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Published on in Vol 18, No 3 (2016): March

An Online Intervention for Co-Occurring Depression and Problematic Alcohol Use in Young People: Primary Outcomes From a Randomized Controlled Trial

An Online Intervention for Co-Occurring Depression and Problematic Alcohol Use in Young People: Primary Outcomes From a Randomized Controlled Trial

An Online Intervention for Co-Occurring Depression and Problematic Alcohol Use in Young People: Primary Outcomes From a Randomized Controlled Trial

Authors of this article:

Mark Deady1 Author Orcid Image ;   Katherine L Mills1 Author Orcid Image ;   Maree Teesson1 Author Orcid Image ;   Frances Kay-Lambkin1, 2 Author Orcid Image
Article Authors Cited by (77) Tweetations (7) Metrics

    Original Paper

    1National Health and Medical Research Council Centre for Research Excellence in Mental Health and Substance Use, National Drug and Alcohol Research Centre, Kensington, Australia

    2The University of Newcastle, Priority Research Centre for Translational Neuroscience and Mental Health, Callaghan, Australia

    Corresponding Author:

    Mark Deady, PhD

    National Health and Medical Research Council Centre for Research Excellence in Mental Health and Substance Use

    National Drug and Alcohol Research Centre

    University of New South Wales

    Kensington, 2052

    Australia

    Phone: 61 293850320

    Fax:61 293850222

    Email: m.deady@unsw.edu.au


    Background: Depression and problematic alcohol use represent two of the major causes of disease burden in young adults. These conditions frequently co-occur and this is associated with increased harm and poorer outcomes than either disorder in isolation. Integrated treatments have been shown to be effective; however, there remains a significant gap between those in need of treatment and those receiving it. The increased availability of eHealth programs presents a unique opportunity to treat these conditions.

    Objective: This study aimed to evaluate the feasibility and preliminary efficacy of an automated Web-based self-help intervention (DEAL Project) in treating co-occurring depressive symptoms and problematic alcohol use in young people.

    Methods: Young people (aged 18 to 25 years) with moderate depression symptoms and drinking at hazardous levels (recruited largely via social media) were randomly allocated to the DEAL Project (n=60) or a Web-based attention-control condition (HealthWatch; n=44). The trial consisted of a 4-week intervention phase with follow-up assessment at posttreatment and at 3 and 6 months postbaseline. The primary outcomes were change in depression severity according to the Patient Health Questionnaire-9 as well as quantity and frequency of alcohol use (TOT-AL).

    Results: The DEAL Project was associated with statistically significant improvement in depression symptom severity (d=0.71) and reductions in alcohol use quantity (d=0.99) and frequency (d=0.76) in the short term compared to the control group. At 6-month follow-up, the improvements in the intervention group were maintained; however, the differences between the intervention and control groups were no longer statistically significant, such that between-group effects were in the small to moderate range at 6 months (depression symptoms: d=0.39; alcohol quantity: d=–0.09; alcohol frequency: d=0.24).

    Conclusions: Overall, the DEAL Project was associated with more rapid improvement in both depression symptoms and alcohol use outcomes in young people with these co-occurring conditions relative to an attention-control condition. However, long-term outcomes are less clear.

    Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12613000033741; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363461 (Archived by WebCite at http://www.webcitation.org/6fpsLEGOy)

    J Med Internet Res 2016;18(3):e71

    doi:10.2196/jmir.5178

    Keywords

    depression; alcohol use, alcohol abuse, problem drinking; young people at risk populations; Internet; intervention online therapy, eHealth; comorbidity 


    Major depressive disorder and alcohol use disorders are two of the top five leading causes of years of life lost to disability in the developed world [1] with young people bearing a disproportionately large share of the burden [2]. Early intervention is imperative to averting the development of more severe, ingrained morbidity [3], yet less than 25% of affected young Australians access traditional health services in a 12-month period [4]. Comorbidity across the disorder classes is common [5] and is associated with considerable adverse outcomes [6,7]. Furthermore, young people with mental health issues rate “‘coping” as a key motive for substance use [8] with comorbid disorders often maintaining and exacerbating one another [9]. Thus, there is increasing support for integrated approaches to comorbidity treatment [10,11]. Baker and colleagues [12] have demonstrated that concurrent treatment of depression and problem drinking is more effective than treating either condition alone and more effective than general counseling.

    The advantages of Web-based interventions, including flexibility, anonymity, and accessibility, appear particularly useful for individuals who are less likely to access traditional services, such as young people [13]. Web-based depression and alcohol interventions have been shown to produce effect sizes equivalent to those of traditional face-to-face therapy (0.28-0.78, 0.22-0.48) [13-21], although it has been argued that this is dependent on therapist guidance; generally, interventions with little or no therapist guidance have significantly smaller treatment effect sizes [22]. Nevertheless, guided interventions are not as cost-effective to disseminate, perhaps limiting their ability to overcome traditional barriers to treatment access, particularly among young people [23]. So far, there are no youth-focused Web-based interventions reported in the research literature for individuals experiencing both depressive and alcohol problems. Furthermore, in the general population only one computerized intervention targeting both of these disorders has been evaluated: Self-Help for Alcohol/other drug use and Depression (SHADE) [24,25]. Evaluations of SHADE indicate electronic forms of treatment for co-occurring disorders are viable and effective, especially when combined with brief therapist guidance. Two randomized controlled trials (RCTs) have found SHADE plus therapist guidance to be associated with equivalent outcomes to those achieved by therapist-delivered treatment, with superior results as far as reducing alcohol consumption over 3 and 12 months [24,25]. The only other study of this kind examined the use of a single session of online personalized feedback and psychoeducation provided to college students; as such, it was not specifically a youth-focused intervention [26]. The study compared alcohol feedback only, depressed mood feedback only, integrated feedback, and an assessment-only condition. At 1-month follow-up, no differences in depressed mood or alcohol use were found across the conditions; however, moderator effects were present, with the interventions being more effective than controls for those with less severe baseline symptoms.

