Published on in Vol 24, No 6 (2022): June

Preprints (earlier versions) of this paper are available at, first published .
Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials

Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials

Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials

Authors of this article:

Rahel Pearson1 Author Orcid Image ;   Emily Carl2 Author Orcid Image ;   Suzannah K Creech1, 3 Author Orcid Image


1Veterans Health Administration Veterans Integrated Service Network 17, Center of Excellence for Research on Returning War Veterans, Central Texas Veterans Affairs Healthcare System, Waco, TX, United States

2Department of Psychology, University of Texas, Austin, TX, United States

3Department of Psychiatry and Behavioral Sciences, Dell Medical School of the University of Texas, Austin, TX, United States

Corresponding Author:

Rahel Pearson, PhD

Veterans Health Administration Veterans Integrated Service Network 17

Center of Excellence for Research on Returning War Veterans

Central Texas Veterans Affairs Healthcare System

4800 Memorial Drive (151C)

Waco, TX, 76711

United States

Phone: 1 2542975155


Background: Computerized psychological interventions can overcome logistical and psychosocial barriers to the use of mental health care in the Veterans Affairs and Department of Defense settings.

Objective: In this systematic review, we aim to outline the existing literature, with the goal of describing: the scope and quality of the available literature, intervention characteristics, study methods, study efficacy, and study limitations and potential directions for future research.

Methods: Systematic searches of two databases (PsycINFO and PubMed) using PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines were conducted from inception until November 15, 2020. The following inclusion criteria were used: the study was published in an English language peer-reviewed journal, participants were randomly allocated to a computerized psychological intervention or a control group (non–computerized psychological intervention active treatment or nonactive control group), an intervention in at least one treatment arm was primarily delivered through the computer or internet with or without additional support, participants were veterans or service members, and the study used validated measures to examine the effect of treatment on psychological outcomes.

Results: This review included 23 studies that met the predefined inclusion criteria. Most studies were at a high risk of bias. Targeted outcomes, participant characteristics, type of support delivered, adherence, and participant satisfaction were described. Most of the examined interventions (19/24, 79%) yielded positive results. Study limitations included participant characteristics limiting study inference, high rates of attrition, and an overreliance on self-reported outcomes.

Conclusions: Relatively few high-quality studies were identified, and more rigorous investigations are needed. Several recommendations for future research are discussed, including the adoption of methods that minimize attrition, optimize use, and allow for personalization of treatment.

J Med Internet Res 2022;24(6):e30065



Most individuals with diagnosable mental health disorders do not have access to adequate care [1]. Computerized psychological interventions are well-positioned to address this treatment gap, as these interventions provide a cost-effective and easily accessible alternative to traditional face-to-face mental health care [2,3]. Computerized psychological interventions, often delivered through the internet, have grown steadily in popularity over the past decade. Computerized psychological interventions may be effective for the treatment of a range of mental health disorders. Meta-analyses have supported the efficacy of computerized psychological interventions in the treatment of depression [4-6], anxiety [4,7], posttraumatic stress disorder (PTSD; [8]), substance use disorders (SUDs; [9,10]), and insomnia [11], with effect sizes ranging from small [5,7,9] to medium-large [4,6,11]. Various meta-analyses have outlined the therapeutic benefit of specific computer-delivered treatment modalities, such as cognitive behavioral therapy (CBT) [4,11], acceptance and commitment therapy [12], and mindfulness-based therapy [13]. There is evidence that computerized psychological interventions are as effective as face-to-face interventions [4,14], suggesting that a variety of mental health conditions can be addressed by computerized treatments with the potential for widespread dissemination of such therapies.

As the body of evidence supporting computerized psychological interventions is robust, it is not surprising that the Department of Veterans Affairs (VA) and the Department of Defense have increasingly implemented these interventions [15], with increasing studies examining computerized psychological interventions in current and former service members. To our knowledge, there has been no systematic review of the literature examining computerized psychological interventions in veterans and service members. Reviews of computerized psychological interventions in general community samples might not generalize to veterans and service members, given the unique considerations in terms of gender ratio, severity of comorbid conditions, socioeconomic factors, and situational or environmental exposures. The use of computerized psychological interventions in veterans and service members will likely continue to rise. The objective of this systematic review is to examine the literature on randomized controlled trials (RCTs) using computerized psychological interventions in veteran and military populations by describing study characteristics and summarizing the efficacy of these interventions. We also aim to examine the quality and limitations of the identified studies. Finally, recommendations for future research will be made.

Study Selection and Data Collection

Systematic searches of two databases (PsycINFO and PubMed) were conducted from inception until November 15, 2020. The search terms are described in Multimedia Appendix 1 (adapted from Moore et al [16]). Duplicates were removed, and the reference lists of the included studies were examined for additional articles. A final list of included studies was circulated among colleagues with subject matter expertise to verify that no relevant papers were omitted. The following inclusion criteria were used: (1) the study was published in an English language peer-reviewed journal, (2) participants were randomly allocated to a computerized psychological intervention or a control group (non–computerized psychological intervention active treatment or nonactive control group), (3) an intervention in at least one treatment arm was primarily delivered through the computer or internet with or without additional support, (4) participants were military veterans or service members, and (5) the study used validated measures to examine the effect of treatment on psychological outcomes. Validated psychological outcome measures are those measures with demonstrated reliability and validity that quantify mood, well-being, emotion, affect, and/or psychosocial functioning. The outcomes examined were mental health disorders, as defined by the Diagnostic Statistical Manual, fifth edition (eg, SUDs and neurocognitive disorders), as well as psychosocial and behavioral correlates of these disorders (eg, romantic relationship dysfunction and anger). As commonly used VA smartphone apps are designed to be used in conjunction with traditional face-to-face treatment [17], they were not included. Papers were reviewed for inclusion at the title, abstract, and full paper levels. RP determined if studies met the inclusion criteria, and any ambiguity was discussed with SC until a final decision was reached. Using a standardized form, intervention characteristics, population characteristics, study design, methods, procedures, and outcomes were recorded. The number of papers identified, screened, and included is reported in Figure 1.

