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Breathing characteristics and symptoms of psychological distress: an exploratory study.

In this exploratory study of the relationship between characteristics of breathing and 3 common psychological issues (I.e., symptoms of anxiety, alexithymia, and depression), 79 college-age adults were examined using self-report, rater observation, and physiological measures. Results indicated significant positive relationships between dysfunctional breath characteristics and symptoms of both anxiety and alexithymia. A significant positive correlation was found between self-reported symptoms of dysfunctional breathing and symptoms of depression, but no significant relationships existed between symptoms of depression and either rater-observed breath characteristics or physiological measures.

Keywords: breath, mind-body-spirit, depression, anxiety, alexithymia


Aided by increasingly sensitive and targeted physiological and psychological measurement instruments, both medical and mental health researchers have found that spiritual, physiological, affective, cognitive, and social health function as an interrelated whole (Fernros, Furhoff, & Wandell, 2008; Myers & Sweeney, 2008; Schore, 2009). Coupled with client preferences and needs, these advances are driving many in the counseling field to integrate mind-body-spirit assessments and interventions in the treatment of symptoms of psychological distress (Ng, Chan, Leung, Chan, & Yau, 2008; Schure, Christopher, & Christopher, 2008). Approaches such as breathwork (Young, Cashwell, & Giordano, 2010), pranayama (Brown & Gerbarg, 2009), mindful exercise (e.g., tai chi, qigong, hatha yoga; La Forge, 2005), and mindfulness and meditation (Kabat-Zinn, 2003) are garnering attention and integration as viable, and increasingly evidence-based, mental health treatments.

Among mind-body-spirit approaches, breathwork interventions are gaining popularity and are considered evidence-based practice in the treatment of some symptoms of psychological distress (Brown & Gerbarg, 2009; Cusens, Duggan, Thorne, & Burch, 2010; Young et al., 2010). In fact, one of the original purposes of psychological treatment was to investigate and heal the spirit (Peres & Nasello, 2008), a word whose origin literally means "breath" (Peres & Nasello, 2008; Young et al., 2010). Breathing is a complex process that involves a variety of body feedback systems, including chemical and neurological systems (Altose & Chemiack, 2005; Courtney, Greenwood, & Cohen, 2011). It involves higher brain centers that afford humans the ability to operate respiration either reflexively/autonomically or consciously/ behaviorally (Altose & Chemiack, 2005). Thus, conscious mechanisms can be used to voluntarily override involuntary reflex mechanisms to create changes in breath pattern (i.e., breathwork; Brown & Gerbarg, 2009). These changes can alter physiological functioning, such as vagal tone and heart rate variability (HRV; Courtney, 2011).

The term breathwork is used to describe a host of spiritual, psychological, and medical practices and interventions that amount to numerous breath-related exercises. A common thread in definitions of the term breathioork is that it exclusively describes breathing that is conscious, intentional, and voluntary as opposed to unattended, reflexive, and autonomic. Breath-based practices are recommended for spiritual (Young et al., 2010), physical (CliftonSmith & Rowley, 2011), and psychological (Brown & Gerbarg, 2009) gains, but the relationships between breathing and psychological health have been only minimally investigated.

Sustained dysfunctional breathing (DB), or faulty breath patterns, likely develops as a maladaptive coping response to real or perceived environmental stressors (Lalande, Bambling, King, & Lowe, 2011). With adequate practice and training, breathwork can be used to restore healthy breathing patterns and positively influence respiratory and cardiovascular functioning (Jeter, Kim, Simon, Ritz, & Meuret, 2012; Tomich et al., 2007). These processes can empower individuals to shape the way in which they mentally, emotionally, physically, and spiritually experience their world (Childre & McCraty, 2001; Young et al., 2010). In fact, breathwork is a foundational approach used to promote holistic wellness, as seen in spiritual practices (e.g., meditation, mindfulness, mindful exercise) and physical exercise (e.g., running, swimming). Breathwork exercises may increase feelings of relaxation and well-being (Brown & Gerbarg, 2009) and may also be a viable treatment for symptoms of psychological distress, including but not limited to anxiety and depression (Kozasa et al., 2008). Despite this, the relationships among the dimensions of DB and the dimensions of holistic wellness are not yet fully understood, and breath-based approaches appear to be underused in many school counseling (Wilkinson, Buboltz, & Seemann, 2001) and clinical mental health settings.

One reason for the gap in counseling literature, training, and practice may be the challenges related to defining and assessing DB (Courtney, Greenwood, & Cohen, 2011). First, no consensus exists on the definition of DB, although DB can be broadly described as disturbances in respiratory functioning that have a negative effect on health (Courtney, Greenwood, & Cohen, 2011). These disturbances may include hyperventilation, poor breath control, and shallow breathing, as well as breath-related symptoms typically measured through self-report, such as chest tightness or pain, tension, blurred vision, dizziness, confusion, shortness of breath, tingling, heart palpitations, and coldness in hands or feet (Courtney, Greenwood, & Cohen, 2011; Courtney, van Dixhoorn, & Cohen, 2008; van Dixhoorn & Duivenvoorden, 1985).

