Heart Rate Variability Profiles and Exposure Therapy ... - Springer Link

Appl Psychophysiol Biofeedback (2012) 37:53–62 DOI 10.1007/s10484-011-9179-5

Heart Rate Variability Profiles and Exposure Therapy Treatment Outcome in Flight Phobia Xavier Bornas • Antonio Riera del Amo Miquel Tortella-Feliu • Jordi Llabre´s

•

Published online: 29 December 2011 Springer Science+Business Media, LLC 2011

Abstract The goal of this study was to explore why certain patients in a previous study on exposure therapy for flight phobia did not experience an improvement in their conditions. Participants from a treatment study (N = 45) were selected according to post-treatment results and divided into two groups: the unsatisfactory treatment outcome group (UTO, N = 10) and the satisfactory treatment outcome group (STO, N = 10). The differences between these two groups prior to receiving exposure therapy were analyzed at the behavioral, physiological, and cognitive levels. The UTO participants had been avoiding flying longer than the STO phobics. Following Thayer and Lane’s neurovisceral model of emotion regulation, heart rate variability was analyzed at two levels: tonic and phasic. Low frequency and high frequency (HF) power were calculated in the frequency domain and Sample Entropy was computed in the time domain. The tonic HF power of the UTO group was higher than the STO group’s tonic HF power. In the phasic level, while the STO group’s HF power decreased under exposure and subsequently returned to baseline level, the UTO group demonstrated a more rigid pattern. Finally, the STO group reported higher emotional involvement than the UTO group when they were shown a sample of the therapy. Based on these results, the challenge of matching exposure therapy to each patient’s profile is discussed. Keywords Specific phobia Fear of flying Heart rate variability Exposure Treatment

X. Bornas (&) A. Riera del Amo M. Tortella-Feliu J. Llabre´s University of the Balearic Islands, University Research Institute on Health Sciences (IUNICS), Palma, Spain e-mail: [email protected]

Introduction The well documented and widely recognized success of exposure therapy has made it the treatment of choice for specific phobias; however, a small and significant minority of patients fail to benefit from it (Choy et al. 2007; McNally 2007; Wolitzky-Taylor et al. 2008). Little attention has been paid to them thus far, perhaps because published studies have been conducted with rather small samples. For example, in an n = 30 study with an 80% success rate, only 6 or fewer participants in the study would be viewed as treatment failures. Furthermore, the criteria for identifying these failures have rarely been reported. One criterion is based on a comparison of levels of self-reported fear of flying at the end of treatment and at pre-treatment. However, it is perhaps more important to determine which patients retain the diagnosis of specific phobia at the end of treatment and which ones are no longer diagnosed with specific phobia than it is to achieve a reduction in selfreported fear. Additionally, reductions in self-reported fear should be analyzed in terms of clinically significant improvements. Avoidance is another criterion to evaluate for therapeutic outcome. For example, some patients might take a flight after simulated exposure, yet they fly with some degree of discomfort or while medicated. Most treatment studies schedule a flight for patients during which they are supported by researchers (who might even accompany the patients). In these cases, flying cannot be considered as strong evidence of exposure therapy success, despite the fact that patients did actually take a flight after treatment. On the other hand, some patients whose fear questionnaire scores are much lower at the end of treatment do not take a flight because they are too busy, or simply because they do not have reason to. In these cases, researchers cannot be sure that they are truly avoiding flying.

123

54

In this study, two main criteria must be met for a case to be considered a treatment failure after exposure therapy: (1) the patient does not fly on his/her own, and (2) the patient receives a clinical phobia diagnosis. Changes in patients’ self-reported fear scores can be considered as additional criteria, but they do not necessarily indicate the effectiveness of the exposure therapy. Once therapy failure has been defined, it should be possible to identify some pre-treatment differences, between patients who ultimately fail to improve and those who do. The first factor to examine for such differences is fear level. For example, perhaps phobics reporting more fear benefit less from exposure than phobics reporting less fear. Questionnaire data can be used to assess fear severity, but structured clinical interviews provide additional information, such as about how long the patient has been avoiding feared situations. Physiological differences might be useful as well. According to Thayer and Lane (2009), the role of HRV in emotional regulation can be studied at two levels: tonic (trait) level and phasic (state) level. At the trait level, ‘‘…individuals with higher levels of resting HRV, compared to those with lower resting levels, produce context appropriate emotional responses’’ (Thayer and Lane 2009, p. 85). Therefore, flight phobics with relatively high levels of resting HRV, would be expected to produce context appropriate emotional responses. Prima facie this would lead to predict better outcomes for patients with high HRV. However, The question is: what exactly is an appropriate emotional response to fearful stimuli? Perhaps avoidance is an appropriate response if understood as a defensive, selfprotective impulse; after all, avoidance is a common response for organisms in dangerous situations. Generally speaking one could argue that high HRV people are more healthy and less vulnerable to specific phobia (among other disorders). The problem arises when these people, for widely unknown reasons in most cases, do become phobic. In this case (and maybe only in this case) it may be that their higher HRV turns out to be a problem. Thus, patients with higher resting HRV would be more able to avoid fearful stimuli longer or more frequently than those with lower resting HRV. The success of exposure is based on repeated presentation of fearful situations to patients who do not avoid them (Emmelkamp 1994 cited by Tryon 2005, p. 73); therefore, exposure will not be effective for patients who cognitively avoid the fearful stimuli and higher rates of failures among patients with higher resting HRV could be expected. As for the state level of analysis, ‘‘…phasic increases in HRV in response to situations that require emotional regulation facilitate effective emotional regulation’’(Thayer and Lane 2009, p. 85). Generally speaking, changes in HRV when patients go from baseline to exposure

