This is a secondary analysis of a 12-week randomized controlled trial comparing Perspectives OCD to HealthWatch, an online general health psychoeducation program validated as an attentional control for DMHIs [19]. In this study, only data from the Perspectives OCD condition were used (N=60). Full descriptions of study design, intervention, eligibility criteria, and procedures are reported in the primary manuscript [16] and clinicaltrials.gov record (NCT04136626). A STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist is provided in Multimedia Appendix 1 [20].

Perspectives OCD is a self-paced Android and iOS-compatible app with seven core modules that cover (1) psychoeducation, which oriented users to the CBT model of OCD and guided personalized goal setting; (2) cognitive restructuring to identify and reappraise OCD-related cognitive distortions; (3) ERP, which guided users through graded exposure to OCD triggers while refraining from compulsions; (4) mindfulness and mindful grounding (2 modules), which included audio exercises teaching mindfulness and attentional retraining skills; (5) values-based living and self-esteem, which helped users set activity goals aligned with their values and identify and modify negative core beliefs; and (6) relapse prevention. During goal setting, participants selected 1-4 OCD symptom domains (ie, contamination, over responsibility, perfectionism, aggressive, sexual, and scrupulosity obsessions). Within the ERP modules, the app then presented symptom-specific graded exposure hierarchies for each selected domain, which participants could complete in any order, skip if irrelevant, and supplement with user-created custom exposures. All app modules unlocked sequentially and contained psychoeducational reading complemented by short interactive exercises that could be repeated as often as desired. Useful exercises could be added to “favorites” for easy repetition.

Perspectives OCD was delivered with light-touch coach support. Each participant was assigned a trained, bachelor’s-level coach who provided 2 brief phone calls at onboarding and midtreatment, respectively, as well as asynchronous in-app messaging. Coach support was focused on encouraging regular use, clarifying CBT content, and troubleshooting practical barriers, without delivering psychotherapy outside the app. Participants were encouraged to use the app daily and progress at their own pace over 12 weeks. See Wilhelm et al [16] for a full description of the intervention and participant journey.

Eligible participants included adults aged 18 years and older in the United States with a primary diagnosis of OCD, confirmed via the Mini International Neuropsychiatric Interview (MINI; [21]), who owned a smartphone with a data plan. Individuals were excluded if they were in concurrent psychotherapy or used another OCD-related app, had received 4 or more prior sessions of CBT for OCD, or had initiated or changed psychotropic medication in the past 2 months; stable medication was permitted. Additional exclusion criteria included current severe substance use disorder, current severe major depressive disorder, current posttraumatic stress disorder, lifetime bipolar disorder or psychosis, and acute suicidal ideation. Participants were recruited nationally from the Massachusetts General Hospital Center for OCD and Related Disorders via social media advertisements, professional organizations, and colleague referrals, between July 2021 and July 2023.

The present analyses were not preregistered and are exploratory. Differences were evaluated at α=.05, and analyses were conducted in SAS (version 9.4; SAS Institute) for Windows. All participants downloaded the app and initiated app use, so app use variables were calculated for all.

This study applied a previously developed approach for comprehensively measuring DMHI behavioral engagement [9] to a new clinical use case. This involved computing 21 objective behavioral engagement variables spanning use volume, frequency, breadth, depth, and temporal patterns. Three key findings emerged. First, overall engagement was exceptionally high and sustained over 12 weeks, contrasting with the rapid attrition commonly reported in DMHIs [7,35]. Second, cluster analysis revealed 3 distinct engagement profiles, multidimensionally high, moderate, and low engagers, yet all 3 groups experienced significant and similar reductions in OCD severity, challenging simple dose-response assumptions. Third, pre-treatment credibility was significantly associated with engagement profiles, even after FDR adjustment, identifying a potential modifiable target for improving DMHI engagement. These findings demonstrate that this measurement approach can be flexibly adapted across DMHIs and conditions, while also yielding novel insights specific to OCD.

Overall, participants’ total app use was high and exceeded the 77-175 minutes documented in prior DMHIs [36]. Use was also remarkably sustained over time; most users were still active at week 12. This pattern of minimal drop-off across 3 months contrasts sharply with usage documented in other DMHI studies; for instance, a report on PTSD Coach found that only 15.6% (26,110/166,861) of users reopened the app 1 week after downloading it [37]. These volume and retention results are particularly notable, given that OCD treatment centers on ERP, which is aversive and can trigger avoidance [13], especially in self-directed interventions that do not involve therapist support.