Second, DB may be assessed in three known dimensions (i.e., breath pattern, breath-related symptoms, and biochemical measures), independently or concurrently. Unfortunately, however, the relationships among these three dimensions are not yet understood (Courtney, Greenwood, & Cohen, 2011). In addition, some measures of breath function require effort on the part of the participant (e.g., exhaling into a spirometer), and other measures are considered independent of effort because no direct effort is required on the part of the participant (e.g., researcher observation of breath pattern). Although a relationship exists among psychological symptoms, emotional states, and respiratory function, it may be variable across individuals, context, and time of day. Accordingly, researchers have suggested a need for studies that focus on explicit psychological states and effort-independent measures of breath function (Ritz, Rosenfield, Dewilde, & Steptoe, 2010).

Noninvasive, effort-independent measures used in the assessment of DB include observation of breath pattern (e.g., respiration rate), HRV, and self-report questionnaires. (Courtney, Cohen, & van Dixhoorn, 2011; Courtney & Greenwood, 2009; Perri & Halford, 2004). HRV refers to the variation in time between heart beats, which is influenced by breath patterns. Some characteristics of DB may be visually observed, including chest-dominant breathing; suppressed chest movement; upward lifting of the chest or shoulders; absence or limited expansion of the ribs; rigid, forced, or paradoxical abdominal movement; asynchrony of chest and abdominal motion; limited or elevated breath volume, breath rate, or both; breath holding; forced inhalation (sucking), forced exhalation (blowing), or both; frequent gasping; frequent sighing; and irregularity (Altose & Cherniack, 2005; Courtney, Cohen, & van Dixhoorn, 2011; Fahri, 1996; Perri & Halford, 2004; Vlemincx et al., 2009). DB patterns are associated with abnormal HRV and a decreased ability to voluntarily regulate HRV. HRV is a noninvasive measure of respiratory and cardiac function that may offer valuable insight into the pathophysiology of distress and the evaluation of treatment outcomes (Yergani, Radhakrishna, Tancer, & Uhde, 2002). In addition, self-report is frequently used in the assessment of DB, although validated questionnaires have not been extensively developed (Courtney & Greenwood, 2009). The Self-Evaluation of Breathing Questionnaire (Version 2; SEBQ; Courtney & Greenwood, 2009) was developed to fill this gap and may be useful in the assessment and treatment of DB.

Researchers and scholars have developed theoretical and limited empirical work on the relationship between breathing and psychological symptomatology. Specifically, breath-related disorders and severity of DB have been associated with psychological distress, including symptoms of alexithymia (i.e., the impaired ability to perceive, identify, and express emotion; Plaza et al., 2006), anxiety (Brenes, 2003), and depression (Blazer & Hybels, 2010). To date, however, no investigations have been conducted into relationships between discrete faulty breath patterns and specific manifestations of psychological distress. Thus, many questions remain. For example, do individuals who experience depression exhibit similar patterns of DB? Are the DB patterns correlated with anxiety different from the DB patterns correlated with depression or alexithymia? Understanding the relationship between breath and distress may aid in the prevention, accurate assessment, and effective treatment of clients experiencing symptoms of psychological distress. Therefore, the purpose of this study was to investigate the relationship between breath patterns and symptoms of psychological distress, including alexithymia, anxiety, and depression. More specifically, we explored the following research questions:

1. What percentage of the variance in anxiety is predicted by breath-related characteristics (i.e., self-reported symptoms of dysfunctional breathing, rater observation of breath pattern, and HRV)?

2. What percentage of the variance in alexithymia is predicted by breath-related characteristics?

3. What percentage of the variance in depression is predicted by breath-related characteristics?



Data were collected from 129 undergraduate and graduate students. Of these, 79 completed the survey, observation of breath pattern, and HRV measurements. One participant did not report complete demographic data, so the following description of the sample is based on the 78 participants for whom we had a complete data set with demographics.

The participants ranged in age from 18 to 54 years (M = 24.00, SD = 5.29). The majority of the participants were female (74.4%, n = 58), undergraduate (55.1%, n = 43), and full-time students (94.9%, n = 74). Self-report of race/ ethnicity indicated that more than half of the participants (60.3%, n = 47) were Caucasian, 26.9% (n = 21) were African American, 7.7% (n = 6) were Asian, and 1.3% (n = 1) were Native American; 3.8% (n = 3) indicated their race/ethnicity as other (e.g., multiracial, Portuguese) or did not respond to the question.

