Mood and Sleep Quality in Peruvian Medical Students During COVID-19 Pandemic

Olarte-Durand, Mely; Roque-Aycachi, Jossiel B.; Rojas-Humpire, Ricardo; Canaza-Apaza, Josué F.; Laureano, Stefani; Rojas-Humpire, Andrea; Huancahuire-Vega, Salomón

doi:10.1016/j.rcp.2021.11.010

Cited by 19 publications

(21 citation statements)

References 24 publications

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“…This study used the PSQI to identify factors associated with poor sleep quality in medical students, and demonstrated that the incidence of poor sleep quality was 31.9%. This figure is consistent with many previous studies (Johnson et al, 2017;Eleftheriou et al, 2021;Gupta et al, 2021;Olarte-Durand et al, 2021).…”

Section: Discussionsupporting

confidence: 93%

Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students

Ding

Guo²,

Zhang³

et al. 2022

Front. Neurosci.

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BackgroundDespite the increasing prevalence of poor sleep quality among medical students, only few studies have identified the factors associated with it sing methods from epidemiological surveys. Predicting poor sleep quality is critical for ensuring medical Students’ good physical and mental health. The aim of this study was to develop a comprehensive visual predictive nomogram for predicting the risk of poor sleep quality in medical students.MethodsWe investigated medical Students’ association with poor sleep quality at JiTang College of North China University of Science and Technology through a cross-sectional study. A total of 5,140 medical students were randomized into a training cohort (75%) and a validation cohort (25%). Univariate and multivariate logistic regression models were used to explore the factors associated with poor sleep quality. A nomogram was constructed to predict the individual risk of poor sleep quality among the medical students studied.Results31.9% of medical students in the study reported poor sleep quality. We performed multivariate logistic analysis and obtained the final model, which confirmed the risk and protective factors of poor sleep quality (p < 0.05). Protective factors included the absence of physical discomfort (OR = 0.638, 95% CI: 0.546–0.745). Risk factors included current drinking (OR = 0.638, 95% CI: 0.546∼0.745), heavy study stress (OR = 2.753, 95% CI: 1.456∼5.631), very heavy study stress (OR = 3.182, 95% CI: 1.606∼6.760), depressive symptoms (OR = 4.305, 95% CI: 3.581∼5.180), and anxiety symptoms (OR = 1.808, 95% CI: 1.497∼2.183). The area under the ROC curve for the training set is 0.776 and the area under the ROC curve for the validation set is 0.770, which indicates that our model has good stability and prediction accuracy. Decision curve analysis and calibration curves demonstrate the clinical usefulness of the predictive nomograms.ConclusionOur nomogram helps predict the risk of poor sleep quality among medical students. The nomogram used includes the five factors of drinking, study stress, recent physical discomfort, depressive symptoms, and anxiety symptoms. The model has good performance and can be used for further research on and the management of the sleep quality of medical students.

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Section: Discussionsupporting

confidence: 93%

Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students

Ding

Guo²,

Zhang³

et al. 2022

Front. Neurosci.

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“…As the university students experienced frequent lockdown, quarantine, and accommodation relocations during the Covid-19 pandemic, their experience of depression, anxiety, negative emotions, and attention incapacity increased, which were threatening their mental health 49 , 50 , 51 , 52 . Sleep difficulties and mood disturbance became more prevalent than before because of the Covid-19 pandemic 53 , 54 , 55 . Incorporating outdoor walking in nature with mindfulness practise was assumed to improve university students’ sleep quality and mood.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of a mindful nature walking intervention on sleep quality and mood in university students during Covid-19: A randomised control study

Williams

Morris

et al. 2023

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“…This result is higher than estimates found in meta-analyses during (47%, 95% CI = 35-59%) and before (34%, 95% CI: 29-39%) the pandemic period [20]. Prevalence of anxiety symptoms was unexpectedly higher than prior Peruvian studies (36-57%) conducted during the first wave in Peru [44][45][46][47]. However, as with depression scores, differences between estimates are related to the instrument used (e.g., GAD-7, DASS-21, BAI-21), different cut-off values, and the study period.…”

Section: Plos Onementioning

confidence: 60%

“…Although PHQ-9 was used, both studies have unclear information about how education was delivered. Another study found a prevalence of 60% with DASS-21 [ 46 ], but data collection period was after the first wave and possibly online education was delivered. Another notable finding is the 37% prevalence reported during suspension of classes [ 47 ], suggesting that academic courses increase the presence rate of depressive symptoms.…”

Section: Discussionmentioning

confidence: 99%

Smartphone overuse, depression & anxiety in medical students during the COVID-19 pandemic

et al. 2022

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Introduction Medical students have made particular use of smartphones during the COVID-19 pandemic. Although higher smartphone overuse has been observed, its effect on mental disorders is unclear. This study aimed to assess the association between smartphone overuse and mental disorders in Peruvian medical students during the COVID-19 pandemic. Methods A cross-sectional study was conducted in 370 students aged between 16 and 41 years (median age: 20) in three universities from July to October 2020. A survey including Smartphone Dependence and Addiction Scale, PHQ-9, and GAD-7 was applied. Prevalence ratios were estimated using generalized linear models. Results Smartphone overuse was a common feature among students (n = 291, 79%). Depressive symptoms were present in 290 (78%) students and anxiety symptoms in 255 (69%). Adjusted for confounders, addictive/dependent smartphone use was significantly associated with presence of depressive symptoms (PR = 1.29, 95% CI: 1.20–1.38 for dependent use; PR = 1.30, 95% CI: 1.12–1.50 for addictive use). Also, addictive/dependent smartphone use was significantly associated with presence of anxiety symptoms (PR = 1.59, 95% CI: 1.14–2.23 for dependent use; PR = 1.61, 95% CI: 1.07–2.41 for addictive use). Conclusions Our findings suggest that medical students exposed to smartphone overuse are vulnerable to mental disorders. Overuse may reflect an inappropriate way of finding emotional relief, which may significantly affect quality of life and academic performance. Findings would assist faculties to establish effective measures for prevention of smartphone overuse.

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Mood and Sleep Quality in Peruvian Medical Students During COVID-19 Pandemic

Cited by 19 publications

References 24 publications

Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students

Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students

Effectiveness of a mindful nature walking intervention on sleep quality and mood in university students during Covid-19: A randomised control study

Smartphone overuse, depression & anxiety in medical students during the COVID-19 pandemic

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