The relationship between weight change and daytime sleepiness: the Sleep Heart Health Study

2017 ◽  
Vol 36 ◽  
pp. 109-118 ◽  
Author(s):  
Winda L. Ng ◽  
Liliana Orellana ◽  
Jonathan E. Shaw ◽  
Evelyn Wong ◽  
Anna Peeters
Keyword(s):  
Weight Change ◽  
Daytime Sleepiness ◽  
Health Study ◽  
Heart Health ◽  
The Relationship

scholarly journals Sleep and Reported Daytime Sleepiness in Normal Subjects: the Sleep Heart Health Study

SLEEP ◽  
2004 ◽  
Vol 27 (2) ◽  
pp. 293-298 ◽  
Author(s):  
Joyce A. Walsleben ◽  
Vishesh K. Kapur ◽  
Anne B. Newman ◽  
Eyal Shahar ◽  
Richard R. Bootzin ◽  
...  
Keyword(s):  
Daytime Sleepiness ◽  
Normal Subjects ◽  
Health Study ◽  
Heart Health

110 Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A45-A45
Author(s):  
Renske Lok ◽  
Jamie Zeitzer
Keyword(s):  
Daytime Sleepiness ◽  
Epworth Sleepiness Scale ◽  
Machine Learning Algorithms ◽  
Important Predictor ◽  
Health Study ◽  
Sleep Physiology ◽  
Cross Correlation Analysis ◽  
Explained Variance ◽  
Sleep Length ◽  
The Relationship

Abstract Introduction The Epworth Sleepiness Scale (ESS) is used as a clinical tool for determining excessive daytime sleepiness. However, the behavior and biology that underlie ESS scores remain to be elucidated. The main objective of this analysis is to determine objective behavioral and physiologic correlates of the ESS. Secondarily, we examine the relationship of the ESS to parallel subjective and objective endpoints that could represent measures of daytime sleepiness. Methods Using two separate machine learning algorithms, Random Forest and Lasso, we determined the association between ESS scores and 55 sleep and medical variables in individuals who participated in the Sleep Heart Health Study (N=2105). These variables include self-reported sleep characteristics (e.g., habitual sleep length and latency, frequency of not getting enough sleep), polysomnographic sleep measures from a single night, medication use, and mental and physical health status. Additional analyses were conducted on data stratified by age and gender. To investigate the relationship between ESS and other measures of daytime sleepiness, cross-correlation analysis was conducted on the ESS and five variables that could analog daytime sleepiness (feeling unrested, nap duration and frequency, sleep latency, frequency of not getting enough sleep). Results Analysis of the main dataset resulted in low explained variance (7.15 - 10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3–13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Habitual sleep length was not an important predictor in any model. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to ESS score. Conclusion Data analyses indicate that ESS scores are not well explained by habitual or polysomnographic sleep values, or a variety of other biomedical characteristics. This suggests that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. Caution should be used when considering the ESS as a clinical measure given that the physiologic correlates still remain to be elucidated. Support (if any):

scholarly journals The impact of diet during adolescence on the neonatal health of offspring: evidence on the importance of preconception diet. The HUNT study

2020 ◽  
pp. 1-13
Author(s):  
Wendy Van Lippevelde ◽  
Frøydis N. Vik ◽  
Andrew K. Wills ◽  
Sofia T. Strömmer ◽  
Mary E. Barker ◽  
...  
Keyword(s):  
Dietary Assessment ◽  
Sociodemographic Factors ◽  
Neonatal Outcomes ◽  
Health Study ◽  
Physical And Mental Health ◽  
Birth Registry ◽  
Meal Patterns ◽  
Dietary Choices ◽  
The Impact ◽  
The Relationship