123

Appl Psychophysiol Biofeedback (2012) 37:53–62

conditions and back to baseline can be taken as indexes of phasic HRV (i.e. autonomic flexibility). As pointed out by Friedman (2007), physiologic variability is essential for maintaining organismic stability. Furthermore, ‘‘in contrast to the view of physiologic lability as an indicator of pathology, patterned variability is seen as reflecting overall systematic coherence and viability’’ (p. 186). Adaptation to changing environmental demands requires such changes, and there may be some association between larger changes and better adaptation capabilities. However, as with tonic HRV, patients with higher phasic HRV (i.e. the ones who show greater HRV changes when conditions change) will benefit less from exposure therapy than those who show smaller or no HRV changes in response to changing environmental stimuli if the former are able to cognitively avoid feared stimuli (thus preventing extinction of fear) while the latter are not; thus, extinction of fear can progress throughout exposure sessions. Again this hypothesis seems to be at odds with the idea that patterned variability reflects overall coherence and viability. It should be noticed, however, that viability through avoidance can be a healthy strategy in the absence of phobia and a non-healthy strategy in phobic patients. Information about heart rate complexity provides additional insight into the system’s dynamics. For variability measures (e.g. standard deviation), the temporal pattern of the data is irrelevant; the focus is to quantify the degree of spread around a central value (e.g. the mean). In contrast, discerning changes in temporal pattern from apparently random to very regular is the primary statistical focus for complexity measures such as Approximate Entropy (Pincus 2000, p. 142). Therefore, a signal might be highly variable but very regular. Sample entropy (Richman and Moorman 2000) is a complexity measure based on the regularity of any system’s output (e.g., heart rate). Previous studies on flight phobia have shown the usefulness of this measure (Bornas et al. 2006a, b; 2007). The trait versus state level rationale (see above) also applies for entropy: complex systems are more flexible and thus can avoid (behaviorally or cognitively) fearful situations. While flexibility is an advantage when real dangers or threats come up in the environment (because flexible people can avoid them effectively), it can be a problem when a threat is not real but only imagined or felt by the patient. As it is known, in this case avoiding the threat will perpetuate the phobia. Therefore, poorer treatment outcome can be expected from flexible systems than from less complex systems, which would not be able to avoid the feared situations. Similarly, complexity changes along conditions (such as those reported in Bornas et al. 2006b) are signs of flexibility; less flexible systems will benefit more from exposure therapy than more flexible ones. SampEn(m, r, N) is the negative natural logarithm of the conditional probability that two


sequences similar for m points remain similar at the next point. SampEn(m, r, N) = - ln [Am(r)/Bm(r)], where Bm(r) is the probability that two sequences will match for m points, whereas Am(r) is the probability that two sequences will match for m ? 1 points. In addition to these behavioral and physiological characteristics, ineffectiveness might be due to differences in cognition. For instance, patients who do not trust exposure therapy and/or do not consider it to be a logical treatment could be less likely to benefit from it (Powers and Emmelkamp 2008). Furthermore, when this therapy is applied in a simulated environment (e.g. through computers and virtual reality) rather than in vivo, the ability of patients to consider themselves to be emotionally involved in the exposure could be a cognitive-emotional factor in therapy ineffectiveness. It seems reasonable to expect more treatment failure for patients who do not experience the sense of presence (feeling the subjective sensation of ‘‘being there’’ in these mediated environments). In summary, if habituation is an essential working mechanism of exposure, patients who cognitively avoid the stimuli will experience fewer or no benefits from therapy. HRV has been found to be related to avoidance, and a theoretical model of this relationship has been recently proposed (Thayer and Lane 2009). On the other hand, being unable to feel simulated scenarios as if they were real can be understood as passive avoidance. The aim of this ex post facto study is to determine if patients who do not benefit from exposure therapy demonstrate lower (as could be predicted from the application of Thayer and Lane’s model to non-phobic people) or higher vagally mediated HRV at rest and less autonomic flexibility (as predicted when we consider the model in phobic people), and to explore the behavioral and cognitive concomitants of these physiological characteristics.

