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. Author manuscript; available in PMC: 2009 May 1.
Published in final edited form as: J Psychiatr Res. 2007 Jul 2;42(6):487–494. doi: 10.1016/j.jpsychires.2007.05.001

Self-Mutilative Behaviors in Male Veterans with Posttraumatic Stress Disorder

Matthew B Sacks 1, Amanda M Flood 3, Michelle F Dennis 3, Michael A Hertzberg 2,3, Jean C Beckham 2,3,4
PMCID: PMC2441874  NIHMSID: NIHMS42691  PMID: 17606271

Abstract

Self-mutilative behaviors (SMB) were examined in a sample of male veterans with posttraumatic stress disorder (PTSD). The primary objective was to determine the prevalence of SMB and any physical, cognitive, or affective antecedents and correlates for these behaviors. Participants included 509 male veterans with PTSD and levels of PTSD, depression, alcohol use, hostility, and impulsivity were evaluated to determine if these variables were related to SMB. Antecedents and sequelae of SMB were also examined to generate hypotheses regarding the functions of these behaviors. A second type of habit behavior, body-focused repetitive behaviors (BFRB), was also examined as part of the study. Findings indicated that veterans who engaged in either type of habit behavior were younger than those who did not engage in SMB or BFRB. Veterans reporting SMB also reported higher levels of PTSD, depression, hostility, and impulsivity compared to the BFRB and no-habit groups. Examination of habit antecedents and sequelae showed support for the automatic-positive reinforcement function of SMB. These findings are discussed in the context of research and treatment involving male veterans with PTSD who engage in SMB.

Keywords: self-mutilation, self-injury, posttraumatic stress disorder, male veterans

Self-mutilative behaviors (SMB) include the direct and deliberate destruction or alteration of one's own body tissue without suicidal intent (Gratz, 2001; Nock and Prinstein, 2005). SMB typically refers to cutting, burning, or otherwise marking of one's own flesh, with self-cutting being the most frequently endorsed type of SMB (Suyemoto, 1998; van der Kolk et al., 1991). Rates of SMB are approximately 4% in the general adult population (Briere, 1998), between 14% to 35% in college students (Favazza et al., 1989; Gratz, 2001), and approximately 21% in adult clinical populations (Briere, 1998; Klonsky et al., 2003). Self-mutilative behaviors have been linked to negative outcomes, including increased risk for successful suicide (Cooper et al., 2005; Suominen et al., 2004) and significant problems in therapeutic and interpersonal relationships (Favazza, 1998). Self-mutilative behaviors are common in many clinical populations including borderline personality disorder (BPD), PTSD, dissociative disorders, and schizophrenia (Brown et al., 2002; Gratz, 2001; Haw et al., 2005).

The functions of SMB have been the source of considerable inquiry and examination. Previous literature has largely centered on the theory that SMB serves to regulate emotions (Brown et al., 2002; Favazza, 1998; Haines et al., 1995; Linehan, 1993a). For instance, Nock and Prinstein (2005) proposed two possible functions of SMB of central interest to the current study: 1) Automatic-negative reinforcement (A-N), where an individual uses SMB to reduce their experience of negative emotional states such as tension, sadness, or anxiety; and 2) Automatic-positive reinforcement (A-P), where an individual engages in SMB in order to create some desired physiological or emotional state. The A-N function of SMB is currently the most widely held view in the literature. Some experimental data has supported this claim, as Haines and colleagues (1995) demonstrated that individuals with a history of SMB showed a decrease in psychophysiological and subjective response during a self-mutilation imagery task, as compared to individuals with no history of SMB. However, Brown and colleagues (2002) found support for the A-P function of SMB, finding that persons with BPD engaging in nonsuicidal SMB reported using these behaviors to regain “normal” feelings, to express anger, as well as to distract themselves.

Little is known about self-harm behaviors within PTSD populations. Most investigations with adult trauma populations have focused on actual suicidal behavior rather than less lethal SMB (Hyer et al., 1990; Price et al., 2004). Several case studies have been reported (Greenspan and Samuel, 1989; Lyons, 1991; Pitman, 1990) and in each of these cases, SMB appeared to occur within the context of PTSD symptomatology, serving as an automatic negative reinforcer. However, studies with a broader scope are necessary to assess and understand the occurrence of SMB within the context of PTSD.

