Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Behav Ther. 2020 Mar 3;52(1):77–85. doi: 10.1016/j.beth.2020.02.003

Predicting treatment outcomes from internet-based cognitive behavior therapy for obsessive–compulsive disorder

Michael G Wheaton 1,2,*, Sapana R Patel 2, Erik Andersson 3, Christian Rück 3, H Blair Simpson 2
PMCID: PMC7826445  NIHMSID: NIHMS1571053  PMID: 33483126

Abstract

Internet-based cognitive-behavioral therapy (ICBT) with therapist support shows promise as a treatment for obsessive-compulsive disorder (OCD). Yet, not all patients respond to ICBT. It is therefore important to identify predictors of ICBT outcomes to determine who is likely to benefit. Relative to the large literature on predictors of outcomes for in person CBT for OCD, very few studies have investigated ICBT predictors. Therefore, we analyzed predictors of outcome in an open trial (n=30) of ICBT for OCD using the OCD-NET platform, which consists of 10 online modules delivered with therapist support. The Yale-Brown Obsessive-Compulsive Scale (YBOCS) was administered by independent raters as the primary outcome measure at baseline and post-treatment. In this sample, greater baseline OCD severity and OCD-related avoidance behaviors were associated with higher end state OCD symptoms (i.e., poorer outcome). Patients with a past history of face-to-face CBT for OCD also had worse outcomes. Although these results require replication, these factors may identify individuals at risk for poor ICBT outcomes.

Keywords: obsessive-compulsive disorder, predictor, internet treatment, cognitive-behavioral therapy

Introduction

Obsessive-compulsive disorder (OCD) is estimated to afflict up to 2% of the population and can be disabling when severe (Ruscio et al., 2010). OCD tends to run a chronic course when not adequately treated (Angst et al., 2004), leading to substantial public health burden (Ayuso-Mateos, 2006). Cognitive-behavioral therapy (CBT), particularly that which includes exposure and response prevention (EX/RP), is a well-established treatment for adults with OCD (Koran et al. 2007, Koran and Simpson 2013, NICE 2013). However, in clinical practice relatively few patients actually receive CBT (Patel et al., 2014). Frequently cited barriers to receiving CBT include lack of access to trained clinicians, time commitment and high treatment costs (Marques et al., 2010).

One practice innovation to overcome these treatment barriers has been to deliver CBT via the internet. Internet-based CBT (ICBT) involves reviewing psychoeducational materials presented in modules over the internet and is commonly paired with therapist-support provided by phone or secure messaging. Internet-based treatments increase efficiency and provide convenient access to treatment. This approach has the benefit of being widely available, including in rural communities without expertly trained CBT therapists. Internet-based treatments are also flexible in that patients can complete online modules corresponding to CBT components at their own pace. The addition of therapist-support to internet-based treatments appears to improve upon the effectiveness of this approach (Andersson et al., 2012; Moritz et al., 2011; Seol et al., 2016). Therapist support includes addressing questions about the treatment rationale and procedures, providing individualized feedback on exposure practices and preventing rituals, problem-solving obstacles and offering general support. Multiple variants of therapist contact have been tested alongside internet-based CBT, including therapist support exclusively via the internet and text message (Andersson, 2012), weekly phone calls (Patel et al., 2018) and combining ICBT with the option for occasional in person sessions (Deifenbach et al., 2015). Although these interventions are new, emerging evidence supports their efficacy. Four research groups have found positive results for therapist-supported ICBT for OCD programs in randomized controlled trials (RCTs) in Sweden (Andersson et al., 2013) and Australia (Kyrios et al., 2018; Mahoney et al., 2014; Wootten et al., 2013). One of these ICBT for OCD platforms (OCD-NET) was recently translated to English and resulted in positive effects in an open trial conducted in the United States (Patel et al., 2018).

Importantly, not all trials of ICBT for OCD have reported positive results. A trial conducted in the United Kingdom (Lovell, et al., 2017) recruited OCD patients who were on the waiting list for face-to-face CBT and randomized them to receive one of three options: 1) a variant of ICBT (therapist support alongside self-help via the “OCFighter” application, n=157), 2) therapist assisted guided self-help with written materials (n=158); or 3) to stay on the waiting list without additional treatment (n=158). Neither form of low intensity approach led to clinically meaningful improvements relative to the wait-list condition. In addition, among the clinical trials that have found positive results for ICBT, response rates (based on clinically significant improvement) have ranged from 31–61% (Andersson et al., 2013; Kyrios et al., 2018; Mahoney et al., 2014; Wootten et al., 2013). Together, these results indicate that ICBT may benefit only some patients.

Given this, it is important to examine patient characteristics that predict ICBT outcomes for several reasons. Firstly, identifying such predictors might allow researchers to refine ICBT treatments to maximize patient outcomes. Similarly, it is important to determine whether established predictors of face-to-face CBT for OCD are also predictors of ICBT for OCD, as doing so could help identify barriers and complicating factors to successful CBT regardless of its modality of delivery. This could help identify core treatment obstacles, which would be clinically relevant for treatment refinement and improvement. Finally, predictor studies can also identify patients at risk for poor outcomes with ICBT so that subsequent studies could then determine in RCTs if individuals displaying risk factors for poor ICBT outcomes would be better served with an alternative treatment. This line of research can ultimately enable personalized treatment recommendations and matching individual patients to the treatments that will work best for them.

