title: A computational physiology approach to personalized treatment models: the beneficial effects of slow breathing on the human cardiovascular system.
abstract: Heart rate variability biofeedback intervention involves slow breathing at a rate of ∼6 breaths/min (resonance breathing) to maximize respiratory and baroreflex effects on heart period oscillations. This intervention has wide-ranging clinical benefits and is gaining empirical support as an adjunct therapy for biobehavioral disorders, including asthma and depression. Yet, little is known about the system-level cardiovascular changes that occur during resonance breathing or the extent to which individuals differ in cardiovascular benefit. This study used a computational physiology approach to dynamically model the human cardiovascular system at rest and during resonance breathing. Noninvasive measurements of heart period, beat-to-beat systolic and diastolic blood pressure, and respiration period were obtained from 24 healthy young men and women. A model with respiration as input was parameterized to better understand how the cardiovascular processes that control variability in heart period and blood pressure change from rest to resonance breathing. The cost function used in model calibration corresponded to the difference between the experimental data and model outputs. A good match was observed between the data and model outputs (heart period, blood pressure, and corresponding power spectral densities). Significant improvements in several modeled cardiovascular functions (e.g., blood flow to internal organs, sensitivity of the sympathetic component of the baroreflex, ventricular elastance) were observed during resonance breathing. Individual differences in the magnitude and nature of these dynamic responses suggest that computational physiology may be clinically useful for tailoring heart rate variability biofeedback interventions for the needs of individual patients.
authors: AIMdyn, Inc., Santa Barbara, California;
authors: Maria
authors: M
authors: Fonoberova
authors: AIMdyn, Inc., Santa Barbara, California; Center for Control, Dynamical Systems and Computation, University of California Santa Barbara, Santa Barbara, California; and.
authors: Igor
authors: I
authors: Mezić
authors: Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, New Jersey.
authors: Jennifer F
authors: JF
authors: Buckman
authors: AIMdyn, Inc., Santa Barbara, California;
authors: Vladimir A
authors: VA
authors: Fonoberov
authors: AIMdyn, Inc., Santa Barbara, California;
authors: Adriana
authors: A
authors: Mezić
authors: Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, New Jersey.
authors: Evgeny G
authors: EG
authors: Vaschillo
authors: Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, New Jersey.
authors: Eun-Young
authors: EY
authors: Mun
authors: Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, New Jersey.
authors: Bronya
authors: B
authors: Vaschillo
authors: Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, New Jersey mebates@rutgers.edu.
authors: Marsha E
authors: ME
authors: Bates
mesh_terms: D017704:Baroreflex; D001945:Breathing Exercises; D025461:Feedback, Physiological; D005260:Female; D006321:Heart / Q000294:innervation / Q000502:physiology*; D006439:Hemodynamics; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular; D057285:Precision Medicine / Q000379:methods; D055815:Young Adult
keywords: adaptability; baroreflex; heart rate variability biofeedback; power spectral density; respiration; respiratory sinus arrhythmia