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. Author manuscript; available in PMC: 2022 May 19.
Published in final edited form as: Addict Behav. 2020 Mar 20;107:106401. doi: 10.1016/j.addbeh.2020.106401

Changes in alcohol use, PTSD hyperarousal symptoms, and intervention dropout following veterans’ use of VetChange

Nicholas A Livingston a,b,c,*, Colin T Mahoney a,b,c, Victoria Ameral b,c, Deborah Brief b,c, Amy Rubin b,c, Justin Enggasser b,c, Scott Litwack b,c, Eric Helmuth d, Monica Roy b,c, Marika Solhan b,c, David Rosenbloom e, Terence Keane a,b,c
PMCID: PMC9118133  NIHMSID: NIHMS1796373  PMID: 32272356

Abstract

Objective:

Veterans of Iraq and Afghanistan conflicts report high rates of drinking, PTSD, and low rates of treatment engagement. Web interventions may help address unmet treatment need; unfortunately, little is known regarding outcomes or adherence to these interventions. In this study, we examined VetChange treatment outcomes and downstream effects of alcohol reduction on PTSD symptoms and intervention dropout rates over six months.

Method:

Participants included 222 veterans (77.5% men, 78.3% White) between 22 and 57 (mean age = 36.02, SD = 7.19). All VetChange users completed a brief alcohol assessment and received personal feedback, then received full access to intervention content including psychoeducation; motivational and cognitive-behavioral modules for relapse prevention, goal-setting, social support, stress, anger, and sleep management; and mood and drink tracking. Veterans completed self-report measures of alcohol use and PTSD symptoms at baseline, one, three, and six months.

Results:

Alcohol use dropped by 43% over six months, p < .001, with the largest decrease occurring within the first month. Greater alcohol reduction in the first month predicted higher subsequent PTSD hyperarousal severity. Over half (52.3%) dropped out by month one, followed by 12.2% and 37.6% by months three and six. Hyperarousal symptoms, hypervigilance specifically, but not alcohol use predicted subsequent intervention dropout.

Conclusion:

These results highlight the importance of attending to the association between alcohol use and PTSD symptom change in web-based interventions for veterans. The fact that hyperarousal symptoms were associated with elevated risk for intervention dropout signifies the need for online intervention refinement aimed at tailoring content to time-varying symptom presentations.

Keywords: Web intervention, Online intervention, Alcohol, PTSD, Veteran

1. Background

Veterans of Iraq and Afghanistan wars (i.e., returning veterans) report high rates of hazardous drinking and alcohol use disorder (AUD; Calhoun, Elter, Jones Jr, Kudler, & Straits-Tröster, 2008; Lan et al., 2016; Wagner et al., 2007), and veterans with lifetime AUD are four times more likely to have lifetime PTSD than veterans without an AUD (Fuehrlein et al., 2016). Co-occurring PTSD symptoms can result in both poorer treatment adherence and treatment outcomes among veterans with a substance use disorder (SUD; Norman, Tate, Anderson, & Brown, 2007; Ouimette, Goodwin, & Brown, 2006; Ouimette, Brown, & Najavits, 1998; Tate, Norman, McQuaid, & Brown, 2007). In addition, not all veterans with mental health problems express an interest in seeking treatment as evidenced in one study by rates of treatment attendance at 38 to 45% in the past year (Hoge et al., 2004).

Web-based interventions circumvent critical barriers to seeking treatment and provide intervention support (Kiluk et al., 2016; Reger et al., 2013) in a manner consistent with many individuals’ preference for self-management (Mojtabai et al., 2011). For instance, veterans report that stigma and inconvenience are some of the primary barriers to seeking care (Hoge et al., 2004), whereas web-based interventions are mobile, highly accessible, and confidential (Brief, Rubin, Enggasser, Roy, & Keane, 2011). Moreover, web-based interventions are more costeffective than in-person therapy (McLean, Schlenger, & Litz, 2010) and their fixed delivery format ensures evidence-based treatment standardization and fidelity (Hester, Delaney, Campbell, & Handmaker, 2009). For veterans with hazardous drinking and co-occurring PTSD symptoms, web-based care may be especially useful for those who prefer online self-disclosure (Kypri, Langley, Saunders, Cashell-Smith, & Herbison, 2008; Leibert & Archer Jr, 2005).