    In response to this gap in evidence-based programs for depressive and alcohol problems among young people, we developed the DEpression-ALcohol (DEAL) Project, a brief, Web-based intervention for young people aged 18 to 25 years based on the SHADE program. Because the program is a self-help intervention, it is primarily aimed at those with moderate symptomatology who may not reach diagnostic cut-offs for disorders but are, nonetheless, experiencing distress and would benefit from early intervention. Such conditions have been associated with substantial impairment [27-29], particularly in young people [30]; these conditions have been shown to escalate into full alcohol use disorder in 17.0% to 38.2% of cases within 5 years [31-33] and full major depressive disorder in 10% to 25% of patients with minor depression within 3 years [27,34].

    The primary aim of this study was to evaluate the feasibility and preliminary efficacy of the DEAL Project and compare outcomes relative to an attention-control condition (HealthWatch) in a RCT. Specifically, this study aimed to determine whether (1) the DEAL Project produces significantly greater pre- to posttreatment reductions in severity of depression symptoms as well as quantity and frequency of alcohol use relative to HealthWatch and (2) changes observed from pre- to posttreatment are maintained through to 6 months postbaseline.


    Study Design

    The study design and flow of participants is shown in Figure 1. Ethical approval was obtained from the University of New South Wales Human Research Ethics Committee and consent was obtained electronically from all participants. The study was conducted entirely online with all contact occurring via automated emails. Following online screening, eligible participants were asked to provide informed consent in order to take part and were randomized to one of two conditions delivered over four weekly sessions: (1) the DEAL Project or (2) HealthWatch. Access to the website for each of the programs was for a period of 10 weeks from the point of randomization. Randomization was automated within the online program; therefore, the trial researcher was blind to randomization. This process occurred immediately after the eligibility screener, consent form, and baseline assessment were completed. Block randomization was conducted with a 1:1 ratio; however, due to a programming error (which included test users within the blocks), a group imbalance occurred resulting in 60 participants being randomized to the DEAL Project and 44 randomized to HealthWatch. Participants then completed a baseline assessment on entry to the study and follow-up assessments at posttreatment (5 weeks), and at 3 and 6 months postbaseline.

    ‎
    Figure 1. Flow of participants through study.
    View this figure

    Recruitment

    Recruitment took place between July 2013 and February 2014 using extensive media coverage across Australia, including tertiary institution flyers and street press, radio and newspaper stories, treatment services websites, and paid Facebook and Google advertisements. Individuals were eligible for the study if they (1) were aged 18 to 25 years, (2) reported current moderate depression symptomology (score of ≥7 on the Depression Anxiety Stress Scale [DASS-21-Depression]) [35], (3) were drinking at hazardous levels as measured by the Alcohol Use Disorders Identification Test (AUDIT; score of ≥8) [36], (4) had the ability to access the Internet (either in the private residence of the participant or willingness to use the public library/other suitable venue with Internet access), and (5) were Australian residents. Exclusion criteria included (1) a Psychosis Screener score ≥3 [37], (2) an inability to speak English, (3) serious risk of suicide in the past 2 weeks (serious thoughts of suicide and desire to act), and (4) daily use of cannabis/weekly use of amphetamines.

    Interventions

    The DEAL Project

    The DEAL Project [38] consists of four 1-hour modules to be completed over a 4-week period (homework is provided at the conclusion of each module and reviewed at the beginning of the subsequent module). The website tracked participants’ completion of each weekly module, with automated email reminders sent to participants’ nominated email addresses. As mentioned, the program is based on the SHADE program, which consists of evidence-based cognitive behavioral therapy and motivational interviewing. The modules were as follows:

    1. Week 1: Where Are You At? Psychoeducation, assessment, goal setting, mood/activity/alcohol use monitoring (homework: mood/drinking monitoring).
    2. Week 2: Getting Moving Again. Behavioral activation, decisional balance (alcohol use), behavior change, activity scheduling (homework: activity planning).
    3. Week 3: Taking Charge of your Thoughts. Mood monitoring, cognitive restructuring (homework: thought monitoring).
    4. Week 4: Coping with Tough Situations. Mindfulness and relaxation, problem solving, drink reduction and refusal, relapse planning and management (mindfulness/relaxation, problem-solving exercises).
    HealthWatch

    HealthWatch is a 12-module attention-control condition first developed for the Australian National University WellBeing Study [39] in which participants read information about various health concerns and complete accompanying surveys. The purpose of this condition was to control for time spent interacting with an online program. Four modules deemed to be most relevant to younger people were selected to act as the attention control in this study: environmental health, physical and mental activity, nutrition, and relationships. These were redesigned to match the DEAL Project in appearance.

    Assessment and Outcome Measures

    A structured online assessment was conducted at each of the four assessment time points. Primary outcomes were (1) depressed mood as measured by the Patient Health Questionnaire-9 (PHQ-9) [40] and (2) quantity and frequency of alcohol use as measured by the TOT-AL [41]. The PHQ-9 is a reliable and valid brief measure of depression severity over the past 2 weeks [42]. The self-report measure utilizes a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). Total scores range from 0 to 27. Scores ≤4 are considered to represent minimal depression, scores 5-9 are considered mild, 10-14 are considered moderate, 16-19 are considered moderately severe, and scores ≥20 are considered indicative of severe depression. The TOT-AL has been found to be a reliable and valid online measure of alcohol consumption [41]. The tool uses a dropdown menu of type, brand, and size of beverages consumed each day for the past week. The TOT-AL calculates the cumulative unit content of the drinks consumed over the previous 7 days (1 unit=approximately 8 g ethanol) to generate an overall consumption score (drinks per week) and frequency score (drinking days per week).

    Automatic email prompts to complete online follow-up assessments were sent to participants at baseline, posttreatment (5 weeks), and 3 and 6 months postbaseline. Reminder emails were sent if the participant did not complete an assessment within 6 days (three emails per assessment in total). On completion of assessments, participants received an AU $10 iTunes voucher as reimbursement for time.

    Data Analysis

    Sample Size Calculation

    Power analysis on the primary outcome variables was conducted using Power Analysis and Sample Size Software (PASS) [43]. Taking into account sample attrition, the sample size had 92.9% power to detect a 5-point time-averaged difference between groups (SD 6.80) on the PHQ-9 at α<.05 (large effect). There was 75.7% power to detect a time-averaged difference between groups of three drinks per drinking day (SD 5.89) at α<.05. A 5-point difference on the PHQ-9 was chosen because this was considered to be clinically significant [44]. No clinical indicators were available for the TOT-AL, but three drinks per drinking day was considered substantial enough to be significant at a public health level.