55

ratings, and number of participants becoming free of diagnosis for specific phobia (situational). Ten of the fortyfive participants did not take a flight during the 2 weeks after exposure sessions terminated; additionally, they received a flight phobia diagnosis when they were clinically interviewed after that period had elapsed. These participants were assigned to the unsatisfactory treatment outcome (UTO) group. Participants who did take a flight after treatment, did not receive a diagnosis of flight phobia at post-treatment assessment and showed the best indexes of clinically meaningful change, calculated for Fear of Flying Questionnaire (FFQ; Bornas et al. 1999) scores according to the Jacobson and Truax (1991) proposal, were candidates for the satisfactory treatment outcome (STO) group (n = 10). In the UTO group there were five participants who received therapist-administered CAFFT, four who self-administered the CAFFT and one participant received VR exposure treatment. In the STO group there were three participants who received therapist-administered CAFFT, three who self-administered the CAFFT and four participants received VR exposure treatment. No statistically significant differences were found between these groups (v2(2) = 2.44, p = .295). Measures Diagnostic Status and Fear of Flying Features The Anxiety Disorders Interview Schedule for DMS-IV (ADIS-IV; Brown et al. 1994) was used at pre-treatment and at post-treatment to determine participants’ diagnostic status and to quantify their levels of fear, avoidance, and interference on a scale of 0–8 (0 = no fear, avoidance or interference, 8 = extreme fear, avoidance or interference). ADIS-IV is an excellent interview for assessing anxiety disorders; it has proven adequate psychometric properties according to Anthony et al. (2001).

Method Clinician Ratings Participants Participants were chosen from a controlled study sample of 45 flight phobics who received computer assisted (self- or therapist-administered) or virtual reality exposure therapy (Tortella-Feliu et al. 2011). These treatment conditions were equally effective in significantly reducing, statistically and clinically, fear of flying as measured by the following: self-reported fear, number of participants taking a flight on their own during a 15-day period after treatment and also during the next year, fear ratings, degree of avoidance and interference in the Anxiety Disorders Interview Schedule for DSM-IV (ADIS-IV; Brown et al. 1994), severity as assessed by independent assessors’

After the individual interviews, independent assessors rated the severity of the patients’ phobias on a scale from 0 to 8, where 0 = symptom free and 8 = extremely severe and disabling, for all aspects of life affected. This scale was the ¨ st et al. (1998). same as that used by O Self-reported Fear of Flying The Fear of Flying Questionnaire (FFQ; Bornas et al. 1999) and the Fear of Flying Scale (FFS; Haug et al. 1987) were used to assess self-reported fear of flying. The FFQ is a 30-item self-report instrument describing situations related to flying. For each item, respondents rated their degree of

123

56

discomfort associated with the situation on a scale of 1–9 (1 = not at all, 9 = very much). Scores ranged from 30 to 270. As reported by Bornas et al. (1999), internal consistency was a = 0.97 and retest reliability (15-day retest period) was r = 0.92. In our sample, Cronbach’s a for the FFQ was 0.90. The FFS consists of 21 items describing air travel situations. Fear elicited by each situation was rated on a five-point scale (0 = not at all, 4 = very much), with scores ranging from 0 to 84. In the original FFS (Haug et al. 1987) Cronbach’s a was 0.94 and retest reliability (at three months) was r = 0.86. For the translated version used in this study, Cronbach’s a and 15-day retest reliability were 0.95 (n = 228) and 0.86 (n = 106) respectively (unpublished results). In our sample, Cronbrach’s a for the FFS was 0.88. Treatment Expectations and Satisfaction In the first session, after the treatment was presented, one question adapted from Borkovec and Nau (1972) was used to quantify usefulness (‘‘To what extent do you think that the treatment is useful in your case?’’). This question was answered on a scale of 0–10 (0 = not at all, 10 = maximum). The scale from which this question was taken has demonstrated good factorial structure, internal consistency and reliability according to Devilly and Borkovec (2000). Motivation for treatment was assessed with a single question to be answered on a scale of 0–10 (0 = not at all, 10 = maximum). Emotional Involvement After the first treatment session, emotional involvement during exposure was assessed by means of the 7-item Emotional Involvement subscale of the Reality Judgement and Presence Questionnaire (RJPQ; Banõs et al. 2000). The alpha reliability (internal consistency) for the questionnaire was 0.82. Stimuli and Apparatus All stimuli presented during psychophysiological recording were displayed on a 17-inch screen, and controlled by a computer (Pentium IV 1.6 GHz). Sounds were played through speakers. Sessions were conducted in a dimly lit and sound attenuated room. ECG was recorded in a Lead III configuration (a positive electrode on the left ankle, a negative electrode on the left wrist, and the ground electrode on the right ankle) using 10 mm Ag/AgCl electrodes. Instructions were given to subjects to avoid arm movements during the experiment. The signal was recorded on a BIOPAC MP150 monitoring system and the sample rate was set to 500 Hz.