To date there is large body of research investigating baseline predictors of outcome with in-person CBT for OCD, although it is characterized by many inconsistent findings (with the exception of patient adherence, a process-level variable robustly linked to outcomes, Simpson et al., 2011). Knopp and colleagues (2013) conducted systematic review of the literature and identified 38 predictor studies of CBT for OCD. Despite a great deal of heterogeneity, pooled estimates suggested potential links with poor OCD outcomes for hoarding symptoms, increased OCD severity, baseline anxiety, unemployment, and being single/unmarried. Subsequent studies have reported further mixed findings for whether baseline OCD severity predicts CBT outcomes, with two studies finding null results (Steketee et al., 2019; Wheaton et al., 2015) while another reported lower baseline severity linked to better outcome (Kyrios et al., 2015). Findings on depression are mixed but suggest that severe depression may also be linked to worse outcome (Keeley et al., 2008). Additional factors recently linked to outcomes for in-person CBT for OCD include significant behavioral avoidance (Wheaton et al., 2018), anxiety sensitivity (Blakey et al. 2017), and work functioning (Wheaton et al., 2015).

Relative to the large number of studies that have evaluated baseline predictors for in person CBT, only a very small number have examined which baseline characteristics predict ICBT outcomes. To our knowledge, only three studies of ICBT for OCD have done so. First, in their RCT in Sweden of 101 OCD patients who received 10 weeks of ICBT via the OCD-NET platform, Andersson et al. (2015) found that three factors were linked with outcomes: higher baseline severity predicted more severe OCD symptoms at post-treatment (but also more improvement), better working alliance with the OCD-NET therapist predicted better outcome and more improvement, and OCD symptoms motivated by disgust predicted worse outcome and less improvement.

Second, in their RCT comparing therapist-assisted ICBT (via a different ICBT platform; n=89) to online progressive muscle relaxation (PMR, n=90) in Australia, Kyrios et al. (2018) found that after looking at many sociodemographic and clinical variables, only higher baseline severity was associated with greater YBOCS change (in both the ICBT and PMR groups). Finally, in a small study (completer n=17) of OCD patients in the U.S. that delivered therapist support paired with the “OCFighter” application, Diefenbach et al. (2015) evaluated multiple variables (baseline OCD severity, depression severity, treatment motivation, executive functioning, and treatment engagement) and found that only readiness to reduce OCD-related avoidance and attendance of therapist sessions significantly correlated with symptom reduction and responder status.

Given that so few studies have investigated this topic, we sought to further investigate predictors of ICBT for OCD outcomes in an independent sample of adults who received ICBT (via the OCD-NET platform) as part of an open trial in the United States as described below. We selected an analytic approach that allowed us to examine a wide range of predictor variables at baseline including demographic variables, OCD features (severity, duration, symptom subtype, insight), comorbidity, and baseline functioning (each of which described below). We chose to predict severity of OCD symptoms at post-treatment (outcome) adjusting for baseline OCD severity rather than calculating individual baseline to post-treatment difference scores because change scores are often considered less reliable (Cronbach & Furby, 1970). In addition, we adopted an analytic approach that balances Type I and Type II error rates as described below to allow us to evaluate a broad set of predictor variables without sacrificing power. This approach is also ideal for allowing us to consider a range of potential predictor variables without forming a specific hypothesis for each. Based on the extent of mixed findings in terms of predictors of outcomes with both face-to-face and internet-delivered CBT we formed an a priori hypothesis that greater baseline severity would predict worse outcome but considered our evaluation of the other factors to be exploratory.

Methods

Trial design and participants

Data came from an open trial of ICBT via OCD-NET for OCD run at an academic research clinic in (New York City); the trial is described in detail elsewhere (Patel et al. 2018). This version of ICBT was comprised of an English translation of the Swedish OCD-NET ICBT platform (Andersson et al., 2012). Eligible patients were adults (aged 18–75 years) with a principal diagnosis of OCD. Exclusion criteria were: 1) diagnosis of bipolar or psychotic disorder; 2) current substance abuse or dependence; 3) suicidal ideation; 4) primary hoarding symptoms or a primary diagnosis of hoarding disorder; 5) unstable medical condition. Participants with comorbid depression and anxiety disorders were allowed to enter the trial as long as OCD was the principal disorder needing clinical attention. Participants taking serotonin reuptake inhibitors (SRIs) for OCD were allowed to enter the trial if they had been on a stable dose for at least 2 months and agreed to maintain a constant dosage during the trial.

Participants were recruited from clinical referrals, advertisements placed in the community, and a study-specific website. Study eligibility was determined via clinical interview conducted by a doctoral-level assessor utilizing the Structured Clinical Interview for DSM-IV (SCID; First et al., 1996). Independent evaluators rated patients at baseline and post-treatment as described below. The Institutional Review Board (IRB) approved the study protocol, and patients provided written informed consent.

ICBT intervention

ICBT was adapted from that previously found effective in Sweden (Andersson et al., 2011, 2012). Treatment consisted of 10 modules delivered in a web-based platform (OCD-NET) via text and audio that included standard CBT components (psychoeducation, self-monitoring, cognitive restructuring, exposure and response prevention [EX/RP] and relapse prevention). All participants were assigned a therapist who provided individualized support throughout treatment. The ICBT modules included worksheets and homework assignments to reinforce concepts and monitor exposure practices. Therapists communicated with participants via the ICBT platform to provide feedback on these worksheets and answer questions. In addition, therapists had weekly phone calls (each lasting up to 30 minutes) during the EX/RP phase of treatment (modules 5–10). Participants were instructed that they had up to12 weeks to complete the 10 modules.

