HRV BIOFEEDBACK - terapia leczenia astmy
Kontynuując temat doniesień z badań dotyczącytch terapeutycznych aplikacji BIOFEEDBACKU przytaczam przykład badań dot. efektywności zastosowania terapii HRV BIOFEEDBACK w leczeniu Astmy i schorzeń układu oddechowego.
Autorami badań są amerykańscy lekarze, pracujący w zespole projektowym po kierunkiem Dr. Paula LEHRERA z "UMDNJ-School of Public Health".
"Heart Rate Variability Biofeedback
Effects of Age on Heart Rate Variability, Baroreflex Gain, and Asthma
1. Paul Lehrer, PhD,
2. Evgeny Vaschillo, PhD,
3. Shou-En Lu, PhD,
4. Dwain Eckberg, MD,
5. Bronya Vaschillo, MD,
6. Anthony Scardella, MD and
7. Robert Habib, PhD
+ Author Affiliations
1. *From UMDNJ–Robert Wood Johnson Medical School (Drs. Lehrer and Scardella), Piscataway and New Brunswick, NJ; Rutgers, The State University of New Jersey (Drs. E. Vaschillo and B. Vaschillo), Piscataway, NJ; UMDNJ-School of Public Health (Dr. Lu), Piscataway, NJ; Virginia Commonwealth University (Dr. Eckberg), Medical College of Virginia, Richmond, VA; and Mercy Children’s Hospital (Dr. Habib), Toledo, OH.
1. Correspondence to: Paul Lehrer, PhD, Department of Psychiatry, UMDNJ–Robert Wood Johnson Medical School, 671 Hoes Lane, Piscataway, NJ 08854; e-mail: firstname.lastname@example.org
Objectives: To present additional analysis of data from a previously published study showing that biofeedback training to increase heart rate variability (HRV) can be an effective component in asthma treatment. HRV and intervention-related changes in HRV are negatively correlated with age. Here we assess the effects of age on biofeedback effects for asthma.
Design: Ten sessions of HRV biofeedback were administered to 45 adults with asthma. Medication was prescribed by blinded physicians according to National Heart, Lung, and Blood Institute criteria. Medication needs were reassessed biweekly.
Results: Decreases in need for controller medication were independent of age. There were larger acute decreases in forced oscillation frequency dependence in the older group but larger increases in HRV variables in the younger group. Differences between age groups were smaller among subjects trained in pursed-lips abdominal breathing as well as biofeedback, than among those receiving only biofeedback.
Conclusions: Age-related attenuation of biofeedback effects on cardiovascular variability does not diminish the usefulness of the method for treating asthma among older patients. Additional training in pursed-lips abdominal breathing obliterates the effects of age on HRV changes during biofeedback.
• breathing exercises
• heart rate variability
Heart rate variability (HRV) biofeedback can easily be used to teach people to increase the amplitude of HRV. We have previously reported1 that HRV biofeedback in healthy subjects also results in significantly increased baroreflex gain, both acutely and chronically.
Recently in CHEST (August 2004),2 we reported that 10 weeks of training in HRV biofeedback produces clinically significant improvement in asthma. Patients receiving this training showed decreases in respiratory resistance and asthma symptoms, while receiving a lower dose of “controller medications” (inhaled steroids, sometimes along with a long-acting β-adrenergic stimulant or a leukotriene inhibitor). Medication was controlled using a strict titration schedule derived from National Heart, Lung, and Blood Institute (NHLBI) guidelines.3
It is known that HRV is negatively correlated with age,4 with most studies5678 finding that the decline levels out after approximately age 40 years. Interventions that affect HRV also show greater effects in younger than older adults, including orthostatic effects,9 sleep,10 and aerobic exercise.11 Women tend to have higher levels of HRV than men, although this difference disappears during and after the fifth decade of life.67
There are no previous data showing how age affects biofeedback response, either for asthma or the cardiovascular system, or whether the two kinds of effects are related. Below we report a complementary analysis of our previously reported data2 exploring the age effects on HRV biofeedback in asthma and, consequently, the implications for use of HRV biofeedback in the treatment of asthma.
Materials and Methods
This research was approved by the Institutional Review Board of the University of Medicine and Dentistry, New Jersey (UMDNJ)–Robert Wood Johnson Medical School. Inclusion criteria were as follows: age 18 to 65 years, history of asthma symptoms and, within the past year, either a positive bronchodilator test result (postbronchodilator FEV1 increase ≥ 12%); a positive methacholine inhalation challenge test; or a documented recent history (within the past year) of clinical improvement and FEV1 increase ≥ 12% following instigation of inhaled steroid therapy among individuals with a protracted history of asthma. Exclusion criteria were as follows: a disorder that would impede performing the biofeedback procedures (eg, abnormal cardiac rhythm); a negative methacholine challenge test result; an abnormal diffusing capacity (tested among all subjects > 55 years old or with > 20 pack years of smoking); or current practice of any relaxation, biofeedback, or breathing technique.
Instrumentation and Software
Instrumentation and physiologic measurement procedures are detailed in our previous report.2 We assessed heart rate and HRV from the ECG, baroreflex gain derived from cross-spectral analysis of beat-to-beat heart rate and BP within the low-frequency (LF) [0.05 to 0.15 Hz] range, and three parameters derived from forced oscillation pneumography: resistance at 6 Hz, frequency dependence of resistance, and resonant frequency of the airways.