123


Data Reduction and Analysis After visual inspection of the ECG recordings, all were cut to the same length (3 min). An automatic R-wave detector was used to identify the interbeat intervals (IBI) in milliseconds. Instantaneous heart rate (HR) was obtained using the algorithm developed by Berger et al. (1986) with a resampling rate of 4 Hz, and the high frequency band power (HF, 0.15–0.4 Hz) as well as the low frequency band power (LF, 0.04–0.15 Hz) was calculated with the fast Fourier transformation method on these HR time series (N = 520). The HRV analysis software (version 1.1; Niskanen et al. 2004) developed by the Biosignal Analysis and Medical Imaging Group at the University of Kuopio was used to detect the HF spectral peak during free breathing and paced breathing conditions. SampEn was calculated using the mse.c software available at Physionet developed by Costa et al. (2002) for scale factor = 1, m was set to 2, and r was 0.2 times the standard deviation of the corresponding series. Physiological measures with highly skewed distributions were ln transformed before analysis. Procedure Participants in the treatment study approved by the Bioethics Committee of the University were recruited through advertisements in local newspapers. Upon arrival, they received general information about the study and signed a written consent form; next they were interviewed by a clinician to determine their diagnostic status and related fear of flying features. They also completed several selfreported measures on fear of flying, treatment expectations, and emotional involvement during exposure (see Measures section above). After being interviewed, participants entered an adjacent room to begin psychophysiological testing. Each subject was seated approximately 1 m from a 17-inch Monitor; sensors were attached to her ankles and wrist for psychophysiological recording. A 3-min adaptation phase was followed by a 5-min paced breathing task (PB) (0.2 Hz), a 5-min resting baseline period (BL1), a 5-min period of exposure to a threatening flying sequence (EXP), and another 5-min resting baseline period (BL2). Paced breathing is one way of isolating the particular vagal component in HRV and it was used to reduce interindividual variability in vagal tone in this study. During adaptation, the subject was asked to stare at a fixed cross on the computer screen. Three minutes later, the paced breathing task began. It was paced using a picture of a human face: arrows pointed to the mouth when inspiring and pointed out of the mouth when expiring; a scrolling bar on the bottom of the screen also indicated inspiration and


expiration phases of identical duration. During both baseline periods the subject was asked to look at the computer screen and relax while the following messages appeared on the screen: ‘‘It’s important that you stay still’’, ‘‘Be relaxed, keep breathing at your own rhythm’’. The message ‘‘Now we need to evaluate your responses to some flying stimuli’’ appeared on the screen 10 s before the 5-min exposure phase. It should be stressed that subjects did not know that feared stimuli would be presented to them until this moment. A modified version of the take-off sequence of the Computer Assisted Fear of Flying Treatment (CAFFT, see Bornas et al. 2002 for a description) was used as the threatening stimulus. We chose this sequence because takeoff is usually the most feared period of a flight for flight phobics. Twenty still pictures with their corresponding sounds (recorded in a real environment) were included. After exposure, actual fear was assessed using a 1–9 point scale (1 = no fear, 9 = extreme fear). All participants received a maximum of 6 sessions of computer-assisted or virtual reality exposure throughout 3 weeks (two sessions per week). They were encouraged to fly within the first 15 days after the last treatment session. Participants were re-evaluated with a clinical interview and the same psychophysiological recording protocol 15 days later, whether they had actually flown or not. Statistics Assumptions of normality, homogenetity of covariances matrices, and sphericity (when appropriate) were carefully tested before conducting statistical analyses due to the small sample size. The Student’s t test or the Mann–Whitney U test (when normality could not be assumed based on ShapiroWilks tests) was used to determine possible differences between participants in the satisfactory and unsatisfactory outcome groups at pretreatment. A Chi-square test was performed to compare gender distribution between groups. Indexes of HRV taken during paced breathing (PB) served as tonic or trait level of analysis measures, and two-way repeated measures ANOVAs with two groups (UTO, STO) and a time factor with two levels (pre-treatment, post-treatment) were performed when the Box’s M test confirmed the homogeneity of the covariances matrices: Box’s M values ranged from M = 7.65, (p = .081) to M = 0.39 (p = .951). Previously, as an initial approach to the question, correlations among pretreatment physiological indexes at paced breathing condition and changes between pre-treatment and post-treatment in self-reported fear of flying were computed. To study HRV at a phasic level of analysis, two-way repeated measures ANOVAs with two groups (UTO, STO) and three conditions (BL1, EXP, BL2) were performed for each measure at pretreatment; Box’s M test and Mauchly’s W test for sphericity were computed to properly conduct these

57

analyses. Homogeneity of covariances matrices could be assumed in all cases as the lowest p value was p = .120 (Box’s M = 12.40). Sphericity assumption was not violated in any case (Mauchly’s W lowest value was W = 0.882, p = .343), and therefore it was not necessary to report any Greenhouse-Geisser epsilon value. The within group effect sizes of change were analyzed using Cohen’s d (Hedges corrected). Unfortunately, a time factor (pre- post-treatment) could not be included: too few patients had good quality recordings along all the experimental conditions before and after treatment. All of the analyses were computed using the SPSS for Windows (version 17).

Results Demographics and Pretreatment Features Groups did not differ in gender (v2(1) = 1.98, p = .160). There were 5 and 8 females in the STO and UTO groups, respectively. Student’s t tests revealed no significant differences between groups in age (t(18) = 0.31, p = .763) and selfreported fear of flying (FFS, t (18) = 0.03, p = .976; FFQ, t(18) = 0.01, p = .994). Mann–Whitney U tests revealed no significant differences between groups in motivation for treatment (U = 41.00, p = .486), self-reported fear of flying (ADIS-IV, U = 42.00, p = .500), degree of avoidance (U = 37.50, p = .241), interference (U = 48.50, p = .907) or severity of flight phobia as rated by clinicians (U = 26.50, p = .067). However, the UTO group participants reported longer avoidance time than the STO group participants (U = 20.00, p = .023) as well as lower levels of usefulness expectations about treatment (t(18) = 2.90, p = .009). Although not statistically significant, a trend appeared with the STO group reporting more emotional involvement during exposure after the first treatment session (t(18) = 2.09, p = .051). Table 1 shows descriptive statistics at pretreatment. Heart Rate Variability and Entropy Because we were analyzing HRV at tonic and phasic levels (following Thayer and Lane’s (2009) proposal), results will be presented accordingly. Tonic Level of Analysis All measures were calculated on the instantaneous HR signal corresponding to the PB condition. Because the spectral amplitude of the HF component is influenced by