    Statistical Analysis

    Data were analyzed using PASW Statistics 18 for Windows (release 18.0.0) [45]. Baseline differences between groups were examined using chi-square (using Yates continuity correction in 2×2 tables to avoid overestimation) and independent-samples t tests.

    Analysis of outcomes was undertaken based on intention to treat using a series of generalized estimating equations (GEE) [46]. Baseline scores were modeled and controlled for in the GEE analysis. Linear and negative binomial (with log link) GEE were used to examine outcomes with normal and count distributions, respectively. An exchangeable correlation structure was used for all models. An interaction term for the intervention variable and time (group × time) was included in each model to test for differential change over time. When this interaction was nonsignificant (P≥.05), it was removed from the model and the analysis was rerun. Alpha was set at .05 and a least significant difference method was used for pairwise comparisons. Results are reported as the unstandardized mean difference (beta) with Wald-type 95% confidence interval (95% CI) and chi-square for linear models and the risk ratio (RR) with 95% CI for negative binomial models. Furthermore, standardized effect sizes (Cohen’s d) were calculated for the primary outcomes [47].

    Sample Retention and Missing Data

    The follow-up rates at posttreatment and at 3 and 6 months postbaseline were 53.8% (56/104), 43.3% (45/104), and 38.5% (40/104), respectively. Those who completed follow-up assessments were consistently more likely to have completed a session (χ21=15.3, P<.001), seen a psychologist in past year at baseline (χ21=5.3, P=.02), and were less likely to be a smoker (χ21=5.1, P=.02). Importantly, attrition was not related to treatment allocation or any of the outcome variables of interest.

    Missing data analysis revealed 35.79% (2866/8008) missing data across all follow-up assessments. According to the results of Little’s missing completely at random test [48], the data could be considered to be missing completely at random (χ2711=652.5, P=.94).


    Participant Characteristics

    The sample consisted of 104 participants (female: 59.6%, 62/104) with a mean age of 21.74 (SD 2.22) years. The mean PHQ-9 score was 16.32 (SD 5.00) and the mean AUDIT score was 17.39 (SD 6.42). Drinking quantities and frequencies were positively skewed with a median of 15.20 standard alcoholic drinks per week (range 0-97) consumed over a median of 2.00 drinking days per week (range 0-7). There were no statistically significant differences between the groups on baseline characteristics (Table 1).

    Table 1. Participant characteristics by group.
    DemographicsDEAL Project treatment (n=60)HealthWatch control (n=44)Total (N=104)t102χ2 (df)P
    Age (years), mean (SD)21.85 (2.32)21.59 (2.08)21.74 (2.22)0.59
    		.56
    Sex (female), n (%)36 (60)26 (59)62 (60)
    		0.0 (1)>.99
    Rural, n (%)5 (8)8 (18)13 (13)
    		1.4 (1).23
    Completed secondary school, n (%)52 (87)33 (75)85 (82)
    		0.2 (1).21
    Postschool qualifications, n (%)



    		3.7 (2).15

    		No postschool11 (18)15 (34)26 (25.0)



    		TAFE student/graduate12 (20)9 (20)21 (20)



    		University student/graduate37 (68)20 (45)57 (55)


    Employment, n (%)



    		1.8 (2).40

    		Unemployed7 (12)4 (9)11 (11)



    		Full/part time employment42 (70)27 (61)69 (66)



    		Student11 (18)13 (30)24 (23)


    Mental health treatment in past year, n (%)






    		GP33 (55)28 (64)61 (59)
    		0.47 (1).49

    		Psychologist19 (32)14 (32)33 (32)
    		3.19 (1).07

    		Psychiatrist21 (35)24 (55)45 (43)
    		0.00 (1)>.99

    		Other mental health worker5 (8)10 (23)15 (14)
    		3.17 (1).08

    		Alcohol/drug worker1 (2)5 (11)6 (6)
    		2.79 (1).09

    		Medication, n (%)17 (28)14 (31.8)31 (30)
    		0.03 (1).87
    Doubtful about computer therapy, n (%)29 (48)22 (50)51 (49)
    		0.00 (1)>.99
    Borderline symptoms (MSI-BPD), mean (SD)5.80 (2.41)6.50 (2.02)6.10 (2.27)0.59
    		.56
    Depression






    		Likely MDD diagnosis (PHQ-9), n (%)35 (58)21 (48)56 (54)
    		0.76 (1).38

    		Likely other depressive diagnosis, n (%)8 (13)5 (11)13 (13)
    		0.00 (1)>.99
    Alcohol in past 12 months






    		≥1 alcohol abuse criteria met, n (%)39 (65)29 (66)68 (66)
    		0.00 (1)>.99

    		≥3 alcohol dependence criteria met, n (%)50 (83)35 (80)85 (82)
    		0.06 (1).81
    AUDIT, mean (SD)17.02 (6.19)17.89 (6.76)17.38 (6.42)–0.68
    		.50
    Smoker, n (%)17 (28)17 (39)34 (33)
    		0.80 (1).37
    Drug use in past month, n (%)






    		Cannabis16 (27)9 (20)25 (24)
    		0.25 (1).62

    		Stimulants7 (12)7 (16)14 (13)
    		0.11 (1).74

    		Other illicit drugs3 (5)2 (5)5 (5)
    		0.00 (1)>.99
    Treatment Retention

    Compared to those in the control group, those in the treatment group attended fewer sessions (t102=–3.14, P=.002). The treatment group completed a mean of 1.50 sessions (SD 1.53), whereas the control group fully completed mean 2.50 sessions (SD 1.69). Overall, 68.3% (71/104) of the sample completed at least one module (treatment: 60.0%, 36/60; control: 79.5%, 21/44). This figure did not statistically differ significantly between groups (χ21=3.4, P=.07). In both groups, missing data at follow-up was associated with fewer modules completed (posttreatment: χ24=263.7, P<.001; 3 month: χ24=82.5, P<.001; 6 month: χ24=102.6, P<.001).

    Service Use

    At each time point, participants reported their use of a range of services for mental health treatment (medication, psychologist, alcohol and drug worker, psychiatrist, general practitioner, other health professional). Although those in the control condition were consistently more likely to use services for mental health problems over the 6-month follow-up, there was no difference in service use over the course of the trial (χ21=3.6, P=.55).