In addition to the possible link between SMB and PTSD, other affective and behavioral variables have been implicated. SMB has been shown to be related to higher levels of negative affect (Klonsky et al., 2003; Sampson et al., 2004); alcohol use (Roberts et al., 2004; Sansone, 2005); anger and hostility (Herpetz et al., 1997; Soloff et al., 1994); and impulsivity (Kingsbury et al., 1999; Maser et al., 2002; Simeon, 2006). Additionally, age may play an important role in the occurrence of SMB, with a higher incidence of SMB often found among younger individuals (Joyce et al., 2006; Sansone et al., 2002; Schmidtke et al., 1996).

Self-mutilative behaviors have been differentiated from body-focused repetitive behaviors (BFRB). BFRBs are nervous “habit” behaviors such as nail biting, hair pulling, and teeth grinding, all of which have been considered to be repetitive and automatic (Hansen et al., 1990; Wilhelm et al., 1999). Such behaviors are generally harmless for most people; however, recent research has focused on the maladaptive side of such habits, including tissue damage, infections, scars, as well as negative psychological effects (Keuthen et al., 2000; Woods et al., 2001). BFRB may serve a similar function of SMB by reducing feelings of anxiety, depression, or boredom (Teng et al., 2002).

The present study sought to differentiate between individuals who engaged in SMB versus those who did not within a selective sample of treatment-seeking veterans meeting current diagnostic criteria for PTSD. We predicted the SMB group would report higher levels of PTSD severity, depression, alcohol use, hostility, and impulsivity. We also predicted support for both the A-N and A-P functions for individuals whose most recent reported habit behavior was self-mutilative.

Method

Participants

Participants were 753 treatment seeking military veterans consecutively evaluated at a southeastern VA Medical Center PTSD Clinic between July 2000 and July 2004, consisting of 736 males and 17 females. Given that the number of females would not allow for proper statistical comparisons between genders, these were excluded from all analyses. Several new questionnaires and structured interviews were newly added to the initial clinic assessment between 2000 and 2004, including three measures used in this study (Habit Questionnaire [HQ]; Clinician Administered PTSD Scale [CAPS] and PAI; see descriptions below). To allow for consistent comparisons between groups, the 210 participants who did not complete one or more of these measures were excluded from the analyses.

Participants were screened for PTSD using the Clinician Administered PTSD Scale (Blake et al., 1990). From the remaining 543 males, the 34 participants that did not meet diagnostic criteria for current PTSD according to the CAPS were excluded from the analyses, resulting in a sample of 509 male veterans with PTSD. There were no significant differences in age and study measures when examining those included versus excluded from the study, with the exception that the sample of 34 non-PTSD participants excluded were significantly older than the overall sample (t = 2.86, p < .05). The sample consisted of 56.6% African Americans, 38.7% Caucasians, 2.4 % Hispanic, and 2.2% from other ethnic backgrounds. Mean age was 53.63 years (age range 24 − 87).

Measures

Habit Questionnaire (HQ; Resnick and Weaver, 1994)

The HQ is an 11-item inventory that measured both BFRB and SMB. Participants were first asked if they had ever engaged in such behaviors (lifetime inquiry). If participants responded affirmatively to the item, they were then asked how often they had engaged in the behavior during the previous two weeks (current inquiry): 0= not at all, 1= once, 2= two to four times, and 3= five or more times. On the second half of the HQ, participants were asked to identify the behavior(s) which they engaged in most recently. They were asked when the most recent behavior(s) occurred: 1= past two weeks, 2= past month, 3= past 6 months, 4= past year, 5= over a year ago. The final sections of the HQ assessed the antecedent and consequent physical sensations, cognitions, and emotions experienced surrounding the most recent behavior(s). For example, participants were asked if they were feeling irritability/frustration/anger before, during, or after their most recent habitual behavior. These items were not forced-choice, but rather participants were allowed to check off any feelings or moods that they had during that time (e.g., sweating, trembling or shaking, relief, calm, sadness, fear).

