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Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.
Sung, Sumi; Kim, Su Hwan; Kim, Youlim; Bae, Ye Seul; Chie, Eui Kyu.
Afiliação
  • Sung S; Department of Nursing Science, Research Institute of Nursing Science, Chungbuk National University, Cheongju, Chungcheongbuk-do, Republic of Korea.
  • Kim SH; Department of Information Statistics, Gyeongsang National University, Jinju, Gyeongsangnam-do, Republic of Korea.
  • Kim Y; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Bae YS; Division of Healthcare Planning, Bigdata Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Chie EK; Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Front Public Health ; 12: 1265848, 2024.
Article em En | MEDLINE | ID: mdl-38660352
ABSTRACT

Background:

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.

Objective:

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.

Methods:

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.

Results:

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).

Conclusion:

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Telemedicina / Depressão / COVID-19 Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: Front Public Health Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Telemedicina / Depressão / COVID-19 Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: Front Public Health Ano de publicação: 2024 Tipo de documento: Article