Potential Predictors

Potential predictor variables are shown in Table 1. As in other predictor studies (Fournier et al., 2009; Wheaton et al., 2015), predictor variables were grouped into conceptually-related categories (domains) as described below.

Table 1.

Demographic and Clinical Characteristics of the Sample

DEMOGRAPHIC VARIABLES
Age (in years), mean (S.D.) 36.61 (11.13)
Female, n (%) 23 (57.5)
Married-partnered, n (%) 11 (27.5)
Non-Hispanic white, n (%) 32 (80)
Years of education, mean (S.D.) 16.35 (1.97)
OCD FEATURES
YBOCS severity, mean (S.D.) 25.85 (4.56)
OCD duration (years), mean (S.D.) 23.04 (12.12)
OCD onset age (years), mean (S.D.) 14.13 (8.89)
Significant OCD-related avoidance, n (%) 24 (60)
YBOCS Insight, mean (S.D.) 0.73 (0.94)
OCI-R Subscales
Obsessing 7.46 (3.24)
Washing 5.84 (4.39)
Checking 6.38 (3.90)
Neutralizing 3.14 (3.73)
Ordering 4.27 (3.76)
Hoarding 2.95 (3.29)
COMORBIDITY AND BASELINE FUNCTIONING
Comorbid Axis I disorders, n (%) having ≥ 1 21 (52.5)
Depression severity (HDRS), mean (S.D.) 9.15 (6.28)
Functioning (GAF), mean (S.D.) 56.00 (8.90)
Quality of Life (Q-LES-Q-SF), mean (S.D.) 55.10 (15.85)
TREATMENT FEATURES
Current medication, n (%) 15 (37.5)
Past CBT for OCD, n (%) 12 (30)
Open in a new tab

Note. YBOCS = Yale Brown Obsessive-Compulsive Scale; OCI-R = Obsessive Compulsive Inventory-Revised; HDRS = Hamilton Depression Rating Scale; GAF = Global Assessment of Functioning; QIDS-SR = Quick Inventory of Depressive Symptoms-Self Report; QLESQ = Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form.

Demographics

This category included age, sex (dummy coded with male=1 and female=0), years of education, race/ethnicity (dummy coded as Non-Hispanic White=1 and all others =0 because the sample was mainly non-Hispanic Caucasian), and current relationship status (dummy coded as single or divorced/separated=0, married or living with partner=1).

OCD Features

This domain included OCD illness duration and OCD age of onset as recorded during the SCID interview. This category also included baseline OCD symptom severity as assessed by the YBOCS (Goodman et al., 1989a; 1989b). Finally, the auxiliary items from the YBOCS were used to categorize degree of insight and severity of OCD-related avoidance behaviors. The avoidance item was coded as in Wheaton et al. (2018) such that patients were grouped into those without avoidance (scores of 0 and 1) and those with significant OCD-related avoidance (scores of 2–4).

OCD Symptom Dimensions

As in a previous study (Wheaton et al., 2015), to balance category size and reduce the likelihood of Type II error (see Data Analysis section), OCD symptom dimensions were analyzed in their own domain, separate from other OCD features. We utilized the Obsessive Compulsive Inventory- Revised (OCI-R; Foa et al., 2002) to quantify the severity of six subscales of OCD symptoms (Obsessing, Washing, Checking, Neutralizing, Ordering, Hoarding). The OCI-R has demonstrated good validity and reliability and is commonly utilized as a measure of self-reported OCD symptom severity (Foa et al., 2002). Reliability in the present sample was good (α = .88).

Comorbidity and Baseline Functioning

This category included the number of comorbid Axis I disorders, as determined via SCID interview. In addition, severity of baseline depressive symptoms was assessed via the Hamilton Depression Rating Scale (HDRS; Hamilton, 1960), an interview-based measure of depression commonly utilized in treatment studies (reliability in the present sample was good, α = .83). To quantify patients’ baseline functioning, this category included the Global Assessment of Functioning (GAF; APA, 1994) from the SCID and the Quality of Life Satisfaction Scale—Short Form (QLESQ-SF; Endicott, Nee, Harrison & Blumenthal, 1993). The QLESQ-SF is a 15-item self-reported measure of quality of life frequently used in research, with good reliability, validity, and sensitivity to change. In the present study the QLESQ-SF had good reliability (α = .90).

Treatment Features

This domain included information about current medication use (as participants were eligible to enter the trial on SRI medications if they had been on a stable dose for at least two months and agreed to maintain a constant dosage during the study). This variable was dummy coded such that participants who were unmedicated were coded=0 and those taking medication were coded=1. Finally, participants were coded=1 if they reported having a past trial of face-to-face CBT for OCD, while those who had never tried CBT for OCD were coded=0.

Outcome Measure

The primary outcome measure was the Yale-Brown Obsessive Compulsive Scale (YBOCS; Goodman et al. 1989a, b), the “gold standard” measure of OCD severity (APA, 2013). The YBOCS was administered at baseline and post-treatment by independent evaluators (doctoral level psychologists) blinded to treatment status. Total scores on the YBOCS range from 0 to 40 with higher scores indicating more severe OCD. Internal consistency in the present sample was excellent (Cronbach’s α = .93).

