从基于多模态传感器的电子健康方法中汲取治疗小儿强迫症的经验教训。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1384540
Carolin S Klein, Karsten Hollmann, Jan Kühnhausen, Annika K Alt, Anja Pascher, Lennart Seizer, Jonas Primbs, Winfried Ilg, Annika Thierfelder, Björn Severitt, Helene Passon, Ursula Wörz, Heinrich Lautenbacher, Wolfgang A Bethge, Johanna Löchner, Martin Holderried, Walter Swoboda, Enkelejda Kasneci, Martin A Giese, Christian Ernst, Gottfried M Barth, Annette Conzelmann, Michael Menth, Caterina Gawrilow, Tobias J Renner
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引用次数: 0

摘要

简介本研究调查了基于传感器的电子健康疗法在儿科强迫症(OCD)心理治疗中的可行性和可用性,并探讨了这种新方法的前景和缺陷。通过电子健康干预,治疗可以在患者的家庭环境中进行,从而使症状评估更符合生态学原理,即使在农村地区也能获得专家的帮助。此外,传感器还能帮助显示患者在治疗过程中的情绪和身体状况。最后,在暴露与反应预防(E/RP)过程中使用传感器有助于个性化治疗和预防回避行为:在这项研究中,我们在 20 名 12-18 岁的强迫症患者接受认知行为疗法(CBT)的 14 个视频疗程期间,开发并评估了基于多模态传感器的电子健康干预措施。在 E/RP 过程中,我们通过眼动追踪器记录眼球运动和注视方向,并通过心电图胸带捕捉心率(HR)以确定压力反应。此外,运动传感器还能检测接近和回避行为:结果:研究结果表明,传感器支持疗法在小儿强迫症治疗中的应用前景广阔,该技术得到了参与者的广泛认可,并且在基于互联网的治疗中成功建立了治疗关系。患者、其父母和治疗师都对这种治疗形式表示高度满意,并认为在家庭环境中采用可穿戴方法很有帮助,在治疗结束时发现强迫症症状有所减少:本研究的目的是更好地了解儿科患者在基于暴露的在线治疗过程中的心理和生理过程。此外,文章末尾还探讨了为患有强迫症的儿童和青少年准备和开展传感器支持的 CBT 的 10 个关键注意事项。一旦解决了技术支持和软硬件可用性方面的挑战,这种方法就有可能克服电子健康干预的局限性,向治疗师实时传输客观数据。临床试验注册:www.ClinicalTrials.gov,标识符(NCT05291611)。
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Lessons learned from a multimodal sensor-based eHealth approach for treating pediatric obsessive-compulsive disorder.

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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