2012, Vol. 9, No. 2, DOI: /a

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1 Psychological Services In the public domain 2012, Vol. 9, No. 2, DOI: /a Assessing Daily Fluctuations in Posttraumatic Stress Disorder Symptoms and Substance Use With Interactive Voice Response Technology: Protocol Compliance and Reactions Kyle Possemato Center for Integrated Healthcare, VA Healthcare Network Upstate New York, Syracuse, New York and Syracuse University Emily Kaier and Michael Wade Center for Integrated Healthcare, VA Healthcare Network Upstate New York, Syracuse, New York Larry J. Lantinga, Stephen A. Maisto, and Paige Ouimette Center for Integrated Healthcare, VA Healthcare Network Upstate New York, Syracuse, New York and Syracuse University PTSD symptoms and substance use commonly co-occur, but information is limited regarding their interplay. We used ecological momentary assessment (EMA) to capture fluctuations in PTSD symptoms and drinking within and across days. Fifty Iraq and Afghanistan War veterans completed four daily Interactive Voice Response (IVR) assessments of PTSD and substance use with cell phones for 28 days. The aims of this study were to (1) describe participant compliance and reactions to the protocol and (2) identify participant characteristics and protocol reactions that predict compliance. Protocol compliance was high, with participants completing an average of 96 out of a total of 112 IVR assessments (86%). While some participants perceived that the IVR assessments increased their drinking (21%) and PTSD symptoms (60%), self-report measures showed significant decreases in PTSD symptoms and nonsignificant decreases in drinking over the assessment period. Analyses revealed demographic (e.g., older than 24, full-time employment, more education), clinical (e.g., less binge drinking, less avoidance symptoms), and perceived benefit from participation predicted better protocol compliance. Results can guide future research on participant predictors of compliance with intensive EMA methods. Keywords: interactive voice response, ecological momentary assessment, protocol compliance, posttraumatic stress disorder, substance use disorders PTSD symptoms and substance use commonly co-occur (Brown, Read, & Kahler, 2003), but there is little information regarding Kyle Possemato, Larry J. Lantinga, Stephen A. Maisto, and Paige Ouimette, Center for Integrated Healthcare, VA Healthcare Network Upstate New York, Syracuse, New York, and Department of Psychology, Syracuse University; Emily Kaier and Michael Wade, Center for Integrated Healthcare, VA Healthcare Network Upstate New York, Syracuse, New York. Emily Kaier is now at the University of Tulsa. This research was supported by a grant VA Clinical Sciences Research and Development Merit awarded to Dr. Ouimette (VHA CSR&D 1I01CX A1). Correspondence concerning this article should be addressed to Kyle Possemato, Center for Integrated Healthcare 116C, VA Healthcare Network Upstate New York, Syracuse VAMC, 800 Irving Avenue, Syracuse, NY Kyle.Possemato@va.gov their interplay. Models often view heavy substance use as a way to cope with negative affect. The self-medication hypothesis (Khantzian, 1997) has been highly influential and is a commonly evoked clinical explanation of PTSD and substance abuse. An alternative theory called the high-risk/susceptibility model (Jacobsen, Southwick, & Kosten, 2001) posits that alcohol use disorders may 1) increase risk of exposure to traumas associated with PTSD development (e.g., sexual and physical assault), 2) inhibit natural processing and resolution of trauma-related distress, and that 3) repeated substance withdrawal may heighten physiologic arousal and therefore increase risk for developing PTSD. A challenge to studying these models is that quantity of substance use and the intensity of PTSD symptoms are known to fluctuate across days (Johnson, Westermeyer, Kattar, & Thuras, 2002). Given that PTSD 185

2 186 POSSEMATO ET AL. symptoms and substance use may vary on a daily basis, often changing within a day, assessment methods that can capture such changes are needed to better understand the nature of PTSD and substance use interactions. Ecological Momentary Assessment (EMA) can be used to obtain frequent samples of symptoms and behavior in the person s environment in near-real time. EMA measures behavior potentially as it occurs and thus can reduce memory-deficit recall biases and maximize ecological validity. However, traditional paperand-pencil EMA, such as daily diary and other self-monitoring methods, often suffer from poor participant compliance, resulting in missing or inaccurate data. Recent studies have enhanced EMA methods by using portable technologies such as hand held computers (e.g., Shiffman, 2009; Swendsen et al., 2000). EMA methods have been further advanced with Interactive Voice Response (IVR) interviews that enable participants to respond to prerecorded assessments by telephone that are automatically administered on a predefined schedule (Helzer, Badge, Searles, Rose, & Mongeon, 2006). IVR assessment can be accessed via any landline or cellular phone to collect daily symptom data in a participant s natural environment. Collins, Kashdan, and Gollisch (2003) randomly assigned 20 social drinkers to complete paperand-pencil EMA or EMA with cell phones to access an IVR system. While few group differences emerged regarding drinking, compliance, and satisfaction, cell phones used to access an IVR system offered several advantages: participants could be called to signal an interview, data were time-stamped and instantaneously entered into a central database, cell phones were less expensive than other electronic devices, and most participants were comfortable using cell phones. One study assessed PTSD and drinking via IVR interviews that were accessed via landlines (Simpson, Kivlahan, Bush, & Mc- Fall, 2005). The data showed a high compliance rate, high participant satisfaction, and minimal