Sung Sumi, Kim Su Hwan, Kim Youlim, Bae Ye Seul, Chie Eui Kyu
Department of Nursing Science, Research Institute of Nursing Science, Chungbuk National University, Cheongju, Chungcheongbuk-do, Republic of Korea.
Department of Information Statistics, Gyeongsang National University, Jinju, Gyeongsangnam-do, Republic of Korea.
Front Public Health. 2024 Apr 10;12:1265848. doi: 10.3389/fpubh.2024.1265848. eCollection 2024.
During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population.
This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial.
A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups.
Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21).
These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.
在新冠疫情高峰期，韩国政府出于安全和实用性的考虑，临时允许全面开展远程医疗服务。然而，通过分析针对韩国人群通过远程医疗收集的新冠确诊患者的身体和/或心理健康数据来评估远程医疗影响的研究有限。
本研究旨在利用基于远程医疗的非接触式临床试验收集的纵向数据，确定临床症状较轻的新冠患者抑郁症状轨迹的亚组。
2022年3月23日至7月20日，共有199名新冠患者参与了使用远程医疗的非接触式临床试验。在隔离开始日、隔离最后一日以及解除隔离1个月后，使用患者健康问卷-9来测量抑郁症状。此外，在隔离期间每天评估急性新冠症状。本研究使用潜在类别混合模型来区分抑郁症状轨迹的亚组，并使用带有费思校正的逻辑回归模型来确定急性新冠症状与这些亚组之间的关联。
确定了两个潜在类别：类别1为缓慢下降的线性变化，类别2为上升的线性变化。在新冠症状中，解除隔离1个月后的发热、胸痛和脑雾与类别2有很强的关联（发热：比值比[OR]，19.43，95%置信区间[CI]，2.30 - 165.42；胸痛：OR，6.55，95% CI，1.15 - 34.61；脑雾：OR，7.03，95% CI 2.57 - 20.95）。睡眠困难和胃肠道症状也与类别2相关（胃肠道症状：OR，4.76，95% CI，1.71 - 14.21；睡眠困难：OR，3.12，95% CI，1.71 - 14.21）。
这些发现强调了使用远程医疗在新冠急性期患者中早期检测抑郁症状的必要性。包括数字疗法在内的积极干预可能有助于症状加重的抑郁患者。
