A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Peng, Pu; Wang, Yaqi; Li, Zhuqing; Zhou, Yanan; Wang, Ji; Qu, Miao; Liu, Tieqiao

doi:10.3389/fpubh.2023.1223429

Cited by 8 publications

(6 citation statements)

References 44 publications

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“…Previous studies have confirmed their satisfactory psychometric properties in Chinese populations [ 39 – 42 ]. Following previous studies [ 43 , 44 ], a cutoff point of 10 for PHQ-9, 10 for GAD-7, and 6 for PSQI was used to determine the presence of depression, anxiety, and sleep disturbances.…”

Section: Methodsmentioning

confidence: 99%

Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis

Peng,

Chen,

Ren

et al. 2023

BMC Psychiatry

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Backgrounds The Smartphone Application-Based Addiction Scale (SABAS) is a validated 6-item measurement tool for assessing problematic smartphone use (PSU). However, the absence of established cutoff points for SABAS hinders its utilities. This study aimed to determine the optimal cutoff point for SABAS through latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses among 63, 205. Chinese adolescents. Additionally, the study explored whether PSU screening with SABAS could effectively capture problematic social media use (PSMU) and internet gaming disorder (IGD). Method We recruited 63,205. adolescents using cluster sampling. Validated questionnaires were used to assess PSMU, IGD, and mental health (depression, anxiety, sleep disturbances, well-being, resilience, and externalizing and internalizing problems). Results LPA identified a 3-class model for PSU, including low-risk users (38.6%, n = 24,388.), middle-risk users (42.5%, n = 26,885.), and high-risk users (18.9%, n = 11,932.). High-risk users were regarded as “PSU cases” in ROC analysis, which demonstrated an optimal cut-off point of 23 (sensitivity: 98.1%, specificity: 96.8%). According to the cutoff point, 21.1% (n = 13,317.) were identified as PSU. PSU adolescents displayed higher PSMU, IGD, and worse mental health. PSU screening effectively captured IGD (sensitivity: 86.8%, specificity: 84.5%) and PSMU (sensitivity: 84.5%, specificity: 80.2%). Conclusion A potential ideal threshold for utilizing SABAS to identify PSU could be 23 (out of 36). Employing SABAS as a screening tool for PSU holds the potential to reliably pinpoint both IGD and PSMU.

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Section: Methodsmentioning

confidence: 99%

Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis

Peng,

Chen,

Ren

et al. 2023

BMC Psychiatry

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“…The current study used network modelling to explore a wide range of long COVID symptoms, departing from previous studies that focused on specific symptoms (e.g., neurobehavioral (4), mental distress (17)). The online format allowed for more convenience and accessibility, providing a broader perspective beyond high-income countries (37).…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…Finally, visualizing the interconnection of different symptoms can be informative for offering new perspectives on pathogenic mechanisms (16). Some previous studies have employed network analysis to explore long COVID (4,17). However, their focus was limited to neurocognitive, behavioral, and psychosocial aspects pertaining to long COVID.…”

Section: Introductionmentioning

confidence: 99%

“…Another study investigated a network of 25 mental distress symptoms in 535 Chinese COVID-19 survivors one year after hospitalization. The results demonstrated excessive worry, intrusion, daytime dysfunction, and mental fatigue as key symptoms associated with the experience of mental distress and a diminished quality of life (17). However, due to the identification of over 200 different long COVID symptoms (6), a more comprehensive network analysis on a larger number of symptoms is in place.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling the Complex Web of Long COVID symptoms: A Network Analysis Approach

van den Houdt,

Mertens,

Slurink

2024

Preprint

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Long COVID is a syndrome of persistent symptoms following a COVID-19 infection. With over 200 identified symptoms and ongoing debate about the central symptoms of this condition, this study aimed to explore the network structure of self-reported long COVID symptoms. Participants with persistent symptoms after a COVID-19 infection (n=458 , Mage = 36.0±11.9; 46.5% male) indicated the prevalence and severity of their long COVID symptoms using a comprehensive 114-item symptom list spanning across 10 health domains. Correlation and regularized partial correlation network analyses were performed to examine the interrelatedness of symptoms focusing on symptoms listed in international health guidelines and those with a prevalence of at least 20% in our sample. Replication analyses were performed using a second dataset from a representative Dutch sample (n=415). We find that some long COVID symptoms are closely interconnected (e.g., congestion and neurocognitive symptoms), whereas others (e.g., fatigue and dizziness) are more dispersed between clusters. Centrality indices indicated that the most common symptoms mentioned in the guidelines were not necessarily the most central in the networks. These findings were largely replicated in the second dataset. The study provides a visualization of the wide array of long COVID symptoms, revealing it as a diverse syndrome marked by different clusters of loosely connected symptoms. Although explorative, the results can be informative for potential pathogenic pathways involved in long COVID-19 symptoms and may inform future research and clinical interventions.

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“…Moreover, there were no statistically significant differences in such network results between participants with or without any history of mental disorders and those who had or hadn’t been hospitalized. Peng P. et al (2023) found in their study on Chinese COVID-19 survivors that the most prominent symptoms included uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction. Additionally, daytime dysfunction and fatigue were identified as bridge symptoms in the mental disorder network and exhibited a strong association with quality of life.…”

Section: Introductionmentioning

confidence: 99%

The relation between mental health and career-related stress among prospective graduates in higher education stage during the COVID-19 pandemic: an evidence based on network analysis

Tang,

Lei,

Zhang

et al. 2024

Front. Psychol.

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IntroductionThe outbreak of the COVID-19 pandemic has disrupted people’s routine, bringing uncertainty and stress, leading to mental health issues. This situation is particularly acute among Chinese prospective graduates in higher education stage as they cannot predict the outcomes of their studies, graduation, and career development, and therefore experience career-related stress.MethodsThis study included 4041 prospective graduates in higher education stage (using handy sampling) recruited in March 2020 as participants (MAge = 22.56, SD = 1.865), utilizing a Sparse Gaussian Graphical Model for regularized partial correlation network analysis of depression symptoms (by PHQ-9), anxiety symptoms (by GAD-7), and career-related stress, which aims to explore the role of career-related stress in the symptom networks of depression and anxiety among prospective graduates in their final semester.ResultsThe results revealed that fatigue, sad mood, and psychomotor symptoms in depression, as well as uncontrollable worry and trouble relaxing in anxiety, were central symptoms in the network. Additionally, sad mood and guilt belonging to depressive symptoms, and feeling afraid, restlessness, and irritability belonging to anxiety symptoms, served as bridge symptoms connecting symptom communities. Specifically, guilt as a depressive symptom showed a strong association with employment stress in career-related stress. There were no significant differences in network structure and global strength based on participants’ gender. However, despite no significant differences in network structure, the global strength of prospective graduates from Wuhan was significantly lower than samples from non-Hubei provinces, possibly indicative of a Typhoon Eye Effect.DiscussionThe findings of this study can inspire psychological professionals in higher education institutions to provide support for mental interventions and therapies for prospective graduates, and addressing career development issues.

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A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Cited by 8 publications

References 44 publications

Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis

Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis

Unraveling the Complex Web of Long COVID symptoms: A Network Analysis Approach

The relation between mental health and career-related stress among prospective graduates in higher education stage during the COVID-19 pandemic: an evidence based on network analysis

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