reactivity to the daily assessment protocol. This paper presents data from a protocol that included the use of IVR assessments accessed via cell phones to collect data on daily PTSD symptoms and substance use in individuals known to have increased rates of PTSD and alcohol use, veterans of Operation Enduring Freedom and Operation Iraqi Freedom (OEF/ OIF) (McDevitt-Murphy et al., 2010; Milliken, Auchterlonie, & Hoge, 2007). Participants completed IVR assessments four times a day for 28 days. Before presenting results on the interplay of PTSD and substance use, it is important to assess the feasibility and usefulness of IVR cell phones in collecting EMA data on PTSD and substance use in OEF/OIF veterans. This paper describes EMA participant compliance and reactions to this protocol. We also aimed to investigate factors that predict compliance to EMA using IVR cell phones. Since this is the first investigation of this type, we have no specific hypotheses, but instead are seeking to explore what variables emerge as significant predictors from a range of participant characteristics. Sociodemographic characteristics such as age, education, employment and income may be related to compliance. For instance, younger and more educated veterans may be more versed in using cell phones, which may result in better compliance. Military and clinical characteristics may also predict compliance. For example, our intensive EMA protocol may be more difficult for veterans with more severe combat exposure, PTSD symptoms, and greater drinking. Participants reactions to the protocol, including whether they found their participation useful or if self-monitoring led to symptom increases or decreases, may also be related to compliance. Exploratory analyses of this type are well-suited to use chi-square Automatic Interaction Detector (CHAID) analysis. CHAID is an exploratory method used to study the relationship between a dependent variable (i.e., compliance) and a series of predictor variables. CHAID modeling selects a set of predictors and their interactions that optimally predict the dependent measure. Results are displayed in a classification tree that shows how participant subgroups formed from the predictor variables differentially predict a dependent variable. Understanding predictors of compliance will help guide future research and clinical applications of IVR-delivered EMA. Method Participants and Procedures A total of 50 OEF/OIF veterans were recruited from VA primary care to take part in an ongoing longitudinal study investigating the

3 IVR COMPLIANCE AND REACTIONS 187 course of PTSD symptoms and alcohol consumption. Veterans who screened positive for PTSD or alcohol misuse on annual screens administered in primary care were referred to the study by their primary care providers. Referred patients were sent a recruitment letter and then contacted by phone and given information about the study. Interested participants were scheduled to meet with study staff at their local VA. During the consent process, veterans were informed that their research participation will not be documented in their medical record and that their participation will have no impact on their benefits or health care at VA. Eligible participants were at least 18 years of age, able to read at the eighth grade level, deployed within the past 5 years as part of OEF or OIF, reported hazardous drinking (AUDIT score 8; Saunders, Aasland, Bebor, De La Fuente, & Grant, 1993), and had at least subthreshold symptoms of combat-related PTSD, defined as functional impairment associated with one reexperiencing symptom, plus three avoidance or two arousal symptoms (Blanchard, Hickling, Taylor, Loos, & Gerardi, 1994). Exclusion criteria included any suicide attempts in the past 6 months, current psychosis, and gross intellectual impairment. Of the 50 eligible participants who completed baseline interview, three participants withdrew from the study before completing the IVR assessment protocol and one was excluded due to very low compliance (i.e., he missed 65 out of 112 assessments). The results could have been unproportionally driven by this one noncomplaint participant and therefore not been representative of the sample. Therefore, 46 participants remained for data analyses. This study was approved by the Institutional Review Board (IRB) at the Syracuse VA Medical Center. At the baseline interview, participants were assessed regarding sociodemographic and military characteristics, warzone experiences and associated traumatic stress symptoms, and alcohol and drug use. Age, education, marital status, employment status, and military branch were assessed as predictors of compliance. Psychiatric symptoms were assessed via structured clinical interviews (described later). Immediately following the baseline interview, participants received an individualized training on how to use the study-issued cell phone to complete the IVR assessment. Participants were given reminder cards with instructions on to how to call into the system using either the study cell phone or a personal phone. Once participants demonstrated mastery of the IVR procedures, they were instructed to use the rest of the day to practice the procedure and to call study staff if they encountered any problems. The IVR system consisted of programmable software and a computer telephony voice board. The software was programmed to automatically administer call participants four times a day for a random prompt interview patterned after those used in previous EMA research (Collins et al., 2003; Helzer et al., 2006). The prompts came during four distinct call blocks; 10 a.m. 1 p.m., 1 4 p.m., 4 7 p.m., and 7 10 p.m., such that participants never received a call before 10 a.m. or after 10 p.m. When receiving a call or calling the system back after noticing they had missed a call from the IVR system, participants first entered their unique identification number into the system and then completed a brief (i.e., less than 