Figure 1. Flow chart of records identified through database screening.
View this figure

Data Synthesis

Findings were grouped by the targeted psychological outcome and population examined (veterans and/or service members). These subgroups used similar methods, such as intervention techniques and recruitment tactics, allowing for a more direct comparison. Study outcomes were examined and visually presented in a harvest plot by these subgroups, as well as the control group (active or inactive), to provide a more nuanced understanding of the study results. If a study included both an active and inactive control group, the computerized psychological intervention was compared with the active control group to provide a more robust test of computerized psychological intervention efficacy. Overall, the studies were heterogeneous, and subgroups of similar studies were very small (≤3), making meta-analysis inappropriate. A narrative synthesis of the effects was conducted to describe the efficacy of interventions on primary outcome measures. Effect sizes (Cohen d of Hedges g if n<20) were extracted or calculated where possible. The authors of the included studies were contacted when insufficient data were provided in the article.

Quality Assessment

The Cochrane risk of bias tool [18] was used to determine the methodological quality of the included studies. The assessed features were sequence generation and allocation sequence concealment (selection bias), blinding of personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective outcome reporting (reporting bias). Two authors (RP and EC) independently rated the risk of bias as high risk (eg, sequence generation: randomization based on birthday), low risk (eg, sequence generation: block randomization with randomly varied blocks sizes), or unclear risk (eg, sequence generation: no information on randomization method). Overall bias was rated as low if ratings of bias were low in all domains or if bias was unclear in 1 domain, and this was unlikely to have biased the study outcome. Overall bias was rated as moderate if bias was high in 1 domain or unclear in 2 domains, and this was unlikely to have biased the study outcome. Overall bias was rated as high if bias was rated as high in 1 domain or unclear in 2 domains, and this was likely to have biased study outcomes. Similarly, overall bias was rated as high if bias was rated as unclear in ≥3 domains or high in ≥2 domains. Ratings were compared, and the 2 raters discussed discrepancies until a consensus was reached.

Intervention Characteristics


Characteristics of the included studies are outlined in Multimedia Appendix 2 [19-42]. Most computerized psychological interventions targeted PTSD and SUDs. Most computerized psychological interventions were web-based, although some studies used software programs installed on study computers [19-23]. Intervention content was often presented with graphics and interactive features that allowed tailored treatment content based on participant characteristics [19,24-32]. Computerized psychological interventions offered varying degrees of guidance in the 23 studies: 3 (13%) studies [24,29,33] provided no human contact during any study phase, 15 (65%) studies [19-23,25-27,31,34-39] provided administrative contact (eg, reminders, use support, and structured or semistructured assessment), and 5 (22%) studies [28,30,32,40,41] included therapeutic contact.

PTSD Interventions

Of the 24 computerized psychological interventions, 8 (33%) addressed PTSD and related symptoms. Of the 23 studies, 3 (13%) studies examined expressive writing interventions of different lengths [38-40] with PTSD symptomatology as the primary outcome, 3 (13%) studies [26,28,36] tested the efficacy of computerized psychological interventions that included a variety of CBT-based techniques, such as cognitive restructuring and behavioral modification. McLean et al [32] computerized prolonged exposure, an evidence-based treatment (EBT) for PTSD, and Larsen et al [37] examined the efficacy of cognitive training on PTSD symptoms.

SUD Interventions

Interventions addressing SUDs were primarily focused on drinking problems (3/23, 13% studies; 4/24, 17% interventions) [22,29,33] or drinking or substance use problems and PTSD [24,35]. One of the studies [25] tested the efficacy of a smoking cessation intervention. Interventions addressing only drinking problems were completed in one sitting [22,29,33]. All interventions included alcohol assessment feedback and psychoeducation on alcohol use. In addition, interventions presented peer-specific norms for alcohol use [22,29,33] and included motivational techniques [22,29]. Interventions addressing comorbid alcohol/substance use and PTSD were CBT based and significantly longer than interventions addressing alcohol use in isolation [24,35]. Both interventions integrated CBT components and motivational techniques aimed at trauma and substance or alcohol use. Acosta et al [35] also offered optional trauma exposure modules, although there was limited engagement with this content. Calhoun et al [25] tested the efficacy of a web-based smoking intervention, Quitnet, which included behavioral goal setting and social support components.

Other Interventions

Approximately 8% (2/24) of interventions targeted depression: Bedford et al [19] examined a 6-session problem-solving intervention, whereas Pfeiffer et al [30] examined Beating the Blues, a well-established CBT for depression intervention. CBT interventions were also used to treat anger [21] and insomnia [31] and decrease suicidality by targeting perceived burdensomeness and thwarted belongingness [23]. Cooper et al [20] examined a brain fitness program to ameliorate the deleterious effects of mild traumatic brain injury. Finally, 2 interventions included participants and their partners. Kahn et al [27] examined an intervention aimed at promoting postdeployment rehabilitation, which included mindfulness-based techniques and massage therapy instructions. Salivar et al [41] tested 2 interventions, combined for data analyses, for low-income couples who experienced relationship distress. Interventions integrated conflict management with improving communication, commitment, and positivity (intervention 1) or acceptance and implementing behavioral change (intervention 2).

