Ethnic Differences in Self-Reported Sleep Problems in Older Adults

SLEEP ◽  
2001 ◽  
Vol 24 (8) ◽  
pp. 926-933 ◽  
Author(s):  
G Jean-Louis ◽  
C M Magai ◽  
C I Cohen ◽  
F Zizi ◽  
H von Gizycki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
African Americans ◽  
Health Risk ◽  
Sleep Disturbance ◽  
Ethnic Differences ◽  
Community Dwelling ◽  
Health Risk Factors ◽  
Sleep Complaints ◽  
European Americans

Abstract Study Objectives: To date, conflicting observations have been made regarding ethnic differences in sleep patterns. Plausibly, differing sampling strategies and disparity in the cohorts investigated might help explain discrepant findings. To our knowledge population-based studies investigating ethnic differences in sleep complaints have not addressed within-group ethnic heterogeneity, although within-group health disparities have been documented. Design Volunteers (n =1118) in this study were community-residing older European Americans and African Americans residing in Brooklyn, New York, which were recruited by a stratified, cluster sampling technique. Trained interviewers of the same race as the respondents gathered data during face-to-face interviews conducted either in the respondent's home or another location of their choice. Data included demographic and health risk factors, physical health, social support, and emotional experience. Relationships of demographic and health risk factors to sleep disturbances were examined in multiple linear regression analyses. Withingroup differences in sleep complaints were also explored. Setting N/A Participants N/A Interventions N/A Measurements and Results Of the factors showing significant associations with sleep disturbance, European American ethnicity was the most significant predictor (r2 = 0.20). Worse sleep and greater reliance on sleep medicine were observed among European Americans. Caribbean Americans reported less sleep complaints than did U.S.-born African Americans, and immigrant European Americans reported greater complaints than did US-born European Americans. Conclusions As expected several health risk factors were predictive of sleep disturbance among urban community-dwelling older adults, but ethnicity was the most significant predictor. The present data suggest both between-group and within-group ethnic differences in sleep complaints. Understanding of demographic and cultural differences between African Americans and European Americans may be critical in interpreting subjective health-related data.

scholarly journals Risk Factors of Sleep Disturbance in Older Adults With Dementia: An Actigraphy-Based Validation Study

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 655-655
Author(s):  
Eunhee Cho ◽  
Jinhee Shin ◽  
Bada Kang ◽  
Sujin Kim ◽  
Sinwoo Hwang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Sleep Disturbance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Sleep Problems ◽  
Neuropsychiatric Symptoms ◽  
Cost Effective ◽  
Community Dwelling ◽  
Activity Level

Abstract Sleep disturbance is a common and significant symptom experienced by older adults with dementia. Early detection and timely treatment of sleep disturbance are critical to prevent adverse consequences including decreased quality of life for persons with dementia and increased caregiver burden. While direct observations and sleep diaries are often unreliable, actigraphy is a cost-effective method in measuring sleep problems in older adults with dementia and provides reliable and rich sleep data. Therefore, this study aimed to examine sleep disturbance objectively measured by actigraphy and its risk factors in community-dwelling older adults with dementia in Korea. This is a prospective study consisting of a two-wave dataset. The model was fitted using Wave 1 data (n=151) and then validated using Wave 2 data (n=59). Independent variables were demographics, cognitive and physical function, depressive symptoms, physical activity level, and neuropsychiatric symptoms measured by Neuropsychiatric Inventory(NPI), and clinical factors including dementia type, sedative use, and comorbidities. Sleep disturbance was defined as less than six nighttime sleep hours and sleep efficacy less than 75%. Using the Youden’s Index, the sample was dichotomized into sleep disturbance group (n=83) and sound sleep group (n=68). The results of the generalized linear mixed model showed that the risk factors for sleep disturbance included vascular dementia, age, step count, and having three neuropsychiatric symptoms (i.e., delusions, depression, and disinhibition). Individuals with dementia at risk for sleep disturbance should be identified to prioritize early prevention strategies and individualized interventions. Particularly, management of delusion, depression, disinhibition is critical in preventing disturbed sleep.

A Study of the Effects of Exercise on Stress, Depression Levels, and Health Risk Factors

2000 ◽  
Author(s):  
Paul A. Thomas ◽  
Jen Hanley ◽  
Christy Tomczak ◽  
Jennifer Wuchteil ◽  
Nathan Underwood ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Health Risk Factors

Behavioral Health Risk Factors for Nonmedical Use of Prescription Opioids in Adolescence

2020 ◽  
Author(s):  
Junhan Cho ◽  
Lorraine I. Kelley-Quon ◽  
Jessica L. Barrington-Trimis ◽  
Afton Kechter ◽  
Sarah Axeen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Behavioral Health ◽  
Prescription Opioids ◽  
Health Risk Factors ◽  
Nonmedical Use

Consumption of carbonated soft drinks among Ghanaian adolescents: associations with socio-demographic factors, health risk factors and psychological distress