    Treatment Outcomes

    Primary Depression Outcomes

    There were no statistically significant differences between the groups on PHQ-9 scores at baseline (see Table 2). There was a statistically significant group × time interaction in relation to depression symptom severity (χ23=11.5, P=.009), indicating that the treatment and control groups differed on PHQ-9 scores over time. As shown in Table 2, the treatment group demonstrated a statistically significant reduction in symptom severity from baseline to posttreatment follow-up (beta=–5.94, 95% CI –8.18 to –3.70; P<.001), representing a large effect (d=1.09). The change in control group PHQ-9 scores over this time was small (d=0.18) and not statistically significant (beta=–1.43, 95% CI –3.46 to 0.60; P=.17). Overall, the degree of improvement in depression symptom severity between baseline and posttreatment follow-up was 4.51 points greater in the treatment group compared to the control group and the treatment group reported significantly better depression scores relative to control at posttreatment follow-up (beta=–3.89, 95% CI –7.09 to –0.68; d=0.71).

    The reduction in severity of depression observed for the treatment group persisted from posttreatment to 3-month follow-up (beta=0.01, 95%CI –2.52 to 2.53; P <.99) and from the 3- to 6-month follow-ups (beta= –1.59, 95% CI –1.38 to 4.57; P=.29; i.e, no statistically significant change). The control group demonstrated a statistically significant reduction in depression symptoms between posttreatment and 3-month follow-up (beta = –2.78, 95% CI –5.33 to 0.23; P=.03) that persisted to the 6-month follow-up (beta= –0.61, 95% CI –2.83 to 1.60; P=.59).

    There was no statistically significant difference in depression scores between groups at either the 3- (beta =–1.10, 95% CI –5.10 to 2.90; P=.59; d=0.15) or 6-month follow-ups (beta=–2.08, 95% CI –6.45 to 2.29; P=.35; d=0.39). The within-group effect between baseline and 3-month follow-up for the treatment group was d=0.96 and d=0.67 for the control group. The within-group effect between baseline and 6-month follow-up was d=1.42 for the treatment group and d=0.78 for the control group.

    Primary Alcohol Outcomes
    Drinks Per Week

    There was no difference at baseline between the groups for alcohol use quantity as measured by the TOT-AL (see Table 3). There was a statistically significant group × time interaction in relation to number of standard drinks per week (χ23=9.3, P=.03). As shown in Table 3, the treatment group demonstrated a significant reduction in drinks per week from baseline to posttreatment follow-up (RR=0.46, 95% CI 0.32-0.65; P<.001) representing a large effect (d=1.07). The change in drinks per week in the control group over this time was small (d=0.03) and not statistically significant (RR=0.97, 95% CI 0.67-1.41; P=.88). Overall, the treatment group reported a two-fold greater reduction in standard drinks consumed per week between baseline and posttreatment follow-up compared to the control group (RR=2.13, 95% CI 1.28-3.54; P=.02). Consequently, the treatment group reported statistically significantly fewer drinks per week relative to control at posttreatment follow-up (RR=0.62, 95% CI 0.39-1.00; P=.05).

    Table 2. Unadjusted comparisons between conditions on Patient Health Questionnaire-9.
    Time pointDEAL ProjectHealthWatchBetween-group difference

    		Mean (95% CI)Change from t0 (95% CI)PMean (95% CI)Change from t0 (95% CI)PMean (95% CI)PChange from t0 (95% CI)P
    t016.58 (15.42, 17.75)—
    		15.95 (14.36, 17.54)—
    		0.63 (–1.34, 2.60)
    		—.53
    t110.64 (8.31, 12.97)–5.94 (–8.18, –3.70)<.00114.53 (12.33, 16.73)–1.43 (–3.46, 0.60).17–3.89 (–7.09, –0.68).024.51 (1.49, 7.54).003
    t210.65 (7.99, 13.31)–5.93 (–8.53, –3.37)<.00111.75 (8.76, 14.74)–4.21 (–7.27, –1.15).01–1.10 (–5.10, 2.90).59–1.73 (–5.74, 2.29).40
    t39.05 (6.21, 11.90)–7.53 (–10.51, –4.55)<.00111.14 (7.82, 14.45)–4.82 (–8.28, 1.36).01–2.08 (–6.45, 2.26).35–2.71 (–7.28, 1.86).24
    Table 3. Unadjusted comparisons between conditions on TOT-AL.
    Time pointDEAL ProjectHealthWatchBetween-group differences, RR (95% CI)

    		Mean (95% CI)RRa (95% CI)PMean (95% CI)RRa (95% CI)PAt each time pointPFrom t0P
    Drinks per week










    		t025.65 (19.52-33.71)—
    		19.43 (14.02-26.93)—
    		1.32 (1.16-2.02)
    		—

    		t111.72 (8.11-16.93)0.46 (0.32-0.65)<.00118.89 (14.00-25.52)0.97 (0.67-1.41).880.62 (0.39-1.00).052.13 (1.28-3.54).02

    		t29.79 (4.66-20.54)0.38 (0.19-0.76).00612.96 (7.65-21.96)0.67 (0.37-1.22).190.76 (0.30-1.88).551.75 (0.70-4.73).23

    		t315.81 (9.89-25.27)0.62 (0.41-0.93).0215.97 (9.87-25.84)0.82 (0.47-1.42).480.99 (0.51-1.94).981.33 (0.67-2.65).41
    Drinking days per week










    		t03.00 (2.49-3.60)—
    		2.64 (2.05-3.41)—
    		1.13 (0.83-1.55).43—

    		t11.56 (1.18-2.07)0.52 (0.41-0.67)<.0012.48 (1.89-3.25)0.93 (0.69-1.26).670.63 (0.43-0.93).021.79 (1.22-2.64).003

    		t21.59 (1.07-2.34)0.53 (0.37-0.76).0011.90 (1.15-3.13)0.72 (0.42-1.24).230.84 (0.44-1.58).581.35 (0.70-2.61).36

    		t32.07 (1.46-3.13)0.69 (0.50-0.96).032.67 (1.71-4.15)1.01 (0.64-1.59).970.78 (0.44-1.37).381.46 (0.83-2.55).19

    a From t0.