5 minutes) interview. Each interview question included a multiple-choice response set to which participants were able to respond to by selecting numbers on the telephone keypad. Error-trapping loops issued an alert and readministered a question if participants pressed a key outside the possible range of responses. Cell phones were also programmed to block all outgoing/incoming calls other than those programmed to the IVR system. Programmed numbers included the IVR survey number, VA crisis/suicide hotline, local suicide hotline, research staff, the VA medical center, and 911. The time and date of each call and the interview responses were recorded in a database on a secure VA computer. The IVR system automatically tracked the participants compliance to the monitoring. The system was checked daily, and when participants failed to complete three daily surveys, a research assistant called them to troubleshoot and resolve any difficulties. After the EMA period, participants attended an in-person 4-week follow-up interview assessing alcohol use and PTSD symptoms since baseline. The participant was also asked to give feedback about the cell phone monitoring period. Participants were compensated for completion of each research assessment ($50 for the baseline, and $25 for the 4-week follow-up) and based on the number of IVR assessments they

4 188 POSSEMATO ET AL. completed. Participants received $10 for completing four daily surveys, $5 for completing three out of four, or a $1.66 per call for every survey fewer than three. Participants received a $10 bonus for completing every survey in a 1-week period. In addition, participants could earn a $100 bonus for completing at least 90% of all assessments. Similar payment schedules and bonus payments have been used to provide incentive for compliance in other studies of daily monitoring (e.g., Searles, Helzer, Rose, & Badger, 2002; Simpson et al., 2005). Measures Baseline. Three clinical interviews were administered at baseline. The Clinician Administered PTSD Scale (CAPS; Blake et al., 1995) assessed the 17 symptoms of PTSD as defined in DSM IV. The CAPS possesses good psychometric properties (Blake et al., 1990). Diagnostic-level and subthreshold PTSD was determined by the CAPS. The Structured Clinical Interview for DSM IV (SCID; Spitzer, Williams, Gibbon & First, 1994) assessed alcohol and substance use disorders and is considered to be the gold standard for diagnosing psychopathology. The total number of substance use disorders were assessed as a predictor of compliance. The Timeline Follow-Back (TLFB; Sobell & Sobell, 1995) is a commonly used interviewing procedure to collect retrospective reports of the quantity of daily alcohol and other drug use. The TLFB gathered past month drinking information at baseline and 4-week follow-up. The total number of binge drinking episodes during the EMA assessment period were assessed as a predictor of compliance. Self-report measures were also administered at baseline. The Deployment Risk and Resilience Inventory (DRRI; King, King, Vogt, Knight, & Samper, 2006) is a series of self-report measures. The Combat Experiences measure of the DRRI assessed 24 combat experiences common among OEF/OIF veterans in a yes/no format. The DRRI has high internal consistency reliability among OEF/OIF veterans (Vogt, Proctor, King, King, & Vasterling, 2008). The total number of combat experiences was used as a combat severity score and assessed as a predictor of compliance. The PTSD Checklist Military Version (PCL-M) is a 17-item self-report measure that asks respondents to rate how much they have been bothered by particular PTSD symptoms in the past month on a 5-point scale (Weathers & Ford, 1996). The PCL-M possesses good psychometric properties. Total PCL scores and total scores for each symptom cluster (i.e., reexperiencing, avoidance, and hyperarousal) were assessed as predictors as compliance. EMA. The IVR system was prerecorded with 41 questions adapted from protocols developed by previous investigators (Searles et al., 2002; Simpson et al., 2005). The first set of questions required participants to enter the number of standard drinks of specific types of alcohol consumed since the last phone call (e.g., How many standard drinks of beer did you drink since the last phone call? ). If alcohol use was reported, an intoxication rating was entered on a scale of 0 3. The IVR interview also asked about the use of tobacco, marijuana, cocaine, opiates, and other illicit drug use (e.g., Did you use any marijuana since the last phone call? ).The second section included six items to assess affect rated on a0(not at all) to3(very much) point scale. Items included tense, angry, sad, relaxed, happy, and overall affect. These items were developed by Collins et al. (1998). The IVR recording then asked participants to focus on their combat experiences and rate their distress level on a0(not at all) to 3 (extremely) point scale for each of the 17 DSM IV PTSD symptoms (e.g., How bothered were you by a disturbing dream about a combat experience since the last phone call? ). The final section assessed coping and self-efficacy. Four items representing approach and avoidance coping were selected from the Coping Responses Inventory (CRI; Moos, 1993). The self-efficacy question asked about the participant s confidence in resisting drinking in the near future. The EMA interview took less than 5 minutes once it is mastered. Four-week follow-up. Section one of the Reactions to Research Participant Questionnaire (RRPQQ; Newman, Willard, Sinclair, & Kaloupek, 2001) was used to assess motivations for participation. Participants were asked to identify the top three reasons why they participated in the study from a list of eight reasons (e.g., help others, curiosity, for the money). Top reasons for participation were assessed as predictors of compliance. Participant feedback regarding the EMA procedure was gathered with six questions developed by Simpson et al.