Concurrent treatment of alcohol use and PTSD symptoms, which is recommended by many clinicians and researchers (e.g., Back et al., 2019), can lead to challenges. For example, reduction or cessation of alcohol use can result in an increase in PTSD symptom severity (Simons, Gaher, Jacobs, Meyer, & Johnson-Jimenez, 2005), and an exacerbation of PTSD symptoms or distress may predict treatment dropout (Eftekhari, Crowley, Mackintosh, & Rosen, 2019; Szafranski et al., 2019; Hundt et al., 2018). Whereas providers can respond to time-varying symptom presentations during in-person therapy and adapt interventions accordingly, web-based interventions currently offer less flexibility. It remains an open question whether web-based interventions must be designed to address this potential concern.

In the current study, we examine outcomes of VetChange, a web-based intervention for returning veterans who report hazardous drinking and PTSD symptoms. This study represents an analysis of an existing data set in which we examine the occurrence and timing of associations between changes in alcohol use and PTSD symptoms, and the relationship of these variables to intervention dropout. We were specifically interested in examining: 1) how changes in alcohol use during the first month of study participation related to changes in PTSD symptoms, and 2) whether alcohol use and PTSD symptoms predict intervention dropout. We believe this extension of extant literature stands to inform adaptations to web intervention content and delivery that may optimize these interventions for time-varying symptom presentations. We hypothesized that veterans would decrease their drinking to a statistically significant degree over time. We further hypothesized that reductions in alcohol use during the first month would predict increases in PTSD symptom severity, and hyperarousal in particular, and that higher PTSD symptom severity would predict treatment dropout.

2. Methods

For parent study, we recruited a sample of 1,140 veterans from 49 out of 50 states and Puerto Rico to evaluate the reach, effectiveness, adoption, implementation, and maintenance of a public version of VetChange. To evaluate its effectiveness, we recruited a subsample of 222 previously deployed veterans of Iraq and Afghanistan conflicts (i.e., returning veterans) who reported drinking above NIAAA guidelines (i.e., > 2/3 drinks/day, 6/13 drinks/week for women/men; Dawson, Grant, & Li, 2005). Inclusion criteria specified anyone 18 or older who had been deployed during Iraq or Afghanistan conflicts, and who reported hazardous drinking in the last 30 days leading up to study enrollment. Exclusions included residing outside of the United States. Although PTSD diagnosis nor symptoms were considered for in/exclusion, over half (55.17%) of the sample screened positive for PTSD based on the clinical cut off of 33 (Bovin et al., 2016). See Table 1 for demographic and baseline characteristics of the sample.

Table 1.

Baseline characteristics of the sample.

M(SD) Range
Age 36.02 (7.19) 22–57
n %
Gender
 Male 172 77.5
 Female 50 22.5
Race/Ethnicity
 White 174 78.3
 Hispanic/Latina/o 11 4.9
 Black/African American 7 3.1
 Native American/Alaskan Native 5 2.2
 Native Hawaiian/Pacific Islander 1 0.4
 Asian 1 0.4
 Multi-racial 2 0.9
 Other 2 0.9
 Unknown/not disclosed 7 3.1
Conflict
 OEF/OIF/OND 222 100
 Persian Gulf 18 8.1
 Kosovo 12 5.4
 Somalia 2 0.9
 Vietnam 0 0
 Panama 2 0.9
 Granada 0 0
 Other 8 3.6
M(SD) Range
Average Weekly Drinking 39.43(25.56) 6–140
PCL-5 35.84(17.69) 0–74
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2.1. Intervention

VetChange is an evidence-based web intervention designed to help returning veterans manage drinking and PTSD symptoms on their own (Brief et al., 2013; Enggasser et al., 2015). Intervention content includes mood and drink tracking and modules based on self-control training (e.g., managing high-risk situations, developing personalized action plans), brief assessment and personalized feedback and motivational enhancement (i.e., decisional balance exercise), personalized goal setting, developing social support, and psychoeducation and intervention content to target PTSD symptoms impacting alcohol use and recovery (e.g., stress, anger, and sleep modules; Brief et al., 2011; Najavits et al., 2007; Miller & Muñoz, 2005; Miller & Wilbourne, 2002; Sobell & Sobell, 1996).

