Background: Adolescent dependency on smartphones is the highest among all age groups. Adolescents can be influenced to evaluate their body image by popular ideals about beautiful bodies via smartphone content, which can cause body image distortion. This study aimed to examine the association between body image distortion and smartphone dependency and the duration of smartphone usage among Korean adolescents. Methods: This study used data from the 16th Korean Youth Risk Behavior Web-Based Survey (2020), and included 42,981 participants, who were grouped according to self-reported duration of smartphone usage and smartphone dependency, as measured by a questionnaire. Body image distortion is defined as an exaggerated subjective body image compared to the actual body image. Multivariable-adjusted logistic regression was used to determine the odds ratios (ORs) with 95% confidence intervals (CIs) of body image distortion on smartphone dependency and usage time after adjusting for various factors related to body image distortion. Results: Among the 42,981 participants, both moderate and high levels of smartphone dependency were associated with body image distortion in boys (moderate: adjusted OR [aOR], 1.11; 95% CI, 1.01–1.22; high: aOR, 1.18; 95% CI, 1.05–1.32) and girls (moderate: aOR, 1.14; 95% CI, 1.05–1.23; high: aOR, 1.30; 95% CI, 1.18–1.42) compared to the low-level dependency group. However, no significant association was found between smartphone usage duration and body image distortion. Conclusion Our study demonstrated that moderate to high levels of smartphone dependency are associated with body image distortion in adolescents. Therefore, it is necessary to establish strategies for checking and managing adolescents’ smartphone dependence.

Background: Adolescent dependency on smartphones is the highest among all age groups. Adolescents can be influenced to evaluate their body image by popular ideals about beautiful bodies via smartphone content, which can cause body image distortion. This study aimed to examine the association between body image distortion and smartphone dependency and the duration of smartphone usage among Korean adolescents. Methods: This study used data from the 16th Korean Youth Risk Behavior Web-Based Survey (2020), and included 42,981 participants, who were grouped according to self-reported duration of smartphone usage and smartphone dependency, as measured by a questionnaire. Body image distortion is defined as an exaggerated subjective body image compared to the actual body image. Multivariable-adjusted logistic regression was used to determine the odds ratios (ORs) with 95% confidence intervals (CIs) of body image distortion on smartphone dependency and usage time after adjusting for various factors related to body image distortion. Results: Among the 42,981 participants, both moderate and high levels of smartphone dependency were associated with body image distortion in boys (moderate: adjusted OR [aOR], 1.11; 95% CI, 1.01–1.22; high: aOR, 1.18; 95% CI, 1.05–1.32) and girls (moderate: aOR, 1.14; 95% CI, 1.05–1.23; high: aOR, 1.30; 95% CI, 1.18–1.42) compared to the low-level dependency group. However, no significant association was found between smartphone usage duration and body image distortion. Conclusion Our study demonstrated that moderate to high levels of smartphone dependency are associated with body image distortion in adolescents. Therefore, it is necessary to establish strategies for checking and managing adolescents’ smartphone dependence.

Background: Adolescent dependency on smartphones is the highest among all age groups. Adolescents can be influenced to evaluate their body image by popular ideals about beautiful bodies via smartphone content, which can cause body image distortion. This study aimed to examine the association between body image distortion and smartphone dependency and the duration of smartphone usage among Korean adolescents. Methods: This study used data from the 16th Korean Youth Risk Behavior Web-Based Survey (2020), and included 42,981 participants, who were grouped according to self-reported duration of smartphone usage and smartphone dependency, as measured by a questionnaire. Body image distortion is defined as an exaggerated subjective body image compared to the actual body image. Multivariable-adjusted logistic regression was used to determine the odds ratios (ORs) with 95% confidence intervals (CIs) of body image distortion on smartphone dependency and usage time after adjusting for various factors related to body image distortion. Results: Among the 42,981 participants, both moderate and high levels of smartphone dependency were associated with body image distortion in boys (moderate: adjusted OR [aOR], 1.11; 95% CI, 1.01–1.22; high: aOR, 1.18; 95% CI, 1.05–1.32) and girls (moderate: aOR, 1.14; 95% CI, 1.05–1.23; high: aOR, 1.30; 95% CI, 1.18–1.42) compared to the low-level dependency group. However, no significant association was found between smartphone usage duration and body image distortion. Conclusion Our study demonstrated that moderate to high levels of smartphone dependency are associated with body image distortion in adolescents. Therefore, it is necessary to establish strategies for checking and managing adolescents’ smartphone dependence.