    The reduction observed for the treatment group persisted from posttreatment to 3-month follow-up (RR=0.84, 95% CI 0.46-1.51; P=.55). Between the 3- and 6-month follow-ups, the number of drinks per week in the treatment group increased (RR=1.62, 95% CI 0.96-1.90; P=.04); however, at 6-month follow-up the number of drinks per week was still significantly lower than baseline (RR=0.62, 95% CI 0.41-0.93; P=.02). No statistically significant change in the number of drinks per week was found for the control group between posttreatment and 3-month follow-up (RR=0.69, 95% CI 0.44-1.07; P=.10) or between the 3- and 6-month follow-ups (RR=1.23, 95% CI 0.61-2.50; P=.56). Similarly, compared to baseline, the number of drinks per week in the control group was no different at the 3- (RR=0.67, 95% CI 0.37-1.22; P=.19) or 6-month follow-ups (RR=0.82, 95% CI 0.47-1.42; P=.48).

    There was no statistically significant difference in the number of drinks per week between the treatment and control groups at either the 3- (RR=0.76, 95% CI 0.30-1.88; d=0.13; P=.55) or 6-month follow-ups (RR=0.99, 95% CI 0.51-1.94; d =–0.09; P=.99). The within-group effect between baseline and 3-month follow-up for the treatment group was d=0.76 and d=0.54 for the control group. The within-group effect between baseline and 6-month follow-up was d=0.38 for the treatment group and d=0.24 for the control group.

    Drinking Days Per Week

    There were no statistically significant differences between the groups for alcohol use frequency as measured by the TOT-AL (see Table 3). There was a statistically significant group × time interaction in relation to number of drinking days per week (χ23=9.6, P=.02). As shown in Table 3, the treatment group demonstrated a statistically significant reduction in drinking days per week from baseline to posttreatment follow-up (RR=0.52, 95% CI 0.41-0.67; P<.001), representing a large effect (d=1.06). The change in weekly drinking days in the control group over this time was small (d=0.10) and not statistically significant (RR=0.93, 95% CI 0.69-1.26; P=.67). Compared to the control group, the treatment group reported a 79% greater reduction in drinking days (RR=1.79, 95% CI 1.22-2.64; P=.003). The treatment group also reported significantly fewer drinking days per week relative to control at posttreatment follow-up (RR=0.63, 95% CI 0.43-0.93; d=0.76; P=.02).

    The reduction observed for the treatment group persisted from posttreatment to 3-month follow-up (RR=1.01, 95% CI 0.70-1.47; P=.94) and from 3- to 6-month follow-ups (RR=1.31, 95% CI 0.93-1.84). No statistically significant change was observed for the number of drinking days per week in the control group between posttreatment and 3-month follow-up (RR=0.77, 95% CI 0.49-1.20; P=.25) or between 3- and 6-month follow-ups (RR=0.71, 95% CI 0.32-1.57; P=.40). Similarly, compared to baseline, the number of drinking days per week in the control group was no different at 3- (RR=0.72, 95% CI 0.42-1.23; P=.23) or 6-month follow-ups (RR=1.01, 95% CI 0.64-1.59; P=.97).

    There was no statistically significant difference in the number of drinking days per week between groups at either the 3- (RR=0.84, 95% CI 0.44-1.58; P=.58; d=0.22) or 6-month follow-ups (RR=0.78, 95% CI 0.44-1.37; P=.38; d=0.24). The within-group effect between baseline and 3-month follow-up for the treatment group was d=0.89 and d=0.45 for the control group. The within-group effect between baseline and 6-month follow-up was d=0.42 for the treatment group and d=0.04 for the control group.

    Discussion

    This RCT evaluated the feasibility and preliminary efficacy of the DEAL Project, a Web-based program that aims to reduce depression and alcohol use in 18- to 25-year-olds. The program demonstrated statistically significant greater reductions in depression and alcohol use compared to a control group at posttreatment. Furthermore, the positive outcomes observed among those randomized to the DEAL Project were maintained at 3- and 6-month follow-ups. However, between-group differences at these later time points disappeared because of statistically nonsignificant shifts in both control and treatment groups. There is evidence to suggest that in brief intervention trials, assessment alone may result in improved outcomes either as a consequence of assessment on subsequent self-report (known as the Hawthorne effect) [49] or as a catalyst to mobilize individuals into actual behavioral change [25]. As such, there is the potential that those in the HealthWatch condition may have derived benefit from not only the assessments, but also the thought involved in completing the surveys. Alternately, because this was a sample that sought out this treatment, the control condition may have been intensive enough for some change to be observed when combined with participant motivation for change. Participants may have also accessed other treatments. However, this was not borne out in our data on service utilization over the follow-up period. Treatment deterioration effects may also have led to this lack of between-group differences at 6 months. Finally, natural recovery cannot be disregarded as an alternative explanation for disappearance of differences between the two groups. Nevertheless, it would appear that the DEAL Project was associated with more rapid improvement in depression and alcohol outcomes compared with control.

    Overall, mean PHQ-9 depression scores in the treatment group dropped from the “moderately severe” range to just outside the range for “mild depression” at 6 months. This was a clinically significant change [44]. At baseline, the treatment group were drinking, on average, 3 days per week and consuming more than 25 drinks per week. At 3-month follow-up, drinking occasions had halved and participants were drinking just over nine drinks per week. Although this reduced figure is still considered above the recommended range for short-term harm [50], it has potentially large public health implications (especially considering the automated nature of the program), including a reduction in risk of harm to the individual and the societal costs associated with heavy alcohol use, including violence, hospital and emergency department visits, road safety and drunk driving, and lost productivity [51]. The posttreatment effect sizes observed in this study were considerably stronger than previous research; however, at 3- and 6-month follow-ups, effect sizes looked similar to the small and moderate effects found in previous Internet-based trials for single disorder interventions [14,16,19,52,53]. Similarly, the overall effects of the DEAL Project program at 3- and 6-month follow-ups were similar to those of the SHADE program trial [24]. The DEAL Project is briefer, unguided, and delivered completely online, which is likely to increase cost-effectiveness and accessibility for youth.

