Department of Psychiatry, Gangnam Severance Hospital, Yonsei University Health System, Seoul, South Korea.
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea.
PLoS One. 2018 Jun 26;13(6):e0199118. doi: 10.1371/journal.pone.0199118. eCollection 2018.
The aims of this study were to design a mobile app that would record daily self-reported Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) ratings in a "Yes" or "No" format, develop two different algorithms for converting mobile K-CESD-R scores in a binary format into scores in a 5-point response format, and determine which algorithm would be more appropriately applied to the newly developed app. Algorithm (A) was designed to improve the scoring system of the 2-week delayed retrospective recall-based original K-CESD-R scale, and algorithm (B) was designed to further refine the scoring of the 24-hour delayed prospective recall-based mobile K-CESD-R scale applied with algorithm (A). To calculate total mobile K-CESD-R scores, each algorithm applied certain cut-off criteria for a 5-point scale with different inter-point intervals, defined by the ratio of the total number of times that users responded "Yes" to each item to the number of days that users reported daily depressive symptom ratings during the 2-week study period. Twenty participants were asked to complete a K-CESD-R Mobile assessment daily for 2 weeks and an original K-CESD-R assessment delivered to their e-mail accounts at the end of the 2-week study period. There was a significant difference between original and mobile algorithm (B) scores but not between original and mobile algorithm (A) scores. Of the 20 participants, 4 scored at or above the cut-off criterion (≥13) on either the original K-CESD-R (n = 4) or the mobile K-CESD-R converted with algorithm (A) (n = 3) or algorithm (B) (n = 1). However, all participants were assessed as being below threshold for a diagnosis of a mental disorder during a clinician-administered diagnostic interview. Therefore, the K-CESD-R Mobile app using algorithm (B) could be a more potential candidate for a depression screening tool than the K-CESD-R Mobile app using algorithm (A).
本研究的目的是设计一款移动应用程序，以“是”或“否”格式记录每日自我报告的韩国版修正版流行病学研究中心抑郁量表（K-CESD-R）评分，开发两种不同的算法将移动 K-CESD-R 评分转换为二进制格式的 5 分响应格式，并确定哪种算法更适用于新开发的应用程序。算法 (A) 旨在改进基于 2 周延迟回顾的原始 K-CESD-R 量表的评分系统，算法 (B) 旨在进一步细化应用算法 (A) 的基于 24 小时延迟前瞻性回忆的移动 K-CESD-R 量表的评分。为了计算移动 K-CESD-R 的总分，每种算法都应用了一定的 5 分制的截断标准，其间隔不同，由用户对每个项目回答“是”的总次数与用户在 2 周研究期间报告每日抑郁症状评分的天数之比来定义。20 名参与者被要求在 2 周内每天完成 K-CESD-R 移动评估，并在 2 周研究结束时将原始 K-CESD-R 评估发送到他们的电子邮件帐户。原始和移动算法 (B) 评分之间存在显著差异，但原始和移动算法 (A) 评分之间没有差异。在 20 名参与者中，有 4 名（原始 K-CESD-R≥13，n=4；移动 K-CESD-R 转换算法 A≥13，n=3；移动 K-CESD-R 转换算法 B≥13，n=1）在原始 K-CESD-R 或使用算法 (A) 或算法 (B) 转换的移动 K-CESD-R 上的评分达到或高于截止标准。然而，所有参与者在临床医生进行的诊断访谈中均被评估为低于精神障碍诊断阈值。因此，与使用算法 (A) 的 K-CESD-R 移动应用程序相比，使用算法 (B) 的 K-CESD-R 移动应用程序可能是一种更有潜力的抑郁筛查工具。