5 IVR COMPLIANCE AND REACTIONS 189 (2005). Participants were asked to rate how do you think doing the monitoring affected your 1) urges to drink, 2) actual drinking, 3) actual drug use, 4) actual tobacco use, and 5) trauma/ptsd symptoms over the past 28 days on a 5-point likert scale ranging from decreased a lot to increased a lot. Additional questions assessed EMA convenience, personal benefit, and potential usefulness of using a cell phone to assess and give support during treatment. Personal benefit was assessed as a predictor of compliance. The PCL-M and the TLFB was also readministered at follow-up to measure changes in PTSD symptoms and drinking since baseline. Data Analytic Plan Measures of central tendency and frequencies were used to describe participants reactions to the protocol. Protocol reactivity was assessed in two ways at 4-week follow-up: 1) participant perceptions of symptom change related to IVR assessments measured by the EMA feedback questions developed by Simpson et al. (2005), and 2) changes in symptoms from baseline to 4-week follow-up on standardized measures of PTSD (i.e., PCL) and drinking (i.e., TLFB). T tests were used to compare baseline and 4-week follow-up PTSD symptoms and drinking. The number of missed calls over the 28-day study period was calculated for each participant. Since the distribution of missed calls was heavily skewed, data was grouped into quartiles. This enables an analysis of homogenous subgroups while maintaining sufficient information about compliance. Consistent with this rationale, other continuous variables were grouped into quartiles. Nominal and ordered discrete variables were left on the original scale. Identification of participant subgroups that predict protocol compliance was done using CHAID analysis (Deal, 2005). CHAID analysis begins by identifying predictors that significantly ( p.05) predict the dependent variable (i.e., compliance). Statistically significant predictors are determined by comparing the distribution of compliance among levels of a potential predictor using the Likelihood Ratio chi-square test. The CHAID algorithm identifies the best way of representing each predictor by combining adjacent categories for ordinal variables and any categories for nominal variables. The best way of representing each predictor is defined as the combination of categories resulting in the lowest p value. P values are adjusted using the Bonferroni method for the exploratory nature of combining categories within a predictor. After any predictor is identified, the user then has the option of repeating the CHAID program within any or all categories within that predictor. Thus, a segmentation tree is formed beginning with a single node (entire sample) followed by a second tree level that is defined by splitting the first predictor into two or more categories. A third tree level is then formed by splitting any categories of the first predictor by the categories of a second predictor. This partitioning process can be terminated at any time, but is usually completed when no variables predict the dependent variable within a subgroup or when the sample size is too small to provide informative results. We chose not to further split subgroups smaller than 10 (20% of sample). For the purpose of analysis, the median number of missed calls was calculated for each quartile (Q1 0, Q2 7, Q3 14.5, Q4 33). These median values were used to represent noncompliance of each participant. Subgroup noncompliance scores are an average of all participants in the subgroup. Noncompliance scores range from 0 (best compliance) to 33 (worst compliance). An index was also calculated by dividing the noncompliance score of a subgroup by the noncompliance score of the entire sample (x 13). An index value of 1.0 indicates equal compliance between a subgroup and the entire sample, whereas an index greater than 1.0 means poorer compliance compared to the sample as a whole. Within- and betweengroup Cohen s d effect sizes were also calculated as a standardized measure of noncompliance. Results Participant Characteristics In full baseline sample participants (n 50) were predominately male (n 39) with a mean age of 30 (SD 7.5). The self-reported racial composition was: 88% (n 44) White, 6% (n 3) Black, 4% (n 2) Hispanic and 1% (n 1) multiracial. Every participant graduated from high school and 88% (n 44) reported at least some college. Approximately two thirds

6 190 POSSEMATO ET AL. (64%, n 32) of the sample was employed with an average annual income of $44,100 (SD $25,984). Most participants served in the Army (72%, n 36), and over half (52%, n 26) were Reservists or National Guards. Participants had returned from their most recent OEF or OIF deployment an average of 2.9 years ago (SD 1.4 years). Eighty percent (n 40) of the participants deployed to Iraq, 38% (n 19) deployed to Afghanistan, and 18% (n 9) to both theaters. Eight participants (16%) reported having a traumatic brain injury resulting from their deployment(s). The majority of the sample (82%, n 41) met criteria for diagnostic-level PTSD, and 18% (n 9) met criteria for subthreshold PTSD. Fifty-eight percent (n 29) of participants had a VA service connection for PTSD and an additional 11 participants were service-connected for other physical or mental health disabilities. Fifty-two percent (n 26) of participants reported symptoms consistent with current alcohol