Recruitment and Sample Size

A subset of trials recruited veterans from one [21,25,37,40] or more [22,30,34-36,38] VA facilities. Approximately 13% (3/23) of trials [22,30,35] contacted veterans who were likely to meet the study inclusion criteria based on information obtained from the VA electronic medical record system, and another sent out eligibility questionnaires and US $5 incentives to a large (N=15,686) random sample of veterans [39]. Several trials [19,24,26,27,33,34] recruited participants through targeted emails on the web (eg, emails sent out to members of veterans’ associations) or through Facebook and other social media advertising. In addition, veterans were recruited from university campuses [19,34,37] and the community [23,34,37]. Approximately 30% (7/23) of studies recruited active duty personnel [20,28,29,31] from military installations and Department of Defense sites or a combination of active duty personnel and veterans [32,36,41]. Of the 23 studies, 4 (17%) studies included all or majority US army personnel [20,31,32,36], 1 (4%) study recruited from various military branches at different installations and sites [29], and 2 (9%) studies did not specify the military branches represented in their sample [28,41]. Pemberton et al [29] recruited a convenience sample at 8 military installations and was able to recruit the largest sample size (N=3070, compared with a sample range of N=40 to N=180 for other studies recruiting service members). However, the study authors noted several sample limitations, such as the low prevalence of drinking problems and high study attrition.

Web-only trials that recruited nationally through social media or by US mail were generally more successful in obtaining large sample sizes [26,27,33,38,39]. Sample sizes were generally reduced in pilot trials [34,37,38], if ≥1 study arm required intensive participant contact (eg, face-to-face treatment and magnetic resonance imaging [21,31,34]), or if eligibility criteria were stringent and/or required specialty assessment [20].

Adherence and Attrition

Adherence to the intervention was addressed in 87% (20/23) of examined studies, although only 9% (2/23) of studies clearly defined the level of engagement that differentiated adherence versus nonadherence (ie, >80% of sessions completed [37] or >5 sessions completed [30]). The remaining studies provided a quantitative assessment of participant engagement with the intervention, such as the time spent or sessions completed. Adherence, defined as completion of all study sessions, ranged from 25% to 100% (based on 12/23, 52% of studies with available data [19,22,24,29,32-36,38,40,41]).

Attrition, defined as a loss to follow-up, was reported in all studies. Across all studies, 36.25% (2994/8260) of participants were lost at the first follow-up time point, which often coincided with the posttreatment assessment. At the second follow-up time point (average follow-up length was 16 weeks), 45.59% (3542/7770) participants across 87% (20/23) of studies were lost. When computerized psychological intervention attrition at the first follow-up time point was examined by support type, attrition was 51.78% (2311/4463) in the no support group (3/23, 13% studies; 4/24, 17% interventions), 17.29% (548/3170) in the administrative support group (15/23, 65% studies), and 21.5% (135/629) in the therapeutic support group (4/23, 17% studies).

Risk of Bias


The Cochrane risk of bias tool [18] assessment of bias consensus ratings for the included studies is found in Table 1.

Table 1. Consensus ratings for Cochrane assessment of bias.
StudyRandom sequence generationAllocation concealmentBlind outcome assessmentIncomplete dataSelective reportingOverall risk of bias
Acosta et al [35]UnclearUnclearLowLowLowModerate
Bedford et al [19]LowLowLowHighUnclearHigh
Brief et al [24]UnclearUnclearLowHighUnclearHigh
Calhoun et al [25]UnclearLowLowUnclearUnclearHigh
Clausen et al [34]HighHighLowHighLowHigh
Cooper et al [20]UnclearUnclearLowHighLowHigh
Cucciare et al [22]LowUnclearLowLowLowLow
Engel et al [36]LowLowLowLowUnclearLow
Hobfoll et al [26]LowUnclearLowHighUnclearHigh
Kahn et al [27]LowUnclearLowLowLowLow
Krupnick et al [40]UnclearUnclearLowHighUnclearHigh
Larsen et al [37]UnclearUnclearLowUnclearLowHigh
Litz et al [28]UnclearUnclearLowHighUnclearHigh
McLean et al [32,42]UnclearUnclearLowHighUnclearHigh
Pedersen et al [33]UnclearUnclearLowLowLowModerate
Pemberton et al [29]HighHighLowHighUnclearHigh
Pfeiffer et al [30]LowUnclearLowUnclearUnclearHigh
Possemato et al [38]UnclearUnclearLowHighUnclearHigh
Salivar et al [41]UnclearUnclearLowUnclearUnclearHigh
Sayer et al [39]LowUnclearLowLowUnclearModerate
Short et al [23]LowUnclearLowHighHighHigh
Taylor et al [31]HighHighLowLowLowHigh
Timmons et al [21]UnclearUnclearLowUnclearUnclearHigh
Sequence Generation

Of the 23 studies, 11 (48%) provided a detailed description of their process of sequence generation, 8 (25%) were rated as low risk for bias, and 3 (13%) were rated as high risk for bias. Studies that were at high risk for bias either discontinued randomization during the study because of technical difficulties [31] or an investigator-moving institution [34]. Pemberton et al [29] were unable to randomize as intended as internet speed limited the availability of interventions at certain study locations. Approximately 52% (12/23) of studies did not provide sufficient detail on their process of sequence generation (eg, specified that block randomization was used but failed to outline the process of selecting block size), and their risk of bias was rated as unclear.

Allocation Concealment

Of the 23 studies, 3 (13%) were rated as having a low risk for bias, 2 (9%) explicitly stated that study staff were blind to treatment allocation [19,25], and 1 (4%) used variable block size, blinding study staff to treatment allocation [36]. The 13% (3/23) of studies that were unable to randomize as intended (see Sequence Generation section; Taylor et al [29], Clausen et al [31], and Pemberton et al [34]) presumably had to unblind study staff to treatment allocation and were rated as having a high risk of bias. The remaining studies did not provide sufficient details about allocation concealment and were rated as having an unclear risk of bias.

Blind Outcome Assessment

All but 17% (4/23) of studies used self-report or physiological measures exclusively to assess outcomes, and these studies were rated as low risk for bias. The studies that used clinician-guided or rated assessment measures [20,28,32,34] specified that raters were blind to the treatment arm and were thus rated as having a low risk of bias.