    Despite these various strengths, this study is not without its limitations. Although there was significant interest at the recruitment level (with more than 900 individuals beginning—and 839 completing—the screener over a 6-month period), the recruitment rate was low. However, this was unsurprising considering this was an opportunistic sample (not treatment-seeking). Consequently, on average, individuals randomized to the DEAL Project fully completed less than half of the four sessions offered to them. Of those who completed one session, approximately one-third went on to fully complete the program. This issue with adherence is unsurprising given the unguided nature of the program [54,55]. Despite considerable efforts in the program development stage to optimize adherence [56], this raises some questions around program acceptability and feasibility. Interestingly, the mean number of DEAL Project modules completed was equivalent to the number of modules completed by the younger participants in the previous SHADE trial [57], suggesting that adherence may be a generalized issue for this demographic. The DEAL Project’s brief structure allows for exposure to more key strategies before dropout. Although only one-third of the treatment group completed at least half of the program, module completion refers to full completion and does not account for participants sampling from different modules in a nonlinear way. Furthermore, the reasons for selective attrition are difficult to interpret because they may reflect the contradictory possibilities of dropout due to dissatisfaction versus dropout due to a sense that the individual feels their needs have been met [58]. Further research is needed to examine methods to improve engagement and retention within online programs. Nevertheless, despite the lack of adherence the positive results are encouraging for the utility of brief interventions. Similarly, although not dissimilar to previous studies without therapist/administrative guidance of participants [59,60], the follow-up rates were low. This may limit the generalizability and conclusions that may be drawn from this study. Telephone or face-to-face contact during online trials has been shown to increase adherence; however, this reduces real-world applicability [61]. Nevertheless, the GEE analysis used is robust to this level of loss to follow-up [62] and missingness was found to be at random.

    Due to difficulties diagnosing disorders online, the sample was nondiagnostic. Nevertheless, the mean baseline PHQ-9 score was in the moderately severe range and the mean baseline AUDIT score indicated high-risk/harmful levels of use. According to the PHQ-9, more than half the sample had a likely depression diagnosis, whereas two-thirds endorsed at least one alcohol abuse diagnostic criterion and three-quarters endorsed more than two dependence diagnostic criteria. Thus, this was not a clinically insignificant sample. Furthermore, subthreshold conditions have been associated with comparable negative outcomes to full-disorder syndromes, especially in younger populations [63,64], and provide an opportunity for early intervention.

    Additionally, this Australian sample may not generalize internationally. The programming error in randomization is a possible study limitation. Nevertheless, few differences were found between the groups at baseline. Finally, as with any study of this kind, there is the potential for self-report bias; evidence suggests self-report provides useful and accurate estimates when conditions are designed to maximize response accuracy [65]. Studies have shown that self-reports of alcohol use correlate with behavioral observations [66]. Furthermore, the anonymity provided by online assessment is likely to be more accurate than other forms of self-report [67].

    Overall, the DEAL Project was associated with significant improvements in both depression symptoms and alcohol use among young people with these co-occurring conditions relative to control at posttreatment. However, although within-group improvements were maintained over the 6-month follow-up period, the significant between-group differences were no longer present at long-term follow-up. This study adds useful evidence to both the eHealth and comorbidity treatment fields. Further studies are required to better understand these long-term outcomes and address the program adherence and trial attrition issues that were present in this study.

    Acknowledgments

    The National Drug and Alcohol Research Centre at UNSW is supported by funding from the Australian Government. The Centre for Research Excellence in Mental Health and Substance Use is funded by the National Health and Medical Research Council, Australia. MT is funded by an Australia National Health and Medical Research Council Research Fellowship. MD is supported by a PhD scholarship from the National Drug and Alcohol Research Centre, which is acknowledged with gratitude. Professor Kathleen Griffiths is acknowledged and thanked for providing the authors with the HealthWatch content.

    Conflicts of Interest

    M Deady and the other authors also worked to develop the DEAL Project website, but derive no personal or financial benefit from its operation.