The majority of the participants (92.3%, n = 72) reported their sexual orientation as heterosexual. In addition, 2.6% (n = 2) reported homosexual, 3.8% (n = 3) reported bisexual, and 1.3% (n = 1) reported other (self-specified as pansexual) sexual orientations. Of the 77 participants who reported relationship status, 42.9% (n = 33) identified as single, with 44.1% (n = 34) reporting that they were in a nonmarital committed relationship and 13.0% (n = 10) reporting that they were married. More than half of the sample (61.5 %, n = 48) reported previous experience with mind-body-spirit training of any kind. Among the participants who reported previous training in at least one mind-body-spirit modality, 12.8% (n = 10) reported experience with breathwork, 28.2% (n = 22) reported experience with yoga, 17.9% (n = 14) reported experience with mindfulness, and 2.6% (n = 2) reported experience with qigong. Regardless of previous experiences, almost two thirds of the participants (62.8%, n = 49) reported that they did not currently engage in any mind-body-spirit practice.

Results of the research questions are based on the 79 participants for whom complete data (CD group) were obtained. It is important to note, however, that an additional 47 participants completed the survey but not the observation (survey-only [SO] group) and three participants completed the observation but not the survey (observation-only [OO] group). The most common cause of missing data was scheduling challenges for participants. To address the issue of missing data, we conducted an analysis to diagnose the type of missing data and assess knowable differences between participants who completed all components of data collection (CD group) and those who completed only the survey (SO group; Sterner, 2011). The results of the analyses are reported in the Results section.


Observational Breath Assessment. We developed a five-part instrument, the Observational Breath Assessment (OBA), to facilitate objective visual assessment of participants' resting breath. In the first part, the observer records the participant's breaths per minute. The second part includes a checklist designed to facilitate the identification and description of breathing patterns. Researchers check all that apply from a list of 48 breath-related characteristics, such as mouth and nose breathing, indications of efforting (e.g., sucking, blowing, sighing), location (e.g., upper torso, midtorso, and lower torso) and duration of pauses, and areas of tension or relaxation in the body.

The third part of the OBA comprises six Likert-type scales, which the observer uses to record the depth of breath and texture of breath in three areas: upper torso (chest), midtorso (upper abdomen or solar plexus), and lower torso (lower abdomen). Depth of breath is assessed on a scale ranging from 1, indicating a shallow breath, to 10, indicating a full-capacity breath. Texture of the breath is assessed on a scale ranging from 1, indicating a smooth breath movement, to 10, indicating a jerky breath movement.

In the fourth part of the OBA, the observer is directed to invite the participant to take three full breaths while the researcher makes note of any differences in breath characteristics between these three full breaths and the participant's resting breath. For example, the participant's observed resting breath may have involved inhaling and exhaling through the nose, whereas when invited to breathe fully, the participant may have inhaled through the mouth. The final part of the OBA involves recording any further comments the researcher had about his or her observations.

Because of the importance of the observations of breath and the subjective nature of some of these assessments, efforts were made to establish interrater agreement to ensure that raters could objectively assess the breath characteristics that were being measured. An initial participant pool of 31 people (not included in this research study) was observed simultaneously by multiple raters, and interrater correlations were calculated. Some of the breath characteristics, such as breaths per minute, were easily measured with high interrater agreement (r = .94). Assessments of depth resulted in a minimally acceptable interrater correlation (r = .72), and assessments of texture were not found to be reliable (r = .19). Discussions between the raters revealed a flaw in the original scoring system. Raters were asked to rate both depth and texture of the breath on a 10-point scale ranging from 1 (shallow) to 10 (deep; for depth) and from 1 (smooth) to 10 (rough/jerky; for texture). When raters observed a participant whose breath was deep in the lower torso but shallow in the chest, they were unclear how to rate the breath. The same was true for texture. For this reason, the rating sheet was adjusted to rate depth and texture in the upper torso (chest), midtorso (upper abdomen or solar plexus), and lower torso (lower abdomen) to account for these variances in breathing styles.

HRV. The emWave is a computer-based HRV feedback system that was developed by the HeartMath Institute (2015). In this study, we used the emWave software to analyze data collected from an ear sensor that recorded participants' real-time heart rate. The analysis of HRV is thought to be a noninvasive estimate of physiological function that reflects both breath-heart-brain relationships and the functioning of the autonomic nervous system (McCraty & Tomasino, 2004). The software estimates the amount of variability as well as the pattern of HRV in a recording period and categorizes the participant's HRV into low, medium, or high heart rhythm coherence. High heart rhythm coherence reflects healthy coordinated physiological functioning, including the autonomic nervous, respiratory, cardiovascular, and neurological systems (Courtney, Cohen, & van Dixhoorn, 2011).

10-item Center for Epidemiologic Studies Depression Scale. The 10-item Center for Epidemiologic Studies Depression Scale (CES-D10; Kohout, Berkman, Evans, & Comoni-Huntley, 1993) is the 10-item short Boston form of the CES-D (Radloff, 1977), which has 20 items. The CES-D was developed to measure current levels of depression in the general U.S. population by means of self-report of depressed affect, positive affect, somatic complaints, and interpersonal problems within the past week. Respondents rate each item on a 4-point Likert-type scale ranging from 0 (rarely or none of the time [less than 1 day]) to 3 (most or all of the time [5-7 days]). The CES-D 10 includes eight negative statements (e.g., "I felt lonely," "I felt that everything I did was an effort") and two positive statements (e.g., "I felt hopeful about the future") that are reverse scored. An acceptable internal consistency ([alpha] = .80) was obtained for the current sample. The maximum score is 60, with higher scores indicating higher reports of depressive symptomatology. Although a score of 16 or greater has frequently been used, no consensus has been reached on what score can be used as a clinical cutoff to indicate clinically significant symptoms. In the current sample, 13.9% (n = 11) reported CES-D 10 scores of 16 or higher.

