Behav. Res. Ther. Vol. 29, No. 5, pp. 469-418, 1991 Printed in Great Britain. All rights reserved

0005-7967/91 $3.00 + 0.00 Copyright 0 1991 Pergamon Press plc

EVALUATION OF THERMAL BIOFEEDBACK TREATMENT OF HYPERTENSION USING 24-HR

AMBULATORY BLOOD PRESSURE MONITORING

ALISON Musso,’ EDWARD B. BLANCHARD’* and GUY C. MCCOY’ ‘Center for Stress and Anxiety Disorders, SUNY-Albany, 1535 Western Avenue, Albany, NY

and 2Albany Medical College, 47 New Scotland Avenue, Albany, NY 12208, U.S.A.

(Received 22 January 1991)

12203

Summary-Ten male hypertensives, whose BPS were controlled on a combination of sympatholytic and diuretic medications, were given 16 sessions of thermal biofeedback prior to attempting withdrawal from the svmnatholvtic drug. Results were evaluated using 24-hr ambulatory BP monitoring (ABPM) as well as cl& and home BPS, both in multi-baseline-across-subject designs and as a sinde‘group.’ Results showed significant treatment effects on 24-hr ABPM data, both at the individual level (SBPs only) and in the aggregate analyses (SBP and DBP). BPS assessed in the clinic by random zero sphygmomanometer and patient-assessed home BPS were also reduced.

Among several so-called stress management approaches (McCaffrey & Blanchard, 1985) to treatment of essential hypertension, thermal biofeedback (TBF) in which patients are taught to “warm their hands and/or feet” that is, to engage in volitional peripheral vasodilation on a regular basis, has gained some popularity. TBF has been the subject of several reports including both a relatively large scale uncontrolled series (Fahrion, Norris, Green, Green & Snarr, 1986) as well as several smaller scale controlled studies (Chesney, Black, Swan & Ward, 1987; Blanchard, McCoy, Musso, Gerardi, Pallmeyer, Gerardi, Catch, Siracusa & Andrasik, 1986; Blanchard, Khramelashvili, McCoy, Aivazyan, McCaffrey, Salenko, MUSSO, Wittrock, Berger, Gerardi & Pangburn, 1988). TBF has been found useful in helping patients eliminate sympatholytic drugs (Blanchard, McCoy, Andrasik, Acerra, Pallmeyer, Gerardi, Halpern & Musso, 1984, 1986) as well as in lowering the blood pressure (BP) of unmedicated patients (Blanchard & Pangburn, 1988). All of the BP effects noted thus far have been on either office or laboratory BPS, or possibly home BPS.

In the field of hypertension, 24-hr ambulatory BP monitoring (ABPM) data is emerging as a possible ‘gold standard’ against which to measure results of therapy (Pickering, Harshfield, Devereux & Laragh, 1985). This emergence is based in part on studies which show stronger prediction of morbidity from 24-hr ABPM data than from office BPS (Perloff, Sokolow & Cowan, 1983; Rowlands, Ireland, Glover, McLeay, Stallard & Littler, 1981; Mann, Miller-Craig & Raftery, 1985).

One of the leaders in 24-hr ABPM, Pickering et al. (1985) has suggested that stress management approaches to hypertension may produce misleading results in that they may teach the patient to have lower BPS in the laboratory or clinic but not otherwise in their general environment. In order to answer this criticism, studies are needed evaluating the effects of stress management techniques such as TBF with 24-hr ABPM. Few reports of the use of 24-hr ABPM data to evaluate therapy, especially in the stress management area, have yet emerged. The one of which we are aware (Jacob, Shapiro, Reeves, Johnsen, McDonald & Coburn, 1986) found no effect on ambulatory BP when relaxation training was added to pharmacotherapy. Thus, the major purpose of this report was to evaluate the effects of TBF on BP using the 24-hr ABPM methodology. Other ways of evaluating outcome were included for comparison purposes.