Before randomization, we stabilized subjects on the lowest possible dose of controller medication that eliminated asthma symptoms and maintained normal pulmonary function. The asthma physicians were blinded to experimental condition. They titrated medications up or down according to symptoms and pulmonary function, according to the protocol described in our previous report,2 based on NHLBI guidelines for asthma treatment.3
Physiologic data were collected during 4 of 10 treatment sessions in the biofeedback condition, and in 4 equivalently spaced sessions in the control group. Data were collected during four 5-min periods: (1) a pretraining rest period (task A), in which subjects were asked to relax as deeply as possible with eyes open, and to try not to move, so as not to disturb the measuring equipment; (2) the first 5 min of biofeedback training (task B); (3) the last 5 min of an approximately 30-min biofeedback training period (task C); and (4) a posttraining rest period (task D), with the same instructions as for the pretest rest period. For control subjects, instructions for tasks B and C were identical to those in tasks A and D.
Procedures for HRV biofeedback training are explained elsewhere in detail.912 Subjects were randomly classified among four treatment groups, of which two groups, reported here, received HRV biofeedback. One of these groups received a “full protocol,” which also included training in pursed-lips abdominal breathing beginning in the second training session. The second group received HRV biofeedback alone.
Subjects were paid $100 for each of the four testing sessions but were not paid for biofeedback sessions or medical evaluations. We only analyzed data from subjects who completed the 10-session biofeedback protocol.
The statistical analysis was done using a mixed-effect model analysis, with unstructured variance-covariance structure, to compare the short-term and long-term within-treatment effects between the age groups, with age treated as a dichotomous variable (> 40 years vs < 40 years). For age as a continuous variable, we used a heterogeneous first-order autoregressive analysis. The model included two repeated measures (sessions, times within sessions [task A = presession rest period, task B = first 5 min of biofeedback, task C = last 5 min of biofeedback, task D = postsession rest period]), treatment conditions (full protocol, HRV alone), and age classes (age < 40 years vs ≥ 40 years). Weight and height were additional covariates included in the model because they correlate strongly with pulmonary function and HRV parameters. Because data were skewed, we applied a log transformation to the cardiovascular and forced oscillation data. Bonferroni criteria were used but only between different physiologic systems, because cardiovascular measures were all related to each other, as were forced oscillation measures. We thus set α = 0.018 as the criterion for statistical significance. We repeated the mixed-models analysis using age as a continuous variable.
In order to normalize data, log transformations were used for all physiologic variables.
Pretest Differences Between Groups
We used the mixed-models analysis main effect for age to examine the effects of age on physiologic variables, across all treatment conditions. With age treated, respectively, as a dichotomous (> 40 years or < 40 years) and continuous variable, values among older subjects were lower than among younger subjects, thus indicating poorer cardiovascular regulation, for LF HRV (p < 0.002, p < 0.0001), high-frequency (HF) HRV (p < 0.002, p < 0.0001), SD of normal R-R intervals (p = not significant [NS], p < 0.011), coefficient of variation in R-R intervals (p = NS, p < 0.0085), and cross-spectral α LF baroreflex gain (p < 0.0001, p < 0.0001). Values were higher for forced oscillation measures, indicating poorer pulmonary function, for frequency dependence (p < 0.006, p < 0.009) and resonant frequency of the lung (p < 0.01, p = NS). With the exception of resonant frequency, the significance of differences was greater when examining age as a continuous variable than as a dichotomous variable, indicating that age continues to affect these physiologic variables past age 40 years. There were no age differences in forced oscillation resistance at 6 Hz.
Age Differences in Effects of Biofeedback
Changes in asthma severity, as measured by medication consumption (the primary outcome variable), improved in both age groups but did not differ between them. Based on our 13-step protocol, medication dropped from an average of that prescribed for moderate asthma to that prescribed for mild persistent asthma (Table 2 , Fig 1 ): a clinically significant improvement, as previously reported.2 This result was maintained after adjusted for age as both a dichotomous and continuous variable. There were no differences between age groups in medication changes.
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Biofeedback effects on cardiovascular measures were smaller among older than among younger patients, consistent with previous studies6791011 of conditions and methods that generally increase HRV. However, age did not appear to decrease the effects of HRV biofeedback on asthma severity, as measured by medication level or oscillation pneumography measures (Fig 2). Indeed, the effects appeared to be slightly greater among older subjects, for reasons not understood.
These results indicate that HRV biofeedback is as effective for asthma among older adults as among younger people, despite the attenuated effects on HRV and baroreflex gain (Fig 3 ). This pattern of results gives further evidence that the effects on asthma may not be mediated by autonomic changes. Other possibilities include the effects of improved gas exchange efficiency that occurs when people breathe at approximately 0.1 Hz,131415 as they did in the present experiment. Hayano et al 16 have shown that gas exchange efficiency is maximized when respiratory sinus arrhythmia occurs in phase with respiration. Vaschillo et al17 have shown that a zero-degree phase relationship between breathing and variations in heart rate occurs only when people breathe at a rate of approximately 0.1 Hz. Also the amplitude of respiratory sinus arrhythmia is maximized at this respiration rate,1518 which also may contribute to gas exchange efficiency. Other possibilities include changes in inflammatory activity, and possible mechanical effects on pulmonary function of practicing slow deep breathing..."