The Habit Questionnaire has not received extensive use in the field (Weaver et al., 2004), and thus an underlying factor structure has yet to be established. Two Q-Sort procedures were conducted to create SMB / BFRB categories, and to divide the list of habit antecedents and sequelae into discrete categories. A group of clinical psychologists and graduate students with knowledge of SMB (n = 9) were given a randomized list of the eleven items of the HQ and instructed to sort the items into a SMB or non-SMB category based on the stated definition of SMB. Results from this Q-Sort revealed an 89% agreement rate on the creation of the SMB and BFRB item categories. We also conducted a factor analysis which revealed a similar two-factor solution for the HQ items.

A second Q-sort was conducted by providing a randomized list of the twenty-three antecedent and sequelae items from the HQ to the same group. These individuals were instructed to sort the 23 items into four categories: 1) Physical, 2) Dissociative / Cognitive, 3) Negative Affect, and 4) Contentment. Results from this Q-Sort showed a 99.25% agreement rate for items in the “Physical” and “Contentment” categories, but only a 74.4% agreement rate for items in the other two categories (two items had < 55% agreement). Given all disagreements for the Dissociative / Cognitive and Negative Affect categories involved placements within these same two categories, a combined category was created: “Cognitive / Negative Affect”, resulting in a total of three categories. Table 1 and 2 depict item sorting based on these Q sorts.

Table 1.

Habit Questionnaire Behavioral Items by Q-Sort

Self-Mutilative Behavior (SMB) Items
Have you ever scratched or picked at skin so that it left a mark?
Have you ever deliberately cut yourself in any way?
Have you ever hit yourself?
Have you ever burned yourself with a cigarette, match or other way?
Body-Focused Repetitive Behavior (BFRB) Items
Have you ever bitten your nails?
Have you ever chewed on your lips?
Have you ever bitten your cheeks?
Have you ever grinded or clenched your teeth?
Have you ever pulled any strands of hair on your head to the point of pulling them out completely?
Have you ever pulled out any hairs in your eyebrows (not by tweezing) or eyelashes?
Have you ever punched a wall or other object?
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Please note. Group item composition based on Q sort methodology; Exploratory factor analysis (EFA) also conducted for comparison. Principle components factor method performed using varimax rotation and number of factors retained determined by eigenvalues > 1 and theoretical interpretability of the resulting factor structure. The EFA yielded similar 2-factor solution as the Q sort methodology.

Table 2.

Habit Questionnaire Antecedent and Sequelae Items by Q-Sort

Physical Sensation Items
Shortness of breath Nausea or abdominal distress
Dizziness or feeling faint Body numbness or tingling sensations
Rapid heart beat Hot flashes or chills
Trembling or shaking Choking
Sweating
 Chest pain or discomfort
Cognitive / Negative Affect Items
Felt like it wasn't really happening Irritability, frustration, or anger
Detached as if in a dream Sadness
Feared going crazy or losing control Fear
Feared you were going to die Disgust
Emptiness or boredom Self-hatred
Guilt
  
Contentment Items
Relief Calm
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Beck Depression Inventory (BDI; Beck and Steer, 1987)

The BDI is a 21-item inventory that measures cognitive and vegetative symptoms of depression. The inventory has a split half reliability of .93. Correlations with clinician ratings of depression range from .62 to .65.(Beck and Steer, 1987)

Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 1992)

The AUDIT is a 10-item self-report screening measure for harmful alcohol consumption with an overall score of 0 (lowest) to 40 (highest). A cut-off of ≥ 8 is used to detect hazardous or harmful alcohol consumption.(Saunders et al., 1993) The measure was found to have a 95−100% sensitivity in a sample of problem drinkers,(Babor et al., 1992) and has shown reasonable reliability (α = 0.84) (Alati et al., 2004).

Cook-Medley Hostility Scale (Cook-Medley; Barefoot et al., 1989)

A short, 27-item, self-report version of the original Cook-Medley scale yielded a total score as well as scores for cynicism, hostile affect, and aggressive responding. The short form has shown good construct validity for the primary hostility components of cynicism and mistrust (Barefoot et al., 1989). The original scale was derived empirically from the Minnesota Multiphasic Personality Inventory and has demonstrated adequate validity (Smith and Frohm, 1985) and test-retest reliability (Barefoot et al., 1983).