Analysis of 24-hr ABPM data is very complex (Marler, Jacob, Lehoczky & Shapiro, 1988; Clark, Denby, Pregibon, Harshfield, Pickering, Blank & Laragh, 1987; Jaccard, Musso, Blanchard &

*Author for correspondence.

469

470 ALISON Musso ef al.

Wan, 1990). Thus, the second purpose of this paper is to illustrate emerging data analytic techniques for use with ABPM data.

Overview METHODS

This study can be conceived as a Multiple Baseline Across Subjects design (Barlow & Hersen, 1984) with 5 replications. In such a design, at a minimum one of a pair of matched Ss begins treatment after a brief baseline while the other continues in baseline for a longer period of measurement before treatment is introduced. Finding a change in the dependent variable of interest coincident with the introduction of treatment, rather than as a function of repeated measurements, is evidence for the effect of treatment. It thus represents a form of controlled evaluation using small numbers of Ss.

Alternatively, the study can be conceived as an uncontrolled single group outcome study. We will present the results in both ways. In all cases, the patients entered the study with BP controlled on two medications. Patients were all changed to the same sympatholytic medication (Metoprolol, ‘Lopressor’) and followed by the study physician until BP was stable at home and at the physician’s office and under control (defined as office pressure of < 140/90). Patients began the formal baseline phase of the study 2 weeks after the physician judged them to be stabilized on the new medication regimen. One patient was unable to tolerate the medication change and was maintained on his entering sympatholytic medication. Patients were treated, and then withdrawn from the second stage (sympatholytic) drug in a fashion similar to that of Blanchard et al. (1986).

Patients

Patients were IO adult males diagnosed as having uncomplicated essential hypertension by both personal physician and the study physician and who required a diuretic andjor a sympatholytic to control their BP. Table 1 lists patient characteristics. Patients ranged in age from 35 to 65 yr of age. Prospective patients with obvious end organ damage, other serious medical problems (e.g. cancer) or serious psychiatric disorder (schizophrenia, major affective disorder, organic brain syndrome) were excluded from the study.

Within the constraints of the small population for the multiple baseline analysis, patients were matched into pairs as closely as possible on sex, age, years hypertensive, medication dosage, and pre-treatment BP.

Treatment

Patients received 16 sessions of TBF on a twice-per-week basis. The sessions consisted of 8 hand-warming sessions with the thermistor attached to the index finger of the patients’ nondominant hand, 4 hand-warming and cooling sessions, and 4 foot-warming sessions where the thermistor was attached to the ventral surface of the great toe.

At the first two sessions the therapist was present in the room and provided Autogenic suggestions of relaxation, heaviness and warmth, as well as verbal feedback and encouragement. In all remaining sessions the therapist was in an adjacent room in voice contact with the patient. Visual and auditory feedback were available to patients at this point and Ss were encouraged to develop their own individual methods of obtaining the warming response.

Table I. Patient demographics

Patient Years of Pretx physician NO. Sex Age HT office BP Medications

P-l M 59 8 119/89 L 50; Hctz 50 P-2 M 65 15 131/78 L 100; M 5,‘50 P-3 M 60 14 131176 L 250; M S/SO P-4 M 58 9 134/86 L 100, M S/SO P-5 M 52 5 131184 L3M);H25 P-6 M 54 9 136/88 L 150; c 150

pp:; M 60 5 147191 C 150; D SO/l00 M 35 9 122119 L 200

P-9 M 46 2 117179 L 100; H 12.5 P-10 M 46 24 127185 L 200; H 25

Medications are in mg/day. HT: hypertension; L: Lopressor; M: Moduretic; Hctz: Hydrochloroth~azid~; H: Hygroton; C: Capoten; D: Dyazide.