Several factors may explain this sustained engagement. First, the present data come from a clinical trial, whereas naturalistic studies capture in-the-wild downloads, representing 2 distinct contexts [38]. Clinical trial participants may be more motivated through self-selection and less psychiatrically complex due to eligibility criteria. They may benefit from reinforcing factors such as study staff contact for assessments. That said, the current pattern of sustained use stands out even relative to our prior randomized controlled trial of smartphone CBT for BDD, which showed active user rates declining from 97% to 27% between weeks 1 and 12 [9]. Thus, sustained engagement in this sample likely extends beyond the effects of clinical trials alone. Another potential factor is the intervention itself. In smartphone CBT for BDD, exposure focuses narrowly on facing situations that trigger appearance concerns. After several weeks of practice, the content of BDD exposures may inherently lack novelty, and users may have less need to interact with the app. On the other hand, CBT for OCD can include widely varying exposures across symptom domains, depending on the user’s clinical presentation. Thus, participants may have returned to Perspectives OCD over time because they continued to benefit from novel content. Finally, it is possible that personality traits associated with OCD, such as conscientiousness [39,40], responsibility [41], perfectionism [42], or need for completion [43], may translate to diligence in completing in-app assignments, although the literature on this is mixed.

The observed pattern of brief and frequent app use suggests that users engage with DMHIs through short interactions rather than the 45‐ to 50-minute concentrated doses typical of traditional psychotherapy [44,45]. This pattern was consistent with our previous findings for BDD [9]. This distinct DMHI usage pattern suggests that app content should be designed to deliver meaningful information and skills practice in brief increments, reinforced through frequent use over time.

Cluster analysis identified 3 user profiles that differed across all measured dimensions. Cluster 1 users showed extensive engagement across all dimensions, including ERP. Cluster 2 users demonstrated moderate, consistent use, completing core content (median 7 ERP exercises) without exhaustive exploration. Cluster 3, despite spending meaningful time on the app, completed few to no ERP exercises and showed a sharp temporal decline in use over time. In contrast to hypotheses, all clusters experienced significant reductions in OCD severity, and there were no statistically significant differences in the magnitude of symptom change by cluster. On the other hand, while we did not analyze response and remission rate differences statistically, rates of full response were descriptively higher in cluster 1 than in cluster 2 or 3, and rates of remission appeared to be considerably higher in cluster 1 than in cluster 2. This pattern of findings is similar to our prior BDD study, which showed a nonsignificant trend toward deeper use being associated with greater improvement [9]. Nonsignificant results may in part reflect the small sample size, particularly in cluster 3, which limited our power to detect what may be clinically relevant differences. Indeed, at a prevalence rate of 40% and a 3:1 group size ratio (ie, assuming participants with very low app use make up only one-quarter of the sample), a sample size of n=224 would be needed to detect a 20% difference in response rates at 80% power. Nonsignificant results also align with broader literature that has inconsistently documented dose-response relationships between engagement and DMHI outcomes [46-48]. These findings invite careful consideration of what constitutes “effective” engagement. One possible interpretation of results is that, when an intervention is potent, as this one appears to be [16], multiple styles of use may be effective. For instance, while ERP is the frontline strategy for OCD, evidence also supports the benefits of other skills such as cognitive strategies for OCD [49,50]. Although cluster 3 users completed few, if any, exposures, they may have benefited from other app content. To this end, 1 potential benefit to self-directed DMHIs is that users who are not willing to engage in certain treatment strategies (eg, ERP) can choose to learn other skills, which may also be therapeutic; in other words, self-directed digital interventions allow users to gravitate toward content that aligns with their readiness.

That said, these null findings may also reflect that engagement was relatively high even within cluster 3, whose median use exceeded that reported in unguided internet-based OCD treatments (129 minutes [46]). A small sample alongside a restricted range of engagement and outcomes may have prevented us from observing what happens to truly disengaged users. This restricted range is in part due to data coming from an efficacy trial; if replicated in the wild, we expect to find greater variability in engagement and treatment outcomes, giving more range from which to identify effective and ineffective engagement styles. Testing the current measurement approach in large real-world data from implemented interventions is a critical next step in the current program of research.

To supplement objective measures of engagement, we asked participants to self-report the time they spent practicing skills both within and outside the app, a complementary variable referred to as enactment in prior DMHI engagement research [18]. These data had limitations related to missingness and data integrity. Despite this, they offered preliminary insights that would be impossible to infer from app analytics alone. All clusters reported spending more time using skills off the app than on the app. This finding underscores the value of asking such a question, as ultimately, the goal is for users to develop skills that they can use independently in daily life. Although we had data from only 2 users in cluster 3, their responses to this item shed further light on why these participants may have improved significantly despite having interacted with the app the least. Cluster 3 participants, in contrast to those in cluster 1, may include people who preferred to visit materials briefly on the app and then implement skills off the app in daily life, still reaping therapeutic benefits despite reduced observed behavioral engagement.