Personality Assessment Inventory (PAI; Morey, 1991)

The PAI is a 344-item self-report measure of personality and psychopathology. The PAI has demonstrated reliability and validity in use with both clinical and non-clinical adult populations (Morey, 1991). The PAI contains a number of subscales, each of which relies on different questions with no overlap of the same questions between scales. Responses to items range from 0 (false) to 3 (always) indicating the degree to which each statement is true. Items are summed to derive subscale scores with higher scores reflecting more severe symptomatology. The current study also utilized the impulsivity (BOR-S) subscale, which is a six-item measure of self-harm and the tendency to act impulsively without attention to consequences (Morey, 1991).

Clinician Administered PTSD Scale (CAPS; Blake et al., 1990)

The CAPS is a structured interview that assessed the 17 symptoms of PTSD identified in the DSM-IV (APA, 1994). The CAPS includes standardized questions which assess the frequency and intensity of PTSD symptoms, as well as standardized questions addressing subjective distress, and impairment in social and occupational functioning due to these PTSD symptoms. Symptoms are assessed for frequency and intensity in the preceding month, using a 5-point Likert scale (e.g., 0 indicates that the symptom does not occur or does not cause distress; 4 indicates that the symptom occurs nearly every day or causes extreme distress and discomfort). The CAPS has strong support for its reliability and validity (Weathers et al., 2001) and has been shown to be sensitive to the detection of PTSD.

Results

Of the 509 veterans in the data set, 54.8% (n=279) reported engaging in one or more self-mutilative behaviors (with or without BFRB) in the previous two weeks (the SMB group) and 33.2% (n=169) reported engaging in one or more body-focused repetitive behaviors but no self-mutilative behaviors in the previous two weeks (the BFRB group). The No Habit group included those subjects who did not report engaging in either type of habit within the previous two weeks. Table 3 displays frequencies of habit endorsement by group membership.

Table 3.

Frequency of Health Questionnaire (HQ) Item Endorsement by Group

Type of Habit Engaged in (Past Two Weeks) SMB BFRB No Habit
Biting Nails 38.1% 36.5% 18.0%
Chewing Lips/Cheeks 39.4% 23.8% 17.1%
Grinding Teeth 48.4% 54.0% 25.4%
Scratching/Picking Skin 72.9% 0% 6.6%
Pulling Hair on Head 15.5% 4.0% 6.1%
Pulling Hair on Brows/Lashes 13.5% 6.3% 5.3%
Cutting 6.5% 0% 0.4%
Punching objects 33.5% 29.4% 21.9%
Hitting self 30.3% 0% 3.9%
Burning self 11.6% 0% 0%
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Group Differences in Demographics

A one-way between-groups analysis of variance was conducted to explore the relationship between age and group membership. There was a statistically significant difference at the p<.05 level in age for the three groups [F(2,506)=7.623, p=.001]. Post-hoc comparisons using the Tukey HSD test indicated that the mean age for the No Habit group (M=57.15, SD=7.75) was significantly higher than the mean age for both the SMB group (M=53.46, SD=7.71) and the BFRB group (M=52.66, SD=7.91). The SMB and BFRB groups did not differ significantly from one another. There were no significant differences between groups for ethnicity or marital status.

Group Differences in Symptom Measures

Table 4 shows mean scores on PTSD severity (CAPS), alcohol use (AUDIT), depressive symptoms (BDI), hostility (Cook-Medley), and impulsivity (PAI-BORS) by habit group membership. A series of one-way between-groups analysis of variance (ANOVA) procedures were conducted to explore the relationship between group membership and the five variables listed above. A Bonferroni corrected alpha level of p < .01 was set for this series of ANOVAs.

Table 4.