2.2. Procedure

All study procedures were approved by the Institutional Review Boards (IRB) at VA Boston Healthcare System and Boston University. We recruited through targeted Facebook and Twitter advertisements and data were collected between July 12, 2015 and August 8, 2017. In order to register, all participants completed a brief alcohol assessment and received personal feedback. They were then free to use intervention components flexibly. All consented participants were asked to complete online assessments at baseline, one, three, and six months and received $20 Amazon gift cards for each completed assessment.

2.3. Measures

Participants completed baseline demographic questions but also the Quick Drink Screen (QDS; Sobell et al., 2003) and 20-item PTSD Checklist for DSM-5 (PCL-5; Weathers, Litz, Keane, Palmieri, Marx, & Schnurr, 2013), anchored to deployment-based Criterion A trauma exposure, at each wave of data collection. The QDS is a valid and reliable four-item self-report screener of past 30-day alcohol use quantity and frequency (Sobell et al., 2003). We used items one (average number of drinks per occasion) and two (average number of drinking days per week) to calculate average weekly drinks (AWD) for each measurement point. The PCL-5 is valid and reliable “gold standard” PTSD screener (Blevins, Weathers, Davis, Witte, & Domino, 2015). The PCL-5 includes 20 items, measured using a 5-point Likert scale (0 = “not at all” to 4 = “extremely”, with total scores ranging from 0−80), anchored to a past Criterion A Trauma and past 30-day PTSD symptom severity in relation to that trauma. Subscales of the PCL-5, which can be summed separately or together for an overall PTSD severity score, correspond to each DSM-5 PTSD symptom cluster: Intrusions (items 1–5), avoidance (6–7), negative alterations in cognitions and mood (8–14), and hyperarousal (15–20).

2.4. Data management and analytic strategy

We used linear multilevel modeling to predict AWD and PTSD symptom cluster scores (restricted maximum likelihood estimation) and multilevel logistic regression to predict treatment dropout. Repeated measures (level 1, fixed slopes) were nested within person (level 2, random intercept) and missing data were omitted listwise. Rates of missing data are reflected in the dropout data (coded 1 for dropout) reported below; all variables associated with missingness/dropout are accounted for below and/or included in the final models. Change in AWD between baseline and one month was created by subtracting AWD at one-month follow up from participants’ baseline drinking level, with higher positive values signifying greater reduction. The “time” variable, originally coded 0 – 3, corresponding to baseline through six-month follow up, was grand-mean centered. Dependent variables included AWD (square root transformed to reduce positive skew), PTSD symptom cluster scores (summed within cluster), and intervention dropout. We modeled intervention dropout, which was based on missing a scheduled follow up assessment without a return login to use the intervention, as a binary dependent variable (coded 1 = dropped out, 0 =retained) using multilevel logistic regression. We used time and change in AWD to predict future PTSD symptoms. Change in AWD only predicted hyperarousal over time so only this PTSD symptom cluster was used in the subsequent analysis. For dropout models, we considered age, race/ethnicity, gender, VetChange use (logins, module completion), treatment received within three months of VetChange registration, and baseline alcohol use and PTSD symptom severity. Number of logins and completion of modules were the only other predictors of dropout (inverse association); no other variables predicted dropout (/missingness). Since the results were unchanged with or without inclusion of login and module completion data, they were excluded from final models, which included AWD, time, and hyperarousal to predict intervention dropout. Following an observed effect for hyperarousal on dropout, we performed follow up exploratory analyses to see which single items/symptoms from the hyperarousal cluster were associated with dropout. We analyzed data and generated plots in R using the nlme (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2020), broommixed (Bolker et al., 2019), and ggplot2 packages (Wickham, 2016).

