背景:青少年强迫症(OCD)的特征是行为,情感,生理反应,和家庭互动模式。治疗的一个重要组成部分是提高对思想之间联系的认识,情感,行为,身体的感觉,家庭互动。使用来自可穿戴生物传感器的生理信号的自动评估工具可以实现诊所内部和外部的连续症状监测并且支持用于OCD的认知行为治疗。
目的:本研究的主要目的是评估使用可穿戴生物传感器监测OCD症状的可行性和可接受性。次要目的是探索开发临床和研究工具的可行性,这些工具可以通过使用语音和行为信号来检测和预测强迫症相关的内部状态和人际过程。
方法:研究的合格标准包括8至17岁被诊断患有强迫症的儿童和青少年,没有精神病诊断的对照,和参与青年的一位家长。年轻人和父母在手腕上佩戴生物传感器来测量脉搏,皮肤电活动,皮肤温度,和加速度。患者和他们的父母标记强迫症发作,而控制青年和他们的父母标志着青年恐惧事件。连续,野外数据收集将持续8周。旨在联系生理的受控实验,演讲,行为,和精神状态的生化信号在基线和8周后进行。实验中的人际互动被拍摄并编码为行为。这些电影还通过计算机视觉和语音信号进行处理。参与者在基线时完成临床访谈和问卷,在第4、7和8周。为招聘设定了可行性标准,保留,生物传感器的功能和可接受性,坚持佩戴生物传感器,以及与生物传感器相关的安全性。作为学习信号和OCD相关参数之间关联的第一步,我们将使用配对t检验和混合效应模型与重复测量来评估催产素之间的关联,个体生物信号特征,以及压力休息和病例对照比较等结果。
结果:第一位参与者于2021年12月3日注册,并于2022年12月31日结束招聘。招募了9个患者二元组和9个对照二元组。16个参与的二元组完成了后续评估。
结论:这项研究的结果将为收集生理信号的可穿戴生物传感器可用于监测年轻人的OCD严重程度和事件的程度提供初步证据。如果我们发现这项研究是可行的,将进行进一步的研究,将生物传感器信号输出集成到机器学习算法中,可以为患者提供,父母,以及对强迫症症状和治疗进展具有可操作见解的治疗师。未来的确定性研究将负责测试机器学习模型的准确性,以检测和预测OCD发作并对临床严重程度进行分类。
背景:ClinicalTrials.govNCT05064527;https://clinicaltrials.gov/ct2/show/NCT05064527。
■DERR1-10.2196/45123。
BACKGROUND: Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. An essential component of therapy involves increasing awareness of the links among thoughts, emotions, behaviors, bodily sensations, and family interactions. An automatic assessment tool using physiological signals from a wearable biosensor may enable continuous symptom monitoring inside and outside of the clinic and support cognitive behavioral therapy for OCD.
OBJECTIVE: The primary aim of this
study is to evaluate the feasibility and acceptability of using a wearable biosensor to monitor OCD symptoms. The secondary aim is to explore the feasibility of developing clinical and research tools that can detect and predict OCD-relevant internal states and interpersonal processes with the use of speech and behavioral signals.
METHODS: Eligibility criteria for the
study include children and adolescents between 8 and 17 years of age diagnosed with OCD, controls with no psychiatric diagnoses, and one parent of the participating youths. Youths and parents wear biosensors on their wrists that measure pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes, while control youths and their parents mark youth fear episodes. Continuous, in-the-wild data collection will last for 8 weeks. Controlled experiments designed to link physiological, speech, behavioral, and biochemical signals to mental states are performed at baseline and after 8 weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, and at weeks 4, 7, and 8. Feasibility criteria were set for recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, and safety related to the biosensor. As a first step in learning the associations between signals and OCD-related parameters, we will use paired t tests and mixed effects models with repeated measures to assess associations between oxytocin, individual biosignal features, and outcomes such as stress-rest and case-control comparisons.
RESULTS: The first participant was enrolled on December 3, 2021, and recruitment closed on December 31, 2022. Nine patient dyads and nine control dyads were recruited. Sixteen participating dyads completed follow-up assessments.
CONCLUSIONS: The results of this
study will provide preliminary evidence for the extent to which a wearable biosensor that collects physiological signals can be used to monitor OCD severity and events in youths. If we find the
study to be feasible, further studies will be conducted to integrate biosensor signals output into machine learning algorithms that can provide patients, parents, and therapists with actionable insights into OCD symptoms and treatment progress. Future definitive studies will be tasked with testing the accuracy of machine learning models to detect and predict OCD episodes and classify clinical severity.
BACKGROUND: ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/ct2/show/NCT05064527.
UNASSIGNED: DERR1-10.2196/45123.