Means and Standard Deviations by Habit Group

Variable
 Groups
SMB (n=279) BFRB (n=169) No Habit (n=61) F
CAPS Total 83.04 (19.19)a 78.88 (19.42) 75.38 (22.17)b 4.943*
AUDIT 9.29 (10.48) 7.57 (9.18) 8.80 (11.27) 1.524
BDI 33.81 (11.43)a 29.76 (10.59)b 29.03 (10.77)b 9.326*
Cook Medley 18.51 (4.78)a 17.12 (5.07)b 16.46 (5.04)b 6.793*
PAI-BORS 60.38 (14.08)a 57.07 (12.72)b 52.90 (11.19)b 9.105*
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*

p < .01

a,b superscripts indicate group differences based on Tukey HSD tests

PTSD Severity

There was a statistically significant difference in overall CAPS scores between the three groups [F(2,506)=4.94, p=.007]. Post-hoc comparisons using the Tukey HSD test indicated that the mean CAPS score for the SMB group was significantly higher than both the BFRB Group and the No Habit group. The effect size r (ES) was .18 between the SMB and No Habit groups. The BFRB and No Habit groups did not differ significantly from one another.

Alcohol Use

There was not a statistically significant difference in alcohol use scores for the three groups [F(2,506)=1.524, p=.219].

Depression

There was a statistically significant difference in BDI scores for the three groups [F(2,506)=9.326, p<.001]. Post-hoc comparisons using the Tukey HSD test indicated that the mean BDI score for the SMB group was significantly higher than both the BFRB group and the No Habit group. The effect size was .18 between the SMB and BFRB groups and .21 between the SMB and No Habit groups. The BFRB and No Habit groups did not differ significantly from one another.

Hostility

There was a statistically significant difference in Cook-Medley scores for the three groups [F(2,506)=6.793, p=.001]. Post-hoc comparisons using the Tukey HSD test indicated that the mean hostility score for the SMB group was significantly higher than both the BFRB group and the No Habit group. The effect size was .14 between the SMB and BFRB groups and .20 between the SMB and No Habit groups. The BFRB and No Habit groups did not differ significantly from one another.

Impulsivity

There was a statistically significant difference in PAI-BORS scores for the three groups [F(2,506)=9.105, p<.001]. Post-hoc comparisons using the Tukey HSD test indicated that the mean PAI-BORS score for the SMB group was significantly higher than both the BFRB group and the No Habit group. The effect size was .12 between the SMB and BFRB groups and .28 between the SMB and No Habit groups. The BFRB and No Habit groups did not differ significantly from one another.

Prediction of Group Membership

A multinomial logistic regression (MLR) analysis was performed to assess prediction of membership in one of three categories of outcome (SMB, BFRB, or No Habit) on the basis of six predictors. This group of predictors included PTSD severity, depression (BDI), alcohol use severity (AUDIT), hostility (Cook-Medley), impulsivity (PAI-BORS), and age. There was a good model fit on the basis of the eight predictors, χ2 (1004, N = 509) = 914.471, p = .980, Nagelkerke η2 = .115 using a deviance criterion. Overall classification was unimpressive. On the basis of the 6 variables, correct classification rates were 89.2% for self-mutilating veterans, 17.8% for veterans with only body-focused repetitive behaviors, and 4.9% for veterans engaging in neither type of habit behavior; the overall correct classification rate was 55.4%.

Table 5 shows the contribution of the individual predictors to the model by comparing models with and without each predictor. Bonferroni corrected alpha level was set at p<.006. Only one predictor, age, showed reliable enhanced prediction [χ2 (2, N = 509) = 14.437, p=.001].

Table 5.

Multinomial Logistic Regression of Habit Status

Variables χ2 to remove df Sig. Model χ2
CAPS 2.174 2 .337
BDI 4.200 2 .122
AUDIT 3.462 2 .177
Cook Medley 3.969 2 .137
PAI-BORS 8.526 2 .014
Age
 14.437
 2
 .001*
  
ALL VARIABLES 54.720
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*

p < .006

Group Differences in Antecedents and Sequelae

To examine antecedents and sequelae, the data set was reduced to include only those individuals who reported engaging in either type of habit behavior within the previous two weeks (n=418). From this reduced sample, 43.1% (n=180) reported that their most recent habit behavior was SMB, while 56.9% (n=238) reported recently engaging in BFRB. Table 6 displays the percentage of individuals who reported experiencing physical, cognitive/negative affect, or contentment experiences broken down by habit group membership and time (“Before” or “After” the recent habit event). A series of chi-square tests were conducted to explore the relationship between group membership and reported habit antecedents and sequelae. Additionally, a series of McNemar's tests were conducted in order to explore the relationship between time and reported habit antecedents and sequelae within groups. The Bonferroni corrected alpha level for both the chi-square and McNemar's analyses was set at p < .004.