3. Results

As expected, veterans reported significant reductions in AWD over time, b =−0.61, p < .001. This result was substantial, particularly between baseline and one month, as demonstrated by rates of 39.43 (SD = 25.56) drinks per week at baseline vs. 24.58 (SD =22.61), 21.48 (SD = 17.69), and 22.48 (SD =24.23) at one, three, and six months, respectively. Change in AWD in the first month was positively associated with hyperarousal symptoms between months one and six, b = 0.05, p =.04 (see Fig. 1), but not intrusive symptoms, avoidance, or negative alterations in cognition and mood.

Fig. 1.

Fig. 1.

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Plot displaying the association between changes in average weekly drinks per week (with higher numbers indicating greater drinking reduction since baseline) and subsequent hyperarousal symptoms, controlling for time. Shaded area represents error.

Over half (52.3%; n = 116) of veterans dropped out by month one, another 12.2% (n = 13) by month three, and 37.6% (n =35) of the remaining veterans by month six. Time was a predictor of intervention dropout between one and six months after accounting for AWD, b = 15.39, p < .001, but not after accounting for hyperarousal, p = .35. Interestingly, after adjusting for time, hyperarousal symptom severity, b =0.06, adjOR =1.07, 95% CI [1.02, 1.12], p = .007 (see Fig. 2), but not change in AWD in the first month, p =.80, predicted intervention dropout over the study duration. Upon further exploration, for which we examined the interaction of individual hyperarousal symptoms and time on dropout, we found a significant interaction, b = −0.31, adjOR =0.72, 95% CI [0.58, 0.89], p = .003, and main effect of hypervigilance, b = 0.39, adjOR =1.49, 95% CI [1.20, 1.85], p < .001. Sleep difficulty was the only other symptom that came close to predicting dropout, b = 0.18, adjOR = 1.21, 95% CI [0.99, 1.47], p = .059.

Fig. 2.

Fig. 2.

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Plot displaying the association hyperarousal symptom severity and the probability of dropout. Shaded area represents error.

4. Discussion

This study highlights the potential role hyperarousal symptoms in predicting dropout from a web-based intervention for hazardous drinking and PTSD symptoms among returning veterans. In addition to demonstrating a substantial 43% reduction in alcohol use over six months, the current analysis focused on the potential downstream effects of the largest decrease in drinking, which occurred in the first 30 days post-registration. Greater reductions in alcohol use during this period were associated with higher PTSD hyperarousal symptom severity between the one- and six-month assessments. Further, hyperarousal symptoms, but not changes veterans’ average weekly drinks, predicted intervention dropout over time. In follow up exploratory analyses, we examined single items comprising the hyperarousal domain on the PCL-5 and found that hypervigilance was the only single item associated with a higher rate of dropout over the study duration. Further, the significant time by hypervigilance interaction observed was in the reverse direction. This suggests a weakened association between hypervigilance and dropout rates over time, likely attributable to individuals with higher hyperarousal leaving the study earlier on.

These results align with previous research showing that reductions in alcohol use can lead to an increase in PTSD hyperarousal symptom severity (Simons et al., 2005). These findings are significant because both hyperarousal symptoms and treatment dropout are associated with poorer alcohol recovery outcomes (Hoge, 2011; Marshall et al., 2006), and hyperarousal symptoms are a risk factor for future drinking (Steindl, Young, Creamer, & Crompton, 2003). Thus, the potential downstream effects of improved alcohol use might actually undermine long-term recovery. These findings point to the importance of addressing changes in hyperarousal and especially hypervigilance in future web-based intervention adaptations for this population, which may offset intervention dropout risk in this population. At the same time, replication is needed to evaluate additional albeit unmeasured predictors of dropout (e.g., dropout to seek in-person treatment). That is, while these results suggest that addressing hyperarousal could decrease dropout and improve outcomes, such replication could greatly build on the relatively narrow focus of our analyses.