Toronto Alexithymia Scale-20. The Toronto Alexithymia Scale-20 (TAS-20; Bagby, Parker, & Taylor, 1994) is a 20-item self-report measure of alexithymia. Alexithymia is considered emotional restriction as evidenced by an inability to verbally express emotions. The TAS-20 consists of 15 negative items and five positive items that are reverse scored. Construct validity was established for the measure through convergent and divergent analyses as well as factor analysis, which found three alexithymia subscales (Bagby et al., 1994). The first subscale, Difficulty Identifying Feelings, includes items such as "I am often confused about what emotions I am feeling." Items such as "I find it hard to describe how I feel about people" are used in the second subscale, Difficulty in Describing Feelings. The third subscale, Externally Oriented Thinking, includes items such as "I prefer talking to people about their daily activities than about their feelings."

Items are rated on a 5-point Likert-type scale ranging from 0 {not at all like me) to 4 {completely like me). In addition to construct validity, the measure has been shown to have acceptable internal consistency. For the derivation sample, the Cronbach's alpha was .81 for the TAS-20 total score, .78 for the Difficulty Identifying Feelings subscale, .75 for the Difficulty in Describing Feelings subscale, and .66 for the Externally Oriented Thinking subscale (Bagby et al., 1994). The unit of analysis for this study was the full-scale score, with higher scores indicating greater symptoms of alexithymia, and an acceptable estimate of internal consistency (a = .78) was obtained for the full scale with the current sample. The cutoff scoring used for the TAS-20 indicates that a score of 51 or less indicates no alexithymia, a score between 52 and 60 indicates the possible presence of alexithymia, and a score that is equal to or greater than 61 indicates alexithymia. In the current sample, 12.7% (n = 10) reported scores indicative of possible alexithymia, and 2.5% (n = 2) reported TAS-20 scores indicative of alexithymia.

Trimodal Anxiety Questionnaire. The Trimodal Anxiety Questionnaire (TAQ; Lehrer & Woolfolk, 1982) is a self-report measure with 36 items that were adapted from the Minnesota Multiphasic Personality Inventory (Hathaway & McKinley, 1943) and the State-Trait Anxiety Inventory (Spielberger, Gorsuch, & Lushene, 1970 [Lehrer & Woolfolk, 1982]). The questionnaire assesses cognitive, somatic, and behavioral symptoms of anxiety separately and also provides an overall measure of anxiety. Sample items are "I picture some future misfortune" (Cognitive subscale; 11 items), "My throat gets dry" (Somatic subscale; 16 items), and "I try to avoid starting conversations" (Behavioral subscale; 9 items). Respondents indicate how often symptoms are experienced on a 9-point Likert-type scale ranging from 0 {never) to 8 {extremely often).

The TAQ's reliability was established as good with split-half reliabilities for the subscales from .83 to .85 in a college sample and from .91 to .93 in a mixed clinical-community sample (Lehrer & Woolfolk, 1982). The factor structure of the TAQ was supported for all three subscales, and convergent validity was established with a measure of trait anxiety as well as a measure of neuroticism for both patient and student samples and significant correlations with a measure of general distress among patients (Lehrer & Woolfolk, 1982). The TAQ total score was the unit of analysis for this study, and an acceptable estimate of internal consistency ([alpha] = .94) was obtained with the current sample. Currently, the TAQ has no cutoff score for clinical significance; the maximum score is 288, with higher scores indicating greater symptoms of anxiety.

SEBQ. The SEBQ (Courtney & Greenwood, 2009) is an 18-item self-report questionnaire that was designed to screen for qualities of DB. The developers of the SEBQ chose items based on previous DB literature and a popular Internet questionnaire (How good is your breathing?, n.d.) for the evaluation of breathing functionality. The measure evaluates three dimensions of breathing: breathlessness, breathing restriction, and breathing pattern dysfunction. Items are rated on a 4-point Likert-type scale ranging from 0 (never/not true at all) to 3 (very frequently/very true). Sample items are "I get breathless even when resting" and "I find myself holding my breath at various times." The SEBQ currently has no cutoff score for clinical significance, although higher scores indicate higher levels of perceived breathing dysfunction. The SEBQ also has good convergent validity, with an expected positive correlation with the Nijmegen Questionnaire (Van Dixhoorn & Duivenvoorden, 1985), a measure of hyperventilation (r = .75, p < .001; Courtney, Greenwood, & Cohen, 2011), as well as an expected negative correlation with forced expiratory volume, a measure of exhale volume commonly used in the diagnosis of breath-related disorders (r = -.26, p = .02). Reliability with the current sample was found to be strong ([alpha] = .92).

