were analyzed. The physiological parameters were extracted using two different methods from heart rate data continuously recorded at one-minute intervals by a wrist device. Conventional cosinor and spline-based methods were used. The study was conducted on 20 healthy individuals aged 25–57 years. Single regression analysis showed a significant correlation of Frequency of Flow ExperienceResilience, and Will for Meaningful Life with heart rate rhythm parameters (p < 0.05), and a trend of significant correlation of Self-Esteem with heart rate rhythm parameters (p < 0.1). On the other hand, Self-Efficacy consistently showed a positive correlation, while the only negative psychological measure; Trait-Anxiety, showed a negative correlation with heart rate rhythm parameters, although statistical significance was not reached (p > 0.1). Principal component analysis extracted two orthogonal components with which multiple principal component regression yielded better R2 (coefficient of determination) values than single regression for Frequency of Flow Experience (R2 = 0.451, p < 0.05), Resilience (R2 = 0.587, p < 0.05), Self-Esteem (R2 = 0.494, p < 0.05), Will for Meaningful Life (R2 = 0.364, p < 0.05), Self-Efficacy (R2 = 0.322, p < 0.1), and Trait-Anxiety (R2 = 0.241, p > 0.1). Based on these results, positive dispositional psychological measures were associated with physiological parameters representing long-term characteristics of autonomic nervous activity. The research outcome may be applied to develop a ubiquitous healthcare monitoring system that integrates both physiological bio-signals and psychological measures.