dependence and 94% (n 47) met criteria for alcohol dependence or abuse sometime during their lives. Participants reported drinking an average of 105 (SD 113) standard drinks and having 10 (SD 10) binge episodes (i.e., 5 or more drinks in one sitting for men, 4 or more drinks for women) in the 30 days prior to their baseline interview. Other substance use disorders were also common in this sample: 58% (n 29) had nicotine dependence, 22% (n 11) had marijuana dependence, 14% (n 7) had cocaine dependence sometime during their lives. Participant Feedback The 46 participants who completed the 4-week follow-up interview provided overall positive feedback after the EMA protocol. Most participants (63%, n 29) found the four times daily monitoring to be not at all or a little bit inconvenient, 13% (n 6) found the monitoring to be moderately inconvenient, and 24% (n 11) found it to be quite a bit to a great deal inconvenient. Approximately two thirds of participants (61%, n 28) felt they personally benefited from the study and most (85%, n 39) thought that using a cell phone with IVR during treatment would be helpful. The most popular reason for participating in this study was to help others (91%, n 42), followed by curiosity (70%, n 32), to help myself (48%, n 22), and for the money (39%, n 18). Participant perceptions of reactivity. When asked about the effect of monitoring on urges to drink, 13% (n 6) reported perceiving decreased urges compared to baseline, 50% (n 23) reported no effect, and 37% (n 17) reported increased urges. Regarding actual drinking, 7% (n 3) reported perceptions of decreased drinking, 72% (n 33) reported no effect, and 22% (n 10) reported increased drinking. All participants reported that the monitoring had no effect on drug use, and 91% (n 42) reported that monitoring had no effect on tobacco use. When asked about the effect of monitoring on PTSD symptoms, 4% (n 2) reported perceptions of decreased symptoms, 35% (n 16) reported no effect, and 61% (n 28) reported increased symptoms. Reactivity based on standardized selfreport measures. Participants reported a significant decrease in mean PTSD symptoms (as measured by the PCL-M) from baseline to follow-up (baseline M 52, SD 15, follow-up M 43, SD 16, t(42) 4.26, p.01). The 28 participants who perceived that their PTSD symptoms increased over the monitoring period also experienced a significant decrease in PTSD symptoms (baseline M 53, SD 15, follow-up M 49, SD 17, t(24) 2.08, p.048). Participants reported a nonsignificant decrease in average number of drinks per day from the 28 days prior to baseline to the 28 days of the IVR monitoring period (baseline M 3.4, SD 3.8, follow-up M 2.7, SD 2.7, t(44) 2.00, p.05). The 10 participants who perceived that their drinking increased over the monitoring period also experienced a nonsignificant decrease over the IVR assessment period (baseline M 4.9, SD 4.4, follow-up M 4.0, SD 2.9, t(9) 1.37, p.20). Compliance Participants had high rates of protocol compliance. On average, participants missed 16 out of the 112 total IVR assessments. Therefore the mean percentage of assessments missed is 14% (SD 14, range 0 58%). The majority of participants missed 5% of assessments (i.e., 5 6 calls). Predictors of compliance. CHAID analysis revealed that several participant subgroups predicted protocol compliance. Four CHAID

7 IVR COMPLIANCE AND REACTIONS 191 trees were created. Figure 1 illustrates one of the trees. The first level shows that the noncompliance score for the 46 participants was 13. The second level shows that moderate hyperarousal scores predicted worse compliance than low and severe hyperarousal scores ( p.019). Large between groups effect sizes were found for the difference between the moderate hyperarousal subgroup and the low and severe hyperarousal subgroups (d 1.3 and 1.0, respectively). The third level shows that 1) among participants with low hyperarousal, working full-time predicted better compliance than working part time or being unemployed ( p.077, d.82); 2) among participants with moderate hyperarousal, 3 8 lifetime substance use disorders predicted worse compliance compared to 1 2 lifetime substance use disorders ( p.016, d 2.5); and 3) among participants with severe hyperarousal, being motivated to participate by money predicted worse compliance than not being motivated by money ( p.044, d 1.5). Table 1 describes each subgroup from all four CHAID trees that predicts protocol compliance (i.e., the subgroups at the bottom level of each tree). Subgroups are ordered from the subgroup that had the poorest compliance (i.e., participants with moderate hyperarousal scores with 2 7 substance use disorders within their lifetime) to one with the best compliance (i.e., participants who report moderate levels of combat and benefiting from the study). In Table 1, the index of 1.69 for participants under the age of 24 indicates that this subgroup had a noncompliance score that is 69% higher than the noncompliance score for the entire sample. The index of 0.43 for participants with low hyperarousal scores who are employed full time means that this subgroup had a noncompliance score that is 57% (100 minus 43) lower than the noncompliance score for the entire sample. Effect sizes in Table 1 take the variability of each subgroup into account to provide a standardized estimate of noncompliance. Figure 2 is a bar graph that visually displays