Demographic questionnaire. The final section of the survey consisted of 12 demographic items, 10 multiple choice and two fill in. The questions were developed to capture participants' enrollment status, degree sought, year in school, employment status, age, gender, race/ethnicity, sexual orientation, relationship status, frequency of exercise, and both previous training and current use of mind-body practices.


Participants were recruited from 10 undergraduate and graduate courses at a midsize public university in the southeastern United States. Participants were scheduled to meet with a researcher individually for data collection. As an incentive for participation, professors in five courses offered extra credit to students who chose to participate in the study as well as an extra-credit project unrelated to the study for students who chose not to participate. There was no incentive for participation in courses in which the professor did not offer extra credit.

All observation and HRV data were collected in individual meetings, and survey data were collected either in class or at the time of observation. Prior to data collection, participants were debriefed on data collection procedures. For the first part of data collection, participants were invited to lie down on a massage table. Once the participant was comfortably positioned, a researcher (who was also a trained rater) placed an ear sensor on the participant to measure HRV. The participant was then instructed to rest comfortably and breathe normally. A researcher waited 2 to 4 minutes to ensure accurate assessment of the participant's resting breath and then used the OBA to visually assess and record qualities of the participant's resting breath. After the OBA was complete, the researcher invited those participants who did not complete the survey packet to sit at a table and complete a paper-and-pencil or electronic survey packet consisting of the demographic questionnaire, CES-D10, TAS-20, TAQ, and SEBQ.


For this single-time-point cross-correlational study, we used simultaneous multiple regression analyses for each of the three research questions. All analyses were conducted with IBM SPSS Statistics (Version 19.0). Preliminary analyses, including correlations, were conducted to verify the assumptions of multiple regression analysis (e.g., normality, linearity, homoscedasticity, multicollinearity) before proceeding with the regression analyses. No assumption violations were identified. We used independent samples f-tests to assess differences on the TAQ, TAS-20, CES-D 10, and SEBQ between the CD group and the SO group. We used listwise deletion for missing data such that only data from the participants who completed all portions of data collection (CD group; n = 79) were used in the analyses of the research questions. An a priori G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) analysis using an alpha level of .05, a moderate effect size of .25, eight predictor variables, and a desired power of .80 indicated that a minimum of 69 participants were needed to achieve the desired statistical power for the multiple regression analysis. The eight predictor variables were the same for each of the three multiple regression analyses: (a) SEBQ total score; (b) dominant HRV coherence; (c) average breaths per minute; (d) lower torso, midtorso, and upper torso breath depth; (e) inhale length equal to exhale length; and (f) irregular breath rate. We calculated the effect sizes ([[eta].sup.2]) by dividing the regression sum of squares by the total sum of squares.

We found no significant differences between the CD and SO groups on the TAS, TAQ-20, and CES-D 10 total scores. The SO group reported significantly lower scores on the SEBQ (M = 7.59, SD = 4.38), t(122) = 5.48, p < .001, and were thus determined not to be missing at random (Sterner, 2011).

Table 1 shows the means, standard deviations, and frequencies for the study's variables. The Pearson product-moment and point biserial correlation coefficients for continuous and dichotomous variables, respectively, are presented in Table 2. In the current sample, all participants had either low (n = 56) or high (n = 23) HRV coherence, with no participants demonstrating medium HRV coherence (n = 0); therefore, we treated the HRV coherence variable as dichotomous rather than categorical data.

Table 3 shows the results of the three multiple regression analyses, including the contributions of the individual predictors. In predicting variance in anxiety, the SEBQ total score was the strongest predictor of anxiety. The overall model of breath-related predictors accounted for 29% of the variance in anxiety (R = .60, [R.sup.2] = .37, adjusted [R.sup.2] = .29), F(8, 69) = 4.95, p = .000, with a medium-to-large effect size ([[eta].sup.2] = .36). In predicting variance in the criterion variable of alexithymia, inhale length equal to exhale length was the strongest predictor of alexithymia. Inhale length equal to exhale length had a significant negative relationship with symptoms of alexithymia ([beta] = -.29). Lower torso (lower abdomen) breath depth was another strong predictor of alexithymia ([beta] = -.28). The overall model of breath predictors accounted for 20% of the variance in alexithymia (R =.53, [R.sup.2] = .28, adjusted [R.sup.2] = .20), F(8, 69) = 3.40, p = .002, with a medium effect size ([[eta].sup.2] = .28). In predicting variance in depression, none of the breath-related variables emerged as significant predictors of depression in the multivariate model. The overall model was not statistically significant and accounted for only 7% of the variance in depression (R = .20, [R.sup.2] = .04, adjusted [R.sup.2] = -.07), F(8, 68) = 0.34, p = .946, with a small effect size ([[eta].sup.2] = .04).