2021 ◽  
pp. 026010602199693
Author(s):  
Prince Atorkey ◽  
Mariam Akwei ◽  
Winifred Asare-Doku
Keyword(s):  
Risk Factors ◽  
Psychological Distress ◽  
Health Risk ◽  
Soft Drink ◽  
Demographic Factors ◽  
Soft Drinks ◽  
Health Risk Factors ◽  
Soft Drink Consumption ◽  
Carbonated Soft Drinks ◽  
Socio Demographic Factors

Background: Carbonated soft drinks consumption is associated with weight gain and other chronic diseases. Aim: To examine whether socio-demographic factors, health risk factors and psychological distress are associated with carbonated soft drink consumption among adolescents in selected senior high schools in Ghana. Methods: Data were obtained from the 2012 Ghana Global School-based Student Health Survey (GSHS). Participants consisted of 1756 school-going adolescents sampled using a two-stage cluster sampling method. Binomial logistic regression was used to determine whether socio-demographic factors, health risk factors and psychological distress were associated with consumption of soft drinks. Results: The prevalence of carbonated soft drinks consumption was 34.9%. Males (odds ratio (OR) = 0.73 (95% confidence intervals (CI) 0.59–0.92); p = 0.007), and participants with high socio-economic status (OR = 0.76 (95% CI 0.48–0.97); p = 0.033) had smaller odds for consumption of soft drinks. Also, adolescents in Senior High School (SHS) 3 (OR = 0.72 (95% CI 0.53–0.97); p = 0.034) and SHS 4 (OR = 0.63 (95% CI 0.43–0.91); p = 0.014) had smaller odds for soft drinks intake compared to those in SHS 1. Health risk factors associated with greater odds of high soft drink consumption were tobacco use (OR = 1.68, (95% CI 1.07–2.65); p = 0.025), fast food consumption (OR = 1.88, (95% CI 1.47–2.41); p = 0.011) and alcohol consumption (OR = 1.43, (95% CI 1.02–1.99); p = 0.039). Consuming adequate fruit (OR = 0.19 (95% CI 0.15–0.24); p = 0.000) and adequate vegetable (OR = 0.55 (95% CI 0.34–0.87); p = 0.011) were associated with lower odds for soft drink consumption. Adolescents who reported feeling anxious had smaller odds for soft drink intake (OR = 0.65, (95% CI 0.47–0.91); p = 0.011). Conclusions: The findings from this study show that socio-demographic characteristics, health risk factors and psychological distress are associated with the soft drink consumption among adolescents in Ghana. Interventions aimed at reducing soft drink consumption and other health risk factors are needed.

Cannabis use among early adolescents and transdiagnostic mental health risk factors

2021 ◽  
pp. 135910452199463
Author(s):  
Sara Moreno-Mansilla ◽  
Jorge J Ricarte ◽  
David J Hallford
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Health Risk ◽  
Suicide Attempts ◽  
Intolerance Of Uncertainty ◽  
Cannabis Use ◽  
Health Risk Factors ◽  
Symptoms Of Depression ◽  
Modifiable Factors ◽  
Wide Range

Introduction: Cannabis is the most widely used psychoactive substance among adolescents worldwide, and the age at which consumption begins to decrease. Cannabis use in adolescents is associated with a wide range of adverse consequences in adulthood including increased vulnerability to psychosis and other mental disorders, as well as suicidal ideation and attempt. The aim of this study is to extend understanding of the link between cannabis use and mental illness by examining whether cannabis use at early ages predicts transdiagnostic variables that are precursors to severe clinical diagnoses. Methods: A descriptive cross-sectional study was conducted. The sample was made up of 605 adolescents from 7th to 9th grades, with a mean age of 13.2 years ( SD = 1.0, 47% girls). The variables evaluated were: anomalous perception of reality, intolerance of uncertainty, rumination, suicide attempt, hopelessness, and symptoms of depression and anxiety. The administration of the questionnaires was carried out in groups of 20 participants under the supervision of a researcher in a unique session of 1 hour. Results: Adolescent cannabis users scored higher on all variables assessed: anomalous perception of reality (Cohen’s d = .60), rumination ( d = .48), intolerance of uncertainty ( d = .11), suicidal attempt (affirmative answer: 25.9% of users vs 7.7% of non-users), hopelessness ( d = .85), symptoms of depression ( d = .80), and anxiety ( d = .39). A binary logistic regression showed that the only variable uniquely related to cannabis use was hopelessness (Wald = 4.560, OR: 1.159, p = .033). Conclusions: Among some mental health risk factors, hopelessness appears uniquely related to cannabis use. Adolescents may use cannabis as a coping strategy for negative thoughts and emotions, or it may be a consequence of cannabis use. Future prevention programs should focus on preventing/treating modifiable factors such as hopelessness, and delaying cannabis use in specific subgroups of adolescents who experience pathologies such as depression or suicide attempts.

