Association of sleep-wake pattern with cognitive performance and academic achievement in young adults

Altaf, Shafaq; Aftab, Anam; Khan, Komal Fayyaz; Anwar, Farah; Imran, Abeeha; Syed, Maira

doi:10.47391/jpma.2087

Cited by 2 publications

(1 citation statement)

References 10 publications

(10 reference statements)

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“…Cumulative cognitive scores were considered as they provide better comprehension of overall cognitive abilities of students, as opposed to the individual domain scores, as observed in previous studies for adolescent subjects. 22,32 The questionnaire was administered to the participants in a controlled setting, ensuring standardized conditions for data collection for all participants.…”

Section: Data Collectionmentioning

confidence: 99%

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Khan,

Perwez,

Gaddam

et al. 2024

NDT

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While previous studies have suggested close association of psychological variables of students withtheir higher-order cognitive abilities, such studies have largely been lacking for third world countries like India, with their unique socio-economiccultural set of challenges. We aimed to investigate the relationship between psychological variables (depression, anxiety and stress) and cognitive functions among Indian students, and to predict cognitive performance as a function of these variables. Patients and Methods: Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables. Results: Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables. Conclusion:The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.

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Section: Data Collectionmentioning

confidence: 99%

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Khan,

Perwez,

Gaddam

et al. 2024

NDT

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Chronotypes and their association with sleep quality among Chinese college students of Anhui Province: a cross-sectional study

Tong,

Gao,

Zhang

et al. 2023

BMJ Open

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ObjectivesTo describe the prevalence of chronotype and sleep quality among Chinese college students and explore the relationship between chronotype and sleep quality.DesignA cross-sectional study.SettingFour colleges and universities in Anhui, China, between November and December 2020.ParticipantsA total of 4768 college students were recruited using a stratified, multistage, cluster sampling survey.Outcome measuresMorningness-Eveningness Questionnaire 19 was used to determine the chronotype of the students and the Pittsburgh Sleep Quality Index (PSQI) was used to measure their sleep quality. The multiple logistic regression model was used to explore the potential association between chronotype and sleep quality.ResultsThe self-reported proportions of evening-type (E-type), neutral-type and morning-type among college students were 51.17%, 45.14% and 3.69%, respectively. The mean PSQI score was 4.97±2.82 and the prevalence of poor sleep quality was 18.2%. After adjusting the covariates by multiple logistic regression analysis, E-type was positively associated with subjective sleep quality (OR=1.671, 95% CI 1.414 to 1.975), sleep latency (OR=1.436, 95% CI 1.252 to 1.647), sleep duration (OR=2.149, 95% CI 1.506 to 3.067), habitual sleep efficiency (OR=1.702, 95% CI 1.329 to 2.180), daytime dysfunction (OR=1.602, 95% CI 1.412 to 1.818) and overall poor sleep quality (OR=1.866, 95% CI 1.586 to 2.196).ConclusionsCollege students mainly exhibited E-type, and an elevated prevalence of poor sleep quality existed among these students. The E-type was positively associated with poor sleep quality.

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Association of sleep-wake pattern with cognitive performance and academic achievement in young adults

Cited by 2 publications

References 10 publications

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Chronotypes and their association with sleep quality among Chinese college students of Anhui Province: a cross-sectional study

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