Incomplete Data

Of the 23 studies, 17 (74%) clearly defined treatment attrition and stated that they used intention-to-treat (ITT) analyses [19,20,22,24-31,35-40]; however, 2 (9%) studies did not include all randomized participants in ITT analyses [20,38] and 1 (4%) study failed to impute missing data [19]. In addition, 30% (7/23) of these studies [19,20,24,26,28,29,38] reported high attrition (>15% difference in missing data between treatment arms and/or >40% missing data overall [43]; as cited in Berge et al [44]), which introduces risk for bias regardless of the statistical methods used to attenuate this risk. The remaining studies either conducted completer analyses [21,23,34,42], had missingness that was not random [23,32,34], or failed to specify attrition rates by treatment arm [41].

Selective Reporting

Approximately 35% (8/23) of trials preregistered their studies [20,22,27,31,34,35,37], and 30% (7/23) reported preregistered outcomes and were rated as having a low risk of bias. One of the studies [23] reported outcomes significantly different from those that were preregistered, and this study was rated as having a high risk for bias. One of the studies published a protocol paper outlining their methods and intended analyses and was rated as having a low risk for bias [33]. The remaining studies were not preregistered, and the risk for bias was rated as unclear.

Outcomes and Satisfaction

Figure 2 presents the outcome data visually in a harvest plot, and study effect sizes are included in Multimedia Appendix 2.

Figure 2. Harvest plot of study outcomes by intervention, population, and control group. PTSD: posttraumatic stress disorder; SUD: substance use disorder; TAU: treatment as usual.
View this figure

Of the 23 studies, 13 (57%) reported positive results when comparing computerized psychological interventions to nonactive control groups or treatment as usual, 9 (39%) reported results that supported the specified hypotheses, and 4 (17%) reported partial support for the hypotheses. For example, Krupnick et al [40] reported that expressive writing (compared with treatment as usual) reduced PTSD hyperarousal symptoms; however, there was no significant movement in other PTSD domains. Sayer et al [39] found that expressive writing (compared with placebo writing) reduced physical complaints, anger, and general psychological distress but did not report a reduction in PTSD symptoms. Similarly, Acosta et al [35] and Engel et al [36] reported that gains in one domain (drinking and PTSD, respectively) failed to generalize to other outcomes.

Approximately 9% (2/23) of studies reported that the computerized psychological intervention under examination outperformed an active control treatment. Litz et al [28] found that self-management CBT (vs computerized supportive counseling) reduced daily measures of PTSD and depression, with 6-month follow-up reductions in depression, PTSD, and anxiety in the completer group. Kahn et al [27] found that their computerized psychological intervention led to improvements in various mental health–related outcomes when compared with residential treatment. Approximately 17% (4/23) of studies found that their computerized psychological intervention performed as well as the face-to-face treatment equivalent. Positive results found in computerized psychological interventions were comparable with those for in-person CBT for insomnia [31], in-person group anger inoculation training [21], therapist-led cognitive rehabilitation [20], and clinic-based smoking cessation care [25]. McLean et al [32] found that computerized prolonged exposure did not outperform non–trauma-focused face-to-face treatment when examining PTSD outcomes.

Approximately 17% (4/23) of studies failed to demonstrate significant treatment effects, with expressive writing [38], executive functioning training [34], and working memory training [37] for PTSD not outperforming placebo. Similarly, 50% (1/2) of the computerized psychological interventions for alcohol use examined by Pemberton et al [29] did not outperform the waitlist control group.

Satisfaction data were provided in several studies. Overall, participants felt that they benefited from computerized psychological intervention engagement: with 82% [35], 93% [38], 96% [41], and 76.1% [39] of participants noting positive treatment effects. One of the studies found similar satisfaction rates between the computerized psychological intervention and the in-person treatment equivalent [21]. Although participants generally reported high treatment satisfaction and acceptability, there were reports of computerized psychological interventions being difficult to complete [34], difficult to understand [35], or impersonal and time consuming [40].

Principal Results and Comparison With Previous Work

We aimed to systematically review the literature examining computerized psychological interventions in veteran and military populations. Across 23 studies, 24 interventions met the inclusion criteria and were reviewed. PTSD and SUDs were the most commonly targeted clinical difficulties, and other outcomes included anger, depression, insomnia, traumatic brain injury, relationship distress, suicidality, and readjustment difficulties. Interventions spanned a range of modalities and mostly focused on veterans, although a subset of studies recruited active military personnel. Approximately 8% (2/24) of interventions included romantic partners, whereas the other interventions followed an individual format. Most studies provided administrative support only, 13% (3/23) of studies provided no support, and 22% (5/23) of studies provided clinically meaningful support. Results were mostly positive; however, only 13% (3/23) of studies reporting positive results were rated as low risk for bias. Similarly, all studies that did not report significant treatment effects were rated as having a high risk of bias. Therefore, we caution against interpreting these results as unambiguous evidence of clinical effectiveness or ineffectiveness. Although it appears that computerized psychological interventions hold promise for the treatment of psychological difficulties in veterans and military service members, there is a need for more high-quality evidence to increase the confidence with which conclusions can be drawn.

