Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review

Introduction

Background

The use of smartphones and wearable devices has increased recently, with an estimated 87% of adults in the United States carrying a smartphone [1] and 1 in 5 Americans using a wearable device [2]. Smartphones provide a near-constant connection to the internet and contain a suite of sensors for estimating parameters such as location, movement, and ambient sound levels [3]. Further, apps are continually being developed for smartphones that range in design from games to music and video streaming services to social media to health monitoring. Data collection from smartphones can be passive, occurring without user awareness or input (eg, accelerometry measurements), or active, where the user is engaged and directly contributes to data collection (eg, answering questionnaires or prompts). Wearable devices are a technology that directly connect to the human body and can sense aspects of physiology (eg, heart rate, oxygen saturation, glucose levels, and lactate levels) or behavior (eg, step count, time of sleep onset, and amount and type of exercise completed) and include items such as wrist-based monitors (eg, fitness trackers and smart watches), smart clothes (eg, shirts and shoes), skin patches, eyeglasses, and contact lenses (see Chan et al [4], Kim et al [5], and Zhang et al [6] for review of this technology). These devices and their associated apps are increasingly finding applications in health and medicine [3-8]. Examples include glucose monitoring in patients with diabetes [5], activity sensing in patients with heart failure [9], and lung function monitoring in patients with chronic obstructive pulmonary disease [10].

In psychiatry, use cases for wearable sensors and smartphone-based apps range in type, design, function, and objective [11,12]. Broadly, these technologies have been used to (1) detect and monitor symptoms [13-16]; (2) estimate diagnostic class, illness severity, and risk for relapse [17-20]; (3) predict response to treatment [21-23]; (4) and deliver digital interventions [24,25]. Populations in which wearable and smartphone-based technologies have been investigated and implemented vary: studies in nonclinical populations have sought to broadly promote and track mental health [26,27]. Within clinical samples, a multitude of psychiatric conditions have been explored, including depression [15,20-22], anxiety [28], schizophrenia [24], bipolar disorder [13,14,17], social anxiety [19], and obsessive-compulsive disorder (OCD) [29]. The extent of the uptake of these technologies in psychiatry varies across conditions, and we are interested in understanding the landscape of the literature covering this within the domain of OCD.

OCD is a chronic and prevalent psychiatric disorder characterized by intrusive and distressing thoughts, images, impulses, and repetitive or ritualistic behaviors [30]. OCD is considered one of the most disabling psychiatric disorders [31] and exacts a significant personal [32] and societal economic toll [33]. The course of OCD is chronic and fluctuating for many individuals [34,35], and treatment response typically hovers near 50% [36,37]. The fluctuating nature and limited treatment responsiveness of OCD present a unique opportunity for wearable and smartphone-based technologies to impact the care for and treatment of individuals with OCD. The use of technology in the treatment of anxiety and obsessive-compulsive spectrum disorders [38], specifically the use of technology in assessing and treating OCD [29], has recently been reviewed. In both reviews, the authors found heterogeneity in the implementation of technology in the care of individuals with OCD as well as an opportunity for advancing research and clinical care.

Objectives of This Review

Given the speed at which new technologies are developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we sought to map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment or monitoring of OCD symptoms and treatment of OCD. Regarding treatment, we focused on novel interventions and excluded studies on the mobile implementation of standard psychotherapy (such as cognitive behavioral therapy [CBT] with exposure-response prevention [ERP]). We aimed to assess domains in which wearable and mobile technologies have had an impact on OCD care while also identifying areas for continued improvement and innovation within this realm.

Methods

Study Design

Given our objectives, the known heterogeneity in implementing technology in individuals with OCD, and the focus on emerging innovative interventions, a scoping review is the most appropriate synthesis approach. The purpose of a scoping review is to identify all available evidence to assess the breadth, depth, and nature of research activity in a topic of interest, and it is particularly useful in rapidly mapping evidence in emerging topics while maintaining rigorous search and study selection processes [39].

The protocol for this review was preregistered at the Open Science Foundation on August 9, 2022 [40]. We consulted a research librarian at the University of Southern California regarding scoping review protocols, topic development, search strategies, and data management. Keywords were initially identified from recent literature reviews relevant to the topic and preliminarily tested using the University of Southern California library database and Google Scholar (Google LLC). We included additional search terms yielded from discussion between the authors. We conducted our initial search in July 2022, with an update in April 2023; we searched multiple databases, including PubMed, Embase, APA PsycInfo, and Web of Science, with no restriction on original study design or 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”). Retrieved records were entered into the Covidence review software (Veritas Health Innovation), and duplicate records were removed.

