Identifying Symptom Dynamics and Profiles of Subgroups at Risk for Major Depression and Suicidal Ideation Among Korean Adults: 2-Week Ecological Momentary Assessment Study.

IF 6.3 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Kyungmi Chung, Jin Young Park
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引用次数: 0

Abstract

Background: Traditional clinical assessments in psychiatric research or clinical practice rely on global retrospective self-report depression measures, which do not adequately capture intra- and interindividual variability in depressive symptoms over time and across contexts.

Objective: This study aimed to (1) assess the sensitivity of mobile ecological momentary assessment (EMA) for monitoring depressive symptoms compared with traditional depression scales, (2) investigate changes in depressive symptoms and recall consistency observed between the first week (FW) and the second week (SW), and (3) identify subgroups at higher risk for depression and suicidal ideation, and characterize their sociodemographic, psychological, and psychiatric profiles.

Methods: Participants' self-reports were collected once daily for 14 consecutive days via a mobile app-based EMA to monitor the presence of 20 depressive symptoms based on a 24-hour recall period, thereby capturing naturalistic severity and variability. Baseline questionnaires measured sociodemographic characteristics, digital sensitivity, and personality traits. On day 14, postquestionnaires were administered to assess their clinical symptoms. In addition, modified depression scales were administered on day 7 for 1-week recall, and day 14 for both 1-week and 2-week recalls. For cluster analysis, 3 active EMA-derived features were included: mean symptom severity, within-person symptom variability, and frequency of suicidal ideation.

Results: Generalized Linear Mixed Model analysis (model 1) revealed that traditional 2-week recall explained only 35.5% of the variance in daily symptom presence (odds ratio 0.666, 95% CI 0.659-0.674; P<.001). Additional Generalized Linear Mixed Model analysis (model 2) identified a robust interaction, indicating that the consistency between weekly retrospective recall and daily EMA differed significantly between FW and SW (F1, 81876=124.550; P<.001). While overall symptom severity scores significantly decreased from FW to SW across both assessment methods (Cohen d=0.10-0.34; P<.001), the estimated mean of the probability of symptom reporting showed a contrasting upward trend from 0.853 to 0.908. In cluster analysis, 695 participants (244 males and 451 females; aged 19-73 years, mean 36.14, SD 10.71 years) who completed at least 7 EMA sessions over a 2-week period were classified into three distinct clusters: (1) no or low risk (n=445, 64.0%), (2) moderate risk (n=223, 32.1%), and (3) high risk (n=27, 3.9%). While depressive symptom severity, frequency of suicidal ideation, and psychiatric profiles progressively increased across clusters (cluster 1<2<3), the highest symptom variability was observed in cluster 2 (cluster 1<3<2).

Conclusions: This study demonstrated that mobile EMA effectively reduced recall bias in assessing depressive symptoms and revealed symptom dynamics. Using a minimal set of active EMA-derived features, we differentiated 3 distinct risk clusters; within-person symptom variability emerged as a clinically significant indicator not captured by traditional 2-week recall-based assessments. Despite the brief 2-week assessment period, these findings suggest that mobile EMA may serve as a robust real-world data collection tool.

韩国成年人重度抑郁和自杀意念风险亚群的症状动态和特征识别:2周生态瞬时评估研究
背景:精神病学研究或临床实践中的传统临床评估依赖于全球回顾性自我报告抑郁测量,这不能充分捕捉抑郁症状随时间和不同背景的个体内部和个体间变异性。目的:本研究旨在(1)评估移动生态瞬间评估(EMA)与传统抑郁量表相比监测抑郁症状的敏感性,(2)调查抑郁症状的变化和第一周(FW)和第二周(SW)之间的回忆一致性,(3)确定抑郁和自杀意念风险较高的亚组,并表征他们的社会人口统计学、心理学和精神病学特征。方法:通过基于移动应用程序的EMA连续14天每天收集一次参与者的自我报告,以监测基于24小时回忆期的20种抑郁症状的存在,从而捕获自然的严重程度和变异性。基线问卷测量了社会人口特征、数字敏感性和人格特征。第14天,进行问卷调查后评估他们的临床症状。此外,改良抑郁量表在第7天进行1周回忆,第14天进行1周和2周回忆。在聚类分析中,包括3个ema衍生的主动特征:平均症状严重程度、个人症状变异性和自杀意念的频率。结果:广义线性混合模型分析(模型1)显示,传统的2周回忆仅解释了35.5%的日常症状存在差异(优势比0.666,95% CI 0.659-0.674)。结论:本研究表明,移动EMA有效地减少了评估抑郁症状的回忆偏差,并揭示了症状动态。使用最小的主动ema衍生特征集,我们区分了3个不同的风险集群;个人症状变异性作为临床重要指标出现,传统的2周回忆评估没有捕捉到。尽管2周的评估期很短,但这些发现表明,移动EMA可以作为一个强大的真实世界数据收集工具。
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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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