Relationship of quality of sleep with cognitive performance and emotional maturity among adolescents

Díaz‐Román

2023

EJIHPE

The relevance of cognitive performance during adolescence requires further studies that analyze potential associated factors. This study aimed to analyze inductive reasoning, reading comprehension, and mathematical thinking (problem-solving and number and calculation) in relation to sleep and depression in 244 students aged 12–17 years (47.6% boys and 52.4% girls). Daytime sleepiness, sleep quality, dysthymia, and euthymia (state and trait) were assessed by self-reported questionnaires. Moreover, correlations between these variables and cognitive performance, and differences depending on sociodemographic variables (sex, age, or academic year) were analyzed using non-parametric tests. Robust regression models were also conducted to evaluate the predictive role of significant variables on cognitive performance. The results showed significant bidirectional relationships between sleep- and depression-related variables, and between the latter ones and cognitive performance. Depression-trait was more related to cognitive performance than depression-state, and euthymia more than dysthymia, but neither daytime sleepiness nor sleep quality significantly correlated with it. As for sociodemographic variables, girls reported worse sleep and more depressive symptoms than boys did, and younger students reported better sleep but performed worse than the older ones. Although these findings should be further explored in forthcoming studies adding other promising variables, they highlight the importance of promoting euthymia to improve cognitive performance in adolescents.

Section: Discussionsupporting

confidence: 91%

Associations between Sleep, Depression, and Cognitive Performance in Adolescence

Díaz‐Román

2023

EJIHPE

“…The present study depicted normal mean cognitive performance scores in older adults. There was no significant difference in mental state in the two groups, which is consistent with research done by Casavi et al (2022), where no statistically significant effects were shown for cognitive performance with sleep quality [ 46 ]. Mahfouz et al (2020) also found no statistical difference in sleep quality according to the stress level, and DASS score [ 7 ], while Aseem et al (2021) highlighted a significant link between cognitive performance and sleep quality [ 47 ].…”

Section: Discussionsupporting

confidence: 91%

The impact of social support, physical and psychological performance on sleep outcomes in Iranian older adults: a case-control study

Salehi,

Pasha,

Hosseini

et al. 2023

BMC Geriatr

Background Sleep quality is one of the most important factors to improve the quality of life in older adults and physical and mental health plays an essential role in better sleep quality. This study aimed to determine the impact of social support, and physical and psychological performance on sleep outcomes in Iranian older adults. Methods In this case-control study, 400 elder people, who were exposed to sleep problems, and 400 people without sleep problems were randomly selected during 2016–2017 in Amirkola, Iran. Subjects in the case and control groups were matched in terms of gender and age. The demographic characteristics, Duke Social Support Questionnaire (DSSI), Physical Activity Scale for the Elderly (PASE), Activity of Daily Living (ADL), Instrumental Activity of Daily Living (IADL), Mini-Mental State Examination (MMSE), and Pittsburgh Sleep Quality Questionnaire (PSQI) questionnaires were used to collect data. T-test, Chi-square, Pearson Correlation coefficient, and multiple Logistic regression were used for data analysis. Results The mean score of DSSI and its domains including social interaction (DSSI.Int) and social satisfaction (DSSI.Sat) were 28.15 ± 3.55, 9.31 ± 1.23, and 18.84 ± 2.88 in the case group and 28.87 ± 3.20, 9.48 ± 1.10, and 19.83 ± 2.44 in the control group, respectively. In this study, the mean scores of MMSE, PASE, ADL, and IADL were 25.36 ± 3.95, 101.71 ± 56.99, 13/97 ± 0.37, 20.59 ± 2/79; respectively. There was a significant inverse correlation between poor sleep quality with DSSI score (rho = -0.165, P < 0.0001), DSSI.Int (rho = -0.113, P < 0.001), DSSI.Sat (rho = -0.160, P < 0.0001), PASE (rho=-0.160, P < 0.0001), and IADL (rho = -0.112, P < 0.001) score. Therefore, more social support and physical activity improved the quality of sleep. There was a significant negative relationship between DSSI, and its domains with sleep quality in terms of gender. DSSI (rho = 0.25, P < 0.0001), DSSI.Int (P < 0.0001, rho=-0.18), and DSSI.Sat (P < 0.0001, rho=-0.22) was significant in men but not in women. The results of the adjusted logistic regression revealed a significant association between sleep quality problems and DSSI (p < 0.045, OR = 1.40), the use of hypnotic drugs (p < 0.0001, OR = 7.56), and occupation (p <0.03, OR= 12.66). Conclusions The results of the present study suggest that low social support and all its domains, PASE, IADL, and using hypnotic drugs may play a role in the development of sleep problems. It can be used as an effective, safe, and low-cost strategy for promoting sleep quality in older adults.

“…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%

“…While originally designed for diagnosis of mild and severe cognitive impairments in the aged population by Nasreddine and colleagues, 20 MoCA and its vernacular versions have been efficiently extended to young adult and adolescent student populations in several studies. [21][22][23][24] Use of MoCA has also been undertaken specifically in Indian students. 25 Using these questionnaire-based instruments, in this study, we investigated the emotional status and cognitive functioning in university students for the exploration of any potential associations.…”

Section: Introductionmentioning

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

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.