Inclusion and Exclusion Criteria

As recommended by the Joanna Briggs Institute Reviewers’ Manual for scoping reviews, we used the Population, Concept, and Context framework to inform our inclusion and exclusion criteria [41].

The inclusion criteria for study population are as follows: individuals with OCD as the primary diagnosis and of any age and sex.

Individuals who did not have OCD as a primary diagnosis were excluded from the study to avoid confounding population factors. We applied no sex or age restrictions because OCD can affect any sex and can develop at any age, including in childhood.

The inclusion criteria for study concept are as follows: biobehavioral technology or smartphone-based technology involved in the assessment or monitoring of OCD symptoms or treatment of OCD and does not use CBT and ERP.

We excluded CBT and ERP digital implementation studies because recent reviews have already covered these interventions [29,42,43].

The inclusion criteria for study context are as follows: any care setting, including inpatient, outpatient, or natural environment (eg, at home, work, or school), and English-language studies.

OCD symptoms can be potentially tracked and treated in multiple environments, including inpatient, outpatient, or naturalistic settings, depending on the severity of symptoms and the specific needs of the individual. Therefore, we chose not to restrict our search criteria to a specific setting. We included full-text English-language studies and excluded studies with only an English translation of the abstract to ensure alignment with the inclusion and exclusion criteria and appropriately extract all relevant items. In addition, our preliminary search indicated that most studies used various metrics to monitor OCD symptom burden, so not all conceptual results of interest were included in the abstract. Finally, our preliminary search yielded only a few non–English-language studies.

We decided to exclude review articles, meta-analyses, conference abstracts, and thesis defenses because these either miss elements relevant to our extraction or lack the academic rigor of the peer-review process. Given the long history of biobehavioral technology research and implementation in health care, we considered any publication date.

Together, based on the Population, Concept, and Context framework, the following study inclusion criteria were used: OCD is a primary diagnosis, studies conducted in any care setting, participants of any age and sex, study uses biobehavioral technology or smartphone-based technology, study is peer reviewed, study contains original content, and study is in the English language. Exclusion criteria were as follows: non-OCD primary diagnosis or nonclinical population; non–mobile-based technology; CBT and ERP digital implementation study; and studies that are review articles, meta-analyses, conference abstracts, or thesis defenses. No restrictions were placed on the date of publication of the included studies.

Two authors, AF and RL, screened all studies separately using the blinded screening feature of Covidence; consensus was achieved through discussion between authors for any records with conflicting screening. From this initial screening, full texts from relevant records were obtained. A total of 3 additional studies were identified for full-text review from the reference lists of other reviewed studies. Finally, AF and RL independently extracted the relevant articles and came to consensus on the final extracted items through regular discussion.

Results

Overview

The scoping review was conducted using the Covidence review software, which facilitates the collation of citations with automatic deduplication, allows for blinded screening and review of articles by individual reviewers, tracks articles through the review process, and records reasons for study exclusion. Figure 1 shows the results of the systematic search, study screening, and review process conducted in Covidence. A total of 2748 records were identified across the 4 databases indicated earlier. Following the removal of duplicate records (1273/2748, 46.32%), 1475 (53.68%) of the 2748 studies remained for title and abstract screening. After screening 1475 studies, 1401 (94.98%) studies were found to be ineligible for inclusion, leaving 74 (5.02%) studies for full-text review; an additional 3 studies were identified from the reference list of the reviewed studies, resulting in a total of 77 studies undergoing full-text review. From these 77 studies, 52 (68%) were excluded, mostly studies using nonmobile devices and assessing nonclinical populations or individuals without a diagnosis of OCD. Studies were also excluded if they were conference abstracts or posters, studied the digital implementation of CBT and ERP, were not peer reviewed or were a review, or were not English-language studies.

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Finally, data were extracted from a total of 25 (N=77, 32%) studies. The key findings from these studies, including the type of wearable device or smartphone app, the method of data collection (eg, active vs passive), whether an intervention was implemented, and the overall study results, are discussed in the tables.