the noncompliance scores of participant subgroups that are at least 50% greater or less than the noncompliance score for the entire sample. The six taller bars represent the six subgroups with the worst compliance, and the four shorter bars represent the subgroups with the best compliance. Several characteristics (e.g., older than 24, full-time employment, less binge drinking, fewer substance use disorders, less avoidance symptoms, and benefiting from study) tended to predict higher compliance. Discussion Our results demonstrate that OEF/OIF veterans with PTSD symptoms and substance use were highly compliant with completing four daily assessments for 28 days using Interactive Figure 1. CHAID tree with noncompliance score and sample sizes for each subgroup. Notes: P-values represent significant differences between subgroups noncompliance scores. CHAID Chi-squared Automatic Interaction Detector; EMA Ecological Momentary Assessment; CAPS Clinician Administered PTSD Scale; SUD Substance Use Disorders.

8 192 POSSEMATO ET AL. Table 1 Participant Subgroups Predicting Protocol Compliance (n 46) Subgroup description n (%) Score Index d Moderate hyperarousal, 2 7 substance use disorders 6 (13) High combat, did not participate to help self 5 (11) Non-army, 3 30 drinking binges in last month 8 (17) Low combat, high school grad/some college, high avoidance 5 (11) years old 11 (24) Older than 24, not married, high school grad/some college 7 (15) High hyperarousal, money as motivator 9 (11) Army, high combat 9 (20) Low avoidance, not employed/working part time 14 (30) Army, low combat 12 (26) Moderate hyperarousal, 0 1 substance use disorders 5 (11) High combat, participated to help self 6 (13) Moderate combat, did not benefit from study 5 (11) Non-army, 0 2 drinking binges in last month 5 (11) Older than 24, not married, college graduate 5 (11) Low combat, high school grad/some college, low avoidance 6 (13) Older than 24, married or living with partner 23 (50) Low combat, college graduate 7 (15) Low hyperarousal, employed full time 10 (22) Army, moderate combat 12 (26) High hyperarousal, money not a motivator 6 (13) Moderate combat, benefited from study 8 (17) Notes Score noncompliance score; Index subgroup noncompliance score divided by the noncompliance score for entire sample; d within group Cohen s d effect size; d could not be calculated because there was no variance within this subgroup; low avoidance 2 14 CAPS score; high avoidance CAPS score; low combat 1 13 DRRI score; moderate combat DRRI score; high combat DRRI score; low hyperarousal 9 25 CAPS score; moderate hyperarousal CAPS score; high hyperarousal CAPS score. Voice Response technology. Most participants thought that they benefited from their participation and that using similar technology when receiving psychological treatment would be helpful. While 21% of participants perceived that the IVR assessments increased their drinking and 60% perceived that the assessments increased their PTSD symptoms, standardized self-report measures of symptom change actually showed significant decreases in PTSD symptoms and nonsignificant decreases in drinking over the assessment period for all participants. In addition, our results provide preliminary evidence that a complex combination of participant characteristics and protocol feedback were associated with compliance. We discuss the descriptive and CHAID results together in this section. However, it is important to highlight that the CHAID results must be viewed as preliminary and in need of replication due to our small sample size and that the predictors interacted in complex ways to make interpretation challenging. Our participants had better compliance than a previous sample of participants with PTSD symptoms and in early recovery from substance use disorders who completed one daily IVR assessment for 28 days (14% missed calls vs. 25% missed calls; Simpson et al., 2005). Simpson s participants has similar monetary reimbursement, but were not assigned cell phones to complete their daily IVR assessments. Protocols that have more frequent monitoring and that issue cell phones may result in better participant compliance. For some individuals, the cost of using a personal cell phone may be a barrier to protocol compliance; however, many veterans in our sample voiced preferences to use their own cell phones. Given the intensity of our EMA protocol, we are pleased that approximately two thirds of participants found completing the four daily IVR assessments not at all to a little inconvenient. Inconvenience did not emerge as a predictor of compliance, thus providing preliminary evidence that participants will complete intensive EMA protocols