There is a clear need for expanded self-management intervention options for veterans who do not seek in-person treatment (Hoge et al., 2004; Hoge et al., 2014), and the potential of technology-based interventions to address this need is just being realized. Unlike in-person therapy, where clinicians can respond immediately to shifts in affect expression and expressed or implied risk (e.g., increases in hyperarousal symptoms, urges to drink), existing technology-based interventions do not yet have such flexibility (Livingston, Shingleton, Heilman, & Brief, 2019). Thus, while technology-based interventions have shown effectiveness (Livingston et al., 2019; Possemato et al., 2016), there is room for improving responsiveness to time-varying symptoms, like increases in hyperarousal symptoms following a reduction in alcohol use.

Limitations of the current study include exclusive reliance on self-reported veteran status and self-report measures of alcohol use and PTSD symptoms. Due to the scope of the parent study, we also did not collect data on length of time since deployment. PTSD hyperarousal symptoms share overlap with symptoms of alcohol withdrawal (e.g., irritability), making it difficult to differentiate between alcohol withdrawal and PTSD hyperarousal symptoms in the current dataset. Corroborating clinical interview data could help overcome this limitation in future studies.

These results represent findings from a secondary analysis of an existing data set. Though our intervention effects were consistent with the previous RCT results (Brief et al., 2013), the current results are limited by the single group design and inability to account for natural change processes, including regression to the mean, which would require a control group. Lastly, although PTSD hyperarousal symptom severity was associated with dropout, we are unable to evaluate reasons or motives for drop out. It is possible that veterans received all the necessary intervention support they desired, did not find the intervention useful, sought in-person treatment instead, or some other undisclosed reason. Such information could provide the necessary insight to maximize adoption, retention, and intervention outcomes among veterans who may benefit from these interventions, and should be examined in future research.

Despite these limitations, the current study provides insight into possible downstream effects of drinking reduction on PTSD hyperarousal symptoms generally, hypervigilance specifically, and potential risks this has for influencing rates of web-based treatment dropout. Though follow up is needed, if replicated these results indicate adaptations to web-based interventions for this population might include modifications to address predictable increases in hyperarousal following reduced alcohol use. Future interventions or adaptations could include hyperarousal- and hypervigilance-specific modular content, as well as incorporate real-time mood and thought tracking features and personalized feedback and intervention content recommendations based on self-rated thoughts and feelings (e.g., hyperlink grounding techniques following a report of increased hypervigilance). Additional contextual modifications might include location-based and state-dependent “just-in-time” intervention delivery capabilities to help veterans manage high-risk situations that trigger hyperarousal symptoms, or help veterans challenge thoughts and feelings stemming from, or leading to, increased hypervigilance. VetChange enhancements could include psychoeducation about the nature and causes of hypervigilance, and exercises to help veterans challenge thoughts (e.g., “the world is unsafe”) and feelings (e.g., fear, anxiety) giving rise to hypervigilance, alcohol use and, as we have shown here, intervention dropout. Additional modifications to the intervention might not relate to programmed content but the manner in which VetChange is delivered. For instance, using VetChange with the help and support of a trained mental health professional might decrease dropout risk, especially if the professional is made aware and trained to help the veteran effectively cope with elevated hyperarousal symptoms following a reduction in their alcohol use. In addition to representing a logical next step in intervention technology development, such innovation could lead to responsive intervention platforms and delivery methods that are capable of adapting to time-varying alcohol use and PTSD symptom presentations in real time.

HIGHLIGHTS.

  • We examined VetChange outcomes and dropout to guide future intervention development.

  • VetChange users reported significant alcohol reductions in the first 30 days.

  • Alcohol reduction predicted higher hyperarousal but not overall PTSD severity.

  • Hyperarousal symptoms and hypervigilance specifically predicted VetChange dropout.

  • Web interventions that are responsive to time-varying symptom change are needed.

Acknowledgments

Funding statement

The first and third author’s work on this project was funded by the Office of Academic Affiliations, U.S. Department of Veteran Affairs. The second author’s work was funded by T32 MH019836. This research was funded by a grant from the Bristol-Myers Squibb Foundation.

