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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).
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11460578 | PMC |
http://dx.doi.org/10.3389/fdgth.2024.1384540 | DOI Listing |
ACS Appl Mater Interfaces
November 2024
National Demonstration Center for Experimental Light Chemistry Engineering Education, College of Bioresources Chemistry and Materials Engineering, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate.
View Article and Find Full Text PDFComput Biol Med
December 2024
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, United Kingdom; UK Dementia Research Institute Care Research and Technology Centre, Imperial College, London, United Kingdom.
Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.
View Article and Find Full Text PDFFront Digit Health
September 2024
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Tübingen, Tübingen, Germany.
JMIR Mhealth Uhealth
November 2024
Institute for Cardiomyopathies Heidelberg, Heidelberg University Hospital, Heidelberg, Germany.
Shoulder Elbow
July 2024
Trauma & Orthopaedic Surgery Department, Leicester Royal Infirmary, Leicester, UK.
Background: Shoulder range of motion (ROM) is traditionally measured using universal goniometry. However, novel devices to measure shoulder ROM digitally are becoming increasingly available. We aimed to synthesise the current evidence to answer: 1) what technologies are currently in use? 2) Are they reliable? 3) How do they compare to goniometry?
Methods: Systematic review of the literature was conducted according to PRISMA guidelines.
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