Background: Adolescent dependency on smartphones is the highest among all age groups. Adolescents can be influenced to evaluate their body image by popular ideals about beautiful bodies via smartphone content, which can cause body image distortion. This study aimed to examine the association between body image distortion and smartphone dependency and the duration of smartphone usage among Korean adolescents. Methods: This study used data from the 16th Korean Youth Risk Behavior Web-Based Survey (2020), and included 42,981 participants, who were grouped according to self-reported duration of smartphone usage and smartphone dependency, as measured by a questionnaire. Body image distortion is defined as an exaggerated subjective body image compared to the actual body image. Multivariable-adjusted logistic regression was used to determine the odds ratios (ORs) with 95% confidence intervals (CIs) of body image distortion on smartphone dependency and usage time after adjusting for various factors related to body image distortion. Results: Among the 42,981 participants, both moderate and high levels of smartphone dependency were associated with body image distortion in boys (moderate: adjusted OR [aOR], 1.11; 95% CI, 1.01–1.22; high: aOR, 1.18; 95% CI, 1.05–1.32) and girls (moderate: aOR, 1.14; 95% CI, 1.05–1.23; high: aOR, 1.30; 95% CI, 1.18–1.42) compared to the low-level dependency group. However, no significant association was found between smartphone usage duration and body image distortion. Conclusion Our study demonstrated that moderate to high levels of smartphone dependency are associated with body image distortion in adolescents. Therefore, it is necessary to establish strategies for checking and managing adolescents’ smartphone dependence.

Objectives: Both shift work and restless legs syndrome (RLS) are common and can disrupt sleep and mood. However, the effects of shift work on RLS remain unclear. The current study aims to explore the prevalence, severity, and related sleep and mood symptoms of RLS in shift workers (SWs) compared to non-shift workers (Non-SWs). Methods: This study recruited 4,562 SWs (age 36.98±9.84, 2,150 males and 2,422 females) and 2,093 NSWs (age 37.79±9.73, 999 males and 1,094 females). All participants completed online self-report questionnaires regarding RLS, sleep, and depression. The presence of RLS was screened using a single standard question for rapid screening of RLS. The severity of RLS was assessed using the International Restless Legs Syndrome Rating Scale (iRLS). Additionally, the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), and short-term Center for Epidemiologic Studies Depression Scale (CES-D) were used to assess daytime sleepiness, insomnia, and depressive symptoms, respectively. Results: Of participants, 1,255 (27.45%) of SWs and 548 (26.18%) of Non-SWs were screened as having RLS, with no significant differences in the prevalence of RLS between the groups. However, among those with RLS, SWs with RLS showed higher iRLS scores compared to Non-SWs with RLS after controlling for age and sex (SWs: 9.55±7.26, NSWs: 6.75±6.05, F=64.15, p<0.01). In addition, SWs with RLS exhibited higher ISI scores (F=46.09, p<0.01), higher ESS scores (F=4.80, p=0.03), and higher CES-D scores (F=33.50, p<0.01) than Non-SWs with RLS. Conclusion Although there was no significant difference in RLS prevalence between SWs and Non-SWs, SWs with RLS exhibited more severe RLS symptoms and RLS-related symptoms, such as insomnia, sleepiness, and depression, compared to NSWs with RLS. The current study suggests that shift work may exacerbate RLS severity and RLS-related sleep and mood symptoms, although it may not be associated with the onset of RLS.