Data Analysis

As in other predictor studies, we used the Fournier approach (Amir, Taylor, & Donohue, 2011; Fournier et al., 2009, Smits et al., 2013, Powers et al., 2014; Wheaton et al., 2015) to evaluate potential predictors. This approach involves a stepwise process that first evaluates whether variables are related to post-treatment YBOCS within each of the five domains identified above, and then uses these variables in a final model predicting post-treatment YBOCS. This approach is designed to represent a balance between risks of Type I and Type II errors. Evaluating many predictor variables separately, one variable at a time would increase Type I error. Alternatively, entering all variables simultaneously would violate guidelines for the ratio of variables to subjects and would likely produce high Type II error (due to low power). Therefore, the stepwise approach allows us to consider a wide range of potential predictors without enhancing false positive rates by only interpreting effects that are significant in the Final Model. Outcomes were evaluated via linear regression models that set observed post-treatment severity (YBOCS score) as the dependent variable and included baseline OCD severity as a predictor. Alpha was set at p<.05.

Results

Descriptive data

Of 40 adults who entered the trial, post-treatment data were available from n=30 (though only n=28 completed all 10 modules). Missing data for the predictors were minimal, with two patients missing information on duration of OCD and two missing GAF scores. Descriptive statistics of the potential predictors are presented in Table 1. Mean scores among those who had post-treatment data showed that on the YBOCS decreased significantly from baseline (M=26.2, SD=4.35) to post-treatment (M=18.57, SD=7.85, t(29)=6.55, p<0.001) with a large effect size (Cohen’s d=1.2).

Results of the Step-Wise Analyses within each Domain

Below we report results from the predictor analysis for each domain, and these are summarized in Table 2. Variables that significantly related to post-treatment YBOCS within a domain were retained for the final model.

Table 2.

Predictors of Post-treatment OCD symptoms from each Domain and the Final Model

Domain/Predictor Post-Treatment Main Effects
DOMAIN: DEMOGRAPHIC VARIABLES
Variable β t p
Age −0.18 −0.94 0.359
Sex 0.01 0.06 0.954
Married-partnered −0.24 −1.28 0.212
Ethnicity 0.01 0.05 0.957
Years of Education 0.23 1.46 0.158
OCD FEATURES
Baseline YBOCS 0.59 3.82 0.001
OCD duration −0.20 −1.23 0.229
OCD onset age −0.29 −1.71 0.100
YBOCS_Avoidance 0.36 2.36 0.027
YBOCS_Insight 0.18 1.19 0.245
OCI-R SUBSCALES
Hoarding 0.11 0.55 0.588
Checking −0.08 −0.39 0.702
Ordering 0.02 0.07 0.995
Neutralizing 0.05 0.20 0.841
Washing −0.20 −0.94 0.356
Obsessing 0.10 0.46 0.651
COMORBITITY AND BASELINE FUNCTIONING
Comorbid disorders 0.21 1.17 0.253
Functioning (GAF) 0.13 0.76 0.457
Depression Severity (HDRS) 0.19 0.98 0.337
Quality of Life (QLESQ-SF) −0.15 −0.78 0.443
TREATMENT FEATURES
Past CBT for OCD 0.38 2.20 0.037
Current medication −0.24 −0.53 0.602
FINAL MODEL
Baseline YBOCS 0.35 2.31 0.029
Avoidance 0.33 2.48 0.020
Past CBT for OCD 0.35 2.33 0.028
Open in a new tab

Note. YBOCS = Yale Brown Obsessive-Compulsive Scale; OCI-R = Obsessive Compulsive Inventory-Revised; HDRS = Hamilton Depression Rating Scale; GAF = Global Assessment of Functioning; QIDS-SR = Quick Inventory of Depressive Symptoms-Self Report; QLESQ-SF = Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form.

Demographic Characteristics

None of the demographic characteristics related to post-treatment YBOCS scores.

Baseline OCD features

Only baseline YBOCS (β=.59, t=3.82, p=.001), and OCD-related avoidance behaviors (β=.36, t=2.36, p=.027), related to post-treatment YBOCS.

OCD Symptom Dimensions

None of the OCI-R subscale scores significantly related to post-treatment YBOCS.

Comorbidity and Baseline Functioning

None of the factors in this category were significantly predictive of post-treatment YBOCS scores.

Treatment Features

History of prior CBT for OCD significantly related to post-treatment YBOCS (β=.38, t=2.20, p=.037), with individuals reporting past CBT having higher post-treatment YBOCS. Current medication use was not linked to outcome.

Final Model

The final model included all variables found significant in the analyses of each domain (baseline YBOCS, Avoidance, and past CBT). This model accounted for 54.4% of the variance in post-treatment YBOCS, which was significant (R2=.544, F(3,29)=10.33 p<.001).

Table 2 presents the significant results for the final model. As shown, baseline YBOCS severity (β=.35, t=2.31, p=.029), avoidance (β =.33, t=2.48, p=.02) and past CBT (β =.35, t=2.33, p=.028) were significant individual predictors of post-treatment YBOCS.