This exploratory study investigated the relationships between characteristics of breathing and symptoms of anxiety, alexithymia, and depression. The results provide preliminary evidence that characteristics of breathing may be related to some symptoms of psychological distress. The results made several novel contributions to the literature. For example, the results indicated that a relationship exists between anxiety and self-reported breathing characteristics as measured by the SEBQ. The emergence of the SEBQ as a predictor of anxiety, whereas physiological measures (i.e., HRV) and observation of breathing characteristics did not predict anxiety, may reflect limitations in clinical measures and observations, but it may also reflect the tendency for individuals who experience symptoms of anxiety to also experience heightened fears or concerns about their physiological experiences (Eifert, Zvolensky, Sorrell, Hopko, & Lejuez, 1999). Consequently, individuals may increase their self-report of perceived breathlessness, breath restriction, and breath pattern dysfunction despite a lack of collateral support (e.g., HRV, observations).

It was somewhat surprising to find that HRV, hyperventilation, and dysfunctional diaphragmatic muscle movement were not associated with anxiety among our sample. Previously, researchers have found symptoms of anxiety to be associated with low HRV coherence (e.g., Pittig, Arch, Lam, & Craske, 2013), hyperventilation (e.g., Huey & West, 1983), and dysfunctional use of the diaphragmatic muscle (e.g., Cuthbert & Rosner, 2011). It is possible that participants were not in a state of heightened anxiety at the time of data collection (in a room with low light, lying on a massage table). As a result, the requisite DB and HRV levels characteristic of anxiety were not observable at that time but might be observable in a situation of greater stress. Furthermore, these findings may be an artifact of sampling or measurement issues between this study and previous findings. In this context, however, the strong relationship between the SEBQ total score and anxiety is noteworthy and bears further exploration. Specifically, it would be useful for future researchers to examine the three SEBQ dimensions (i.e., breathlessness, breathing restriction, and breathing pattern dysfunction) to understand more fully how attributes of the breath are related to anxiety.

The significant relationships between alexithymia, lower torso breath depth, and inhale length equal to exhale length are another novel contribution to the literature. The implication is that individuals who experience alexithymia tend to have patterns of limited diaphragmatic movement (evidenced by decreased observation of lower torso breath depth) and an unequal ratio of inhale and exhale length. Alexithymia was not found

to be associated with HRV or the SEBQ total score, which is consistent with previous findings indicating that symptoms of alexithymia are associated with the minimization of respiratory symptoms (Plaza et al., 2006). That is, people who suppress emotions may also be likely to underreport DB patterns because of reduced awareness of both physical and emotional experiences. Unfortunately, this finding creates a measurement paradox for researchers and calls for systematic integration of physiological measures to supplement self-report and observational measures.

Although depression was significantly correlated with the SEBQ total score in the bivariate correlations, the results of this study did not reveal breathing characteristics as significant predictors of depression in the multivariate model. Previous researchers have associated depression with low HRV (Carney et al., 2001) and breathing difficulty (Silverstein et al., 2013). Therefore, the nonsignificant findings for depression may be, in part, an artifact of restricted variance in the criterion variable for the current sample. Although CES-D 10 scores range from 0 to 60, in our sample, the mean CES-D 10 total score was 9.91, with a standard deviation of 5.56. Thus, the majority of the participants in our sample reported low rates of depressive symptoms with restricted variance.

Finally, it seems noteworthy that we found a significant difference on the SEBQ between individuals who completed all phases of the data collection and those who completed the survey only but did not follow through on the observation assessment. It is interesting that participants who completed all phases of the data collection self-reported higher levels of breathing dysfunction than did those who completed only the survey. Why this occurred is unknown, but it is possible that participants with lower self-perceived breathing dysfunction were less motivated or interested in the observation phase of data collection. Future researchers should keep this finding in mind.

Implications and Directions for Future Research

Although this study was exploratory, it has several important implications for counselors, counselor training, and future research. First, self-report and clinical assessment of breathing characteristics may offer valuable insight into the psychophysiology of client distress, particularly among clients experiencing symptoms of anxiety and alexithymia. Furthermore, assessment of client breathing may offer key insights into the development of client-tailored treatment of symptoms of distress. Understanding relationships between breathing characteristics and symptoms of distress may facilitate buy-in for both counselors and clients in the importance and potential effect of breath-related mind-body-spirit intervention in counseling. Also, in counselor training, the psychophysiology of distress typically gets limited attention. Without additional training, counselors may not have adequate understanding and experience to implement breath-related assessments and interventions.

For counselors and counselor educators to expand the effective use of breath-related mind-body-spirit intervention, further research is greatly needed. First, there is a need for rigorous methodology and development of reliable and valid assessments of breathing characteristics and patterns. Moreover, there is a need to compare the breath characteristics and patterns of clinical and nonclinical samples. Despite some valuable conceptual articles (Lalande et al., 2011; Young et al., 2010), there is a dearth of literature investigating the relationships between breathing characteristics and psychological and psychospiritual constructs. For example, the relationships between breath attributes and psychospiritual constructs such as spiritual wellness, grief and loss, compassion, empathy, spiritual development, and spiritual experiences are largely uninvestigated in the current literature. Further research on the relationships between breathing and psychological distress would also be useful. For example, the relationships between breathing characteristics and patterns and clinical levels of anxiety, anxiety sensitivity, depression, or trauma are unknown. Finally, the current study is correlational in nature, and causation cannot be inferred. Researchers might examine breath-related interventions, particularly with clients with clinical levels of anxiety and alexithymia, to see how changes in breath patterns affect symptoms of anxiety and alexithymia.