Objectives: Both shift work and restless legs syndrome (RLS) are common and can disrupt sleep and mood. However, the effects of shift work on RLS remain unclear. The current study aims to explore the prevalence, severity, and related sleep and mood symptoms of RLS in shift workers (SWs) compared to non-shift workers (Non-SWs). Methods: This study recruited 4,562 SWs (age 36.98±9.84, 2,150 males and 2,422 females) and 2,093 NSWs (age 37.79±9.73, 999 males and 1,094 females). All participants completed online self-report questionnaires regarding RLS, sleep, and depression. The presence of RLS was screened using a single standard question for rapid screening of RLS. The severity of RLS was assessed using the International Restless Legs Syndrome Rating Scale (iRLS). Additionally, the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), and short-term Center for Epidemiologic Studies Depression Scale (CES-D) were used to assess daytime sleepiness, insomnia, and depressive symptoms, respectively. Results: Of participants, 1,255 (27.45%) of SWs and 548 (26.18%) of Non-SWs were screened as having RLS, with no significant differences in the prevalence of RLS between the groups. However, among those with RLS, SWs with RLS showed higher iRLS scores compared to Non-SWs with RLS after controlling for age and sex (SWs: 9.55±7.26, NSWs: 6.75±6.05, F=64.15, p<0.01). In addition, SWs with RLS exhibited higher ISI scores (F=46.09, p<0.01), higher ESS scores (F=4.80, p=0.03), and higher CES-D scores (F=33.50, p<0.01) than Non-SWs with RLS. Conclusion Although there was no significant difference in RLS prevalence between SWs and Non-SWs, SWs with RLS exhibited more severe RLS symptoms and RLS-related symptoms, such as insomnia, sleepiness, and depression, compared to NSWs with RLS. The current study suggests that shift work may exacerbate RLS severity and RLS-related sleep and mood symptoms, although it may not be associated with the onset of RLS.

Objectives: Both shift work and restless legs syndrome (RLS) are common and can disrupt sleep and mood. However, the effects of shift work on RLS remain unclear. The current study aims to explore the prevalence, severity, and related sleep and mood symptoms of RLS in shift workers (SWs) compared to non-shift workers (Non-SWs). Methods: This study recruited 4,562 SWs (age 36.98±9.84, 2,150 males and 2,422 females) and 2,093 NSWs (age 37.79±9.73, 999 males and 1,094 females). All participants completed online self-report questionnaires regarding RLS, sleep, and depression. The presence of RLS was screened using a single standard question for rapid screening of RLS. The severity of RLS was assessed using the International Restless Legs Syndrome Rating Scale (iRLS). Additionally, the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), and short-term Center for Epidemiologic Studies Depression Scale (CES-D) were used to assess daytime sleepiness, insomnia, and depressive symptoms, respectively. Results: Of participants, 1,255 (27.45%) of SWs and 548 (26.18%) of Non-SWs were screened as having RLS, with no significant differences in the prevalence of RLS between the groups. However, among those with RLS, SWs with RLS showed higher iRLS scores compared to Non-SWs with RLS after controlling for age and sex (SWs: 9.55±7.26, NSWs: 6.75±6.05, F=64.15, p<0.01). In addition, SWs with RLS exhibited higher ISI scores (F=46.09, p<0.01), higher ESS scores (F=4.80, p=0.03), and higher CES-D scores (F=33.50, p<0.01) than Non-SWs with RLS. Conclusion Although there was no significant difference in RLS prevalence between SWs and Non-SWs, SWs with RLS exhibited more severe RLS symptoms and RLS-related symptoms, such as insomnia, sleepiness, and depression, compared to NSWs with RLS. The current study suggests that shift work may exacerbate RLS severity and RLS-related sleep and mood symptoms, although it may not be associated with the onset of RLS.

Objectives: Both shift work and restless legs syndrome (RLS) are common and can disrupt sleep and mood. However, the effects of shift work on RLS remain unclear. The current study aims to explore the prevalence, severity, and related sleep and mood symptoms of RLS in shift workers (SWs) compared to non-shift workers (Non-SWs). Methods: This study recruited 4,562 SWs (age 36.98±9.84, 2,150 males and 2,422 females) and 2,093 NSWs (age 37.79±9.73, 999 males and 1,094 females). All participants completed online self-report questionnaires regarding RLS, sleep, and depression. The presence of RLS was screened using a single standard question for rapid screening of RLS. The severity of RLS was assessed using the International Restless Legs Syndrome Rating Scale (iRLS). Additionally, the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), and short-term Center for Epidemiologic Studies Depression Scale (CES-D) were used to assess daytime sleepiness, insomnia, and depressive symptoms, respectively. Results: Of participants, 1,255 (27.45%) of SWs and 548 (26.18%) of Non-SWs were screened as having RLS, with no significant differences in the prevalence of RLS between the groups. However, among those with RLS, SWs with RLS showed higher iRLS scores compared to Non-SWs with RLS after controlling for age and sex (SWs: 9.55±7.26, NSWs: 6.75±6.05, F=64.15, p<0.01). In addition, SWs with RLS exhibited higher ISI scores (F=46.09, p<0.01), higher ESS scores (F=4.80, p=0.03), and higher CES-D scores (F=33.50, p<0.01) than Non-SWs with RLS. Conclusion Although there was no significant difference in RLS prevalence between SWs and Non-SWs, SWs with RLS exhibited more severe RLS symptoms and RLS-related symptoms, such as insomnia, sleepiness, and depression, compared to NSWs with RLS. The current study suggests that shift work may exacerbate RLS severity and RLS-related sleep and mood symptoms, although it may not be associated with the onset of RLS.