Discussion

We examined baseline predictors of outcome in an open trial of ICBT for OCD (specifically the OCD-NET platform) in the United States. Of the predictor variables examined, greater baseline OCD severity, avoidance behavior, and past history of face-to-face CBT for OCD significantly predicted poorer outcomes. Together these predictors accounted for more than half of the variability in post-treatment YBOCS scores. Each of these findings is discussed below.

Our finding that higher baseline OCD severity predicted greater post-treatment symptoms is consistent with a prior predictor study of ICBT for OCD in Sweden that also used OCD-NET (Andersson et al., 2015) and suggests that baseline severity (regardless of country) is important in determining who benefits from the OCD-NET platform. In contrast, with other platforms, baseline severity was linked to more change in OCD symptoms in another study (Kyrios et al. (2018) and not to outcome or responder status in a third (Diefenbach et al., 2016). Similarly, the literature on baseline severity predicting outcomes in face-to-face CBT for OCD is mixed (Knopp et al., 2013; Olatunji et al., 2013; Maher et al., 2010; Tolin et al., 2004; Wheaton et al. 2015). Increasing the number of published reports on the relationship between severity and outcome may allow consensus to emerge through meta analytic methods.

Significant behavioral avoidance (as indexed by the auxiliary avoidance item on the YBOCS) also significantly predicted outcomes (accounting for overall baseline OCD severity and the other predictors), with patients with higher avoidance having higher post-treatment severity. This same measure also predicted outcomes in patients receiving face-to-face CBT (Wheaton et al., 2018). In addition, Diefenbach et al. (2016) found readiness to reduce OCD-related avoidance was correlated with greater improvement in 17 individuals who completed internet self-help with therapist support. Together these data suggest that individuals with significant behavioral avoidance patterns have increased risk of poor CBT outcome for both modalities of treatment. Future studies should examine ways to optimize outcomes for more avoidant patients in both ICBT and face-to-face CBT.

Finally, patients who reported a past history of individual face-to-face CBT for OCD had worse ICBT outcomes (even accounting for the other predictors). We did not collect details on the frequency, duration or helpfulness of the past CBT, and so future research is needed to explore this finding. The literature on predictors of outcome for in person CBT finds that past history of a CBT trial is not a reliable predictor of worse CBT outcome (Knopp et al., 2013). Thus, whereas baseline severity and avoidance may predict outcomes for both face-to-face and ICBT, it is possible that past history of in person CBT may be worth investigating as a particular factor in predicting ICBT outcomes. If past in person CBT were more important in determining ICBT outcomes than face-to-face CBT outcomes, this would have important implications for developing treatment recommendations. However, determining that a predictor is treatment-specific can only be concluded from a direct comparison in a study that randomizes patients to different treatment modalities. Thus, this question deserves further study in an RCT directly comparing these two ways to deliver CBT, as is currently underway in Sweden (Rück et al., 2018).

The results from this study could therefore be utilized as initial data towards future studies personalizing care for OCD. Some researchers have suggested that ICBT represents ideal initial treatment option, to be offered prior to more intensive and costly options such as in person face-to-face CBT, medications, or residential treatments (Tolin et al. 2011). These intensive options could then be offered to patients who are not sufficiently helped by ICBT. However, a critique of a sequential staging of treatments (meaning that patients progress through treatments in order) is that they may experience multiple unsuccessful treatments before finding the most beneficial approach, leading to more time suffering with symptoms. Thus, work to identity treatment-specific predictors to match patients to particular treatments is essential.

Many variables of interest were not significantly related to ICBT outcomes in this sample. For example, none of the OCI-R subscales measured at baseline were related to post-treatment OCD severity, suggesting that ICBT was equally helpful across OCD dimensions (though it should be noted that individuals with a primary diagnosis of hoarding disorder were excluded from the present study). Other baseline variables also not significantly related to ICBT outcomes, included demographic variables, use of concomitant medications, functioning/quality of life, OCD duration, and insight. The literature on predictors of outcomes for face-to-face CBT is decidedly mixed for all of these variables (Knopp et al., 2013; Olatunji et al., 2013), so it is perhaps not surprising that they were not strongly linked to poor ICBT outcomes. Restriction of range may have affected some of our results. Specifically, depressive severity was low in our sample, and few patients had poor insight.

Our findings require interpretation in light of study limitations. First, our sample size was relatively small. Post-hoc power analysis using the program G*Power 3 (Faul et al., 2007) revealed that our final model had good power (.93) to detect large effects (f2 ≥ 0.35; Cohen, 1988) but our observed power for medium effects (f2 ≥ 0.15) was only .66, indicating that our study may suffer from an increased change of a Type II error for detecting predictors with medium or small effects, though we were adequately powered to detect predictors with large effects on outcomes. In the context of the limited published literature on ICBT predictors, our results should be considered as initial and subject to replication in future studies. Second, due to the inclusion/exclusion criteria of the parent study, we were unable to test some potential predictors. For example, patients with substance abuse, bipolar disorder, suicidal ideation or primary hoarding problems were excluded, and we did not measure disgust, readiness for change, or therapeutic alliance. Moreover, our sample was predominantly non-Hispanic white. Finally, this was an open trial without a control group. This design precludes us knowing whether the predictive factors we identified are specific to ICBT. Only a study that randomizes patients to different treatments can address that question. Such a trial, comparing ICBT (with or without therapist support) and face-to-face CBT, is currently ongoing (Rück et al. 2018).

