Biofeedback treatment of prehypertension: analyses of efficacy, heart rate variability and EEG approximate entropy

The effect of relaxation-assisted electromyographic (EMG) biofeedback on blood pressure in 49 college students with prehypertension was observed. Following 10 sessions of biofeedback training, the participants could lower their systolic blood pressures 10.6 mm Hg and diastolic blood pressure 4.7 mm Hg with an remarkable increment in the high-frequency (HF) power spectrum of heart rate variability (HRV) and the approximate entropy of electroencephalogram at electrodes Fp1 and Fp2, but with a significant decrement in the lowfrequency (LF) power spectrum of HRV and LF/HF ratio. This efficacy may be due to the effect that the relaxation-assisted EMG biofeedback could shift the autonomic interaction towards vagal dominance with the involvement of cognitive processing. Prehypertension (blood pressure (BP) ranged 120/ 80–139/89 mm Hg) is a new category designated by the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC7) in 2003. The report showed that prehypertension is associated with an elevated risk of cardiovascular diseases. Managing prehypertension may benefit to prevent hypertension and other cardiovascular diseases. Biofeedback, as a non-pharmacological intervention, is effective to treat hypertension. A meta-analysis conducted by Nakao et al. showed that biofeedback intervention decreased systolic blood pressure (SBP) and diastolic blood pressure (DBP) more than nonintervention controls; but only the relaxation-assisted biofeedback and not the simple biofeedback significantly decreased both SBP and DBP versus sham or non-specific behavioural interventions. Whether biofeedback could decrease BP in prehypertensives is unclear. Therefore, in this study we determined the effect of relaxation-assisted electromyographic (EMG) biofeedback on prehypertension. We also assessed the changes of heart rate variability (HRV) (reflecting the sympatho–vagal interaction) and electroencephalogram (EEG) approximate entropy (ApEn) (reflecting the complexity of a series of data) to investigate the functional changes of autonomic nerve and brain activity during biofeedback training and supply some experimental data to elucidate the neural mechanism by which biofeedback regulates BP (Supplementary Tables 1 and 2 and Supplementary Figure 1). Fifty-eight asymptomatic students (aged 19–23 years) diagnosed with prehypertension in Zhongshan School of Medicine, Sun Yat-sen University, enrolled in this study voluntarily. They were randomized to biofeedback group (30) and control group (28) according to the random digits table. Nine participants who failed to complete the study were excluded from the statistic analysis. Thus, only 49 participants (27 in the biofeedback group and 22 in the control group) underwent the whole study. This protocol was approved by the local ethics committee. Informed written consents were obtained from all participants. Both groups received 10 sessions of treatment once every 3 days. Each session lasted 25 min: the initial 5 min for baseline recording and the remaining 20 min for treatment. The biofeedback group received ten sessions of EMG biofeedback training coupled with relaxation techniques that are similar to autogenic training and guided imagery. The relaxation techniques include relaxing the frontal muscle, mimetic muscles, masseter, shoulders and limbs gradually, feeling the arms and legs limp and heavy, and imagining the hands and feet getting warmer and warmer. During the interval between two sessions, the participants were told to practice the relax skills learned from the biofeedback training at least twice every day at home with each period lasting 20 min. The participants in the control group underwent the same protocol as the biofeedback group except the relaxation-assisted biofeedback treatment. Electrocardiogram (lead II) and EEG (16 leads) were recorded simultaneously during each session to evaluate the HRV and EEG ApEn, respectively. BP was measured before and after each session by the same research assistant using a mercury sphygmomanometer according to the standard guidelines. Three months after this study period, all participants’ BP was measured as follow-up value. The HRV analysis software (HRV Analysis Software, version 1.1 SP1), designed and donated by Biomedical Signal Analysis Group (Department of Physics, University of Kuopio, Finland), was utilized to assess several measures of HRV power spectra (nu, based on fast Fourier transform). The HRV index includes high-frequency (HF) power Journal of Human Hypertension (2007) 21, 973–975 & 2007 Nature Publishing Group All rights reserved 0950-9240/07 $30.00