Table 6.

Habit Antecedents and Sequelae by Group and Time

Variable  Groups
SMB (n=180) BFRB (n=238) χ2
Any Physical Sensation
Before .52 .36 10.17*
After
 .47
 .31
 10.64*
McNemar's Test
 1.53
 2.32
  
Any Cognitive / Negative Affect
Before .67 .51 10.08*
After
 .63
 .42
 16.17*
McNemar's Test
 1.07
 5.88
  
Any Contentment
Before .11 .05 4.56
After
 .38
 .23
 10.57*
McNemar's Test 33.82* 39.34*
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*

p < .004

Before and After Habit Behavior

The proportion of individuals in the SMB group who reported experiencing physical sensations prior to engaging in a habit behavior was 52%, while the proportion of individuals in the BFRB group reporting physical sensations prior to engaging in a habit behavior was 36%. This difference in proportions (16%) was significant, χ2(1, N = 418) = 10.17, p=.001. Forty-seven percent of the SMB group reported experiencing physical sensations after engaging in SMB, compared to 31% of the BFRB group who reported physical sensations after BFRB. This difference in proportions (16%) was also significant, χ2(1, N = 418) = 10.64, p=.001. The within group analyses indicated a 5% decrease in the proportion of the SMB group that reported physical sensations before engaging in SMB (52%) compared to after SMB (47%); this difference in proportions was not significant, McNemar's χ2(1, N = 180) = 1.53, p>.004. The BFRB group also showed a 5% decrease in the proportion of individuals who reported physical sensations prior to engaging in BFRB (36%), compared to afterwards (31%); this difference in proportions was not significant, McNemar's χ2(1, N = 238) = 2.32, p>.004.

Sixty-seven percent of the SMB group reported experiencing cognitive/negative affect antecedents while 51% of the BFRB group reported cognitive/negative affect antecedents. This difference in proportions (16%) was significant, χ2(1, N = 418) = 10.08, p=.001.

The proportion of the SMB group reporting cognitive/negative affect sequelae was 63% while 43% of the BFRB group reported such experiences. This difference in proportions (20%) was also significant, χ2(1, N = 418) = 16.17, p<.001. The within group analysis indicated a 4% decrease in the proportion of the SMB group that reported cognitive/negative affect antecedents (67%) compared to cognitive/negative affect sequelae (63%); this difference in proportions was not significant, McNemar's χ2(1, N = 180) = 1.328, p>.004. The BFRB group showed a 9% decrease in the proportion of individuals who reported cognitive/negative affect antecedents (51%), compared to cognitive/negative affect sequelae (42%); this difference in proportions was not significant, McNemar's χ2(1, N = 238) = 5.88, p>.004.

There was a 6% difference between the proportion of the SMB group reporting contentment antecedents (11%) compared to the proportion of BFRB group members reporting contentment antecedents (5%). This difference in proportions was not significant, χ2(1, N = 418) = 3.55, p=.033. There was a 15% difference between the proportion of the SMB group who reported contentment sequelae (38%) compared to the proportion of the BFRB group who reported contentment sequelae (23%). This difference in proportions was significant, χ2(1, N = 418) = 10.571, p=.001. In the within group analysis, there was a 27% increase in the proportion of the SMB group that reported contentment antecedents (11%) compared to contentment sequelae (38%); this difference in proportions was significant, McNemar's χ2(1, N = 180) = 33.817, p< .0001. The BFRB group showed an 18% increase in the proportion of individuals who reported contentment antecedents (5%), compared to contentment sequelae (23%); this difference in proportions was significant, McNemar's χ2(1, N = 238) = 39.340, p< .0001.

