Objective: The purpose of this study is to test three different ways of predicting which persons with hypertension will be able to lower their blood pressure using biofeedback techniques. Specifically, the first set of predictive criteria is that proposed by Weaver & McGrady (1995). This model is derived from five variables: heart rate, finger temperature, forehead muscle tension, plasma renin response to furosemide, and mean arterial pressure response to furosemide. The second prediction model is based on the magnitude of circadian variations in blood pressure as measured by 24-hour ambulatory blood pressure monitoring. The third prediction model is based on locus of control of behavior.
Design: This is a quasi-experimental one group pretest-posttest design.
Population, Sample, Setting, Years: Sixty adults with Stage 1 or 2 hypertension are being studied from June 2000-December 2002 in the College of Nursing Biofeedback Laboratory.
Intervention, Outcome Variables, and Prediction Variables:
Intervention: Thermal, EMG, and respiratory sinus arrhythmia biofeedback using Procomp+ / Multitrace Biofeedback System (STENS Corporation, Oakland, CA).
Outcome Variables: Clinic blood pressure, beat-to beat blood pressure (Tonometric Patient Monitor, Colin Medical Instruments Corp, San Antonio, TX), and 24-hour ambulatory blood pressure monitor (Model 90207, SpaceLabs Medical, Inc, Redmond, WA)
Prediction Variables: Weaver & McGrady predictive criteria - heart rate, finger temperature, forehead muscle tension, plasma renin response to furosemide, and mean arterial pressure response to furosemide; 24-hour ambulatory blood pressure - daytime and nighttime mean and standard deviation; locus of control - locus of control of behavior (LCB) scale and multidimensional health locus of control (MHLC) scale.
Methods: Baseline prediction variables are measured weekly for 3 weeks. Plasma renin activity and mean arterial pressure in response to furosemide are measured in week 4. Subjects then receive 8 sessions of biofeedback training. Outcome variables are measured 1 week, 1 month, and 3 months post biofeedback training.
Findings: Prediction criteria have not yet been analyzed. Preliminary data will be reported on approximately 50 subjects who will have complete data by August 2002.
Conclusions: We hope to suggest a means of predicting which persons with hypertension may be able to lower their BP using biofeedback.
Implications: Hypertension, present in more than 50 million Americans, increases the risk of cardiovascular disease and its associated morbidity and mortality. Thus is it critical that adherence to treatment of hypertension be increased. While medications are effective in certain patients, their adverse effects make compliance with treatment difficult to ensure. Yet biofeedback therapy is time-intensive and technician-intensive. The results of this study will enable those caring for hypertensive persons to recommend nonpharmacological treatment (i.e., biofeedback) in an individualized way.
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