Abstract Emerging evidence suggests that parents’ nutritional status before and at the time of conception influences the lifelong physical and mental health of their child. Yet little is known about the relationship between diet in adolescence and the health of the next generation at birth. This study examined data from Norwegian cohorts to assess the relationship between dietary patterns in adolescence and neonatal outcomes. Data from adolescents who participated in the Nord-Trøndelag Health Study (Young-HUNT) were merged with birth data for their offspring through the Medical Birth Registry of Norway. Young-HUNT1 collected data from 8980 adolescents between 1995 and 1997. Linear regression was used to assess associations between adolescents’ diet and later neonatal outcomes of their offspring adjusting for sociodemographic factors. Analyses were replicated with data from the Young-HUNT3 cohort (dietary data collected from 2006 to 2008) and combined with Young-HUNT1 for pooled analyses. In Young-HUNT1, there was evidence of associations between dietary choices, meal patterns, and neonatal outcomes, these were similar in the pooled analyses but were attenuated to the point of nonsignificance in the smaller Young-HUNT3 cohort. Overall, energy-dense food products were associated with a small detrimental impact on some neonatal outcomes, whereas healthier food choices appeared protective. Our study suggests that there are causal links between consumption of healthy and unhealthy food and meal patterns in adolescence with neonatal outcomes for offspring some years later. The effects seen are small and will require even larger studies with more state-of-the-art dietary assessment to estimate these robustly.

scholarly journals The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016

2021 ◽  
Vol 18 (8) ◽  
pp. 4365
Author(s):  
Kundadak Ganesh Kudva ◽  
Edimansyah Abdin ◽  
Janhavi Ajit Vaingankar ◽  
Boon Yiang Chua ◽  
Saleha Shafie ◽  
...  
Keyword(s):  
Mental Health ◽  
Suicidal Ideation ◽  
Psychiatric Disorders ◽  
Population Level ◽  
Risk Groups ◽  
Epidemiological Survey ◽  
Health Study ◽  
Physical Disorders ◽  
Mental Health Study ◽  
The Relationship

Suicidality encompasses suicidal ideation, plans, and attempts. This paper aims to establish associations between suicidality and sociodemographic variables, physical disorders, and psychiatric disorders. The Singapore Mental Health Study 2016 was a population-level epidemiological survey, which determined the prevalence of physical disorders, psychiatric disorders, and suicidality. Questionnaires were used to determine socio-demographic information. A total of 6216 respondents were interviewed. Lifetime prevalence of suicidal ideation, planning, and attempts were 7.8%, 1.6%, and 1.6%, respectively. All components of suicidality were more likely in those with major depressive disorder, bipolar disorder, generalized anxiety disorder, alcohol use disorder, and chronic pain. Suicidal ideation and attempts were more likely in those with diabetes. Age above 65, being male, and a monthly household income of ≥ SGD 10,000 were associated with a lower likelihood of suicidal ideation. These findings indicate that there are high-risk groups for whom suicidality is a concern, and for whom interventions may be needed.

scholarly journals Association between Anxiety Levels and Weight Change in the Multiethnic Study of Atherosclerosis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Katherine Rieke ◽  
Ramon Durazo-Arvizu ◽  
Kiang Liu ◽  
Erin D. Michos ◽  
Amy Luke ◽  
...  
Keyword(s):  
Weight Change ◽  
Inclusion Criteria ◽  
Mixed Effect ◽  
Effect Model ◽  
Lower Baseline ◽  
Multiethnic Cohort ◽  
Baseline Weight ◽  
Study Population ◽  
The Relationship

Objective. To examine the association between anxiety and weight change in a multiethnic cohort followed for approximately 10 years.Methods. The study population consisted of participants of the multiethnic study of atherosclerosis who met specified inclusion criteria (n= 5,799). Weight was measured at baseline and four subsequent follow-up exams. Anxiety was analyzed as sex-specific anxiety quartiles (QANX). The relationship between anxiety level and weight change was examined using a mixed-effect model with weight as the dependent variable, anxiety and time as the independent variables, and adjusted for covariates.Results. Average annual weight change (range) was −0.17 kg (−6.04 to 4.38 kg) for QANX 1 (lowest anxiety), −0.16 kg (−10.71 to 4.45 kg) for QANX 2, −0.15 kg (−8.69 to 6.39 kg) for QANX 3, and −0.20 kg (−7.12 to 3.95 kg) for QANX 4 (highest anxiety). No significant association was noted between QANX and weight change. However, the highest QANX was associated with a −2.48 kg (95% CI = −3.65, −1.31) lower baseline weight compared to the lowest QANX after adjustment for all covariates.Conclusions. Among adults, age 45–84, higher levels of anxiety, defined by the STPI trait anxiety scale, are associated with lower average baseline weight but not with weight change.

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