背景:精神病学中的预防为解决精神疾病的负担提供了一种有希望的方法。然而,既定的方法侧重于特定的诊断,没有解决早期识别服务中出现的寻求帮助人群的异质性和多种潜在结果.从相互作用症状的网络角度对寻求帮助的人群中表现出的精神病理学进行概念化,可以超越二元疾病类别进行诊断性调查。此外,智能手机等现代技术促进了经验采样方法(ESM)的应用。
目的:本研究将ESM与网络分析相结合,为寻求帮助的人群提供超出既定评估工具的有效见解。
方法:我们将检查科隆早期识别中心寻求帮助人群中的75个人(18-40岁)。对于一个最大自然的样本,只有最低限度的排除标准将适用。我们将使用移动应用程序收集14天的数据,以评估10种诊断症状(即,抑郁,焦虑,和精神病症状)以及每天5次的痛苦水平。有了这些数据,我们将使用2步多级向量自回归模型生成平均群体级症状网络和个性化症状网络.此外,我们将探索症状网络和社会人口统计学之间的关联,风险,和弹性因素,以及心理社会功能。
结果:该方案于2020年2月设计,并于2020年10月获得科隆大学医院伦理委员会的批准。该协议于2020年9月由科隆财富计划审查和资助。数据收集于2020年11月开始,并于2021年11月完成。在接受筛查的258名参与者中,93人(36%)符合纳入标准,愿意参与研究。在这93名参与者中,86(92%)完成了研讨。首批结果预计将于2022年公布。
结论:这项研究将提供有关ESM在早期识别中心的寻求帮助人群中的可行性和实用性的见解。在该人群中首次提供诊断性精神病理学的探索性表型分析,我们的研究将有助于精神病学早期认知的创新.这些结果将有助于为更广泛的患者群体的预防和有针对性的早期干预铺平道路,因此,在减轻精神疾病的负担方面实现更大的预期效果。
■DERR1-10.2196/35206。
BACKGROUND: Prevention in psychiatry provides a promising way to address the burden of mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing the psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of the Experience Sampling Method (ESM).
OBJECTIVE: This study is a combination of ESM with network analyses to provide valid insights beyond the established assessment instruments in a help-seeking population.
METHODS: We will examine 75 individuals (aged 18-40 years) of the help-seeking population of the Cologne early recognition center. For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days using a mobile app to assess 10 transdiagnostic symptoms (ie, depressive, anxious, and psychotic symptoms) as well as distress level 5 times a day. With these data, we will generate average group-level symptom networks and personalized symptom networks using a 2-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk, and resilience factors, as well as psychosocial functioning.
RESULTS: The protocol was designed in February 2020 and approved by the Ethics Committee of the University Hospital Cologne in October 2020. The protocol was reviewed and funded by the Köln Fortune program in September 2020. Data collection began in November 2020 and was completed in November 2021. Of the 258 participants who were screened, 93 (36%) fulfilled the inclusion criteria and were willing to participate in the study. Of these 93 participants, 86 (92%) completed the study. The first results are expected to be published in 2022.
CONCLUSIONS: This study will provide insights about the feasibility and utility of the ESM in a help-seeking population of an early recognition center. Providing the first explorative phenotyping of transdiagnostic psychopathology in this population, our study will contribute to the innovation of early recognition in psychiatry. The results will help pave the way for prevention and targeted early intervention in a broader patient group, and thus, enable greater intended effects in alleviating the burden of psychiatric disorders.
UNASSIGNED: DERR1-10.2196/35206.