Sleep Quality and Related Factors in Turkish High School Adolescents

Tekcan, Pınar; Çalışkan, Zehra; Kocaöz, Semra

doi:10.1016/j.pedn.2020.07.020

Cited by 9 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2017). Sleep quality is determined by: the total amount of sleep, the difficulty of falling asleep, the amount of sleep disturbance, the amount of awake time and incidence of sleep disorders such as sleep apnoea and spontaneous stimulation (Tekcan et al. , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although the concept of sleep quality has continued to defy a widely accepted definition in mainstream literature (Buysse, 2014;Fabbri et al, 2021), a consensus pertaining to its determinants remains apparent (Ohayon et al, 2017). Sleep quality is determined by: the total Prevalence of poor sleep quality amount of sleep, the difficulty of falling asleep, the amount of sleep disturbance, the amount of awake time and incidence of sleep disorders such as sleep apnoea and spontaneous stimulation (Tekcan et al, 2020). It plays a vital role in brain and body functions and physical and mental well-being (Coronel-Ocampos et al, 2020) and affects the cognitive capacities, learning and attention, physical development, behavioural and emotional development of children (Chattu et al, 2019).…”

Section: Literature Review 21 Measuring Sleep Qualitymentioning

confidence: 99%

See 1 more Smart Citation

An assessment of prevalence of poor sleep quality among construction workers in Southern India

Sathvik

Krishnaraj²,

Awuzie³

2022

BEPAM

View full text Add to dashboard Cite

PurposeSleep quality has been associated with health and safety issues influencing construction labour productivity in developing countries. Despite its significantly adverse contribution to these facets if left unattended to, limited studies have sought to establish its prevalence and causal factors in labour-intensive contexts. This study aims to bridge the gap between the prevalence and casual factors of poor sleep quality among construction workers.Design/methodology/approachAdopting a case study research design, data were collected from a randomly selected sample of construction workers (n = 475) recruited from four construction-based corporations in Southern India. Self-administered questionnaires comprising a Pittsburgh Sleep Quality Index (PSQI) scale was used to measure sleep quality among respondents alongside demographic characteristics, lifestyle preferences and work-habits data. Data were analysed using multivariate logistic regression analysis and descriptive statistical techniques.FindingsSlightly more than a third of the construction workers (n = 33.9%) surveyed experienced poor sleep quality based on a PSQI method score of = 5. Also, the nexus between demographic characteristics, lifestyle preferences and work habits on the incidence of poor sleep quality among construction workers was confirmed.Practical implicationsThe study contributes to the contemporary discourse on improving sleep health of construction workers to enhance their well-being thereby enabling their contribution towards achieving improved construction labour productivity.Originality/valueThis study makes an original contribution to the extent that it seeks to not only determine the prevalence of sleep quality within the construction industry and associated causal factors but to explore its implications on construction labour productivity.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Review 21 Measuring Sleep Qualitymentioning

confidence: 99%

An assessment of prevalence of poor sleep quality among construction workers in Southern India

Sathvik

Krishnaraj²,

Awuzie³

2022

BEPAM

View full text Add to dashboard Cite

show abstract

“…This process began by training the model with all the variables. The importance of each feature was then evaluated based on the change in a specific performance score following a random shuffle of the feature's values 26 . Consequently, this shuffle disrupted the relationship between the feature and target outcome, revealing the significance of the feature 28 .…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the area under the curve (AUC) was computed to assess the overall performance of each model. The analysis was conducted in Python using the Scikit-learn library (version 0.23.2) 26 . The receiver operating characteristic (ROC) curves and their comparative analyses were performed using MedCalc software (version 14.0) 30 .…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies on prediction models for the Indian population have predominantly utilized multivariate analysis or support vector machine (SVM). One study on sleep deprivation (SD) in construction workers utilized logistic regression (LR) with data from 433 individuals, achieving a sensitivity of 74.6% and a specificity of 66.3% 24 – 26 . Another study employed an SVM to create an SD prediction model based on data from 566 individuals, resulting in an accuracy rate of 84.15% 27 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms

Sathvik,

Alsharef,

Singh

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

show abstract

Determining the relationship between sleep disturbances in children and parental stress during COVID-19 pandemic

Sarıalioğlu

Ayran

Köse

2023

Psychology, Health & Medicine

View full text Add to dashboard Cite

Sleep Quality and Related Factors in Turkish High School Adolescents

Cited by 9 publications

References 28 publications

An assessment of prevalence of poor sleep quality among construction workers in Southern India

An assessment of prevalence of poor sleep quality among construction workers in Southern India

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms

Determining the relationship between sleep disturbances in children and parental stress during COVID-19 pandemic

Contact Info

Product

Resources

About