Footnotes

CRediT authorship contribution statement

Nicholas A. Livingston: Conceptualization, Data curation, Formal analysis. Colin T. Mahoney: Conceptualization. Victoria Ameral: Conceptualization. Deborah Brief: Study execution. Amy Rubin: Study execution. Justin Enggasser: Study execution. Scott Litwack: Study execution, Manuscript revision. Eric Helmuth: Study execution, Manuscript revision. Monica Roy: Study execution. Marika Solhan: Study execution. David Rosenbloom: Study execution, Manuscript revision. Terence Keane: Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Back SE, Killeen T, Badour CL, Flanagan JC, Allan NP, Santa Ana E, … Brady KT (2019). Concurrent treatment of substance use disorders and PTSD using prolonged exposure: A randomized clinical trial in military veterans. Addictive Behaviors, 90, 369–377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Blevins CA, Weathers FW, Davis MT, Witte TK, & Domino JL (2015). The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. Journal of Traumatic Stress, 28(6), 489–498. [DOI] [PubMed] [Google Scholar]
  3. Bolker B, Robinson D, Gabry J, Buerkner P, Hua C, & Petry W. (2019). Broom. Mixed: Tidying methods for mixed models. Retrieved from https://cran.r-project.org/web/packages/broom.mixed/broom.mixed.pdf. [Google Scholar]
  4. Bovin MJ, Marx BP, Weathers FW, Gallagher MW, Rodriguez P, Schnurr PP, & Keane TM (2016). Psychometric properties of the PTSD checklist for diagnostic and statistical manual of mental disorders-fifth edition (PCL-5) in veterans. Psychological Assessment, 28(11), 1379–1391. 10.1037/pas0000254. [DOI] [PubMed] [Google Scholar]
  5. Brief DJ, Rubin A, Enggasser JL, Roy M, & Keane TM (2011). Web-based intervention for returning veterans with symptoms of posttraumatic stress disorder and risky alcohol use. Journal of Contemporary Psychotherapy, 41(4), 237–246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brief DJ, Rubin A, Keane TM, Enggasser JL, Roy M, Helmuth E, … Rosenbloom D. (2013). Web intervention for RETURNING veterans with problem drinking and PTSD symptoms: A randomized clinical trial. Journal of Consulting And Clinical Psychology, 81(5), 890–900. 10.1037/a0033697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Calhoun PS, Elter JR, Jones ER Jr, Kudler H, & Straits-Tröster K. (2008). Hazardous alcohol use and receipt of risk-reduction counseling among US veterans of the wars in Iraq and Afghanistan. The Journal of Clinical Psychiatry, 69(11), 1686–1693. [DOI] [PubMed] [Google Scholar]
  8. Dawson DA, Grant BF, & Li TK (2005). Quantifying the risks associated with exceeding recommended drinking limits. Alcoholism: Clinical and Experimental Research, 29(5), 902–908. [DOI] [PubMed] [Google Scholar]
  9. Eftekhari A, Crowley JJ, Mackintosh MA, & Rosen CS (2019). Predicting treatment dropout among veterans receiving prolonged exposure therapy. Psychological Trauma: Theory, Research, Practice, and Policy. Advance online publication. [DOI] [PubMed] [Google Scholar]
  10. Enggasser JL, Hermos JA, Rubin A, Lachowicz M, Rybin D, Brief DJ, … Keane TM (2015). Drinking goal choice and outcomes in a Web-based alcohol intervention: Results from VetChange. Addictive Behaviors, 42, 63–68. 10.1016/j.addbeh.2014.10.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fuehrlein BS, Mota N, Arias AJ, Trevisan LA, Kachadourian LK, Krystal JH, … Pietrzak RH (2016). The burden of alcohol use disorders in US military veterans: results from the National Health and Resilience in Veterans Study. Addiction, 111(10), 1786–1794. [DOI] [PubMed] [Google Scholar]