123

58


Table 1 Mean scores (SD) of demographics and pretreatment features derived from interview and self-reports STO (N = 10) M (SD)

UTO (N = 10) M (SD)

39.50 (8.54)

38.20 (10.36)

8.40 (1.84)

8.00 (1.56)

62.9 (8.31) 192.2 (27.28)

62.8 (6.41) 192.3 (34.28)

Fear ADIS-IV

7.10 (0.99)

7.40 (0.84)

Avoidance ADIS-IV

7.00 (1.41)

7.70 (0.67)

Interference ADIS-IV

6.30 (1.25)

6.40 (1.17)

Severity (clinician’s rating)

6.00 (1.05)

7.00 (1.41)

16.40 (21.37)

68.40 (72.21)

Age Motivation for treatment (0–10) Fear of Flying Scale Fear of Flying Questionnaire

Time From Last Flight Usefulness Expectations

8.30 (1.42)

6.50 (1.35)

Emotional Involvement

47.30 (7.42)

39.60 (8.98)

ADIS-IV anxiety disorders interview schedule for DSM-IV, STO satisfactory treatment outcome, UTO unsatisfactory treatment outcome

unstable respiration cycles, these cycles should be constantly regulated (see Tripathi 2004). As a preliminary approach to posterior analysis we examined whether pretreatment HRV measures at paced breathing condition were associated with pre-post treatment changes in selfreported fear of flying. As expected, Fear of Flying Questionnaire pre-treatment minus post-treatment scores were negatively associated with HF power (r = -0.494, p = .02) and with sample entropy (r = -0.501, p = .02) at pre-treatment, but not with LF power (r = -0.162, p = .49). The same was true when changes were measured through the Fear of Flying Scale (HF power r = -0.445, p = .05; sample entropy r = -0.493, p = .02; LF power r = -0.175, p = .46). High Frequency Power As shown in Table 2, the repeated measures ANOVA only revealed a significant main effect for Time. Interestingly,

Table 2 Mean scores (SD) for heart rate variability and entropy at paced breathing condition (tonic level analysis) before and after treatment: results from repeated-measures ANOVA

STO M (SD)

Fig. 1 Heart rate sample entropy scores at pretreatment and posttreatment for the unsatisfactory and satisfactory treatment outcome groups. Note h = Unsatisfactory treatment outcome; D = Satisfactory treatment outcome. * p \ 0.05; ** p \ 0.01

the means’ difference between groups before treatment was marginally significant (t(18) = 2.07, p = .053, d = 0.89). When examined alone the UTO group showed a significant decrease from pre- to post-treatment assessment (t(18) = 3.59, p = .002, d = 0.67). Low Frequency Power No main effects were revealed by the ANOVA for this variable. Sample Entropy As shown in Table 2, the Group 9 Time interaction effect was clearly significant. The UTO group showed a significant decrease from pre- to post-treatment assessment (t(18) = 3.49, p = .003, d = 1.46); the between groups difference which was significant before treatment (t(18) = 2.16, p = .045, d = 0.91) was no longer significant at post-treatment (t(18) = 1.22, p = .237, d = 0.55). See Fig. 1.

UTO M (SD)

Group F(1,18) (p)

Time F(1,18) (p)

Group 9 Time F(1,18) (p)

2.11 (0.163)

9.11 (0.007)

4.23 (0.055)

ln HF power Pre

5.82 (1.06)

6.67 (0.74)

Post

5.71 (1.07)

6.07 (0.96)

ln LF power

STO satisfactory treatment outcome, UTO unsatisfactory treatment outcome, HF high frequency; LF low frequency; SampEn sample entropy

123

Pre Post

6.01 (1.12) 5.97 (0.87)

6.45 (0.94) 6.46 (0.72)

1.42 (0.248)

0.01 (0.933)

0.05 (0.828)

Pre

0.59 (0.14)

0.69 (0.05)

0.25 (0.624)

3.57 (0.075)

9.39 (0.007)

Post

0.62 (0.10)

0.56 (0.11)

SampEn

Appl Psychophysiol Biofeedback (2012) 37:53–62 Table 3 Mean scores (SD) for heart rate variability and entropy at pretreatment (phasic level analysis): results from repeated-measures ANOVA

59

STO M (SD) UTO M (SD)

Group F(1,18) (p)

Condition F(2,36) (p)

Group 9 Condition F(2,36) (p)

4.53 (0.047)

0.32 (0.726)

5.56 (0.008)

0.57 (0.814)

1.01 (0.374)

6.81 (0.003)

1.03 (0.324)

1.48 (0.241)

1.58 (0.220)

ln HF power BL1

4.91 (1.03)

5.65 (0.92)

EXP

4.60 (0.75)

5.76 (0.79)

BL2

5.06 (0.84)

5.39 (0.69)

ln LF power BL1 baseline prior to exposure, EXP exposure, BL2 baseline following exposure, STO satisfactory treatment outcome, UTO unsatisfactory treatment ouctome, HF high frequency, LF low frequency, SampEn sample entropy