Conclusions

In summary, in this open trial of ICBT delivered via the OCD-NET platform, we found three predictors of worse outcomes: 1) higher baseline severity, 2) behavioral avoidance, and 3) prior face-to-face CBT. Future research is needed to determine the mechanisms by which these factors relate to poor outcomes and to determine whether individuals with these risk factors might have better outcomes if matched to alternative treatments. As digital health technologies offer the promise of greatly increased access to CBT for OCD, it is important to determine who will benefit most from these treatments in order to take a personalized medicine approach to treatment (Shoham & Insel, 2011).

Highlights.

  • Examined outcome predictors with internet-based cognitive-behavior therapy for OCD

  • Higher baseline OCD severity predicted worse treatment outcome

  • Pretreatment avoidance behaviors were predictive of poor outcomes

  • Patients with a prior history of face-to-face CBT also had poorer outcomes

Acknowledgements

Funding: A New York State Office of Mental Health Policy Scholar Award (SRP), K24 MH091555 (HBS), and the New York State Office of Mental Hygiene supported this work. The funding sources had no role in study design, collection analysis interpretation of data, writing of the report and decision to submit the article for publication.

Declaration of competing interest

H. Blair Simpson has Dr. Simpson has received research support from Biohaven, royalties from Cambridge University Press and UpToDate, Inc, and a stipend from JAMA for her role as Associate Editor at JAMA Psychiatry. All other authors report no financial disclosures or competing interests