Given the broad inclusion criteria that allowed great heterogeneity in intervention content and outcomes, a limited number of RCTs were identified. This is especially true in comparison with the number of RCTs that examine computerized psychological interventions in the general population. For example, Andrews et al [4] identified 53 RCTs that targeted depression and anxiety. Although the literature search returned a substantial number of pilot trials and process papers, limited RCTs were identified, indicating that the step from examining feasibility to establishing efficacy has not been decisively made. Furthermore, a high or unclear risk of bias across multiple features was common, with attrition bias being a concern for 48% (11/23) of the studies reviewed, greatly reducing the confidence with which inferences can be made. There are generally high rates of attrition in computerized psychological intervention trials [45], and veterans might be at a higher risk of attrition from treatment [46]. Although 74% (17/23) of the studies included in this review attempted to compensate for missing data by using ITT analyses, 30% (7/23) of studies had such a significant loss of data that high risk for bias was introduced regardless of the statistical methods used to attenuate this risk. Future research should attempt to reduce data loss by incorporating procedures associated with improved study retention. For example, clear study completion deadlines and prescheduled posttreatment assessments have been shown to reduce attrition [47], and adherence is improved when interventions are designed to include persuasive technology (ie, technology designed to include elements of social influence, such as praise, personalization, and social learning [48]). Therapeutic support (vs no support or administrative support) is also associated with improved retention in computerized psychological intervention trials [49], although it is not clear whether the benefits of including therapist contact outweigh the limitations placed on intervention scalability. Recently, evidence [50,51] has supported the efficacy of single-session web-based interventions, which can maximize recruitment while minimizing attrition. Of the studies reviewed here, Pedersen et al [33] reported low attrition rates and medium effect sizes for a single-session intervention administered in a help-seeking sample, demonstrating that these interventions can be successfully adapted for use in veterans.

Attrition is associated with insufficient statistical power, especially when the initial sample sizes are small, as was the case in many of the studies reviewed. Future research should anticipate high rates of attrition and set recruitment goals to ensure adequate statistical power for detecting treatment effects. In addition to dropout, low study use is another common concern in studies examining computerized psychological interventions. Notably, only 9% (2/23) of studies [30,37] defined study use (eg, minutes spent in the program or modules completed) as constituting adherence versus nonadherence. Similarly, only 30% (7/23) of studies described the relationship between use metrics and study outcomes. It is important to further consider these metrics in computerized psychological intervention research, as there is evidence that a dose–response relationship exists for computerized psychological interventions. For example, increases in modules completed [52] and more frequent use [53] are related to greater improvement at posttreatment. Examining study use and its relationship to outcome would also elucidate which intervention components are associated with change, allowing for the optimization of treatment.

Several studies have noted that sample characteristics limit the generalizability of the results. As is the case with much veteran and military research, samples tended to be mostly male. There is a need for computerized psychological interventions addressing the unique needs of female veterans, with recent evidence suggesting that these interventions are feasible, satisfactory, and potentially beneficial [54]. Male veterans who experienced military sexual trauma and transgender veterans are other subpopulations that might benefit from computerized psychological interventions because of high mental health disorder rates and numerous barriers to establishing care [55,56]. Further, some studies limited enrollment to post–9/11 war veterans. This cohort is younger and might be more computer literate than the overall veteran population; however, given that a substantial proportion of veterans served in VA are ages >65 years [57], it is important to investigate the utility of computerized psychological interventions for this population. Although evidence suggests that computerized psychological interventions can provide additional care options for rural patients [58], there is a need to establish ways of extending coverage to veterans who live in rural areas without adequate internet infrastructure or transportation.

Several studies noted sample heterogeneity within treatment arms as a limitation. Although limiting sample variability can enhance the interpretability of findings, it also constrains generalizability. Alternatively, given a robust sample size, statistical methods can be leveraged to identify characteristics within heterogeneous samples associated with beneficial and/or adverse computerized psychological intervention treatment effects [59]. This would allow a stepped care approach, where minimally invasive and cost-effective treatments such as computerized psychological interventions are initially offered to those who are most likely to benefit, and resource-intensive face-to-face treatments are reserved for veterans or service members who require a higher level of care. Optimizing service delivery by identifying subsets of veterans or military personnel who are well-suited for computerized psychological intervention treatment is an important avenue of research, and initial explorations in this area are being reported [60].

VA is increasingly focusing on offering EBTs for mental health disorders. Although various interventions reviewed here included evidence-based practices (eg, exposure and motivational techniques), only 13% (3/23) of studies digitalized an EBT [30-32]. There is evidence that face-to-face EBTs used at VA can be successfully offered in a computerized format, as there have been positive trials of web-based exposure-based trauma treatment [61] and CBT for chronic pain [62] in general community samples. Similarly, there is an evidence base for web-based CBT [63] and acceptance and commitment therapy [64] for depression. Future research should continue to focus on digitalizing those treatments that have proven efficacy.


This review has several limitations. First, owing to small sample sizes and high rates of potential bias, conclusions that computerized psychological interventions are potentially beneficial for veterans and service members are tentative. Lack of reporting was common across studies, resulting in many unclear bias ratings, which introduces uncertainty in our overall assessment of bias. Second, findings might not generalize to the broader veteran population, as many of the reviewed studies limited enrollment to post–9/11 veterans. Third, only English language articles were included, and all the studies reviewed were conducted in North America with US veterans or service members, and results cannot be extrapolated to other contexts. Fourth, only 1 author (RP) evaluated the results of the search; having an additional reviewer of these results would have strengthened the methodology. Finally, the methods of the studies included in this review are diverse, which in some instances complicates direct comparisons. The study heterogeneity also precluded conducting a meta-analysis, which would be the most robust way of assessing intervention efficacy. As the literature examining computerized psychological interventions in veterans and service members grows, efforts should be made to synthesize results by conducting a meta-analysis, which would provide evidence for intervention utility in this population.


Computerized psychological interventions are uniquely positioned to optimize treatment access and use for service members and veterans. These interventions could be integrated into a stepped care framework and reduce the burden on the health care system while increasing engagement with mental health services in this vulnerable population. Despite increased research interest, significant work remains in the development and evaluation of computerized psychological interventions targeted at veterans and service members. Although initial outcomes suggest that computerized psychological interventions are potentially beneficial for this population, much of the available research is at high risk for bias and fails to fully incorporate known evidence-based practices. There is an opportunity to design treatments that minimize threats to internal validity (eg, attrition and limited engagement) by including strategies to increase user motivation and by distilling treatments to include the most active intervention components. As veterans and service members report complex mental health challenges, as well as perceived and actual barriers in obtaining adequate care, there is an acute need to address the limitations of the existing literature.