9 IVR COMPLIANCE AND REACTIONS 193 Figure 2. Protocol compliance for selected participant subgroups. despite experiencing some inconvenience. We are also pleased that so many participants felt they benefited from our assessment protocol. In fact, perceived study benefit in combination with experiencing moderate levels of combat (combat is discussed further later) was the strongest predictor of good compliance. This subgroup s noncompliance score was 80% lower than the noncompliance score for the entire sample (see Table 1). Veterans motivations for participation also predicted compliance. Among participants with more severe problems (i.e., those with high hyperarousal scores or high combat scores), being motivated by money predicted lower compliance than participating to help themselves. Therefore, individuals who participate because they think the research will help them directly may be more compliant then those who participate to earn money. Participants also felt that using cell phones with IVR would be helpful while receiving psychological treatment. Participants commonly remarked that IVR assessments helped them to be more mindful of when they did and did not have PTSD symptoms and helped them to keep track of how much they were drinking and smoking. Participants also commented that checking in with the cell phones helped them feel as if someone cared about their problems. One previous study investigated whether IVR assessments during substance abuse treatment were helpful. Kranzler, Abu-Hasaballah, Tennen, Feinn, and Young (2004) found that daily IVR monitoring was a reliable and valid method of assessing the dayto-day effects of a substance use intervention. It appears that IVR assessments may be useful in clinical practice for patients who need help monitoring symptoms and behaviors that often fluctuate. The majority of participants reported that they perceived no changes in their drinking, drug use, or smoking behaviors related to the EMA protocol. However, a subset of participants did report that they perceived the assessments increased their urges to drink and actual drinking. Also, 60% of participants perceived that the protocol increased their PTSD symptoms. Participant perceptions of reactivity were unrelated to compliance and inconsistent with

10 194 POSSEMATO ET AL. symptom change as measured by the PCL-M and TLFB. These measures reveal improvements in PTSD symptoms and drinking over the assessment period. It is possible that the IVR assessments increased participants awareness or mindfulness of their PTSD symptoms and drinking and this caused them to perceive that their symptoms increased. Symptoms measured by standardized self-report instruments are likely to be more valid than questions used to assess participants reactions to the protocol (e.g., How much do you think doing the monitoring affected any trauma/ PTSD symptoms you might have had over the past 28 days? ). Compared to the once daily IVR monitoring of PTSD and substance used in the Simpson et al. (2005) study, our participants perceived greater protocol reactivity (e.g., 60% vs. 17% for increased PTSD symptoms, 21% vs. 0% for increased drinking). This may be due to our protocol using more frequent monitoring and participant differences. We recruited a sample of recent combat veterans with risky drinking patterns who may or may not have been enrolled in mental health or substance abuse treatment. Simpson et al. (2005) recruited veterans and nonveterans in early substance use recovery. Active drinkers and recent combat veterans may perceive more reactivity. Simpson et al. (2005) also found evidence of inconsistency between perceptions of reactivity and reports of symptoms on standardized self-report measures. Several sociodemographic characteristics combined with other factors to predict compliance. One clear finding was that veterans 24 years of age or younger were less compliant then older veterans. However, veterans older than 24 who were unmarried and in the lower education category (those who had not graduated from college) were also less compliant. Full-time employment was associated with good compliance. Taken together, these results indicated a general trend that older veterans who are married or living with a partner, who have more education, and who have full-time employment may be more compliant. These results are interesting for several reasons. They provide evidence that older veterans are at least as capable of using cell phone and IVR technologies as younger veterans and that managing full-time employment and family responsibilities is not associated with lower compliance. Two military characteristics (Army membership and level of combat) and four clinical characteristics (hyperarousal symptoms, avoidance symptoms, number of lifetime substance use disorders, and number of drinking binges in the last month) combined with other predictors to predict compliance. Military characteristics and hyperarousal combined with so many other predictors that it is not possible to see general trends as to whether these variables are related to poor or good compliance. Other clinical characteristics are more informative. More substance use disorders and more drinking binges appear to be related to lower compliance. It is possible that participants with more substance use disorder diagnoses and more binge drinking episodes are less complaint because their drinking and substance use distract them from the IVR assessments. More avoidance symptoms also seem to be associated with lower compliance. It makes sense that individuals who avoid trauma-related stimuli would avoid IVR assessments that inquire about PTSD symptoms. It must be emphasized that no military, clinical, or participant feedback characteristics predicted compliance in isolation. Therefore, it may also be helpful to speculate why specific subgroups were more or less compliant. For instance, individuals with high hyperarousal scores who were not motivated to participate by money had a noncompliance score that was 73% lower than the noncompliance score for the entire sample. It is possible that the interaction