Studies Without Digital or Mobile Interventions and With Passive Data Collection

We broadly divided the studies into those that contained digital or mobile interventions and those that did not. We further divided studies that did not include a digital or mobile intervention based on whether they collected mobile or wearable data completely passively or had an active data collection component (ie, requiring participants to enter data or directly engage with an app). Table 1 lists studies lacking a mobile or digital intervention and collecting mobile or wearable data in a passive manner.

Overall, the studies in this category recruited a mix of youth and adult participants and a mix of male and female participants. A total of 2 (18%) of the 11 studies in this group collected wearable data using advanced body sensors that required monitoring in an outpatient setting. One study used a shirt embedded with sensors to collect physiological data, including heart rate and heart rate variability (HRV; calculated from a continuous electrocardiogram), respiration rate, and postural data in patients with anxiety disorders, including OCD. Briefly, the authors found reduced high-frequency HRV at baseline and during hyperventilation in patients with anxiety compared with control participants. They also found a higher heart rate in patients with panic disorder and generalized anxiety disorder during hyperventilation [47]. The other study piloted the ability of a variety of sensors to capture OCD symptomology for a larger study. The team used multiple sensors, including an electrocardiogram chest belt, wrist-based sensors, and a custom-built eye-tracking device to measure gaze fixation during OCD-induced stress as well as heart rate, HRV, and hand motor activity during planned OCD-triggering events [53]. HRV decreased with higher stress levels and increased during rest, whereas heart rate either increased or remained stable with higher stress levels and decreased during rest. Additionally, HRV and motor activity data yielded data patterns that distinguished OCD-induced stress from physical activity.

All other studies in this group used actigraphy as an objective measure of activity. Most studies (8/11, 73%) used actigraphy at night as a measure of sleep while also collecting self-report, parent report, or nursing report as additional metrics of sleep quality and quantity. A majority of studies (7/11, 64) collected actigraphy measures in a naturalistic home environment; however, 2 (18%) of the 11 studies were conducted in an inpatient setting, with 1 study focusing on individuals with treatment-refractory OCD [46] and the other study requiring inpatient hospitalization to draw concurrent blood samples [44]. In general, studies found differences in objective and subjective measures of sleep in patients with OCD, such as decreased total sleep time [45,49], increased number of awakenings after sleep onset [45,50], increased duration of awakenings [45], increased time to fall asleep [49,50], later midsleep timing [52], and presence of delayed sleep phase disorder [46,51,52]. However, 1 study provided contrary evidence on sleep disturbance, which was largely nonsignificant between participants with OCD and control participants in subjective and actigraphy measures [52]. Another study analyzed actigraphy and self-reported sleep measures between responders and nonresponders to repetitive transcranial magnetic stimulation (rTMS); the authors found that a circadian rhythm sleep disorder model could discriminate between responders and nonresponders to rTMS treatment with a sensitivity of approximately 84%. An insomnia model could not discriminate between these groups [49]. A more recent study from the same group, however, did not identify the circadian rhythm sleep disorder model as a potential predictor of rTMS response but did find measures of sleep disturbance, as measured by self-report but not actigraphy measures, to be predictive of rTMS response, with an area under the curve of 0.813, sensitivity of 76%, and specificity of 50% [54]. Other investigators have used actigraphy to measure activity in patients with OCD during daytime hours. One study reported no abnormalities in circadian variability compared with controls [44]. Another study that assessed behavioral activation from selective serotonin reuptake inhibitor treatment reported lower daytime activity in girls and an association of parent reports with actigraphy measures of activity [48].

Studies Without Digital or Mobile Interventions and With Active or Mixed Data Collection

All the study participants in this category were adults. Ecological momentary assessments (EMAs) occur in a naturalistic setting and were used in all studies that collected mobile or wearable data in an active or mixed fashion (active and passive concurrently, albeit only Brown et al [55] used a mixed collection method) (Table 2).

A series of studies assessed whether the use of EMA could outperform retrospective symptom recall [56], uncover new OCD symptoms [57], and feasibly monitor symptoms [55]. EMA reported a slightly lower frequency [56] and burden [57] of OCD symptoms than clinician-administered Yale-Brown Obsessive Compulsive Scale (Y-BOCS) or Obsessive Compulsive Inventory-Revised, although EMA captured novel, previously unreported OCD symptoms [57]. Although patients’ perspectives on the use of EMA were generally positive, clinicians expressed some concern about the amount of data collected and the integration of EMA into clinical care [55]. Rupp et al [58] assessed the feasibility of using EMA to assess OCD symptom burden before and after participants completed detached mindfulness or cognitive restructuring psychotherapy. Participants generally rated EMA highly in terms of acceptability, practicability, and representativeness. The data were quite noisy; however, approximately 28.11% (851/3027) of the noise was removed during data cleansing [58]. A separate study assessed the results of these psychotherapy interventions and found no significant differences between the detached mindfulness and cognitive restructuring therapies in the frequency of their use, perceived difficulty, or the experience of relief after treatment [60].

