Reutimann Stefan, Steiner Jasmin, Hübscher Noah, Voderholzer Ulrich, Meule Adrian, Augsburger Mareike
University of Zurich Start-Up, Klenico Health AG, Zürich, Switzerland.
Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland.
JMIR Form Res. 2025 Jul 24;9:e50504. doi: 10.2196/50504.
The accurate diagnosis of mental disorders, such as depression, requires comprehensive, valid, and reliable tools to ensure evidence-based treatments and effective outcome monitoring. Existing diagnostic practices often lack standardization, leading to missed comorbidities and variable diagnostic accuracy. The Klenico system is an innovative, web-based diagnostic tool that integrates patient self-reports with clinical validations by mental health professionals. This system covers a broad spectrum of mental disorders, including depression.
This research aimed to evaluate the psychometric properties of the Klenico Depression Domain (KDD), the component of the Klenico system that measures depressive symptomatology, in a real-world clinical setting. Specifically, the evaluation focused on the assessment of its construct validity, internal consistency, and sensitivity to change in symptom severity.
Anonymized data from 496 inpatients with mental disorders collected between 2019 and 2022 were analyzed. Patients completed the KDD alongside parts of the Patient Health Questionnaire (PHQ), Beck Depression Inventory (BDI-II), and Satisfaction With Life Scale (SWLS) at both admission and discharge. Internal consistency was measured using Cronbach α. Exploratory factor analysis was conducted to examine the factor structure. Construct validity was assessed via Pearson correlations with PHQ-9 and BDI-II, while divergent validity was tested against the PHQ Somatic Symptoms Scale (PHQ-15), PHQ-Generalized Anxiety Disorder-7, and SWLS. Sensitivity to change was evaluated using paired 1-tailed t tests, effect sizes, and repeated measures correlations.
The KDD demonstrated excellent internal consistency (Cronbach α=0.91 at admission and 0.93 at discharge). Factor analysis revealed a 7-factor structure encompassing dimensions like "inadequacy," "anhedonia," and "self-hatred," aligning with core depressive symptoms outlined in the International Statistical Classification of Diseases, Tenth Revision. The correlations with the convergent questionnaires PHQ-9 (r=0.68; P<.001) and BDI-II (r=0.70; P<.001) were high. While the KDD showed a moderate correlation with the divergent PHQ-15 (r=0.35; P<.001), it was more strongly associated with the divergent SWLS (r=-0.51; P<.001) and Generalized Anxiety Disorder-7 (r=0.51; P<.001). Sensitivity to change was high, with significant reductions in KDD scores for patients with improved symptoms (t=5.36, P<.001; Cohen d=0.79) and high repeated measures correlation with both the BDI-II (r=0.61; P<.001) and the PHQ-9 (r=0.59; P<.001).
The KDD shows promise as a reliable and valid instrument for diagnosing depression and monitoring treatment outcomes in psychotherapeutic settings. Its alignment with International Statistical Classification of Diseases, Tenth Revision diagnostic criteria and sensitivity to symptom change underlines its potential utility. These findings highlight the Klenico system's potential to enhance clinical diagnostics by addressing current gaps in mental health care, thus improving diagnostic accuracy and consistency. Further research is recommended to validate its performance across different populations and settings.
抑郁症等精神障碍的准确诊断需要全面、有效且可靠的工具,以确保循证治疗和有效的结果监测。现有的诊断方法往往缺乏标准化,导致漏诊共病情况且诊断准确性参差不齐。Klenico系统是一种创新的基于网络的诊断工具,它将患者自我报告与心理健康专业人员的临床验证相结合。该系统涵盖了广泛的精神障碍,包括抑郁症。
本研究旨在评估Klenico抑郁症领域(KDD)——Klenico系统中测量抑郁症状的组成部分——在真实临床环境中的心理测量特性。具体而言,评估重点在于其结构效度、内部一致性以及对症状严重程度变化的敏感性。
对2019年至2022年间收集的496名精神障碍住院患者的匿名数据进行分析。患者在入院和出院时均完成了KDD以及部分患者健康问卷(PHQ)、贝克抑郁量表(BDI-II)和生活满意度量表(SWLS)。使用克朗巴哈α系数测量内部一致性。进行探索性因素分析以检验因素结构。通过与PHQ-9和BDI-II的皮尔逊相关性评估结构效度,同时针对PHQ躯体症状量表(PHQ-15)、PHQ广泛性焦虑障碍-7和SWLS检验区分效度。使用配对单尾t检验、效应量和重复测量相关性评估对变化的敏感性。
KDD表现出出色的内部一致性(入院时克朗巴哈α系数 = 0.91,出院时为0.93)。因素分析揭示了一个包含“不足”“快感缺失”和“自我厌恶”等维度的七因素结构,与《国际疾病分类第十次修订本》中概述的核心抑郁症状一致。与收敛性问卷PHQ-9(r = 0.68;P <.001)和BDI-II(r = 0.70;P <.001)的相关性很高。虽然KDD与区分性的PHQ-15(r = 0.35;P <.001)显示出中等相关性,但它与区分性的SWLS(r = -0.51;P <.001)和广泛性焦虑障碍-7(r = 0.51;P <.001)的相关性更强。对变化的敏感性很高,症状改善的患者KDD得分显著降低(t = 5.36,P <.001;科恩d = 0.79),并且与BDI-II(r = 0.61;P <.001)和PHQ-9(r = 0.59;P <.001)的重复测量相关性都很高。
KDD有望成为心理治疗环境中诊断抑郁症和监测治疗结果的可靠有效工具。它与《国际疾病分类第十次修订本》诊断标准的一致性以及对症状变化的敏感性突出了其潜在效用。这些发现凸显了Klenico系统通过弥补当前精神卫生保健中的差距来提高临床诊断的潜力,从而提高诊断准确性和一致性。建议进一步研究以验证其在不同人群和环境中的性能。