    1. World Health Organisation. The Global Burden of Disease, 2004 update. Geneva: World Health Organisation Press (WHO) Press; 2008.
    2. Patel V, Flisher AJ, Hetrick S, McGorry P. Mental health of young people: a global public-health challenge. Lancet 2007 Apr 14;369(9569):1302-1313. [CrossRef] [Medline]
    3. McGorry PD, Goldstone SD, Parker AG, Rickwood DJ, Hickie IB. Cultures for mental health care of young people: an Australian blueprint for reform. Lancet Psychiatry 2014 Dec;1(7):559-568. [CrossRef] [Medline]
    4. Reavley NJ, Cvetkovski S, Jorm AF, Lubman DI. Help-seeking for substance use, anxiety and affective disorders among young people: results from the 2007 Australian National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 2010 Aug;44(8):729-735. [CrossRef] [Medline]
    5. Chan Y, Dennis ML, Funk RR. Prevalence and comorbidity of major internalizing and externalizing problems among adolescents and adults presenting to substance abuse treatment. J Subst Abuse Treat 2008 Jan;34(1):14-24 [FREE Full text] [CrossRef] [Medline]
    6. Sullivan LE, Fiellin DA, O'Connor PG. The prevalence and impact of alcohol problems in major depression: a systematic review. Am J Med 2005 Apr;118(4):330-341. [CrossRef] [Medline]
    7. Lubman DI, Allen NB, Rogers N, Cementon E, Bonomo Y. The impact of co-occurring mood and anxiety disorders among substance-abusing youth. J Affect Disord 2007 Nov;103(1-3):105-112. [CrossRef] [Medline]
    8. Hides L, Lubman DI, Cosgrave EM, Buckby JA, Killackey E, Yung AR. Motives for substance use among young people seeking mental health treatment. Early Interv Psychiatry 2008 Aug;2(3):188-194. [CrossRef] [Medline]
    9. Quello SB, Brady KT, Sonne SC. Mood disorders and substance use disorder: a complex comorbidity. Sci Pract Perspect 2005 Dec;3(1):13-21 [FREE Full text] [Medline]
    10. Hesse M. Integrated psychological treatment for substance use and co-morbid anxiety or depression vs. treatment for substance use alone. A systematic review of the published literature. BMC Psychiatry 2009;9:6 [FREE Full text] [CrossRef] [Medline]
    11. Kelly TM, Daley DC, Douaihy AB. Treatment of substance abusing patients with comorbid psychiatric disorders. Addict Behav 2012 Jan;37(1):11-24 [FREE Full text] [CrossRef] [Medline]
    12. Baker AL, Kavanagh DJ, Kay-Lambkin FJ, Hunt SA, Lewin TJ, Carr VJ, et al. Randomized controlled trial of cognitive-behavioural therapy for coexisting depression and alcohol problems: short-term outcome. Addiction 2010 Jan;105(1):87-99. [CrossRef] [Medline]
    13. White A, Kavanagh D, Stallman H, Klein B, Kay-Lambkin F, Proudfoot J, et al. Online alcohol interventions: a systematic review. J Med Internet Res 2010;12(5):e62 [FREE Full text] [CrossRef] [Medline]
    14. Cowpertwait L, Clarke D. Effectiveness of web-based psychological interventions for depression: a meta-analysis. Int J Ment Health Addiction 2013 Jan 18;11(2):247-268. [CrossRef]
    15. Barak A, Hen L, Boniel-Nissim M, Shapira N. A comprehensive review and a meta-analysis of the effectiveness of Internet-based psychotherapeutic interventions. J Technol Hum Serv 2008 Jul 03;26(2-4):109-160. [CrossRef]
    16. Cuijpers P, Donker T, Johansson R, Mohr DC, van Straten A, Andersson G. Self-guided psychological treatment for depressive symptoms: a meta-analysis. PLoS One 2011;6(6):e21274 [FREE Full text] [CrossRef] [Medline]
    17. Cuijpers P, Smit F, Bohlmeijer E, Hollon SD, Andersson G. Efficacy of cognitive-behavioural therapy and other psychological treatments for adult depression: meta-analytic study of publication bias. Br J Psychiatry 2010 Mar;196(3):173-178 [FREE Full text] [CrossRef] [Medline]
    18. Cuijpers P, van Straten A, Andersson G, van Oppen P. Psychotherapy for depression in adults: a meta-analysis of comparative outcome studies. J Consult Clin Psychol 2008 Dec;76(6):909-922. [CrossRef] [Medline]
    19. Riper H, Spek V, Boon B, Conijn B, Kramer J, Martin-Abello K, et al. Effectiveness of E-self-help interventions for curbing adult problem drinking: a meta-analysis. J Med Internet Res 2011;13(2):e42 [FREE Full text] [CrossRef] [Medline]
    20. Tait RJ, Christensen H. Internet-based interventions for young people with problematic substance use: a systematic review. Med J Aust 2010 Jun 7;192(11 Suppl):S15-521. [Medline]
    21. Carey KB, Scott-Sheldon LA, Elliott JC, Garey L, Carey MP. Face-to-face versus computer-delivered alcohol interventions for college drinkers: a meta-analytic review, 1998 to 2010. Clin Psychol Rev 2012 Dec;32(8):690-703 [FREE Full text] [CrossRef] [Medline]
    22. Johansson R, Andersson G. Internet-based psychological treatments for depression. Expert Rev Neurother 2012 Jul;12(7):861-869. [CrossRef] [Medline]
    23. Batterham P, Sunderland M, Calear A, Davey C, Christensen H, Teesson M. Developing a roadmap for the translation of e-mental health services for depression. Aust N Z J Psychiatry 2015 Sep;49(9):776-784. [CrossRef]
    24. Kay-Lambkin F, Baker A, Kelly B, Lewin T. Clinician-assisted computerised versus therapist-delivered treatment for depressive and addictive disorders: a randomised controlled trial. Med J Aust 2011 Aug 1;195(3):S44-S50. [Medline]
    25. Kay-Lambkin FJ, Baker AL, Lewin TJ, Carr VJ. Computer-based psychological treatment for comorbid depression and problematic alcohol and/or cannabis use: a randomized controlled trial of clinical efficacy. Addiction 2009 Mar;104(3):378-388. [CrossRef] [Medline]
    26. Geisner IM, Varvil-Weld L, Mittmann AJ, Mallett K, Turrisi R. Brief web-based intervention for college students with comorbid risky alcohol use and depressed mood: does it work and for whom? Addict Behav 2015 Mar;42:36-43 [FREE Full text] [CrossRef] [Medline]
    27. Oxman TE, Sengupta A. Treatment of minor depression. Am J Geriatr Psychiatry 2002;10(3):256-264. [Medline]
    28. Beekman AT, Geerlings SW, Deeg DJ, Smit JH, Schoevers RS, de Beurs E, et al. The natural history of late-life depression: a 6-year prospective study in the community. Arch Gen Psychiatry 2002 Jul;59(7):605-611. [Medline]
    29. McBride O, Adamson G, Bunting B, McCann S. Diagnostic orphans: comparing self-report lifetime course to groups with DSM-IV alcohol abuse and dependence. Addict Behav 2009 Jan;34(1):86-91. [CrossRef] [Medline]
    30. Lewinsohn PM, Rohde P, Seeley JR. Major depressive disorder in older adolescents: prevalence, risk factors, and clinical implications. Clin Psychol Rev 1998 Nov;18(7):765-794. [Medline]
    31. Eng MY, Schuckit MA, Smith TL. A five-year prospective study of diagnostic orphans for alcohol use disorders. J Stud Alcohol 2003 Mar;64(2):227-234. [Medline]
    32. Harford TC, Yi H, Grant BF. The five-year diagnostic utility of "diagnostic orphans" for alcohol use disorders in a national sample of young adults. J Stud Alcohol Drugs 2010 May;71(3):410-417 [FREE Full text] [Medline]