Studies With Digital or Mobile Interventions

All studies in this section include some form of mobile or digital intervention; however, we excluded studies that focused on CBT and ERP implementation. All but 1 study (8/9, 89%) also involved the collection of active or passive digital or mobile data (Table 3).

All studies applied interventions in a natural environment, with some providing constant treatment via deep brain stimulation (DBS) [65,66] and others using a 2-pronged at-home and in-clinic interventional approach [67,68]. A total of 2 (22%) of the 9 studies developed novel interventions aimed specifically at OCD symptoms. In a single-participant case report, Le Boeuf [61] created a wearable device that provided a mild electric shock to the user if an electrical circuit was completed when the user’s hands were in contact with water, presumably during a washing compulsion. The participant had severely impairing, compulsive handwashing before treatment and had a marked and durable improvement in symptom burden soon after beginning the use of the wearable device [61]. Even more notable is that this study was completed in 1974—well before the advent of smartphones or modern wearable biosensors. In another single-participant case report, Olbrich et al [62] developed a smartphone app to address severe harm-based obsessions and checking compulsions that prevented the participant from attending psychotherapy appointments. The smartphone app tracked the user’s location and sent a reminder signal if the participant had not moved a predefined distance. Use of the app reduced the time required for the participant to reach the clinic by 50% (2 hours to 1 hour for a travel distance of 1 mile); once the patient was able to re-engage in ERP (and continue to use the app), he reached the clinic in 20 minutes. He endorsed that the app served as a negative reinforcer in that he feared drawing attention to himself if the app made a signal noise [62].

A total of 2 (22%) of the 9 studies used DBS as a treatment modality, collected longitudinal intracranial physiological measures from the DBS electrodes, and collected passive and active digital and wearable metrics. In a case report of a patient receiving dual-site stimulation in the ventral capsule/ventral striatum and supplementary motor area, Olsen et al [65] found a small improvement in Y-BOCS score and more robust improvement in patient global impression of improvement following dual-site stimulation compared with single ventral capsule/ventral striatum stimulation. Unexpectedly, cortical-striatal synchrony increased with dual-site stimulation, and random forest modeling showed that cortical-striatal gamma and theta synchrony predicted patient global impression of improvement with 92% accuracy [65]. Provenza et al [66] took a multimodal approach to studying OCD by chronically recording intracranial local field potentials (LFPs) while also densely collecting other measures, including heart rate, self-report symptom burden, facial features, and speech samples. The authors presented data on the first few participants in a planned, larger study. They found significant correlations of facial affect with subjective improvement during DBS programming, negative correlations of delta band power with OCD symptom severity, and correlations of delta band power with ERP [66]. Fridgeirsson et al [69] used machine learning models to analyze LFP data collected during rest and symptom provocation in an outpatient setting while DBS was turned off. Machine learning modeling of resting LFP identified individual patients significantly above the chance level. Modeling of LFP collected during symptom provocation and relief of symptoms predicted symptom state with an average accuracy of 32.5% and 38.8% for the boosted trees and deep learning model, respectively, with the latter reaching an average area under the curve of 78.2% for compulsions, 62.1% for obsessions, 58.7% for baseline, and 59.7% for relief [69].

A total of 2 (22%) of the 9 studies from 1 research group used a mobile electroencephalogram device for monitoring during biofeedback treatment. They found that active treatment reduced Y-BOCS self-report scores and increased electroencephalogram alpha and beta power in proportion to the improvement in OCD symptoms [67]. They also found that alpha power and the perfectionism or certainty subscores from the Obsessive Beliefs Questionnaire were reciprocally associated with one another across time [68].