Evaluation of thermal biofeedback treatment 471

All sessions had a similar format:

(1) Discussion of home practice and BP measurement: 5-10 min (2) Adaptation and baseline (therapist out of room): 10 min (3) Self-control 1 (try to warm hands with no feedback): 5 min (4) Feedback training: 20 min (5) Self-control 2 (try to warm hands with no feedback): 5 min (6) Discussion of session and BP measurement: 5-10 min

All Ss were given a small electronic home trainer with digital readout to O.l”F and instructed to practice the hand-warming for about 20 min once-per-day.

BP measures

The following BP measurements were taken for each S. Physician ofice BP. The patient sat quietly in a straight-backed chair for 20 min. Then three BP

determinations were made, 5 min apart. BP was measured in the right arm supported on a firm surface, using a random-zero mercury sphygmomanometer. Scheduled measurement sessions were made once at pre-treatment, post-treatment, and post-drug-withdrawal. The same procedures were followed for any other physician office BP (e.g. determination of failure of drug withdrawal).

Treatment clinic BP. BP readings were taken using identical procedures to those at the physician’s office. Measurement took place at baseline (2 or 4 times), mid-treatment, post-treatment, and post-drug-withdrawal.

Home BP. Patients were taught to take their own BP at home using a dual stethoscope and sphygmomanometer. Patients were asked to take their BP twice daily: once in the morning and once in the evening. Both readings consisted of one standing and one reclining BP. The BPS were recorded on mail-back postcards. The cards were mailed weekly and weekly averages were obtained. Home BP monitoring began at the first physician’s office visit and continued throughout the study.

24-hr A BPM. A SpaceLabs model 5200 was used to measure 24-hr BP. Five calibration readings of BP were taken at each ABPM session. The monitor was considered calibrated if it was within 5 mm Hg of a mercury sphygmomanometer. The SpaceLabs monitor recorded BP every 15 min from 6 a.m. to 12 midnight and every 30 min from 12 midnight to 6 a.m. Ss were asked to record the time, their activity, location and postural position at each measurement. ABPM was measured at baseline (2 or 4 times, approx. once-per-week), then once at mid-treatment, post-treatment, and post-drug-withdrawal.

Drug withdrawal

At the completion of the post-treatment assessment, the patient was given a schedule by the physician for tapering the sympatholytic medication. Patients continued sending in home BPS and received a booster session of TBF during tapering phase. Two weeks after the drug was discontinued the post-drug-withdrawal assessment was completed.

The following criteria from Blanchard et al. (1986) were employed to judge if a patient had been successfully withdrawn from the second-stage medication. A positive response on any criterion led to the patient’s being declared a failure and returned to the sympatholytic medication.

(1) Physician Office BP > 140/90 (average of 3 determinations in sitting position) (2) Onset of symptoms (3) Excessive (> 20 mm Hg) rise in home BP (4) Failure to achieve baseline home BP levels (based on patient’s mail-back reports) (5) The patient’s personal physician believed the patient should resume second-stage

medication

Patient’s weight was meausred on a balance beam scale at pre-treatment and post-treatment.

Data analytic procedures for 24-hr ABPM

For internal data analytic purposes, patients were judged as ‘successes’ if any significant decrease in 24-hr ABPM levels occurred, either systolic or diastolic BP. This was obtained by comparing

472 ALSON Musso et al.

the 24-hr average BP at post-treatment with the average of the first two baseline 24-hr ABPM averages.

Several issues need to be considered in the analysis of 24-hr ABPM data. These include artifactual readings and outliers, missing data, differences across Ss in residual variances, interaction effects, patterns of serial correlation, and distribution of outliers. In addition, nonhomogeneity of variance and differential effects of activity, location, and position across Ss are factors which make the analysis of 24-hr ABPM complex.

Marler et al. (1986) has outlined an approach which takes these factors into account. They are spelled out in more detail in Jaccard et al. (1990) and are only summarized here. They suggest an approach where the effects of interest are computed separately on each individual. The results are then combined or aggregated via meta-analysis in order to make statements about the sample as a whole. The analysis has the advantage of isolating treatment effects on a single S while also allowing for nomothetic analyses. This approach avoids the violation of assumptions of more standard nomothetic methods. The strategy generally employed consists of the following.