This work was supported by the resources of the Central Texas Veterans Health Care System (Temple, Texas) and the Veterans Integrated Service Network 17 Center of Excellence for Research in Returning War Veterans (Waco, Texas). The contents of this manuscript do not represent the views of the US Department of Veterans Affairs or the United States Government.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Search terms for review.

DOCX File , 13 KB

Multimedia Appendix 2

Characteristics of the studies included in the review.

DOCX File , 18923 KB

  1. Kazdin AE. Addressing the treatment gap: a key challenge for extending evidence-based psychosocial interventions. Behav Res Ther 2017;88:7-18. [CrossRef] [Medline]
  2. Webb CA, Rosso IM, Rauch SL. Internet-based cognitive-behavioral therapy for depression: current progress and future directions. Harv Rev Psychiatry 2017;25(3):114-122 [FREE Full text] [CrossRef] [Medline]
  3. Paganini S, Teigelkötter W, Buntrock C, Baumeister H. Economic evaluations of internet- and mobile-based interventions for the treatment and prevention of depression: a systematic review. J Affect Disord 2018;225:733-755. [CrossRef] [Medline]
  4. Andrews G, Basu A, Cuijpers P, Craske MG, McEvoy P, English CL, et al. Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: an updated meta-analysis. J Anxiety Disord 2018;55:70-78 [FREE Full text] [CrossRef] [Medline]
  5. Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, et al. Efficacy of self-guided internet-based cognitive behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. JAMA Psychiatry 2017;74(4):351-359. [CrossRef] [Medline]
  6. Sztein DM, Koransky CE, Fegan L, Himelhoch S. Efficacy of cognitive behavioural therapy delivered over the Internet for depressive symptoms: a systematic review and meta-analysis. J Telemed Telecare 2018;24(8):527-539. [CrossRef] [Medline]
  7. Cuijpers P, Marks I, van Straten A, Cavanagh K, Gega L, Andersson G. Computer-aided psychotherapy for anxiety disorders: a meta-analytic review. Cogn Behav Ther 2009;38(2):66-82. [CrossRef] [Medline]
  8. Lewis C, Roberts NP, Simon N, Bethell A, Bisson JI. Internet-delivered cognitive behavioural therapy for post-traumatic stress disorder: systematic review and meta-analysis. Acta Psychiatr Scand 2019;140(6):508-521. [CrossRef] [Medline]
  9. Boumparis N, Karyotaki E, Schaub MP, Cuijpers P, Riper H. Internet interventions for adult illicit substance users: a meta-analysis. Addiction 2017;112(9):1521-1532 [FREE Full text] [CrossRef] [Medline]
  10. Riper H, Blankers M, Hadiwijaya H, Cunningham J, Clarke S, Wiers R, et al. Effectiveness of guided and unguided low-intensity internet interventions for adult alcohol misuse: a meta-analysis. PLoS One 2014;9(6):e99912 [FREE Full text] [CrossRef] [Medline]
  11. Zachariae R, Lyby MS, Ritterband LM, O'Toole MS. Efficacy of internet-delivered cognitive-behavioral therapy for insomnia - a systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev 2016;30:1-10. [CrossRef] [Medline]
  12. Brown M, Glendenning A, Hoon AE, John A. Effectiveness of web-delivered acceptance and commitment therapy in relation to mental health and well-being: a systematic review and meta-analysis. J Med Internet Res 2016;18(8):e221 [FREE Full text] [CrossRef] [Medline]
  13. Spijkerman MP, Pots WT, Bohlmeijer ET. Effectiveness of online mindfulness-based interventions in improving mental health: a review and meta-analysis of randomised controlled trials. Clin Psychol Rev 2016;45:102-114 [FREE Full text] [CrossRef] [Medline]
  14. Andersson G, Cuijpers P, Carlbring P, Riper H, Hedman E. Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry 2014;13(3):288-295 [FREE Full text] [CrossRef] [Medline]
  15. Ruzek J, Hoffman J, Ciulla R, Prins A, Kuhn E, Gahm G. Bringing internet-based education and intervention into mental health practice: Eur J Psychotraumatol 2011;2(1):7278 [FREE Full text] [CrossRef] [Medline]
  16. Moore BA, Fazzino T, Garnet B, Cutter CJ, Barry DT. Computer-based interventions for drug use disorders: a systematic review. J Subst Abuse Treat 2011;40(3):215-223 [FREE Full text] [CrossRef] [Medline]
  17. Rodriguez-Paras C, Tippey K, Brown E, Sasangohar F, Creech S, Kum HC, et al. Posttraumatic stress disorder and mobile health: app investigation and scoping literature review. JMIR Mhealth Uhealth 2017;5(10):e156 [FREE Full text] [CrossRef] [Medline]
  18. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Cochrane Bias Methods Group, Cochrane Statistical Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928 [FREE Full text] [CrossRef] [Medline]
  19. Bedford LA, Dietch JR, Taylor DJ, Boals A, Zayfert C. Computer-guided problem-solving treatment for depression, PTSD, and insomnia symptoms in student veterans: a pilot randomized controlled trial. Behav Ther 2018;49(5):756-767. [CrossRef] [Medline]
  20. Cooper DB, Bowles AO, Kennedy JE, Curtiss G, French LM, Tate DF, et al. Cognitive rehabilitation for military service members with mild traumatic brain injury: a randomized clinical trial. J Head Trauma Rehabil 2017;32(3):E1-15. [CrossRef] [Medline]