    33. Schuckit MA, Danko GP, Smith TL, Bierut LJ, Bucholz KK, Edenberg HJ, et al. The prognostic implications of DSM-IV abuse criteria in drinking adolescents. Drug Alcohol Depend 2008 Sep 1;97(1-2):94-104 [FREE Full text] [CrossRef] [Medline]
    34. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand 2004 May;109(5):325-331. [CrossRef] [Medline]
    35. Lovibond S, Lovibond P. Manual for the Depression Anxiety Stress Scales. 2nd edition. Sydney: Psychology Foundation; 1995.
    36. Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction 1993 Jun;88(6):791-804. [Medline]
    37. Degenhardt L, Hall W, Korten A, Jablensky A. Use of Brief Screening Instrument for Psychosis: Results of a ROC Analysis. Sydney: National Drug and Alcohol Research Centre; 2005.
    38. Deady M, Kay-Lambkin F, Teesson M, Mills K. Developing an integrated, Internet-based self-help program for young people with depression and alcohol use problems. Internet Interventions 2014 Jun;1(3):118-131. [CrossRef]
    39. Griffiths KM, Crisp D, Christensen H, Mackinnon AJ, Bennett K. The ANU WellBeing study: a protocol for a quasi-factorial randomised controlled trial of the effectiveness of an Internet support group and an automated Internet intervention for depression. BMC Psychiatry 2010;10:20 [FREE Full text] [CrossRef] [Medline]
    40. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA 1999 Nov 10;282(18):1737-1744. [Medline]
    41. Khadjesari Z, Murray E, Kalaitzaki E, White IR, McCambridge J, Godfrey C, et al. Test-retest reliability of an online measure of past week alcohol consumption (the TOT-AL), and comparison with face-to-face interview. Addict Behav 2009 Apr;34(4):337-342. [CrossRef] [Medline]
    42. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [Medline]
    43. Hintze J. PASS 11. Kaysville, UT: NCSS, LLC; 2011.
    44. Kroenke K, Spitzer R. The PHQ-9: a new depression diagnostic and severity measure. Psychiat Ann 2002;32(9):1-7.
    45. SPSS: An IBM program version 18.0.2. Chicago, IL: PASW Statistics; 2010.
    46. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986 Mar;42(1):121-130. [Medline]
    47. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988.
    48. Little R. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc 1988 Dec;83(404):1198-1202. [CrossRef]
    49. McCambridge J, Kypri K. Can simply answering research questions change behaviour? Systematic review and meta analyses of brief alcohol intervention trials. PLoS One 2011;6(10):e23748 [FREE Full text] [CrossRef] [Medline]
    50. NHMRC. Australian Guidelines to Reduce Health Risks from Drinking Alcohol. Canberra: NHMRC; 2009.
    51. Gibb SJ, Fergusson DM, Horwood LJ. Burden of psychiatric disorder in young adulthood and life outcomes at age 30. Br J Psychiatry 2010 Aug;197(2):122-127 [FREE Full text] [CrossRef] [Medline]
    52. Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev 2012 Jun;32(4):329-342. [CrossRef] [Medline]
    53. 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]
    54. Eysenbach G. The law of attrition. J Med Internet Res 2005;7(1):e11 [FREE Full text] [CrossRef] [Medline]
    55. Lillevoll KR, Vangberg HC, Griffiths KM, Waterloo K, Eisemann MR. Uptake and adherence of a self-directed internet-based mental health intervention with tailored e-mail reminders in senior high schools in Norway. BMC Psychiatry 2014;14:14 [FREE Full text] [CrossRef] [Medline]
    56. Titov N, Dear BF, Johnston L, Lorian C, Zou J, Wootton B. Improving adherenceclinical outcomes in self-guided Internet treatment for anxiety and depression: randomised controlled trial. PLoS ONE 2013;8(7):e62873. [CrossRef]
    57. Deady M. Comorbid Depression and Problematic Alcohol Use in Young People: The Development and Evaluation of an Online Intervention. Sydney: University of New South Wales, Australia; 2015.
    58. Christensen H, Griffiths K, Groves C, Korten A. Free range users and one hit wonders: community users of an Internet-based cognitive behaviour therapy program. Aust N Z J Psychiatry 2006 Jan;40(1):59-62. [CrossRef] [Medline]
    59. Leykin Y, Muñoz RF, Contreras O, Latham MD. Results from a trial of an unsupported internet intervention for depressive symptoms. Internet Interv 2014 Oct 1;1(4):175-181 [FREE Full text] [CrossRef] [Medline]
    60. Donker T, Bennett K, Bennett A, Mackinnon A, van SA, Cuijpers P, et al. Internet-delivered interpersonal psychotherapy versus internet-delivered cognitive behavioral therapy for adults with depressive symptoms: randomized controlled noninferiority trial. J Med Internet Res 2013;15(5):e82 [FREE Full text] [CrossRef] [Medline]
    61. Clarke G, Kelleher C, Hornbrook M, Debar L, Dickerson J, Gullion C. Randomized effectiveness trial of an Internet, pure self-help, cognitive behavioral intervention for depressive symptoms in young adults. Cogn Behav Ther 2009;38(4):222-234 [FREE Full text] [CrossRef] [Medline]
    62. Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag; 2000.
    63. Fergusson DM, Horwood LJ, Ridder EM, Beautrais AL. Subthreshold depression in adolescence and mental health outcomes in adulthood. Arch Gen Psychiatry 2005 Jan;62(1):66-72. [CrossRef] [Medline]
    64. McBride O, Adamson G, Bunting BP, McCann S. Characteristics of DSM-IV alcohol diagnostic orphans: drinking patterns, physical illness, and negative life events. Drug Alcohol Depend 2009 Jan 1;99(1-3):272-279. [CrossRef] [Medline]
    65. Del Boca FK, Noll J. Truth or consequences: the validity of self-report data in health services research on addictions. Addiction 2000 Nov;95 Suppl 3:S347-S360. [Medline]
    66. Del Boca FK, Darkes J. The validity of self-reports of alcohol consumption: state of the science and challenges for research. Addiction 2003 Dec;98(Suppl 2):1-12. [Medline]
    67. Booth-Kewley S, Larson G, Miyoshi D. Social desirability effects on computerized and paper-and-pencil questionnaires. Comput Hum Behav 2007 Jan;23(1):463-477. [CrossRef]

    ‎
    DASS: Depression Anxiety Stress Scale
    AUDIT: Alcohol Use Disorders Identification Test
    GEE: generalized estimating equations
    MSI-BPD: McLean Screening Instrument for Borderline Personality Disorder
    PHQ-9: Patient Health Questionnaire
    RCT: randomized controlled trial
    RR: risk ratio
    SHADE: Self-Help for Alcohol and other drug use and DEpression

    Edited by G Eysenbach; submitted 29.09.15; peer-reviewed by J Torous, T Elgan, D Crisp, N Arnaud, I Geisner; comments to author 29.10.15; revised version received 02.12.15; accepted 04.01.16; published 23.03.16

    Copyright

    ©Mark Deady, Katherine L Mills, Maree Teesson, Frances Kay-Lambkin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.03.2016.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), 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 http://www.jmir.org/, as well as this copyright and license information must be included.