Editing of artifactual readings and outliers. Artifactual readings associated with technical malfunction can be a problem with ambulatory recording devices and were hand edited based on the following criteria established by Marler et al. (1986):

(1) Test readings in the clinic (2) SBP < 70 mm Hg or >250 mm Hg (3) DBP <45mmHg or >150mmHg (4) SBP/DBP < 1.0625 + 0.00125 (DBP) or >3.0 (5) Monitor error (air leak, low batteries, etc.)

Despite the efforts to edit artifactual readings from the data, a number of readings occur that are sufficiently different from other data points so as to bias the outcome of the data. These ‘outliers’ can also mask major trends in the data and were thus edited out via a bivariate outlier analysis based on the Mahalnobis D squared statistic. The analysis was conducted simultaneously on SBP and DBP and on baselines (BL) 1 and 2 simultaneously, then on BLs 3 and 4 simultaneously for Ss having 4 BLs.

Missing data estimating. A multiple regression equation was derived from other variables to estimate missing data. The BP just prior to (y - 1) and the BP just after (y + 1) the missing reading and the sine and cosine of the circadian rhythm were entered into the multiple regression to predict the missing score. If MR-squared was equal to at least 0.65 the equation was used to predict the missing score.

Serial dependency. Three variables were used to model the serial dependency in the data: the first-order autoregression, and the sine and cosine of the circadian rhythm. These variables were subsequently used as covariates in further analyses.

Determination of coeflcients of interests. The data for a given patient were treated as a large matrix with rows representing the BP readings across the BL, mid-treatment, post-treatment, and post-withdrawal assessments. The k columns represented different independent variables such as location or activity. Each independent variable was represented by various dummy-coded vectors representing time of assessment, activity type, location, and position. Interactions between variables were represented by appropriate product terms. An ordinary least squares regression analysis was performed, regressing a given dependent measure on the relevant independent variables (Cohen & Cohen, 1983). The resulting coefficients provided information about the presence and nature of the effects of different independent variables. This analysis also allowed for the elimination of interactions that were not statistically significant, thus making later analyses more manageable.

Aggregate analyses (meta-analyses). Once a model was fitted to the data of an individual patient, we used the procedures of Hedges and Olkin’s (1985) to derive and analyze effect sizes; d, the differences between the means of two conditions, divided by the within-S standard deviation served as the index of effect size. These analyses are the same as those used in so-called ‘meta-analysis’.

Multiple baseline data

Since the multiple baseline

473 Evaluation of thermal biofeedback treatment

RESULTS

analysis provides our ‘controlled’ evaluation of TBF effects on BP, it will be presented first. Two sets of BPS appeared to show consistent results across matched pairs of patients. These include 24-hr standardized average systolic BP (all location, postural position etc., effects controled for) (Fig. 1) and 24-hr home SBP (Fig. 2) with 4 of the 5 patient pairs replicating a decrease in SBP with the onset of treatment. Patients 7 and 8, however, showed subsequent increases in SBP after treatment, perhaps making their inclusion as treatment successes somewhat tenuous.

These results are also replicated when one looks at treatment outcome. In other words, the majority of patients in this subset were also able to be withdrawn from their second stage medication while maintaining 24-hr average SBP and 24-hr home SBP below 140/90 mm Hg.. No other consistent advantage for treatment was noted for other sets of BPS.

24-hr ABPM aggregate statistical analyses

As mentioned earlier, the aggregate statistical analyses was based on the procedures of meta-analysis for combining the results of multiple studies. The 24-hr ABPM data on each S were analyzed individually using the procedures described earlier. These individual analyses yield possible effects of the various independent variables, including activity, location, postural position and most importantly time, that is, pre-treatment to mid-treatment, pre-treatment to post- treatment etc., as well as interactions of time with the other independent variables.

