How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review

Abstract

BackgroundNew technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example.ObjectiveThe impact of new technologies on health professionals’ practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD.MethodsWe conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video].ResultsWe analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention.ConclusionsThe review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

The impact of coronavirus (COVID‐19) in the diagnosis and treatment of obsessive‐compulsive disorder

“…(Fineberg et al, 2020) It further indicated that, for safety reasons, in vivo cognitive‐behavioral therapy with exposure and response prevention (ERP) may need to be substituted by imaginal exposure. (Fineberg et al, 2020) By the same token, we also feel that the pandemia provides increased room for testing the role of ERP delivered thought new technologies, such as virtual reality (Ferreri et al, 2019). Given the importance given to pharmacotherapy, another topic of concern is drug interactions of experimental drugs for COVID‐19 and medications prescribed for patients with OCD.…”

Clinical advances in obsessive-compulsive disorder: a position statement by the International College of Obsessive-Compulsive Spectrum Disorders

“…Harnessing smartphone technology along with smart wearables (e.g., smart watches) is expected to be a valuable source of continuous, objective and reliable data for clinical characterization, behavioural monitoring and treatment support (Marzano et al, 2015). This is true for several disorders, but especially true for obsessive-compulsive problems such as PUI, as the digital media that is directly linked to the disorder is the same one that can accurately monitor the behaviour (Ferreri et al, 2019).…”

“…Accordingly, using digital technology along with big data analyses may enable the potential to characterize the 'digital phenotype' of the disorder (Ferreri et al, 2019) and to identify those individuals most at risk (e.g., by monitoring online internet usage in comparison with changes in diurnal variation, lack of human contact, lack of geographical movement, restricted circles of friends, etc.). A research avenue in this direction is to use (real time) big data analysis, alongside machine learning algorithms, to establish identifiable OCRD-specific illness patterns and use those real-time results to create an immediate feedback loop with the patient, which could then be used therapeutically by providing direct feedback on their behaviour and progress.…”

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity

“…As it has in the present study, investigating higher brain functions such as mental imagery might expand our physiological and pathological understanding of psychiatric disorders and facilitate the search for their biomarkers. Furthermore, neural decoding might help treat psychiatric disorders by application of its detailed results to neuromodulation methods such as neurofeedback (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) and transcranial stimulation (88,89).…”

“…In recent years, neural decoding research has progressed along with the expansion of machine learning (ML). Neural decoding has also been applied for interpreting mental states (1,2) and for treating various psychiatric disorders with neurofeedback (3)(4)(5)(6)(7)(8)(9)(10)(11)(12). With the development of deep learning, decoding performance improvement is accelerating.…”