A novel contribution of this study was its incorporation of participants’ baseline treatment credibility and expectancy scores. While clusters 1 and 2 perceived app CBT for OCD to be similarly credible and had comparable treatment expectations, cluster 3 perceived treatment to be significantly less credible and had lower expectations than cluster 2; the difference in credibility (but not expectancy) ratings remained significant after FDR adjustment. Results are consistent with research showing significant associations between treatment expectancy and adherence [51,52]. Relatedly, recent research on OCD has shown that treatment credibility and expectancy predict outcomes in concentrated exposure-based treatment [14]. It is possible that those who most strongly believe in the treatment’s premise and who have high expectations that treatment will work are most likely to engage deeply and in a targeted way (eg, focused on ERP modules), and this deeper engagement may be a mechanism through which credibility and expectancy predict outcomes. Current results are correlational; we cannot conclude that higher credibility or expectancy caused greater engagement. If further experimental studies confirm a causal relationship, these novel results highlight potential intervention targets to enhance engagement and, potentially, outcomes [53]. App developers can boost credibility by providing a detailed rationale for the treatment approach during onboarding and partnering with credible codevelopers, such as professional experts. To enhance expectancy, psychoeducational content can instill hope by providing information about anticipated outcomes (drawing on the literature).

End-of-treatment satisfaction also varied by cluster, with cluster 3, the least engaged participants, reporting significantly lower treatment satisfaction than cluster 2. Interestingly, all clusters gave the app similar, high-quality ratings on the uMARS, suggesting that lower satisfaction in cluster 3 reflected aspects of the treatment experience beyond app quality. Results linking lower satisfaction ratings to reduced behavioral engagement begin to address a gap identified in recent reviews [3,6], which have noted that prior DMHI studies tend to examine either subjective or behavioral engagement, in isolation.

This study has several strengths, including the demonstration that a comprehensive behavioral engagement measurement approach generalizes across clinical samples, the integration of objective and select subjective engagement measures to provide a more complete picture than either alone, and the novel examination of pretreatment credibility and expectancy as predictors of objective engagement patterns. Additionally, several limitations should be noted. This was an exploratory, non-preregistered secondary analysis of an efficacy trial, which included eligibility criteria and participant interactions with research staff, thereby limiting its generalizability to real-world implementation contexts. High engagement and strong treatment outcomes across clusters likely in part reflect this and create a restriction of range that may have obscured engagement-outcome associations. The sample was relatively small, affecting the stability of the cluster solution and homogeneity of the chosen clusters. Although a bootstrap stability analysis of our sample indicated reasonable cluster stability, analyses of larger samples of app users are needed. Relatedly, the small size of cluster 3 (n=8), in particular, limits statistical power for comparisons involving this group. The number of tests of statistical significance may have resulted in type I errors. We sought to mitigate this risk by also evaluating P values adjusted for multiple testing via an FDR adjustment. Although comprehensive in its treatment of behavioral engagement, our approach does not capture subjective dimensions such as cognitive absorption or emotions during use [4]. We incorporated posttreatment satisfaction and quality ratings, which reflect user attitudes about the DMHI, but future studies should additionally measure subjective cognitive and affective engagement during use (eg, via ecological momentary assessment, in-the-moment ratings of willingness, and affective participation). This gap may be especially important for a DMHI targeting OCD; as noted previously, ERP is aversive, and one’s willingness to actively participate and experience anxiety during ERP is critical. It is possible that the 3 clusters differed in their attentional, cognitive, and affective participation (and conversely, experiential avoidance) during app-delivered ERP, and capturing these variables might further clarify results related to treatment outcomes across clusters. Additionally, OCD is a heterogeneous condition, and this study was not powered to examine whether symptom subtypes or the number of symptom domains endorsed influenced engagement profiles. Replication in large, real-world samples is a critical next step.

This work demonstrates that our approach to comprehensive behavioral engagement measurement is feasible and informative across different DMHIs. The methodological approach provides a nuanced view of the quantity and quality of users’ behavioral engagement with digital tools that researchers can adapt to their own DMHIs. The novel findings that pretreatment credibility and expectancy are associated with different behavioral engagement patterns identify an actionable target to potentially boost behavioral engagement. Based on our experience across 2 DMHIs, we recommend that researchers measuring engagement include, at minimum, variables capturing use volume (eg, total time and number of sessions), temporal patterns (eg, consistency of use over treatment phases and inactivity periods), feature-level engagement (eg, completion of therapeutic content theorized to be most active for the given clinical target), and session characteristics (eg, duration distribution). We further recommend supplementing objective metrics with self-reported off-app skills practice, pre- and posttreatment attitudes, and measures of cognitive and affective engagement, alongside behavioral engagement.