  21. Timmons PL, Oehlert ME, Sumerall SW, Timmons CW, Borgers SB. Stress inoculation training for maladaptive anger: comparison of group counseling versus computer guidance. Comput Hum Behav 1997;13(1):51-64. [CrossRef]
  22. Cucciare MA, Combs AS, Joshi G, Han X, Humphreys K. Computer-delivered brief alcohol intervention for patients with liver disease: a randomized controlled trial. Addiction 2021;116(5):1076-1087. [CrossRef] [Medline]
  23. Short NA, Fuller K, Norr AM, Schmidt NB. Acceptability of a brief computerized intervention targeting anxiety sensitivity. Cogn Behav Ther 2017;46(3):250-264. [CrossRef] [Medline]
  24. Brief DJ, Rubin A, Keane TM, Enggasser JL, Roy M, Helmuth E, et al. Web intervention for OEF/OIF veterans with problem drinking and PTSD symptoms: a randomized clinical trial. J Consult Clin Psychol 2013;81(5):890-900. [CrossRef] [Medline]
  25. Calhoun PS, Datta S, Olsen M, Smith VA, Moore SD, Hair LP, et al. Comparative effectiveness of an internet-based smoking cessation intervention versus clinic-based specialty care for veterans. J Subst Abuse Treat 2016;69:19-27 [FREE Full text] [CrossRef] [Medline]
  26. Hobfoll SE, Blais RK, Stevens NR, Walt L, Gengler R. Vets prevail online intervention reduces PTSD and depression in veterans with mild-to-moderate symptoms. J Consult Clin Psychol 2016;84(1):31-42. [CrossRef] [Medline]
  27. Kahn JR, Collinge W, Soltysik R. Post-9/11 veterans and their partners improve mental health outcomes with a self-directed mobile and web-based wellness training program: a randomized controlled trial. J Med Internet Res 2016;18(9):e255 [FREE Full text] [CrossRef] [Medline]
  28. Litz BT, Engel CC, Bryant RA, Papa A. A randomized, controlled proof-of-concept trial of an internet-based, therapist-assisted self-management treatment for posttraumatic stress disorder. Am J Psychiatry 2007;164(11):1676-1683. [CrossRef] [Medline]
  29. Pemberton MR, Williams J, Herman-Stahl M, Calvin SL, Bradshaw MR, Bray RM, et al. Evaluation of two web-based alcohol interventions in the U.S. military. J Stud Alcohol Drugs 2011;72(3):480-489. [CrossRef] [Medline]
  30. Pfeiffer PN, Pope B, Houck M, Benn-Burton W, Zivin K, Ganoczy D, et al. Effectiveness of peer-supported computer-based CBT for depression among veterans in primary care. Psychiatr Serv 2020;71(3):256-262. [CrossRef] [Medline]
  31. Taylor D, Peterson AL, Pruiksma KE, Young-McCaughan S, Nicholson K, Mintz J, STRONG STAR Consortium. Internet and in-person cognitive behavioral therapy for insomnia in military personnel: a randomized clinical trial. Sleep 2017;40(6):zsx075. [CrossRef] [Medline]
  32. McLean CP, Foa EB, Dondanville KA, Haddock CK, Miller ML, Rauch SA, et al. The effects of web-prolonged exposure among military personnel and veterans with posttraumatic stress disorder. Psychol Trauma 2021;13(6):621-631. [CrossRef] [Medline]
  33. Pedersen ER, Parast L, Marshall GN, Schell TL, Neighbors C. A randomized controlled trial of a web-based, personalized normative feedback alcohol intervention for young-adult veterans. J Consult Clin Psychol 2017;85(5):459-470 [FREE Full text] [CrossRef] [Medline]
  34. Clausen AN, Thelen J, Francisco AJ, Bruce J, Martin L, McDowd J, et al. Computer-based executive function training for combat veterans with PTSD: a pilot clinical trial assessing feasibility and predictors of dropout. Front Psychiatry 2019;10:62 [FREE Full text] [CrossRef] [Medline]
  35. Acosta MC, Possemato K, Maisto SA, Marsch LA, Barrie K, Lantinga L, et al. Web-delivered CBT reduces heavy drinking in OEF-OIF veterans in primary care with symptomatic substance use and PTSD. Behav Ther 2017;48(2):262-276 [FREE Full text] [CrossRef] [Medline]
  36. Engel CC, Litz B, Magruder KM, Harper E, Gore K, Stein N, et al. Delivery of self training and education for stressful situations (DESTRESS-PC): a randomized trial of nurse assisted online self-management for PTSD in primary care. Gen Hosp Psychiatry 2015;37(4):323-328 [FREE Full text] [CrossRef] [Medline]
  37. Larsen SE, Lotfi S, Bennett KP, Larson CL, Dean-Bernhoft C, Lee HJ. A pilot randomized trial of a dual n-back emotional working memory training program for veterans with elevated PTSD symptoms. Psychiatry Res 2019;275:261-268 [FREE Full text] [CrossRef] [Medline]
  38. Possemato K, Ouimette P, Knowlton P. A brief self-guided telehealth intervention for post-traumatic stress disorder in combat veterans: a pilot study. J Telemed Telecare 2011;17(5):245-250. [CrossRef] [Medline]
  39. Sayer NA, Noorbaloochi S, Frazier PA, Pennebaker JW, Orazem RJ, Schnurr PP, et al. Randomized controlled trial of online expressive writing to address readjustment difficulties among U.S. Afghanistan and Iraq war veterans. J Trauma Stress 2015;28(5):381-390. [CrossRef] [Medline]
  40. Krupnick JL, Green BL, Amdur R, Alaoui A, Belouali A, Roberge E, et al. An internet-based writing intervention for PTSD in veterans: a feasibility and pilot effectiveness trial. Psychol Trauma 2017;9(4):461-470. [CrossRef] [Medline]
  41. Georgia Salivar E, Knopp K, Roddy MK, Morland LA, Doss BD. Effectiveness of online OurRelationship and ePREP programs for low-income military couples. J Consult Clin Psychol 2020;88(10):899-906. [CrossRef] [Medline]