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Amir N, Taylor CT, & Donohue MC (2011). Predictors of response to an attention modification program in generalized social phobia. Journal of Consulting and Clinical Psychology, 79(4), 533–541. DOI: 10.1037/a0023808 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.), APA, Washington, DC [Google Scholar]
  3. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Arlington, VA: American Psychiatric Publishing; DOI: 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
  4. Andersson E, Enander J, Andrén P, Hedman E, Ljótsson B, Hursti T, … & Rück C (2012). Internet-based cognitive behaviour therapy for obsessive–compulsive disorder: a randomized controlled trial. Psychological Medicine, 42(10), 2193–2203. DOI: 10.1017/S0033291712000244 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Andersson E, Ljótsson B, Hedman E, Enander J, Kaldo V, Andersson G, … & Rück C (2015). Predictors and moderators of Internet-based cognitive behavior therapy for obsessive–compulsive disorder: Results from a randomized trial. Journal of Obsessive-Compulsive and Related Disorders, 4, 1–7. DOI: https://www.sciencedirect.com/science/article/pii/S2211364914000773?via%3Dihub [Google Scholar]
  6. Andersson E, Ljótsson B, Hedman E, Kaldo V, Paxling B, Andersson G, … & Rück C (2011). Internet-based cognitive behavior therapy for obsessive compulsive disorder: a pilot study. BMC Psychiatry, 11(1), 125 DOI: 10.1186/1471-244X-11-125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Angst J, Gamma A, Endrass J, Goodwin R, Ajdacic V, Eich D, & Rössler W (2004). Obsessive-compulsive severity spectrum in the community: prevalence, comorbidity, and course. European Archives of Psychiatry and Clinical Neuroscience, 254(3), 156–164. DOI: 10.1007/s00406-004-0459-4 [DOI] [PubMed] [Google Scholar]
  8. Ayuso-Mateos J (2006). Global Burden of obsessive-compulsive disorder in the year 2000. World Health Organization: Geneva. [Google Scholar]
  9. Blakey SM, Abramowitz JS, Reuman L, Leonard RC, & Riemann BC (2017). Anxiety sensitivity as a predictor of outcome in the treatment of obsessive-compulsive disorder. Journal of behavior therapy and experimental psychiatry, 57, 113–117. DOI: 10.1016/j.jbtep.2017.05.003 [DOI] [PubMed] [Google Scholar]
  10. Cohen JE (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc [Google Scholar]
  11. Cronbach LJ, & Furby L (1970). How we should measure” change”: Or should we?. Psychological Bulletin, 74(1), 68–80. DOI: 10.1037/h0029382 [DOI] [Google Scholar]
  12. Diefenbach GJ, Wootton BM, Bragdon LB, Moshier SJ, & Tolin DF (2015). Treatment outcome and predictors of internet guided self-help for obsessive-compulsive disorder. Behavior Therapy, 46(6), 764–774. DOI: 10.1016/j.beth.2015.06.001 [DOI] [PubMed] [Google Scholar]
  13. Endicott J, Nee J, Harrison W, & Blumenthal R (1993). Quality of Life Enjoyment and Satisfaction Questionnaire: a new measure. Psychopharmacology Bulletin, 29, 321–326. [PubMed] [Google Scholar]
  14. First MB, Spitzer RL, Gibbon M, & Williams JB (1996). Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), version 2. Biometrics Research Department, New York State Psychiatric Institute: New York, NY. [Google Scholar]
  15. Faul F, Erdfelder E, Lang AG, & Buchner A (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175–191. 10.3758/BF03193146 [DOI] [PubMed] [Google Scholar]
  16. Foa EB, Huppert JD, Leiberg S, Langner R, Kichic R, Hajcak G, & Salkovskis PM (2002). The Obsessive-Compulsive Inventory: development and validation of a short version. Psychological Assessment, 14(4), 485–496. 10.1037/1040-3590.14.4.485 [DOI] [PubMed] [Google Scholar]
  17. Fournier JC, DeRubeis RJ, Shelton RC, Hollon SD, Amsterdam JD, & Gallop R (2009). Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. Journal of Consulting and Clinical Psychology, 77(4), 775–787. DOI: 10.1037/a0015401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, … & Charney DS (1989a). The Yale-Brown obsessive compulsive scale: I. Development, use, and reliability. Archives of General Psychiatry, 46(11), 1006–1011. DOI: 10.1001/archpsyc.1989.01810110048007 [DOI] [PubMed] [Google Scholar]
  19. Goodman WK, Price LH, Rasmussen SA, Mazure C, Delgado P, Heninger GR, & Charney DS (1989b). The Yale-Brown obsessive compulsive scale: II. Validity. Archives of General Psychiatry, 46(11), 1012–1016. DOI: 10.1001/archpsyc.1989.01810110054008 [DOI] [PubMed] [Google Scholar]
  20. Hamilton M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56–62. DOI: 10.1136/jnnp.23.1.56 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Keeley ML, Storch EA, Merlo LJ, & Geffken GR (2008). Clinical predictors of response to cognitive-behavioral therapy for obsessive–compulsive disorder. Clinical psychology review, 28(1), 118–130. DOI: 10.1016/j.cpr.2007.04.003 [DOI] [PubMed] [Google Scholar]
  22. Knopp J, Knowles S, Bee P, Lovell K, & Bower P (2013). A systematic review of predictors and moderators of response to psychological therapies in OCD: Do we have enough empirical evidence to target treatment? Clinical Psychology Review, 33, 1067–1081. DOI: 10.1016/j.cpr.2013.08.008 [DOI] [PubMed] [Google Scholar]
  23. Koran LM, Hanna GL, Hollander E, Nestadt G, Simpson HB; American Psychiatric Association (2007). Practice guideline for the treatment of patients with obsessive-compulsive disorder. American Journal of Psychiatry, 164, (suppl):5–53. [PubMed] [Google Scholar]
  24. Koran L, & Simpson H (2013). Guideline Watch (March 2013): Practice Guideline for the Treatment of Patients with Obsessive-Compulsive Disorder In APA Practice Guidelines. American Psychiatric Publishing, Inc. [Google Scholar]
  25. Kyrios M, Ahern C, Fassnacht DB, Nedeljkovic M, Moulding R, & Meyer D (2018). Therapist-Assisted Internet-Based Cognitive Behavioral Therapy Versus Progressive Relaxation in Obsessive-Compulsive Disorder: Randomized Controlled Trial. Journal of Medical Internet Research, 20(8), e242 DOI: 10.2196/jmir.9566 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kyrios M, Hordern C, & Fassnacht DB (2015). Predictors of response to cognitive behaviour therapy for obsessive-compulsive disorder. International Journal of Clinical and Health Psychology, 15(3), 181–190. DOI: 10.1016/j.ijchp.2015.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lovell K, Bower P, Gellatly J, Byford S, Bee P, McMillan D, … & Reynolds S (2017). Low-intensity cognitive-behaviour therapy interventions for obsessive-compulsive disorder compared to waiting list for therapist-led cognitive-behaviour therapy: 3-arm randomised controlled trial of clinical effectiveness. PLoS Medicine, 14(6), e1002337 DOI: 10.1371/journal.pmed.1002337 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Maher MJ, Huppert JD, Chen H, Duan N, Foa EB, Liebowitz MR, & Simpson HB (2010). Moderators and predictors of response to cognitive-behavioral therapy augmentation of pharmacotherapy in obsessive–compulsive disorder. Psychological Medicine, 40(12), 2013–2023. DOI: 10.1017/S0033291710000620 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mahoney AE, Mackenzie A, Williams AD, Smith J, & Andrews G (2014). Internet cognitive behavioural treatment for obsessive compulsive disorder: a randomised controlled trial. Behaviour Research and Therapy, 63, 99–106. DOI: 10.1016/j.brat.2014.09.012 [DOI] [PubMed] [Google Scholar]