Arevian et al [64] developed and tested the usability and feasibility of a mobile texting app cocreated by therapists and patients for use in an OCD treatment clinic. The app prompted patients with SMS text messages, and some SMS text messages also requested patient engagement and response. The authors found that the types of SMS text messages created generally focused on personalizing treatment for the individual and using humor to aid in treatment. Approximately 80 to 90% of the patients expressed positive sentiments about the development and use of the app. Themes from working groups of therapists regarding app development and use included treatment engagement, personalization of treatment, motivation, and provision of after-hours care [64].

Finally, 1 (11%) of the 9 studies used a custom mobile phone app aimed at improving cognition in individuals with OCD; this was coupled with therapist-led treatment in a single individual with subjective cognitive complaints [63]. The authors reported that this multimodal approach was associated with improvements in subjective and objective measures of cognition.

Discussion

Overview

Wearable sensors and smartphone-based apps are increasingly being used in medicine and health broadly and in psychiatry specifically to monitor symptoms, diagnose diseases, and predict responses to treatment [3-5,7,8,11,12]. Given that OCD is a chronic, fluctuating condition with significant personal and economic costs, there is a unique opportunity to implement this evolving digital data collection framework to improve our understanding of disease mechanism and improve treatment and clinical outcomes [32-35]. This scoping review maps the extant literature on the use of wearable and smartphone-based technologies in tracking, diagnosing, and predicting clinical outcomes in individuals with OCD. The included studies were broadly divided into studies with digital or mobile interventions and those without. Studies without such interventions were further categorized based on whether they solely collected mobile or wearable data passively or involved an active component in data collection. The results of recent reviews of technology use in OCD do not meaningfully overlap with our results: Cooper et al [38] explored the use of technology in facilitating therapist-delivered psychotherapy in person or by webcam, assessment and prediction of OCD symptoms, and interventions in treating OCD, with the results of none of the reviewed studies overlapping ours; Ferreri et al [29] focused broadly on the use of technology in the assessment and prediction of and interventions for OCD, with the results of only 2 reviewed studies overlapping with ours [57,62].

Principal Findings

We found several broad themes through this study. First, except for 2 (8%) of the 25 studies [44,61], the reviewed studies indicate that the use of wearable sensors or mobile apps in evaluating and treating OCD has developed within the past 15 years, with over half (15/25, 60%) of the studies having been conducted in the last 5 years. This speaks not only to the novelty of these methods in psychiatry but also to the increasing pace of adoption of mobile and wearable technologies in health and medicine. Second, regarding the types of technology, most studies using fully passive mobile or wearable data collection used actigraphy to assess sleep or, less frequently, daytime movement patterns. These studies generally reported good agreement between objective actigraphy data and patient-, parent-, or nurse-reported subjective metrics. However, no study used actigraphy as the sole measure of sleep, which may be indicative of the current limitations of actigraphy. Several studies used extensive, nonconsumer sensors and modalities, including custom-built hardware and software, to passively track OCD symptoms. These efforts highlight the potential therapeutic benefit of tracking OCD symptoms passively and the desire for higher-performance sensors that are not available in off-the-shelf solutions. Many studies that actively collect mobile or wearable data use EMA to assess OCD symptom severity and burden in a naturalistic manner. In general, EMA is well tolerated by participants and appears to uncover new OCD symptoms not reported on retrospective questionnaires, although it may underestimate the overall OCD symptom burden.

We found that mobile or digital interventions are varied and diverse. They include apps and devices that provide negative reinforcement, apps that provide cognitive training, apps facilitating bidirectional texting and SMS text messaging between providers and patients, electroencephalogram-based biofeedback devices, and open-loop DBS with concurrently recorded intracranial LFP. Studies that leveraged mobile or digital interventions were often case reports with a single male participant. The dearth of studies involving a larger and more diverse participant pool highlights the novelty of such interventions. Nevertheless, given the rapidity of technology development and adoption, we anticipate the depth and breadth of mobile and digital interventions to continue to expand with increasing speed. Finally, approaches to data privacy and security are often underreported. This is a critical issue to address given the user concerns about these technologies [70-72], the ongoing integration of technology into health care, and the potential for malicious use of data [73].