In the interest of brevity, only the results of the individual analyses on Time, specifically, pre-treatment to mid-treatment and pre-treatment to post-treatment, are presented here, in Table 2, for SBP and DBP. The measure of effect size, d, is presented for each S for each analysis. Effects of the other possible influences on ABP (activity, postural position, etc.), are controlled for in these analyses. Also, for informational purposes, there is included an indication of whether a particular S showed a significant Time effect (reduction) on SBP and DBP.

These data, and similar ones on all of the other main effects and interactions, were the ‘raw data’ for the aggregate analyses (meta-analyses). Several hypotheses were tested using the effect size measure (d statistic) mentioned above. For example, for the comparison of systolic BP between baseline and post-treatment, effect sizes were designated as negative if BP at baseline was higher than BP at post-treatment. Likewise, effect sizes were designated as positive if the reverse was true. Each individual effect size was corrected for the bias from d’s overestimate of the population effect size for small sampfes (Hedges & Olkin, 1985) and weighted by the reciprocal of its variance before it was averaged with other relevant individual effect sizes. This correction gives additional weight to effect sizes that are more reliably estimated.

The hypotheses for SBP and diastolic BP (DBP) along with the associated average effect size (d) and the 95% confidence interval for d for each comparison are presented in Tables 3 and 4.

Table 2. Individual S effect sizes (df for changes in SBP and DBP from baseline to mid-treatment and baseline to post-treatment

Effect sizes (d) -. Significant

Systolic BP Diastolic BP Patient

change at individual

NO. BL-mid BL-post BL-mid EL-post SBP DBP

P-f -0.6656 -0.2374 -0.7436 -0.3667 No No P-2 - 0.0803 -1.0192 -0.0167 -0.5598 Yes Yes P-3 -0.1479 -0.3373 0.4072 -0.0800 No No P-4 -0.6576 -0.2667 0.1532 -0.6185 No Yes P-5 -0.7155 - I .4290 -0.5594 - 0.7995 Yes Yes P-6 -1.1489 - 1.5280 I .3308 - I .2950 Yes Yes P-7 0.0775 -0.4546 0.0333 -0.2139 Yes NO P-8 0.1280 - 0.0983 0.0434 1 -0.0150 No No P-9 -0.9364 0.0294 -0.4823 -0.2513 No* P-IO -0.2380 -0.4sO4 -0.3485 0.1678 :: No

A minus (-) sign for d indicates a lower BP at mid-treatment or post-treatment than at baseline.

‘SBP and DBP for P-9 significantly lower at post-treatment than baseline for Work pressures but not for Home pressures.

474 ALISON Muss0 et al.

Table 3. Hypotheses and effect size statistics for aggregate analysis-SBP

Hypothesis ” d 95% Cl

Regression effects (I) BL2 SBP= BLI SBP

Location effects (2) Home SBP = Work SBP

Position effects (3) Standing SBP = Sitting SBP (4) Reclining SBP = Sitting SBP (5) Reclining SBP = Standing SBP

Acrioity effects (6) Sleep SBP = Mental SBP (7) Nonmental SBP = Mental SBP (8) Nonmental SBP = Sleep SBP

Treafmenr efecls (9) Midtx SBP = BL SBP

(10) Postx SBP = BL SBP (11) Postx SBP = Midtx SBP

10

10 0.28 0.21-0.36’

8 0.32 0.224-1.42~ 10 -0.52 -0.6&-0.44* 8 -0.82 -0.93-0.71*

10 10 IO

10 IO 10

0.02

-0.45 -0.55-0.36’ -0.19 -0.27--0.11’

0.30 0.21-0.39’

-0.43 -0.5%-0.31’ -0.53 -0.6%-0.41’ -0.02 -0.1&0.10

-O.l&O.l4

CI: confidence interval; BL: baseline; Midtx: mid-treatment; Postx: post-treatment. *Cl does not include 0 therefore null hypothesis is rejected. A negative value for d indicates that the BP value on the left is lower than the BP value

on the right side of the equation.

