Background
Obsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). Previous ...factor analytic and clustering studies suggest the presence of three or four subtypes of OCD symptoms. However, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased toward researchers’ previous conceptualizations of OCD.
Objective
In this study, we examine a large data set of freely reported obsession symptoms obtained from an OCD mobile app as an alternative to uncovering potential OCD subtypes. From this, we examine data-driven clusters of obsessions based on their latent semantic relationships in the English language using word embeddings.
Methods
We extracted free-text entry words describing obsessions in a large sample of users of a mobile app, NOCD. Semantic vector space modeling was applied using the Global Vectors for Word Representation algorithm. A domain-specific extension, Mittens, was also applied to enhance the corpus with OCD-specific words. The resulting representations provided linear substructures of the word vector in a 100-dimensional space. We applied principal component analysis to the 100-dimensional vector representation of the most frequent words, followed by k-means clustering to obtain clusters of related words.
Results
We obtained 7001 unique words representing obsessions from 25,369 individuals. Heuristics for determining the optimal number of clusters pointed to a three-cluster solution for grouping subtypes of OCD. The first had themes relating to relationship and just-right; the second had themes relating to doubt and checking; and the third had themes relating to contamination, somatic, physical harm, and sexual harm. All three clusters showed close semantic relationships with each other in the central area of convergence, with themes relating to harm. An equal-sized split-sample analysis across individuals and a split-sample analysis over time both showed overall stable cluster solutions. Words in the third cluster were the most frequently occurring words, followed by words in the first cluster.
Conclusions
The clustering of naturally acquired obsessional words resulted in three major groupings of semantic themes, which partially overlapped with predefined checklists from previous studies. Furthermore, the closeness of the overall embedded relationships across clusters and their central convergence on harm suggests that, at least at the level of self-reported obsessional thoughts, most obsessions have close semantic relationships. Harm to self or others may be an underlying organizing theme across many obsessions. Notably, relationship-themed words, not previously included in factor-analytic studies, clustered with just-right words. These novel insights have potential implications for understanding how an apparent multitude of obsessional symptoms are connected by underlying themes. This observation could aid exposure-based treatment approaches and could be used as a conceptual framework for future research.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Exposure and response prevention (ERP) therapy, a form of cognitive-behavioral therapy, is a first-line, evidence-based treatment for obsessive-compulsive disorder (OCD) for adults and children. It ...is effective for the majority of those who engage in it, but treatment adherence can be challenging for some due to the stress involved in the treatment as well as different life circumstances that arise. To help improve treatment adherence, NOCD, a provider of video teletherapy ERP, identifies those at risk of non-adherence using a prediction algorithm trained on a data set of
N
= 13,809 and provides targeted peer support interventions by individuals (“Member Advocates”) who successfully completed ERP treatment for OCD. Member Advocates, using lived OCD experience as well as experience with ERP, engage at-risk patients through digital messaging to engage, educate, and encourage patients in the early stages of treatment. From June 2022 to August 2022,
N
= 815 patients deemed at risk were reached out to and
n
= 251 responded and engaged with the Member Advocates. In the at-risk patients who engaged, the intervention resulted in a significant mean 30.4% more therapy hours completed compared to those who did not engage. Additionally, engaged patients had greater reductions in OCD severity. These results have implications for how data science, digital interventions, and strategic peer-to-peer communication and support can be combined to enhance the effectiveness of treatment.
Exposure and response prevention, a type of cognitive-behavioral therapy, is an effective first-line treatment for obsessive-compulsive disorder (OCD). Despite extensive evidence of the efficacy of ...exposure and response prevention (ERP) from clinical studies and in real-world samples, it is still underused as a treatment. This is likely due to the limits to access to care that include the availability of adequately trained therapists, as well as geographical location, time, and cost barriers. To address these, NOCD created a digital behavioral health treatment for OCD using ERP delivered via video teletherapy and with technology-assisted elements including app-based therapy tools and between-session therapist messaging.
We examined treatment outcomes in a large naturalistic sample of 3552 adults with a primary OCD diagnosis who received NOCD treatment.