The findings of this review highlight several important considerations for future studies and the implementation of digital health technologies in clinical practice. First, the consistency and standardization of data collection and analysis are important and likely to improve both study quality and public perception of digital or wearable technology research. To facilitate this effort, future studies will benefit from the use of a conceptual framework that allows one to identify important metrics to assess, determine on what timescale to collect these measures, and decide how to implement appropriate and statistically sound analytic methods. Two commonly used and conceptually overlapping frameworks—behavioral signal processing [74] and digital phenotyping [75,76]—share important features and aims: acquisition of multimodal and ecologically valid data, selection of analytic methods suited to the acquired data, and development of models to predict clinical course and treatment response. Both approaches have been usefully implemented in psychiatric conditions as diverse as schizophrenia [77], depression [78,79], anxiety [80,81], and autism spectrum disorder [82,83].

Second, wearable- and smartphone-based studies have the potential to improve treatment outcomes through the development of intervention decision models, which are collections of strategies and policies for the evaluation and treatment of patients and are commonly used in diverse fields of medicine [84]. Decision models operate most effectively when the illness phenomenology (ie, signs and symptoms) maps onto an understanding of the pathophysiology. Wearable devices and smartphones will allow for the ongoing collection of diverse, dense, longitudinal data sets that can improve our understanding of the signs and symptoms of psychiatric disorders; in conjunction with research on the pathophysiology of mental illness, these complementary approaches will lead to the development of much-needed decision models in psychiatry [84].

Third, the included studies were conducted across the globe in countries, including India, Australia, Canada, the United States, and several European nations. Globally, smartphone use ranges from 70% to 85% of the population and is steadily increasing [85,86]. Furthermore, mental illness is prevalent throughout the world [87,88], and even within the United States, there are disparities in access to care based on race and ethnicity [89,90]. We also found that studies were conducted across age groups, from children and adolescents to those in their 60s, and in both males and females (although not all studies reported age). Taking these themes together, wearable- and smartphone-based studies can, and should, be conducted in diverse settings and populations around the world. This naturally lends itself to large, concurrent studies that are scaled up to include many more participants so that variability in measures can effectively be captured and analyzed.

Fourth, the declining costs of technology, ubiquitous use of smartphones and their associated functionalities (eg, user interface, cloud connection, and data storage and sharing), and integration of artificial intelligence for high-dimensional data processing have enabled real-time monitoring of various health-related biomarkers via wearable biosensors [6]. For instance, a recently proposed study uses an armband biosensor to passively monitor diverse physiological parameters in patients with COVID-19. An associated smartphone app receives and stores real-time data from the sensor and subsequently uploads them to a cloud-based server, where further processing occurs via machine learning. The results can then be displayed to a clinician via a web-based dashboard with an overall goal of early detection of disease progression [91]. From our review, Provenza et al [66] took a similar approach in capturing diverse streams of wearable and mobile data concurrently. They demonstrated an approach to combining these data into a broader scientific and clinical picture [66]. We anticipate that future studies in psychiatry will further integrate actively and passively collected wearable and mobile data, on-device and cloud-based storage, and real-time data extraction and analysis to produce actionable information that patients and clinicians can use to guide care.

Finally, the ethical and legal frameworks surrounding mobile and wearable data collection and use continue to evolve, particularly as the definitions of devices and apps change [92]. Currently, most devices are not regulated by the Food and Drug Administration, although their features or marketing suggest medical diagnostic capabilities; this leaves manufacturers and, potentially, physicians open to state and federal liabilities should these devices malfunction or fail to perform as advertised [93]. Simon et al [93] suggested changes to state and federal regulations to mitigate this liability, although they also note that best practices developed by physician organizations that specifically address mobile or wearable devices may reduce some legal risk.

Strengths and Limitations

We present an overview of the use of mobile and wearable technologies in the monitoring and treatment of OCD. Our systematic approach to the literature ensured that all indexed studies were included, supported by our identification of an older study not previously captured in reviews [61]. It is possible that relevant non–English-language studies were overlooked, as we focused our review on manuscripts published in English. We divided the included studies into studies passively collecting data, studies actively collecting data, and studies implementing treatment; this decision was based on the structure and findings of these studies and was intended to highlight the current landscape of the field, although other organizational approaches could be validly implemented. We chose not to focus on digital or mobile implementation of CBT and ERP, given the existing recent reviews highlighting the literature covering CBT and ERP [29,42,43], and to identify studies reporting novel digital or mobile treatment approaches. Finally, we briefly suggest areas for ongoing consideration when designing studies and considering the clinical implementation of mobile and wearable technologies in OCD. This field is evolving rapidly, and continued publication of high-quality research is paramount for a safe and trusted uptake of technology by patients and providers.