  30. Marques L, LeBlanc NJ, Weingarden HM, Timpano KR, Jenike M, & Wilhelm S (2010). Barriers to treatment and service utilization in an internet sample of individuals with obsessive–compulsive symptoms. Depression and Anxiety, 27(5), 470–475. DOI: 10.1002/da.20694 [DOI] [PubMed] [Google Scholar]
  31. Moritz S, Wittekind CE, Hauschildt M, & Timpano KR (2011). Do it yourself? Self-help and online therapy for people with obsessive-compulsive disorder. Current Opinion in Psychiatry, 24(6), 541–548. DOI: 10.1097/YCO.0b013e32834abb7f [DOI] [PubMed] [Google Scholar]
  32. NICE (2005) Obsessive-compulsive Disorder: Core Interventions in the Treatment of Obsessive-compulsive Disorder and Body Dysmorphic Disorder. NICE clinical guideline 31. Available at http://guidance.nice.org.uk/CG31 [NICE guideline] [PubMed] [Google Scholar]
  33. Olatunji BO, Davis ML, Powers MB, & Smits JA (2013). Cognitive-behavioral therapy for obsessive-compulsive disorder: A meta-analysis of treatment outcome and moderators. Journal of psychiatric research, 47(1), 33–41. [DOI] [PubMed] [Google Scholar]
  34. Patel SR, Humensky JL, Olfson M, Simpson HB, Myers R, & Dixon LB (2014). Treatment of obsessive-compulsive disorder in a nationwide survey of office-based physician practice. Psychiatric Services, 65(5), 681–684. DOI: 10.1176/appi.ps.201300192 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Patel SR, Wheaton MG, Andersson E, Rück C, Schmidt AB, La Lima CN, … & Simpson HB (2018). Acceptability, feasibility, and effectiveness of internet-based cognitive-behavioral therapy for obsessive-compulsive disorder in New York. Behavior Therapy, 49(4), 631–641. DOI: 10.1016/j.beth.2017.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Powers MB, Warren AM, Rosenfield D, Roden-Foreman K, Bennett M, Reynolds MC, Davis ML, Foreman ML, Petrey LB, & Smits JAJ (2014). Predictors of PTSD symptoms in adults admitted to a Level I trauma center: A prospective analysis. Journal of Anxiety Disorders, 28, 301–309. DOI: 10.1016/j.janxdis.2014.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Ruscio AM, Stein DJ, Chiu WT, & Kessler RC (2010). The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey Replication. Molecular Psychiatry, 15(1), 53–63. DOI: 10.1038/mp.2008.94 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rück C, Lundström L, Flygare O, Enander J, Bottai M, Mataix-Cols D, & Andersson E (2018). Study protocol for a single-blind, randomised controlled, non-inferiority trial of internet-based versus face-to-face cognitive behaviour therapy for obsessive–compulsive disorder. BMJ Open, 8(9), e022254 DOI: 10.1136/bmjopen-2018-022254 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Seol SH, Kwon JS, Kim YY, Kim SN, & Shin MS (2016). Internet-based cognitive behavioral therapy for obsessive-compulsive disorder in Korea. Psychiatry Investigation, 13(4), 373–382. DOI: 10.4306/pi.2016.13.4.373 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Shoham V, & Insel TR (2011). Rebooting for whom? Portfolios, technology, and personalized intervention. Perspectives on Psychological Science, 6(5), 478–482. DOI: 10.1177/1745691611418526 [DOI] [PubMed] [Google Scholar]
  41. Simpson HB, Foa EB, Liebowitz MR, Huppert JD, Cahill S, Maher MJ, … & Campeas R (2013). Cognitive-behavioral therapy vs risperidone for augmenting serotonin reuptake inhibitors in obsessive-compulsive disorder: a randomized clinical trial. JAMA Psychiatry, 70(11), 1190–1199. DOI: 10.1001/jamapsychiatry.2013.1932 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Smits JAJ, Hofmann SG, Rosenfield D, DeBoer LB, Costa P, Simon NM, … & M. H.( 2013). D-cycloserine augmentation of cognitive behavioral group therapy of social anxiety disorder: prognostic and prescriptive variables. Journal of Consulting & Clinical Psychology, 81, 1100–1112. DOI: 10.1037/a0034120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Simpson HB, Maher MJ, Wang Y, Bao Y, Foa EB, & Franklin M (2011). Patient adherence predicts outcome from cognitive behavioral therapy in obsessive-compulsive disorder. Journal of consulting and clinical psychology, 79(2), 247 DOI: 10.1037/a0022659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Steketee G, Siev J, Yovel I, Lit K, & Wilhelm S (2019). Predictors and moderators of cognitive and behavioral therapy outcomes for OCD: A patient-level mega-analysis of eight sites. Behavior therapy, 50(1), 165–176. 10.1016/j.beth.2018.04.004 [DOI] [PubMed] [Google Scholar]
  45. Tolin DF, Diefenbach GJ, & Gilliam CM (2011). Stepped care versus standard cognitive–behavioral therapy for obsessive–compulsive disorder: A preliminary study of efficacy and costs. Depression and Anxiety, 28(4), 314–323. DOI: 10.1002/da.20804 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Tolin DF, Maltby N, Diefenbach GJ, Hannan SE, & Worhunsky P (2004). Cognitive-behavioral therapy for medication nonresponders with obsessive-compulsive disorder: a wait-list-controlled open trial. Journal of Clinical Psychiatry, 65, 1169–1175. DOI: 10.4088/jcp.v65n0708 [DOI] [PubMed] [Google Scholar]
  47. Wheaton MG, Gershkovich M, Gallagher T, Foa EB, & Simpson HB (2018). Behavioral avoidance predicts treatment outcome with exposure and response prevention for obsessive–compulsive disorder. Depression and Anxiety, 35(3), 256–263. DOI: 10.1002/da.22720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wheaton MG, Rosenfield D, Foa EB, & Simpson HB (2015). Augmenting serotonin reuptake inhibitors in obsessive–compulsive disorder: What moderates improvement?. Journal of Consulting and Clinical Psychology, 83(5), 926–937. DOI: 10.1037/ccp0000025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Wootton BM, Dear BF, Johnston L, Terides MD, & Titov N (2013). Remote treatment of obsessive-compulsive disorder: a randomized controlled trial. Journal of Obsessive-Compulsive and Related Disorders, 2(4), 375–384. DOI: 10.1016/j.jocrd.2013.07.002 [DOI] [Google Scholar]

RESOURCES