The treatment model consisted of twice-weekly, live, face-to-face video teletherapy ERP for 3 weeks, followed by 6 weeks of once-weekly brief video teletherapy check-ins for 30 minutes. Assessments were conducted at baseline, at midpoint after completion of 3 weeks of twice-weekly sessions, and at the end of 6 weeks of brief check-ins (endpoint). Longitudinal assessments were also obtained at 3, 6, 9, and 12 months after endpoint.
Treatment resulted in clinically and statistically significant improvements, with a 43.4% mean reduction in obsessive-compulsive symptoms (g=1.0; 95% CI 0.93 to 1.03) and a 62.9% response rate. Treatment also resulted in a 44.2% mean reduction in depression, a 47.8% mean reduction in anxiety, and a 37.3% mean reduction in stress symptoms. Quality of life improved by a mean of 22.7%. Reduction in OCD symptoms and response rates were similar for those with mild, moderate, or severe symptoms. The mean duration of treatment was 11.5 (SD 4.0) weeks, and the mean total therapist time was 10.6 (SD 1.1) hours. Improvements were maintained at 3, 6, 9, and 12 months.
In this sample, representing the largest reported treated cohort of patients with OCD to date, video teletherapy treatment demonstrated effectiveness in reducing obsessive-compulsive and comorbid symptoms and improved quality of life. Further, it achieved meaningful results in less than half the total therapist time compared with standard once-weekly outpatient treatment, an efficiency that represents substantial monetary and time savings. The effect size was large and similar to studies of in-person ERP. This technology-assisted remote treatment is readily accessible for patients, offering an advancement in the field in the dissemination of effective evidence-based care for OCD.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Obsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). While specific ...obsessions and compulsions can manifest in vastly different ways, previous factor analytic and clustering studies suggest the presence of three or four "subtypes" of OCD symptoms. Yet, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased towards researchers' prior conceptualizations of OCD.
As an alternative to uncovering potential OCD subtypes, we examined a large data set of freely-reported obsession symptoms obtained from an OCD mobile app. From this we examined data-driven clusters of obsessions based on their latent semantic relationships in the English language, using word embedding, a type of natural language processing.
We extracted free-text entry words describing obsessions in a large sample of users of the mobile application, "NOCD," who self-identified as having OCD. Semantic vector space modeling was applied using Global Vectors for Word Representation algorithm (GloVe), an unsupervised learning algorithm for obtaining vector representations for words based on word-word co-occurrence statistics from a 6 billion word corpus. A domain-specific extension, "Mittens," was also applied to enhance the corpus with OCD-specific words. After cleaning the obsessions words, we created a word co-occurrence matrix. Resulting representations provided linear substructures of the word vector in 100-dimensional space. We applied principal components analysis to the 100-dimensional vector representation of the most frequent words, followed by k-means clustering to obtain clusters of related words.
We obtained unique 7,001 words representing obsessions from 25,369 individuals. Heuristics for determining optimal numbers of clusters pointed to a three-cluster solution, with themes relating to doubt/checking, contamination/somatic/physical harm/sexual harm, and relationship/just-right. All three clusters showed relatively close semantic relationships to each other in a central area of convergence, with themes relating to harm. An equal-sized split-sample analysis across individuals and a split-sample analysis over time both showed overall stable cluster solutions. Words in the contamination/somatic/physical harm/sexual harm cluster were the most frequently occurring, followed by words in the relationship/just-right cluster.
Clustering of naturalistically-acquired obsessional words resulted in three major groupings of semantic themes, which partially overlap with previous studies' results using predefined checklists. Further, the closeness of the overall embedded relationships across clusters and their central convergence on harm suggests that, at least at the level of self-reported obsessional thoughts, the majority of obsessions have close semantic relationships. Harm to self or others may be an underlying organizing theme across many obsessions. Notably, "relationship" themed words, not previously included in factor analytic studies, clustered with "just-right" words. These novel insights have potential implications for understanding how an apparent multitude of obsessional symptoms are connected by underlying themes. This could aid in exposure-based treatment approaches and could be used as a conceptual framework for future research.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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