Confidence intervals that do not include 0.00 indicate that the mean effect size is significantly different from zero; in other words, that the null hypothesis was rejected. Also, effect sizes in the negative direction indicate that the BP to the left of the equation is lower than the BP on the right.

The most significant findings have to do with the treatment effect. Both SBP and DBP at mid-treatment and post-treatment were significantly lower than at BL. No significant difference was found between mid- and post-treatment BPS. [Analysis of post-withdrawal ABPs was not conducted because of the small number of patients (n = 6) with post-withdrawal 24-hr ABP data.]

Differences in setting appeared to have an unusual effect with SBP at home being significantly higher than SBP at work. No differences in work and home pressures were found for DBP.

The impact of activity was as expected with sleep SBP and DBP being significantly lower than BP during mental or nonmental activity. Also, BPS during nonmental activity were significantly lower than BP during mental activity.

Position effects were also not unusual with standing producing the highest BPS followed by sitting and reclining. These results were consistent across both SBP and DBP.

Although two individual Ss showed significant interactions of Location x Time, there were no significant interactions in the aggregate analyses. For the two individuals, there were significant

Table 4. Hypotheses and effect size statistics for aggregate analysis-DBP

Hypothesis n d 95% CI

Regression effects (1) BL2 DBP=BLI DBP IO 0.12 0.01-0.24’

Locution @ecrs (2) Home DBP = Work DBP 10 -0.02 -O.I(M.O6

Posirion effects (3) Standing DBP = Sitting DBP

1: 0.48 0.374.58’

(4) Reclining DBP = Sitting DBP -0.55 -0.63--0.47* (5) Reclining DBP = Standing DBP 8 -0.99 -l.ll--0.87’

Acrioify effecrs (6) Sleep DBP = Mental DBP 10 -0.41 -0.51--0.31. (7) Nonmental DBP = Mental DBP 10 -0.23 -0.32--0.15* (8) Nonmental DBP = Sleep DBP IO 0.39 0.3IXl.48’

Treatment eflecrs (9) Midtx DBP = BL DBP IO -0.24 -0.3&-0.12*

(10) Postx DBP = BL DBP 10 -0.17 -0.29--0.06’ (1 I) Postx DBP = Midtx DBP 10 -0.04 -0.1&0.09

Cl: confidence interval; BL: baseline; Midtx: mid-treatment; Postx: post-treatment. *CI does not include 0 therefore null hypothesis is rejected. A negative value for d indicates that the BP value on the left is lower than the BP value

on the right side of the equation.

Evaluation of thermal biofeedback treatment 475

Table 5. Average pre-treatment and post-treatment blood pressures for entire sample under different measurement conditions

Measurement condition Pre-TX Post-TX I P

Home blood pressures Supine

Standing

Physician’s &ice pressures (Seated, random zero)

Clinic pressures (Seated, random zero)

SBP 128 125 2.22 <0.05 DBP 77 75 2.23 <0.05 SBP 132 131 0.67 NS DBP 87 85 2.22 < 0.05

SBP 130 129 0.27 NS DBP 84 81 0.99 NS

SBP 135 127 2.40 <0.025 DBP 89 87 I .20 NS

Baseline to Post-Treatment x Location effects: for P-9 there was a significant reduction in SBP at Work but not at Home. For both P-8 and P-9, there were significant reductions in DBP at Work but not at Home.

‘Standard’ nomothetic analyses

Since 24-hr ABPM data are rare in the stress management literature, we have also presented and analyzed more conventional measures of BP. These data can be considered those from a single group, and as such are uncontrolled. In Table 5 are presented the pre-treatment and post-treatment values for physician office, treatment clinic, and supine and standing home BPS. Correlated t’s were calculated on each set of values. One-tailed probabilities are presented since we predicted decreases from pre- to post-treatment.

Examining Table 5, one finds significant decreases in self-measured home supine SBP and DBP and in home standing DBP. There was also a significant decrease in SBP measured by random zero sphygmomanometer at the Treatment Clinic [t(9) = 2.40, P < 0.0251.