Objectives: Both shift work and restless legs syndrome (RLS) are common and can disrupt sleep and mood. However, the effects of shift work on RLS remain unclear. The current study aims to explore the prevalence, severity, and related sleep and mood symptoms of RLS in shift workers (SWs) compared to non-shift workers (Non-SWs). Methods: This study recruited 4,562 SWs (age 36.98±9.84, 2,150 males and 2,422 females) and 2,093 NSWs (age 37.79±9.73, 999 males and 1,094 females). All participants completed online self-report questionnaires regarding RLS, sleep, and depression. The presence of RLS was screened using a single standard question for rapid screening of RLS. The severity of RLS was assessed using the International Restless Legs Syndrome Rating Scale (iRLS). Additionally, the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), and short-term Center for Epidemiologic Studies Depression Scale (CES-D) were used to assess daytime sleepiness, insomnia, and depressive symptoms, respectively. Results: Of participants, 1,255 (27.45%) of SWs and 548 (26.18%) of Non-SWs were screened as having RLS, with no significant differences in the prevalence of RLS between the groups. However, among those with RLS, SWs with RLS showed higher iRLS scores compared to Non-SWs with RLS after controlling for age and sex (SWs: 9.55±7.26, NSWs: 6.75±6.05, F=64.15, p<0.01). In addition, SWs with RLS exhibited higher ISI scores (F=46.09, p<0.01), higher ESS scores (F=4.80, p=0.03), and higher CES-D scores (F=33.50, p<0.01) than Non-SWs with RLS. Conclusion Although there was no significant difference in RLS prevalence between SWs and Non-SWs, SWs with RLS exhibited more severe RLS symptoms and RLS-related symptoms, such as insomnia, sleepiness, and depression, compared to NSWs with RLS. The current study suggests that shift work may exacerbate RLS severity and RLS-related sleep and mood symptoms, although it may not be associated with the onset of RLS.

Background: Delirium is a recognized neurological complication following cardiac surgery and is associated with adverse clinical outcomes, including elevated mortality and prolonged hospitalization. While several clinical risk factors for post-cardiac surgery delirium have been identified, the pathophysiology related to the immune response remains unexamined. This study was conducted to investigate the immunological factors contributing to delirium in patients after thoracic aortic surgery. Methods: We retrospectively evaluated 43 consecutive patients who underwent thoracic aortic surgery between July 2017 and June 2018. These patients were categorized into 2 groups: those with delirium and those without it. All clinical characteristics were compared between groups. Blood samples were collected and tested on the day of admission, as well as on postoperative days 1, 3, 7, and 30. Levels of helper T cells (CD4), cytotoxic T cells (CD8), B cells (CD19), natural killer cells (CD56+CD16++), and monocytes (CD14+CD16−) were measured using flow cytometry. Results: The median patient age was 71 years (interquartile range, 56.7 to 79.0 years), and 21 of the patients (48.8%) were male. Preoperatively, most immune cell counts did not differ significantly between groups. However, the patients with delirium exhibited significantly higher levels of interleukin-6 and lower levels of tumor necrosis factor-alpha (TNF-α) than those without delirium (p<0.05). Multivariate analysis revealed that lower TNF-α levels were associated with an increased risk of postoperative delirium (p<0.05). Conclusion Postoperative delirium may be linked to perioperative changes in immune cells and preoperative cytokine levels. Additional research is required to elucidate the pathophysiological mechanisms underlying delirium.