BACKGROUND Use of smartphones and wearable biosensors has increased rapidly, with an estimated 87% of adult Americans currently carrying a smartphone and 20% using wearable sensors. These devices can continuously measure various aspects of behavior and physiology, while also collecting data that require user input (such as mood, context, and social interactions). Mobile and wearable technologies have been implemented in healthcare and psychiatry to monitor symptom burden, estimate diagnosis and risk for relapse, predict treatment response, and deliver digital interventions, though the uptake of mobile and wearable devices in psychiatry is varied. Obsessive-compulsive disorder (OCD) is a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course. Use of mobile and wearable devices in monitoring and treating OCD is highly variable, though these technologies present a novel approach to improving care for individuals suffering from this disorder. Given the speed at which new technologies are developed and implemented in clinical settings, continual reappraisal of this field is needed. OBJECTIVE In this scoping review we sought to map the literature on the use of wearable- and smartphone-based devices or applications in the assessment, monitoring, or treatment of OCD. METHODS We conducted a search of multiple databases including PubMed, EMBASE, APA Psycinfo, and Web of Science from 07/08/2022 – 07/27/2022 using the following search strategy: ("OCD" OR "obsessive" OR "obsessive-compulsive") AND ("smartphone" OR "phone" OR "wearable" OR "sensing" OR "biofeedback" OR "neurofeedback" OR "neuro feedback" OR "digital" OR "phenotyping" OR "mobile" OR "heart rate variability" OR "actigraphy" OR "actimetry" OR “biosignals” OR “biomarker” OR “signals” OR “mobile health”). RESULTS We analyzed 2,487 records, reviewed the full text of 67 articles, and included 21 studies in this review. We divided our review into three parts: 1) studies without mobile/digital intervention and with passive data collection, 2) studies without mobile/digital intervention and with active or mixed data collection, and 3) studies including a digital/mobile intervention. CONCLUSIONS Use of mobile and wearable technology in OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally recapitulate subjective report. Ecological momentary assessment is well-tolerated for naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse, though generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for safe and trusted uptake of technology by patients and providers.
Background Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies. Objective Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD. Methods In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: (“OCD” OR “obsessive” OR “obsessive-compulsive”) AND (“smartphone” OR “phone” OR “wearable” OR “sensing” OR “biofeedback” OR “neurofeedback” OR “neuro feedback” OR “digital” OR “phenotyping” OR “mobile” OR “heart rate variability” OR “actigraphy” OR “actimetry” OR “biosignals” OR “biomarker” OR “signals” OR “mobile health”). Results We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention. Conclusions Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.
OBJECTIVES/GOALS: This study will collect multimodal and longitudinal data in adults with obsessive-compulsive disorder and healthy controls. A mixed effects random forest machine learning approach will be taken to develop a model that can predict individualized longitudinal OCD symptom burden. METHODS/STUDY POPULATION: Baseline resting state functional MRI (rsfMRI) and measures of symptom burden will be collected in adults with OCD and healthy controls. Longitudinal measures of behavior and physiology–such as heart rate, activity, and sleep metrics - will be collected using Fitbit Charge 5 tracker. Daily assessments of symptom burden and functional status will be collected through a smartphone app. Individuals with OCD will start pharmacotherapy during the study period and all participants will be followed for a total of 10 weeks. Repeat rsfMRI imaging will occur at study conclusion. Data will be analyzed using a mixed effects random forest machine learning algorithm with assessment of model performance. RESULTS/ANTICIPATED RESULTS: Prior studies of symptom severity in psychiatric illness and affect in non-clinical populations have found longitudinal features - such as lexical and acoustic measures, participant context, heart rate, and sleep metrics–that were predictive of these states over time. It is anticipated that the present study will extend these results to individuals with OCD and identify physiologic and behavioral features that track personalized symptom burden longitudinally in this patient population. A model able to predict when symptoms are elevated could allow for provision of additional treatment or interventions targeted to times of high symptom burden. DISCUSSION/SIGNIFICANCE: This study will be the first to collect and analyze longitudinal measures of behavior, symptoms, and physiology in patients with OCD with a goal of predicting symptom burden. Identification of elevated symptom burden would allow for implementation of just-in-time treatment, during these periods.
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