Some of the unexpected results in this study, including inconsistencies with the current literature and the hypothesis that guided this investigation, are likely an artifact of study limitations. First, this study used a cross-correlational design; therefore, the results cannot be used to draw conclusions about causal relationships. The study used a convenience sample of college students and experienced attrition. Thus, representativeness and generalizability of the findings are limited. That is, it is unknown how the results from our sample of college students generalize to nonstudents, nor is it known how the participants may differ systematically from those invited to participate but who declined the invitation or those who agreed to participate but did not complete all of the components of data collection. In addition, the study findings were limited by fairly low levels of reported symptoms of psychological distress in the current sample, and some of the instruments used did not indicate cutoff scores for clinically significant symptomatology. Furthermore, how the results may have been affected by the variance in the timing and the sequence of data collection is unknown because some participants completed the survey during class time, and others completed it after breath observation and HRV measurement.

The study also had several challenges related to construct measurement. The majority of the measures in this study were self-report; therefore, there was no way to verify their accuracy. There were also limitations to clinical measures and observations. For example, the measure of HRV, the emWave software, was designed for biofeedback use and may not have appropriate sensitivity for use as a measure in empirical research. Furthermore, the majority of the sample was categorized as low HRV coherence; for this reason, there may have been inadequate variance in the sample's HRV. It is also possible that self-consciousness about being observed may have had an impact on participants' resting breath, and, thus, the presence of the researchers during the data collection process may have had a confounding effect. In future investigations, video recording rather than in vivo observation of breath pattern may offer the potential benefits of reducing self-consciousness while also allowing researchers to replay the video for multiple observations of the participant's breath pattern.

In addition, it was not possible to consider all potential confounds. For example, what activities participants engaged in before observation (e.g., exercise, food intake, caffeine intake) is unknown. Consequently, how activity or diet before the observation may have affected results is unknown. How factors such as weight, pregnancy, smoking or substance use history, or diagnosis of an existing respiratory or cardiovascular problem may have affected results is also unknown.


In this empirical study of the relationship between breathing characteristics and symptoms of three common psychological issues (i.e., anxiety, alexithymia, and depression), we used self-report, rater observation, and physiological measures to explore these complex but clinically relevant issues. Breath characteristics were found to be significant predictors of both anxiety and alexithymia but not depression. Specifically, self-reported breathing issues significantly correlated with anxiety levels. Relatedly, observable DB correlated significantly with alexithymia. Researchers should conduct additional studies to further refine attributes of the breath that best predict anxiety and alexithymia. Given that other researchers have empirically connected depression to breathing difficulty (Silverstein et al., 2013), additional research is needed to better understand this relationship. Finally, future researchers should examine how breath-related interventions affect psychological distress, including anxiety and alexithymia as outcome variables. Conducting future research with clinical samples would allow for the measurement of breath dysfunction among individuals similar to those whom counselors encounter in practice. Although additional research is needed to refine understanding in this area, it seems clear that breath-related attributes cannot be ignored in research or clinical practice. Furthermore, our findings suggest that practicing counselors should consider addressing DB with clients, in particular those reporting symptoms of anxiety.

Received 08/11/14

Revised 07/06/15

Accepted 08/04/15

DOI: 10.1002/cvj.12023


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Jamie E. Crockett, Department of Counseling, Wake Forest University: Craig S. Cashwell, Jodi L. Tangen, and J. Scott Young, Department of Counseling and Educational Development, University of North Carolina at Greensboro; K. Hridaya Hall, Counselor Education and School Psychology Department, Plymouth State University. Correspondence concerning this article should be addressed to Jamie E. Crockett, Department of Counseling, Wake Forest University, PO Box 7406, Winston-Salem, NC 27109 (e-mail:

Means, Standard Deviations, and Frequencies for the Study Variables

Variable                                               M      SD     f

Anxiety (Trimodal Anxiety Questionnaire)
  Total score                                        81.43   35.10
Alexithymia (Toronto Alexithymia Scale-20)
  Total score                                        40.90   8.56
Depression (10-item Center for Epidemiologic
  Depression Scale)
    Total score                                      9.91    5.56
Breath-related variables
    1. Dominant heart rate variability coherence
       Low                                                           56
       High                                                          23
    2. Self-Evaluation of Breathing Questionnaire    14.30   9.55
       total score
    3. Breaths per minute                            15.04   5.24
    4. Inhale length equal to exhale length
       Yes                                                           64
       No                                                            15
5. Irregular breath rate
      Yes                                                            59
      No                                                             20
6. Upper torso breath depth                          2.14    0.79
7. Midtorso breath depth                             2.56    0.59
8. Lower torso breath depth                          3.06    0.74