Discussion

This study revealed a high incidence of SMB in a treatment seeking male veteran sample, with over half of the participants reporting SMB in the previous two weeks. Further, significant associations between SMB and heightened levels of PTSD symptomatology, depression, hostility, and impulsivity were detected. Age was the only demographic variable that uniquely predicted group membership (SMB, BFRB, or No Habit) within the presence of all other variables, with younger veterans more likely to report having engaged in SMB or BFRB than older veterans. Based on our chi-square analyses on contentment antecedents and consequences, the findings most strongly support the automatic-positive reinforcement function of SMB, while no direct support was found for the proposed automatic-negative reinforcement function of SMB. This suggests that in this sample of participants, self-mutilative behaviors appeared to function to increase positive affect rather than diminish or reduce negative emotional states.

The present study's results regarding age suggested that younger male veterans with PTSD may be more likely to call upon this type of maladaptive behavior. While awareness may be one aspect of the relationship, other possibilities should be considered. For instance, younger participants may be more impulsive and more likely to engage in dangerous behaviors. Alternatively, they may have been too young to have experienced many medical issues and other problems that would cause them to stop engaging in SMB. Further investigation regarding age and SMB is warranted based on the current study's findings.

Veterans in the SMB group reported heightened levels of distress across a range of domains. These individuals may have engaged in SMB in an attempt to cope with severe levels of emotional and psychological pain. From an operant conditioning perspective, their use of SMB was positively reinforced via the subjective feelings of relief and calmness experienced afterwards, thus functioning to reinforce the use of self-mutilative behaviors. This theory may also explain the persistence of BFRB within the current sample. However, these hypothesized mechanisms need further investigation.

Clinicians working with individuals similar to the present population may consider specifically assessing for and treating SMB in individuals with PTSD. There are specific intervention techniques designed to reduce SMB and to increase effective coping skills, such as components of Dialectical Behavior Therapy (DBT; Linehan et al., 1999). While DBT was initially designed for individuals with borderline personality disorder, it has also been effective in treating other populations (Gratz et al., 2005). Problem solving interventions have been successful in the treatment of SMB (Townsend et al., 2001) and the prescribing of selective serotonin reuptake inhibitors (SSRIs) may be a helpful adjunct intervention for the SMB, PTSD, and depression constellation (Bohne et al., 2005; Cipriani et al., 2005; Nemeroff et al., 2006).

Given the retrospective nature of the measures used in this study, it was not possible to identify causality in the relationships previously discussed. Future research should evaluate a possible causal link by administering questionnaires before and after military deployment, for example. Longitudinal study would provide baseline incidence rates for SMB, and with multiple time points could help explain the trajectory of SMB among veterans with and without PTSD. It may also be useful to use a less restricted sample including a more diverse sampling of male and female veterans and non-veterans with varying levels of PTSD severity, depression, alcohol use, hostility and impulsivity.

Relatedly, since respondents are asked to retrospectively recall their physical, emotional and mental states in the Habit Questionnaire, this methodology is suspect due to the transitory nature of emotional experience, and the fallibility of emotional memory (Gentzler and Kearns, 2006; Safer and Keuler, 2002). Future research in this area could offer a more definitive assessment of the functions of SMB and BFRB through the use of advanced data collection methods, such as real time electronic palm pilot recordings(Barrett and Barrett, 2001) .

Despite these limitations, the current study is the first to evaluate SMB in a large sample of treatment-seeking male veterans with PTSD. The negative consequences of self-destructive behaviors (e.g., abuse of substances, risk taking behaviors) have been previously identified in male veterans with PTSD (Drescher et al., 2003), but results from the current study underscore the importance of evaluating and treating self-mutilative behaviors in these veterans as well. We hope that these findings will stimulate future research on traumatized veterans and SMB.

Role of the Funding Source

Preparation of this manuscript was supported by R01MH62482, K24DA016388, 2R01CA081595, R21DA019704, and Veterans Affairs Merit Award MH-0018. The views expressed in this presentation are those of the authors and do not necessarily represent the views of the NIH or Department of Veterans Affairs.

Footnotes

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