BL1

6.32 (0.99)

6.40 (0.59)

EXP

6.05 (0.64)

6.53 (0.66)

BL2

6.37 (0.93)

6.05 (0.78)

BL1

0.56 (0.11)

0.57 (0.15)

EXP BL2

0.59 (0.06) 0.56 (0.08)

0.61 (0.11) 0.65 (0.12)

SampEn

Phasic Level of Analysis As previously described, three experimental conditions (baseline prior to exposure, exposure, and baseline following exposure) were taken into account to analyze HRV and entropy at a phasic level. High Frequency Power As shown in Table 3, the repeated measures ANOVA revealed significant main effects for Group, and Group 9 Condition. The mean HF power of the UTO group was significantly higher (t(18) = 3.36, p = .004, d = 1.44) than the mean HF power of the STO group during the exposure condition. Low Frequency Power A significant Group 9 Condition interaction effect emerged when the ANOVA of this variable was performed. The UTO group showed a significant LF power decrease from exposure to baseline (t(17) = 2.94, p = .026, d = 0.64). Sample Entropy No main effects were revealed by the ANOVA for this variable.

Discussion Despite its well documented efficacy for treating phobias, exposure therapy is not always successful in treating flight phobia. Treatment studies report high or at least acceptable rates of success; however, they also state that a

number of patients did not actually take a flight after treatment (e.g. 37% in Bornas et al. 2007; 35% in Maltby et al. 2002). The aim of this study was to compare the patients who did not improve after therapy with those who did. For a case to be considered a failure, the patient must not fly after exposure therapy and he/she must receive a clinical diagnosis of flight phobia after therapy. Because this therapy is based on the repeated presentation of fearful stimuli, patients who are not sufficiently attentionally and emotionally involved when confronted with these stimuli during exposure sessions are less likely to overcome their fears. However, such patterns of lesser cognitive and emotional involvement, which can be considered a form of cognitive avoidance, can only be detected once therapy has begun; therefore, pre-treatment markers could help to evaluate the appropriateness of exposure therapy for specific patients. Differences between the UTO and the STO groups in self-reported fear of flying (FFQ and FFS data) before treatment were not found. This is in agreement with the lack of predictive power of these variables reported by Bornas et al. (2007); they found that ‘‘a predictive model could not be built with the pre-treatment FFQ scores alone. It should include the psychophysiological variables [i.e. heart rate variability and complexity] as predictors’’ (p. 193). In general, the use of demographic or clinical variables as outcome predictors has yielded very inconsistent ¨ st results (Fullana and Tortella-Feliu 2001; Hellstrom and O 1996). The lack of differences in several self-reported measures are in line with inconsistency of results. Thus, motivation for treatment, degree of avoidance, and interference or severity of flight phobia as rated by clinicians were statistically equivalent in both groups. However, the UTO group’s usefulness expectations about treatment were lower than the STO group’s, in agreement with Powers and Emmelkamp’s (2008) meta-analytic study.

123

60

The analysis of the patients’ HRV and heart rate complexity was the main goal of this study. Following recent theoretical work of Thayer and Lane (2009), two levels of HRV analysis were explored, namely the tonic (trait) level and the phasic (state) level. At the tonic level of analysis, the UTO group showed marginally higher HF power and higher sample entropy, thus giving support to the hypothesis that patients with more variable and complex heart rates receive less benefit from exposure therapy than their counterparts with less variable and more regular heart rates. This is consistent with the model’s prediction that individuals with higher levels of resting HRV produce contextappropriate emotional responses. In general, avoiding dangerous or threatening stimuli is considered an appropriate response (from a phylogenetic perspective); therefore, people with high resting HRV more readily avoid such stimuli. Patients with specific phobia, on the other hand, avoid stimuli such as flying which can be considered threatening only from a subjective, ontogenetic perspective. Our results show that among these patients, those with high resting HRV and HR entropy will not benefit from exposure: they are likely to avoid the fearful stimuli during exposure sessions, thus preventing extinction of fear. Interestingly, both the UTO group’s HRV and entropy decreased significantly from pre- to post-treatment evaluations: at post-treatment, their scores were statistically the same as those of the STO group. It seems that therapy brought the HRV and entropy levels of the UTO group close to those of the STO group, although these changes were not followed by the expected behavioral changes (i.e. flying after therapy). If this is the case, then the UTO group at the end of therapy would be physiologically ready to start successful exposure. These results are partially consistent with data suggesting that an increase in the number of treatment sessions is related to a larger effect size for exposure therapy as compared to no treatment (see Powers and Emmelkamp 2008, Wolitzky-Taylor et al. 2008 for meta-analytic reviews of virtual reality exposure therapy). Perhaps the need for more treatment sessions is related to participants exhibiting a certain psychophysiological pattern, as observed in the STO group in the present study. The analysis of HRV and entropy at phasic or state level was performed to determine if the corresponding measures changed from baseline to exposure and back to baseline. Changes would be indexes of the system’s autonomic flexibility. Results provide only partial support to the hypothesis that patients who demonstrate small or no HRV changes when conditions change will benefit more from exposure therapy than those who demonstrate larger HRV changes due to changing environmental stimuli. Regarding HF power, while no changes across conditions were found in the UTO group, a significant recovery of HF power from EXP to BL2 was observed in the STO group. As mentioned