between increased hyperarousal and being motivated by factors other than money (e.g., to help other veterans) resulted in participants being more likely to respond to cell phone prompts. In other subgroups it may be one or two characteristics that are driving compliance. For example, participants with low combat, less education, and high avoidance have a noncompliance score that is 95% higher than the noncompliance score for the sample. High levels of avoidance and less education may explain low compliance better than low levels of combat. Our findings regarding participant characteristics that were unrelated to compliance are also important to highlight. Most deployment characteristics, including number of combat tours, theater of deployment, and traumatic brain injury (TBI), were unrelated to compliance. It is helpful to know that OEF/OIF veterans with a TBI appear able to adhere to intensive EMA

11 IVR COMPLIANCE AND REACTIONS 195 protocols. Substance use outcomes such as whether a participant currently has alcohol dependence and total number of drinks in the last month were unrelated to compliance, although binge drinking and total substance use disorders predict poorer compliance. Total PTSD symptoms and reexperiencing symptoms were unrelated to compliance unlike avoidance and hyperarousal symptoms. These preliminary results from an ongoing longitudinal study demonstrate that OEF/OIF veterans were highly compliance with an intensive protocol assessing PTSD symptoms and substance use four times a day for 28 days using IVR technology. While participants provided positive feedback about the IVR assessments, some also perceived increased PTSD symptoms and drinking. These perceptions were inconsistent with self-reports of PTSD symptoms and drinking. Several demographic (e.g., older than 24, full-time employment, more education), clinical (e.g., less binge drinking, fewer substance use disorders, less avoidance symptoms), and research feedback (e.g., benefiting from study, being motivated to participate to help-self) predicted better protocol compliance. However, these results must be interpreted cautiously due to our small sample size and that the predictors interacted in complex ways making interpretation challenging. Given these limitations, our analyses need replication. Nonetheless, we believe our results will be useful for generating future research hypotheses on participant predictors of compliance with intensive EMA methods. For example, one could hypothesize that total number of drinks per month does not decrease compliance, but frequent binge drinking does. Future research confirming this hypothesis could conclude that IVR assessments may not be the best self-monitoring approach for populations with high rates of binge drinking. Our results also provide clinicians with information regarding how individuals with PTSD and substance use disorders react to and comply with frequent IVR monitoring. This information can help clinicians consider if IVR assessments would be useful to increase patient s self-monitoring, measure symptom changes in reaction to treatment, and provide recommendations for coping strategies. References Blake, D. D., Keane, T. M., Wine, P. R., Mora, C., Taylor, K. L., & Lyons, J. A. (1990). Prevalence of PTSD symptoms in combat veterans seeking medical treatment. Journal of Traumatic Stress, 3, doi: /jts Blake, D. D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, D. S., & Keane, T. M. (1995). The development of a clinician administered PTSD scale. Journal of Traumatic Stress, 8, doi: /jts Blanchard, E. B., Hickling, E. J., Taylor, A. E., Loos, W. R., & Gerardi, R. J. (1994). Psychological morbidity associated with motor vehicle accidents. Behaviour Research and Therapy, 32, doi: / (94) Brown, P. J., Read, J. P., & Kahler, C. W. (2003). Comorbid PTSD and substance use disorders: Treatment outcomes and role of coping. In P. C. Ouimette & P. J. Brown (Eds.), Trauma and substance abuse: Causes, consequences, and treatment of comorbid disorders, (pp ). Washington, DC: American Psychological Association. doi: / Collins, R. L., Kashdan, T. B., & Gollnisch, G. (2003). The feasibility of using cellular phones to collect ecological momentary assessment data: Application to alcohol consumption. Experimental and Clinical Psychopharmacology, 11, doi: / Collins, R. L., Morsheimer, E. T., Shiffman, S., Paty, J. A., Gnys, M., & Papandonatos, G. D. (1998). Ecological momentary assessment in a behavioral drinking moderation training program. Experimental and Clinical Psychopharmacology, 6, Deal, K. (2005). Deeper into the trees: A new hybrid CHAID application analyzes multiple dependent variables. Marketing Research, Summer, Helzer, J. E., Badge, G. J., Searles, J. S., Rose, G. L., & Mongeon, J. A. (2006). Stress and alcohol consumption in heavily drinking men: 2 years of daily data using interactive voice response. Alcoholism, Clinical and Experimental Research, 30, doi: /j x Jacobsen, L. K., Southwick, S. M., & Kosten, T. R. (2001). Substance use disorders in patients with posttraumatic stress disorder: A review of the literature. The American Journal of Psychiatry, 158, doi: /appi.ajp Johnson, D. R., Westermeyer, J., Kattar, K., & Thuras, P. (2002). Daily charting of posttraumatic stress symptoms: A pilot study. Journal of Nervous and Mental Disease, 190, doi: /

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