  12. Hester RK, Delaney HD, Campbell W, & Handmaker N. (2009). A web application for moderation training: Initial results of a randomized clinical trial. Journal of Substance Abuse Treatment, 37(3), 266–276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hoge CW (2011). Interventions for war-related posttraumatic stress disorder: Meeting veterans where they are. Jama, 306(5), 549–551. [DOI] [PubMed] [Google Scholar]
  14. Hoge CW, Castro CA, Messer SC, McGurk D, Cotting DI, & Koffman RL (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. New England Journal of Medicine, 351(1), 13–22. [DOI] [PubMed] [Google Scholar]
  15. Hoge CW, Grossman SH, Auchterlonie JL, Riviere LA, Milliken CS, & Wilk JE (2014). PTSD treatment for soldiers after combat deployment: Low utilization of mental health care and reasons for dropout. Psychiatric Services, 65(8), 997–1004. [DOI] [PubMed] [Google Scholar]
  16. Hundt NE, Ecker AH, Thompson K, Helm A, Smith TL, Stanley MA, & Cully JA (2018). “It didn’t fit for me:” A qualitative examination of dropout from prolonged exposure and cognitive processing therapy in veterans. Psychological Services. Advance online publication. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kiluk BD, Devore KA, Buck MB, Nich C, Frankforter TL, LaPaglia DM, … Carroll, K. M. (2016). Randomized trial of computerized cognitive behavioral therapy for alcohol use disorders: efficacy as a virtual stand-alone and treatment add-on compared with standard outpatient treatment. Alcohol Clin Exp Res, 40(9), 1991–2000. 10.1111/acer.13162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kypri K, Langley JD, Saunders JB, Cashell-Smith ML, & Herbison P. (2008). Randomized controlled trial of web-based alcohol screening and brief intervention in primary care. Archives of internal medicine, 168(5), 530–536. [DOI] [PubMed] [Google Scholar]
  19. Lan CW, Fiellin DA, Barry DT, Bryant KJ, Gordon AJ, Edelman EJ, … Marshall BD (2016). The epidemiology of substance use disorders in US Veterans: A systematic review and analysis of assessment methods. The American journal on addictions, 25(1), 7–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Leibert T, & Archer J, Jr (2005). An exploratory study of client perceptions of internet counseling and the therapeutic alliance. Journal of Mental Health Counseling, 28(1), 69–83. [Google Scholar]
  21. Livingston NA, Shingleton R, Heilman ME, & Brief D. (2019). Self-help smartphone applications for alcohol use, PTSD, anxiety, and depression: Addressing the new research-practice gap. Journal of Technology in Behavioral Science, 1–13. [Google Scholar]
  22. Marshall RD, Turner JB, Lewis-Fernandez R, Koenan K, Neria Y, & Dohrenwend BP (2006). Symptom patterns associated with chronic PTSD in male veterans: New findings from the National Vietnam Veterans Readjustment Study. The Journal of nervous and mental disease, 194(4), 275–278. [DOI] [PubMed] [Google Scholar]
  23. McLean C, Schlenger WE, & Litz B. (2010). Using the internet to deliver treatments for PTSD. In Brunet ARAA, & Herbert CF (Ed.), Internet use in the aftermath of trauma. . Amsterdam: IOS Press. [Google Scholar]
  24. Miller WR, & Muñoz RF (2005). Controlling your drinking. New York, NY: Guilford. [Google Scholar]
  25. Miller WR, & Wilbourne PL (2002). Mesa Grande: A methodological analysis of clinical trials of treatments for alcohol use disorders. Addiction, 97(3), 265–277. [DOI] [PubMed] [Google Scholar]
  26. Mojtabai R, Olfson M, Sampson NA, Jin R, Druss B, Wang PS, … Kessler RC (2011). Barriers to mental health treatment: Results from the National Comorbidity Survey Replication. Psychological Medicine, 41(8), 1751–1761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Najavits LM, Harned MS, Gallop RJ, Butler SF, Barber JP, Thase ME, & Crits-Christoph P. (2007). Six-month treatment outcomes of cocaine-dependent patients with and without PTSD in a multisite national trial. Journal of Studies on Alcohol and Drugs, 68(3), 353–361. [DOI] [PubMed] [Google Scholar]