123


previously, the STO’s mean was significantly lower than the UTO’s mean under condition EXP. If HF power changes indicate autonomic flexibility, then the STO group was more flexible than the UTO group. Results regarding LF power, however, do agree with that hypothesis. In this case, the UTO group’s LF power significantly decreased from EXP to BL2, while no changes across conditions were seen in the STO group. Therefore, the less flexible group was not the UTO one, but rather the group that reached the best clinical outcomes. As the LF power may reflect baroreflex activity, perhaps the UTO group showed active baroreflex control during EXP, thus indicating active withdrawal from the feared situation. Unfortunately, however, baroreflex was not measured and therefore this is a very speculative interpretation. Additionally, no changes in HR entropy were detected in any group across all conditions. Behavioral avoidance was indexed as the number of months from the participant’s last flight to the day of his/ her interview, and as the severity of avoidance as measured on a scale from 0 to 8 (0 = no avoidance, 8 = extreme avoidance) following the ADIS-IV criteria. A behavioral avoidance test was only formally conducted at the end of treatment; however, at pre-treatment all participants in the study refused to take a flight on their own before starting the intervention. The UTO group reported longer avoidance time than the STO group, despite fear intensity ratings that were almost identical at pre-treatment. This difference supports the hypothesis that physiologically variable and complex patients (i.e. the more flexible phobics) are more prone to using avoidance strategies. In this case, however, differences in self-reported data should also be found. Consistent with this hypothesis, the STO group reported marginally more emotional involvement than the UTO group (p = .058) upon finishing the first treatment session. This result can be interpreted as a sign of greater cognitive avoidance in the UTO group if cognitive avoidance is understood as an attentional correlate of the enhanced tonic HRV of this group, given that the importance of vagal cardiac control in attentional processes has been shown in phobic patients (e.g. Johnsen et al. 2003, in a study with dental phobics). Among the limitations of the present study the small sample size must be stressed. We are aware that because there were only ten subjects in each group, extreme caution must be taken when interpreting results. Furthermore, more research on therapeutic failures should be conducted to obtain reliable data on why some patients fail to benefit from otherwise effective therapeutic techniques. Because the number of such patients is relatively small in existing published studies on fear of flying treatments, studying large samples would require mixing patients from two or more treatment studies. Nevertheless, the results of this study can be considered (cautiously) to be in agreement


with the HRV trait level hypothesis, indicating that exposure therapy is not appropriate (or it will require much more sessions) for flight phobics with high resting HRV and HR entropy. The reason might be that they would be unable to fully focus their attention and/or become sufficiently emotionally involved in the therapeutic fearful environment (e.g. virtual reality scenarios, flight-related sounds and pictures, etc.) thus preventing the extinction of fear. The short length of the time series from which the heart measures were calculated can be seen as another limitation of this study. Short-term measures of HRV do not correlate well with 24-h measures which are the gold standard for assessing the level of overall HRV a person has. It would be interesting in future studies on flight phobia to use longer ECG recordings to test if the relationship found in this study were found in these longer series also. We are aware that the results of this study are somehow surprising. It is a well validated believe that higher resting HRV and HR entropy characterize healthy systems, and therefore reporting that flight phobics with these features get unsatisfactory treatment outcomes seems to be the opposite. Nevertheless, the same enhanced ability to avoid real danger or threat might be a problem when threat is irrational (as it is in specific phobia) because exposurebased treatments require people to not avoid the feared stimuli. Furthermore, it should be stressed that from our results we cannot attribute any causal role to HRV or entropy. Perhaps higher HRV and entropy could predispose to avoidance, but the other way around can be true: a subject with an avoidant mental set will show greater entropy. Further research is necessary to clarify which one of these interpretations is better. Finally, the analysis of HRV and HR entropy at a phasic or state level could not be performed because ECG signals from some conditions were of unsatisfactory quality, which further reduced the number of patients in each group that could be entered in the corresponding ANOVAs. Future studies should investigate if state HRV and entropy (i.e. flexibility) changes from pretreatment to posttreatment. Based on the decrease in tonic HRV and entropy in the UTO group found in this study, we would also expect a decrease in autonomic flexibility in patients who do not benefit from exposure therapy. Acknowledgments This research was funded by the Spanish Ministry of Science and Innovation (Grants SEJ2006-14301/PSIC and PSI2009-12711).

References Anthony, M. M., Orsillo, S. M., & Roemer, L. (2001). Practitioner’s guide to empirically based measures of anxiety. New York: Plenum.