Process data

All patients experienced an average increase in hand temperature over BL across training sessions. Only three patients were able to increase their hand temperature to 95”F, however. All patients also showed an average within session decrease in BP across sessions.

Seven of the 10 patients recorded and returned home practice data regularly (the other 3 claimed to practice but did not return data regularly). They reported having greater average increases in hand temperature during home practice than at the clinic; 6 of the 10 were able to reach 95°F at home.

The sample was further divided into treatment successes (patients with a pre- to post-treatment decrease in 24-hr BP) and treatment failures (patients without a decrease in 24-hr BP). Treatment failures were significantly less likely to turn in home practice data (Fisher’s exact P < 0.05), suggesting that treatment successes may be more compliant leading to greater eventual decreases in BP. No other significant differences were found between treatment successes and failures.

There was a net weight gain for the sample of about 2 lb per patient which was not statistically significant [t(9) = 0.24, NS]. However, it rules out weight loss as a possible explanation for BP changes.

Drug withdrawal and follow -up

Although extended maintenance and follow-up were not a formal part of the study, short-term follow-up was conducted on all patients. Two patients were initial failures at drug withdrawal (P-7 because of the onset of symptoms, despite lowered BP, and P-10 because office BPS were too high). P-2 resumed medication shortly after drug withdrawal because of the onset of migraine symptoms. One other patient (P-8) resumed the sympatholytic drug on his own despite controlled office BPS. Thus, only 6 patients remained off the sympatholytic for the l-month follow-up. Only one of these was still off the drug at 4 months. Thus, the ability to maintain physician office BP below 140/90 mm Hg while remaining on only one drug appeared to be fairly short term. 24-hr BPS were not measured beyond the post-withdrawal point so it is uncertain whether they remained below pre-treatment levels after this point.

476 ALISON Mum et ai.

DISCUSSION

The results of this study represent, to the best of knowledge, the first evidence of pre- to post-treatment decreases in 24-hr ABP associated with TBF thus addressing the primary purpose of the study. These findings lend support to previous studies demonstrating the effectiveness of TBF in lowering physician ofice, clinic, and home BPS (Blanchard et al., 1984, 1986). Although the average decrease was not large (5/l mm Hg), the decrease is remarkable for several reasons. First, all patients’ BPS were well controlled on two medications upon entering treatment. Given the initial low average BPS (129/83), one would not predict a further decrease in BP (Jacob, Kraemer & Agras, 1977). Second, the results are significant even after controlling for statistical dependencies in the data, circadian effects, and location, position, and activity effects and are thus independent of these influences. Finally, the decrease in 24-hr BP occurred in the presence of a net weight gain of about 2 lb per patient.

The observed decrease in overall 24-hr BP suggests that TBF produces a basal decrease in BP rather than a temporary, event-related decrease. This provides some evidence to refute the suggestion by Pickering et al. (1985) that stress management procedures may only decrease response to environmental stressors which produce pressor responses. If this were the case, physician ofhce and clinic BPS would show decreases with 24-hr BPS showing no change. The significant pre- to post-treatment decrease in clinic measured SBP suggest that TBF may also be effective in reducing pressor responses in stressful situations (having BP measured by a health professional).

It is difficult to determine whether the amount of BP decrease is clinically meaningful. Previous studies have employed physician office or clinic BPS which tend to be higher than 24-hr BPS and would thus be expected to show more of a decrease post-treatment. Studies examining the effects of antihypertensive medication on 24-hr BP do provide some basis for comparison. Neusy, Steele and Lowenstein (1984), whose population most closely resembled that of the current study (in that most patients required two medications to control BP), found a decrease of 3/3 mm Hg in 24-hr BP after Propranolol administration and a 2/l mm Hg decrease after Clonidine administration. All patients initially entered the study on a diuretic. Thus, our results are comparable to those of Neusy et al. (1984). Somewhat larger decreases in BP, ranging from 5.7/2.6 to 15/14 mm Hg for mild, unmedicated hypertensives after administration of Chlorthalidone or Atenolol have been found (Jacob er al., 1986; Ventura, Messerli, Dunn & Frohlich, 1984).

