Lessons learned from a multimodal sensor-based eHealth approach for treating pediatric obsessive-compulsive disorder

Abstract

Introduction: The present study investigates the feasibility and usability of a sensor-based eHealth treatment in psychotherapy for pediatric obsessive-compulsive disorder (OCD), and explores the promises and pitfalls of this novel approach. With eHealth interventions, therapy can be delivered in a patient's home environment, leading to a more ecologically valid symptom assessment and access to experts even in rural areas. Furthermore, sensors can help indicate a patient's emotional and physical state during treatment. Finally, using sensors during exposure with response prevention (E/RP) can help individualize therapy and prevent avoidance behavior.

Methods: In this study, we developed and subsequently evaluated a multimodal sensor-based eHealth intervention during 14 video sessions of cognitive-behavioral therapy (CBT) in 20 patients with OCD aged 12-18. During E/RP, we recorded eye movements and gaze direction via eye trackers, and an ECG chest strap captured heart rate (HR) to identify stress responses. Additionally, motion sensors detected approach and avoidance behavior.

Results: The results indicate a promising application of sensor-supported therapy for pediatric OCD, such that the technology was well-accepted by the participants, and the therapeutic relationship was successfully established in the context of internet-based treatment. Patients, their parents, and the therapists all showed high levels of satisfaction with this form of therapy and rated the wearable approach in the home environment as helpful, with fewer OCD symptoms perceived at the end of the treatment.

Discussion: The goal of this study was to gain a better understanding of the psychological and physiological processes that occur in pediatric patients during exposure-based online treatment. In addition, 10 key considerations in preparing and conducting sensor-supported CBT for children and adolescents with OCD are explored at the end of the article. This approach has the potential to overcome limitations in eHealth interventions by allowing the real-time transmission of objective data to therapists, once challenges regarding technical support and hardware and software usability are addressed.

Clinical trial registration: www.ClinicalTrials.gov, identifier (NCT05291611).

Keywords: children and adolescents; exposure; internet-based cognitive behavioral therapy; obsessive-compulsive disorder; sensor technology; usability.

Figure 1

Study design. Figure 1 illustrates the study design and the process of online-based psychotherapy. After an onsite diagnostic assessment and introduction to the use of the sensor system, a 14-week digital CBT consisting of 4 treatment modules took place in the home environment of the patients. At the end of the therapy, the families received a further diagnostic assessment on site and returned the sensor system.

Figure 2

The SSTeP KiZ sensor system. This figure shows the entire sensor system with the ECG chest strap, the motion sensors, and the eye tracking glasses. The tablet with the Aggregator Software, which collects the sensor data, is carried by a small backpack. The University Hospital of Tübingen holds the rights to all images in this paper.

Figure 3

In Figure 3, an exposure session during iCBT is displayed. (A) Shows the heart rate data (subjective stress level, beats per minute and heart rate variability) during the exposure. (B) Shows the summarized heat maps of estimated gaze points. The patient and his family consented to the presentation of the image material.

Figure 4

Displays the therapist UI with its different components of gaze estimation, HR and HRV as well as the bar to operate different tags during the sessions. The patient and his family gave their consent to the publication of the image.

Figure 5

(A) Shows the schedule system with the upcoming daily questionnaire. (B) displays an example of the questionnaires that were completed by the patients (“I found it easy to use the eye tracking glasses”).

Figure 6

Daily and weekly questionnaires from the patients. Compliance rates as the frequency of completed weekly (A) and aggregated daily (B) ambulatory questionnaires as a function of weeks into the study.