61 Banõs, R. M., Botella, C., Garcıá-Palacios, A., Villa, H., Perpinã, C., & Alcan˜iz, M. (2000). Presence and reality judgement in virtual environments: A unitary construct? Cyberpsychology and Behavior, 3, 327–335. Berger, R. D., Akselrod, S., Gordon, D., & Cohen, R. J. (1986). An efficient algorithm for spectral analysis of heart rate variability. IEEE Transactions on Biomedical Engineering, 33, 900–904. Borkovec, T. D., & Nau, S. D. (1972). Credibility of analogue therapy rationales. Journal of Behaviour Therapy and Experimental Psychiatry, 3, 257–260. Bornas, X., Tortella-Feliu, M., Garcıá de la Banda, G., Fullana, M. A., & Llabre´s, J. (1999). Validacioń factorial del cuestionario de miedo a volar (QPV) [The factor validity of the fear of flying questionnaire]. Ana´lisis y Modificacioń de Conducta, 25, 885–907. Bornas, X., Tortella-Feliu, M., Llabre´s, J., Barcelo´, F., Pauli, P., & Mu¨lhberger, A. (2002). Clinical usefulness of a simulated exposure treatment for fear of flying. International Journal of Clinical and Health Psychology, 2, 247–262. Bornas, X., Llabre´s, J., Noguera, M., & Lo´pez, A. M. (2006a). Sample entropy of ECG time series of fearful flyers: Preliminary results. Nonlinear Dynamics, Psychology, and Life Sciences, 10, 301–318. Bornas, X., Llabre´s, J., Noguera, M., Lo´pez, A. M., Gelabert, J. M., & Vila, I. (2006b). Fear induced complexity loss in the electrocardiogram of flight phobics: A multiscale entropy analysis. Biological Psychology, 73, 272–279. Bornas, X., Llabre´s, J., Tortella-Feliu, M., Fullana, M. A., Montoya, P., Lo´pez, A., et al. (2007). Vagally mediated heart rate variability and heart rate entropy as predictors of treatment outcome in flight phobia. Biological Psychology, 76, 188–195. Brown, T. A., DiNardo, P. A., & Barlow, D. H. (1994). Anxiety disorders interview schedule for DSM-IV (ADIS-IV), adult version. Albany (NY): Graywind Publications Inc. Choy, Y., Fyer, A. J., & Lipsitz, J. D. (2007). Treatment of specific phobia in adults. Clinical Psychology Review, 27, 266–286. Costa, M., Goldberger, A. L., & Peng, C. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 98(6), 068102-1–068102-4. Devilly, G. J., & Borkovec, T. D. (2000). Psychometric properties of the credibility/expectancy questionnaire. Journal of Behavior Therapy and Experimental Psychiatry, 31, 73–86. Friedman, B. H. (2007). An autonomic flexibility—neurovisceral integration model of anxiety and cardiac vagal tone. Biological Psychology, 74, 185–199. Fullana, M. A., & Tortella-Feliu, M. (2001). Predictores de resultado terapeútico en el miedo a volar en avioń. Psicothema, 13, 617–621. Haug, T., Brenne, L., Johnsen, B. H., Berntzen, D., Gotestam, K. G., & Hugdahl, K. (1987). A three-systems analysis of fear of flying: A comparison of a consonant vs a non-consonant treatment method. Behaviour Research and Therapy, 25, 187–194. ¨ st, L. G. (1996). Prediction of outcome in the Hellstrom, K., & O treatment of specific phobia. A cross validation study. Behaviour Research and Therapy, 34, 403–411. Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychoterapy research. Journal of Consulting and Clinical Psychology, 59, 12–19. Johnsen, B. H., Thayer, J. F., Laberg, J. C., Wormnes, B., Raadal, M., Skaret, E., et al. (2003). Attentional and physiological characteristics of patients with dental anxiety. Journal of Anxiety Disorders, 17, 75–87. Maltby, N., Kirsch, I., Mayers, M., & Allen, G. J. (2002). Virtual reality exposure therapy for the treatment of fear of flying: A

123

62 controlled investigation. Journal of Consulting and Clinical Psychology, 70, 1112–1118. McNally, R. J. (2007). Mechanisms of exposure therapy: How neuroscience can improve psychological treatments for anxiety disorders. Clinical Psychology Review, 27, 750–759. Niskanen, J. P., Tarvainen, M. P., Ranta-Aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine, 76, 73–81. ¨ st, L. G., Stridh, B. M., & Wolf, M. (1998). A clinical study of O spider phobia: Prediction of outcome after self-help and therapist-directed treatments. Behaviour Research and Therapy, 36, 17–35. Pincus, S. M. (2000). Approximate entropy in cardiology. Herzschr Elektrophys, 11, 139–150. Powers, M. B., & Emmelkamp, P. M. (2008). Virtual reality exposure therapy for anxiety disorders: A meta-analysis. Journal of Anxiety Disorders, 22, 561–569. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology—Heart and Circulatory Physiology, 278, H2039–H2049.

123

Appl Psychophysiol Biofeedback (2012) 37:53–62 Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart– brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33, 81–88. Tortella-Feliu, M., Botella, C., Llabre´s, J., Bretoń, J., Riera del Amo, A., Banõs, R. M., et al. (2011). Virtual reality versus computeraided exposure treatments for fear of flying. Behavior Modification, 35, 3–30. Tripathi, K. K. (2004). Respiration and heart rate variability: A review with special reference to its application in aerospace medicine. Indian Journal of Aerospace Medicine, 48, 64–75. Tryon, W. W. (2005). Possible mechanisms for why desensitization and exposure therapy work. Clinical Psychology Review, 25, 67–95. Wolitzky-Taylor, K. B., Horowitz, J. D., Powers, M. B., & Telch, M. J. (2008). Psychological approaches in the treatment of specific phobias: A meta-analysis. Clinical Psychology Review, 28, 1021–1037.