Relations among dfleering BP measures

As mentioned earlier, the data from this study can be considered either as a controlled evaluation with four replications utilizing a multiple baseline across Ss design or as an uncontrolled single group outcome study. As a controlled study, in four out of the five replications, there is evidence from the 24-hr ABP for home SBP and for overall average SBP, that the introduction of treatment led consistently to decreases in BP. No such consistency was found for any measure of ambulatory DBP. Moreover, neither SBP measured at home by the patient nor SBP measured at the treatment clinic, showed this effect in the multiple baseline analyses. Thus, in terms of the controlled evaluation, treatment effects are inconsistent.

When in turn from the multiple baseline design to evaluating the data in terms of changes in a single group of patients from baseline to post-treatment, somewhat more consistency emerges. The aggregate analysis of 24-hr ABPM shows significant effects for both SBP and DBP, controlling for activity, position etc. Moreover, for 3 of the self-recorded home BPS, there is a significant decrease by correlated t-test from pre- to post-treatment, with only standing SBP not showing a decrease. Finally, the seated SBPs measured by random zero sphygmomanometer in the treatment clinic also decrease significantly but DBPs do not. The major inconsistency is in physician office BPS: neither shows a significant decrease.

Finally, at the level of the individual patient, 5 of 10 patients showed statistically significant decreases at post-treatment in 24-hr ABP measures of SBP while 4 of 10 showed similar significant decreases in DBP. By our criteria, 6 of 10 patients were thus classified as successes based on individual analyses of ABPM data. In terms of the clinical end point of successful withdrawal from the sympatholytic medication, while 8 were successfully withdrawn in terms of our a priori criteria, two of these became failures by returning to the medication for non-BP-related reasons. The 60%

Evaluation of thermal biofeedback treatment 477

short-term success rate is similar to what we have seen before (Blanchard et al., 1986). However, maintenance was noticeably poorer.

Thus, although not perfect, there is a reasonable degree of internal consistency across the various measures used in this study. Some degree of inconsistency among various BP measures, especially 24-hr ABP and other measures, is generally the rule rather than the exception (Pickering, Harshfield, Kleinert, Blank & Laragh, 1982; Marolf, Hany, Battig & Vetter, 1987; Kleinert, Harshfield, Pickering, Devereux, Sullivan, Marion, Mallary & Laragh, 1984).

A possible explanation for the higher failure rate during follow-up involves the primary purpose of the study which was to evaluate short-term 24-hr ABPM changes. Thus, no systematic effort was made to provide formal maintenance training such as booster sessions. This may have decreased patients’ ability to maintain BP decreases (Blanchard et al., 1986; Agras, Schneider & Taylor, 1984). Also, patients were aware that no formal follow-up (other than BP monitoring) would take place perhaps decreasing motivation to continue practicing the hand-warming skills. These results along with other literature suggesting the importance of continued practice in maintaining relaxation-induced BP reductions (Hoelscher, 1987; Hoelscher, Lichstein & Rosenthal, 1986) suggest the need for more systematic follow-up along with some type of formal maintenance training.

In conclusion, the present study suggests that TBF may play a significant adjunctive role in reducing 24-hr BP levels in mild to moderate hypertensives. However, the present study was conducted with a fairly small sample size and thus should be considered a preliminary study. As ambulatory BP monitoring becomes more widely used in medicine, and given its important prognostic power, stress management approaches will need to be evaluated in terms of 24-hr ABP changes as well as the more traditional office and home BP changes.

Ackno,r,ledgemenrs-This research was supported in part by grants from NHLBI, HL-27622 and HL-31189. The authors wish to acknowledge the assistance of Drs James Jaccard and Choi Wan in the analyses of the 24-hr ABPM data.

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