  42. McLean CP, Miller ML, Gengler R, Henderson J, Sloan DM. The efficacy of written exposure therapy versus imaginal exposure delivered online for posttraumatic stress disorder: design of a randomized controlled trial in veterans. Contemp Clin Trials 2020;91:105990. [CrossRef] [Medline]
  43. Institute of Medicine. Treatment of PTSD: an assessment of the evidence. Washington, D.C: National Academies Press; 2008.
  44. Berge EE, Hagen R, Øveraas Halvorsen J. PTSD relapse in veterans of Iraq and Afghanistan: a systematic review. Mil Psychol 2020;32(4):300-312. [CrossRef]
  45. McDonald A, Eccles JA, Fallahkhair S, Critchley HD. Online psychotherapy: trailblazing digital healthcare. BJPsych Bull 2020;44(2):60-66 [FREE Full text] [CrossRef] [Medline]
  46. Goetter EM, Bui E, Ojserkis RA, Zakarian RJ, Brendel RW, Simon NM. A systematic review of dropout from psychotherapy for posttraumatic stress disorder among Iraq and Afghanistan combat veterans. J Trauma Stress 2015;28(5):401-409. [CrossRef] [Medline]
  47. Andersson G. Internet-delivered psychological treatments. Annu Rev Clin Psychol 2016;12:157-179. [CrossRef] [Medline]
  48. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res 2012;14(6):e152 [FREE Full text] [CrossRef] [Medline]
  49. Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev 2012;32(4):329-342. [CrossRef] [Medline]
  50. Schleider JL, Weisz JR. Little treatments, promising effects? Meta-analysis of single-session interventions for youth psychiatric problems. J Am Acad Child Adolesc Psychiatry 2017;56(2):107-115. [CrossRef] [Medline]
  51. Tanner-Smith EE, Lipsey MW. Brief alcohol interventions for adolescents and young adults: a systematic review and meta-analysis. J Subst Abuse Treat 2015;51:1-18 [FREE Full text] [CrossRef] [Medline]
  52. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res 2011;13(3):e52 [FREE Full text] [CrossRef] [Medline]
  53. Manwaring JL, Bryson SW, Goldschmidt AB, Winzelberg AJ, Luce KH, Cunning D, et al. Do adherence variables predict outcome in an online program for the prevention of eating disorders? J Consult Clin Psychol 2008;76(2):341-346. [CrossRef] [Medline]
  54. Creech SK, Pulverman CS, Shin ME, Roe KT, Tzilos Wernette G, Orchowski LM, et al. An open trial to test participant satisfaction with and feasibility of a computerized intervention for women veterans with sexual trauma histories seeking primary care treatment. Violence Against Women 2021;27(3-4):597-614. [CrossRef] [Medline]
  55. Schvey NA, Klein DA, Pearlman AT, Kraff RI, Riggs DS. Stigma, health, and psychosocial functioning among transgender active duty service members in the U.S. military. Stigma Health 2020;5(2):188-198 [FREE Full text] [CrossRef]
  56. Morris EE, Smith JC, Farooqui SY, Surís AM. Unseen battles: the recognition, assessment, and treatment issues of men with military sexual trauma (MST). Trauma Violence Abuse 2014;15(2):94-101. [CrossRef] [Medline]
  57. Amaral EF, Pollard MS, Mendelsohn J, Cefalu M. Current and future demographics of the veteran population, 2014–2024. Popul Rev 2018;57(1):28-60. [CrossRef]
  58. Schueller SM, Hunter JF, Figueroa C, Aguilera A. Use of digital mental health for marginalized and underserved populations. Curr Treat Options Psych 2019;6(3):243-255. [CrossRef]
  59. Pearson R, Pisner D, Meyer B, Shumake J, Beevers CG. A machine learning ensemble to predict treatment outcomes following an internet intervention for depression. Psychol Med 2019;49(14):2330-2341 [FREE Full text] [CrossRef] [Medline]
  60. Frankfurt S, Frazier P, Litz BT, Schnurr PP, Orazem RJ, Gravely A, et al. Online expressive writing intervention for reintegration difficulties among veterans: who is most likely to benefit? Psychol Trauma 2019;11(8):861-868. [CrossRef] [Medline]
  61. Spence J, Titov N, Johnston L, Jones MP, Dear BF, Solley K. Internet-based trauma-focused cognitive behavioural therapy for PTSD with and without exposure components: a randomised controlled trial. J Affect Disord 2014;162:73-80. [CrossRef] [Medline]
  62. Buhrman M, Fredriksson A, Edström G, Shafiei D, Tärnqvist C, Ljótsson B, et al. Guided internet-delivered cognitive behavioural therapy for chronic pain patients who have residual symptoms after rehabilitation treatment: randomized controlled trial. Eur J Pain 2013;17(5):753-765. [CrossRef] [Medline]
  63. Foroushani PS, Schneider J, Assareh N. Meta-review of the effectiveness of computerised CBT in treating depression. BMC Psychiatry 2011;11:131 [FREE Full text] [CrossRef] [Medline]
  64. Sierra MA, Ruiz FJ, Flórez CL. A systematic review and meta-analysis of third-wave online interventions for depression. Rev Latinoam Psicol 2018;50(2):126-135. [CrossRef]

CBT: cognitive behavioral therapy
EBT: evidence-based treatment
ITT: intention to treat
PTSD: posttraumatic stress disorder
RCT: randomized controlled trial
SUD: substance use disorder
VA: Veterans Affairs

Edited by A Mavragani; submitted 03.05.21; peer-reviewed by A Raines, F Beyer, MDG Pimentel; comments to author 30.07.21; revised version received 07.10.21; accepted 20.12.21; published 03.06.22


©Rahel Pearson, Emily Carl, Suzannah K Creech. Originally published in the Journal of Medical Internet Research (, 03.06.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.