  28. Norman SB, Tate SR, Anderson KG, & Brown SA (2007). Do trauma history and PTSD symptoms influence addiction relapse context? Drug and Alcohol Dependence, 90(1), 89–96. [DOI] [PubMed] [Google Scholar]
  29. Ouimette P, Goodwin E, & Brown PJ (2006). Health and well being of substance use disorder patients with and without posttraumatic stress disorder. Addictive Behaviors, 31(8), 1415–1423. [DOI] [PubMed] [Google Scholar]
  30. Ouimette PC, Brown PJ, & Najavits LM (1998). Course and treatment of patients with both substance use and posttraumatic stress disorders. Addictive Behaviors, 23(6), 785–795. [DOI] [PubMed] [Google Scholar]
  31. Pinheiro J, Bates D, DebRoy S, Sarkar D, & R Core Team (2020). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–145, https://CRAN.R-project.org/package=nlme. [Google Scholar]
  32. Possemato K, Kuhn E, Johnson E, Hoffman JE, Owen JE, Kanuri N, … Brooks E. (2016). Using PTSD Coach in primary care with and without clinician support: A pilot randomized controlled trial. Gen Hosp Psychiatry, 38, 94–98. 10.1016/j.genhosppsych.2015.09.005. [DOI] [PubMed] [Google Scholar]
  33. Reger GM, Hoffman J, Riggs D, Rothbaum BO, Ruzek J, Holloway KM, & Kuhn E. (2013). The “PE coach” smartphone application: An innovative approach to improving implementation, fidelity, and homework adherence during prolonged exposure. Psychological Services, 10(3), 342. [DOI] [PubMed] [Google Scholar]
  34. Simons JS, Gaher RM, Jacobs GA, Meyer D, & Johnson-Jimenez E. (2005). Associations between alcohol use and PTSD symptoms among American Red Cross disaster relief workers responding to the 9/11/2001 attacks. The American Journal of Drug and Alcohol Abuse, 31(2), 285–304. [PubMed] [Google Scholar]
  35. Sobell LC, Agrawal S, Sobell MB, Leo GI, Young LJ, Cunningham JA, & Simco ER (2003). Comparison of a quick drinking screen with the timeline followback for individuals with alcohol problems. Journal of studies on alcohol, 64(6), 858–861. [DOI] [PubMed] [Google Scholar]
  36. Sobell MB, & Sobell LC (1996). Problem drinkers: Guided self-change treatment. The Guilford Press. [Google Scholar]
  37. Steindl SR, Young RM, Creamer M, & Crompton D. (2003). Hazardous alcohol use and treatment outcome in male combat veterans with posttraumatic stress disorder. Journal of Traumatic Stress, 16(1), 27–34. [DOI] [PubMed] [Google Scholar]
  38. Szafranski DD, Gros DF, Acierno R, Brady KT, Killeen TK, & Back SE (2019). Heterogeneity of treatment dropout: PTSD, depression, and alcohol use disorder reductions in PTSD and AUD/SUD treatment noncompleters. Clinical Psychology & Psychotherapy, 26(2), 218–226. [DOI] [PubMed] [Google Scholar]
  39. Tate SR, Norman SB, McQuaid JR, & Brown SA (2007). Health problems of substance-dependent veterans with and those without trauma history. Journal of Substance Abuse Treatment, 33(1), 25–32. [DOI] [PubMed] [Google Scholar]
  40. Wagner TH, Harris KM, Federman B, Dai L, Luna Y, & Humphreys K. (2007). Prevalence of substance use disorders among veterans and comparable nonveterans from the National Survey on Drug Use and Health. Psychological Services, 4(3), 149–157. [Google Scholar]
  41. Weathers FW, Litz BT, Keane TM, Palmieri PA, Marx BP, & Schnurr PP (2013). The PTSD Checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov. [Google Scholar]
  42. Wickham H. (2016). Ggplot2: Elegant graphics for data analysis. Springer International Publishing. [Google Scholar]

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