Correlations Among the Study Variables

Variable         1       2        3       4       5       6

1. TAQ          --
2. TAS-20      .54 *     --
3. CES-D 10    .52 *   .40 *      --
4. HRV (a)     .16    -.23 *    .05      --
5. SEBQ (a)    .49 *   .15      .23 *   .07      --
6. BPM (a)    -.10     .10     -.02    -.56 *   .00      --
7. IEE (a)    -.07    -.35 *   -.46     .24 *  -.09    .13
8. IBR (a)     .11     .23 *    .07    -.01    -.12   -.08
9. UTD (a)     .21     .12      .04     .08     .08   -.19
10. MTD (a)    .12    -.03     -.03    -.03    -.03   -.25 *
11. LTD (a)   -.18    -.32 *   -.10    -.20    -.20   -.32 *

Variable        7      8       9      10     11

1. TAQ
2. TAS-20
3. CES-D 10
4. HRV (a)
5. SEBQ (a)
6. BPM (a)
7. IEE (a)      --
8. IBR (a)    -.13     --
9. UTD (a)     .00    .16     --
10. MTD (a)    .12   -.03    .48 *    --
11. LTD (a)    .04   -.11    .11    .37 *   --

Note. TAQ = Trimodal Anxiety Questionnaire total score; TAS-
20 = Toronto Alexithymia Scale-20 total score; CES-D 10 =
10-item Center for Epidemiologic Studies Depression Scale
total score; HRV = dominant heart rate variability
coherence; SEBQ = Self-Evaluation of Breathing Questionnaire
total score; BPM = breaths per minute; IEE = inhale length
equal to exhale length; IBR = irregular breath rate; UTD =
upper torso breath depth; MTD = midtorso breath depth; LTD =
lower torso breath depth.

(a) Breath-related variables.

* p < .05.


Multiple Regression Analyses for Breath Variables
Predicting Psychological Symptoms

Predictor Variable                               [beta]     t      P

Research Question 1 Criterion: Anxiety
  Model fit                                                       .00
    Dominant heart rate variability coherence     .13     0.96    .33
    SEBQ total score                              .49     4.82    .00
    Breaths per minute                            .00    -0.02    .98
    Inhale length equal to exhale length         -.06    -0.53    .60
    Irregular breath rate                         .17     1.65    .10
    Upper torso breath depth                      .07     0.66    .51
    Midtorso breath depth                         .16     1.36    .18
    Lower torso breath depth                     -.16    -1.41    .17
Research Question 2 Criterion: Alexithymia
  Model fit                                                       .00
    Dominant heart rate variability coherence    -.11    -0.79    .44
    SEBQ total score                              .12     1.08    .28
    Breaths per minute                            .04     0.31    .76
    Inhale length equal to exhale length         -.29    -2.52    .01
    Irregular breath rate                         .17     1.54    .13
    Upper torso breath depth                      .09     0.77    .44
    Midtorso breath depth                         .10     0.80    .43
    Lower torso breath depth                     -.28    -2.38    .02
Research Question 3 Criterion: Depression
  Model fit                                                       .95
    Dominant heart rate variability coherence     .05     0.32    .75
    SEBQ total score                              .14     1.14    .26
    Breaths per minute                            .00    -0.02    .98
    Inhale length equal to exhale length         -.03    -0.22    .83
    Irregular breath rate                         .06     0.46    .65
    Upper torso breath depth                      .03     0.22    .83
    Midtorso breath depth                        -.03    -0.19    .85
    Lower torso breath depth                     -.07    -0.50    .62

Predictor Variable                               [R.sup.2]   [R.sup.2]

Research Question 1 Criterion: Anxiety
  Model fit                                         .37         .29
    Dominant heart rate variability coherence
    SEBQ total score
    Breaths per minute
    Inhale length equal to exhale length
    Irregular breath rate
    Upper torso breath depth
    Midtorso breath depth
    Lower torso breath depth
Research Question 2 Criterion: Alexithymia
  Model fit                                         .28         .20
    Dominant heart rate variability coherence
    SEBQ total score
    Breaths per minute
    Inhale length equal to exhale length
    Irregular breath rate
    Upper torso breath depth
    Midtorso breath depth
    Lower torso breath depth
Research Question 3 Criterion: Depression
  Model fit                                         .04        -.07
    Dominant heart rate variability coherence
    SEBQ total score
    Breaths per minute
    Inhale length equal to exhale length
    Irregular breath rate
    Upper torso breath depth
    Midtorso breath depth
    Lower torso breath depth

Note. SEBQ = Self-Evaluation of Breathing Questionnaire.
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Title Annotation:Research
Author:Crockett, Jamie E.; Cashwell, Craig S.; Tangen, Jodi L.; Hall, K. Hridaya; Young, J. Scott
Publication:Counseling and Values
Article Type:Report
Date:Apr 1, 2016
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