scholarly journals Diagnostic Power of Circulatory Metabolic Biomarkers as Metabolic Syndrome Risk Predictors in Community-Dwelling Older Adults in Northwest of England (A Feasibility Study)

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2275
Author(s):  
Razieh Hassannejad ◽  
Hamsa Sharrouf ◽  
Fahimeh Haghighatdoost ◽  
Ben Kirk ◽  
Farzad Amirabdollahian
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Metabolic Syndrome ◽  
Roc Analysis ◽  
Cross Validation ◽  
Community Dwelling ◽  
Tnf Α ◽  
Diagnostic Power ◽  
Metabolic Biomarkers ◽  
Community Dwelling Older Adults

Background: Metabolic Syndrome (MetS) is a cluster of risk factors for diabetes and cardiovascular diseases with pathophysiology strongly linked to aging. A range of circulatory metabolic biomarkers such as inflammatory adipokines have been associated with MetS; however, the diagnostic power of these markers as MetS risk correlates in elderly has yet to be elucidated. This cross-sectional study investigated the diagnostic power of circulatory metabolic biomarkers as MetS risk correlates in older adults. Methods: Hundred community dwelling older adults (mean age: 68.7 years) were recruited in a study, where their blood pressure, body composition and Pulse Wave Velocity (PWV) were measured; and their fasting capillary and venous blood were collected. The components of the MetS; and the serum concentrations of Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), Plasminogen Activator Inhibitor-I (PAI-I), Leptin, Adiponectin, Resistin, Cystatin-C, C-Reactive Protein (CRP), insulin and ferritin were measured within the laboratory, and the HOMA1-IR and Atherogenic Index of Plasma (AIP) were calculated. Results: Apart from other markers which were related with some cardiometabolic (CM) risk, after Bonferroni correction insulin had significant association with all components of Mets and AIP. These associations also remained significant in multivariate regression. The multivariate odds ratio (OR with 95% confidence interval (CI)) showed a statistically significant association between IL-6 (OR: 1.32 (1.06–1.64)), TNF-α (OR: 1.37 (1.02–1.84)), Resistin (OR: 1.27 (1.04–1.54)) and CRP (OR: 1.29 (1.09–1.54)) with MetS risk; however, these associations were not found when the model was adjusted for age, dietary intake and adiposity. In unadjusted models, insulin was consistently statistically associated with at least two CM risk factors (OR: 1.33 (1.16–1.53)) and MetS risk (OR: 1.24 (1.12–1.37)) and in adjusted models it was found to be associated with at least two CM risk factors and MetS risk (OR: 1.87 (1.24–2.83) and OR: 1.25 (1.09–1.43)) respectively. Area under curve (AUC) for receiver operating characteristics (ROC) demonstrated a good discriminatory diagnostics power of insulin with AUC: 0.775 (0.683–0.866) and 0.785 by cross validation and bootstrapping samples for at least two CM risk factors and AUC: 0.773 (0.653–0.893) and 0.783 by cross validation and bootstrapping samples for MetS risk. This was superior to all other AUC reported from the ROC analysis of other biomarkers. Area under precision-recall curve for insulin was also superior to all other markers (0.839 and 0.586 for at least two CM risk factors and MetS, respectively). Conclusion: Fasting serum insulin concentration was statistically linked with MetS and its risk, and this link is stronger than all other biomarkers. Our ROC analysis confirmed the discriminatory diagnostic power of insulin as CM and MetS risk correlate in older adults.

scholarly journals A Modified CAIDE Risk Score as a Screening Tool for Cognitive Impairment in Older Adults

2021 ◽  
pp. 1-14
Author(s):  
Magdalena I. Tolea ◽  
Jaeyeong Heo ◽  
Stephanie Chrisphonte ◽  
James E. Galvin
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Risk Score ◽  
Clinical Sample ◽  
Community Sample ◽  
Community Dwelling ◽  
Cognitive Testing ◽  
Risk Scores ◽  
Predictive Values

Background: Although an efficacious dementia-risk score system, Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) was derived using midlife risk factors in a population with low educational attainment that does not reflect today’s US population, and requires laboratory biomarkers, which are not always available. Objective: Develop and validate a modified CAIDE (mCAIDE) system and test its ability to predict presence, severity, and etiology of cognitive impairment in older adults. Methods: Population consisted of 449 participants in dementia research (N = 230; community sample; 67.9±10.0 years old, 29.6%male, 13.7±4.1 years education) or receiving dementia clinical services (N = 219; clinical sample; 74.3±9.8 years old, 50.2%male, 15.5±2.6 years education). The mCAIDE, which includes self-reported and performance-based rather than blood-derived measures, was developed in the community sample and tested in the independent clinical sample. Validity against Framingham, Hachinski, and CAIDE risk scores was assessed. Results: Higher mCAIDE quartiles were associated with lower performance on global and domain-specific cognitive tests. Each one-point increase in mCAIDE increased the odds of mild cognitive impairment (MCI) by up to 65%, those of AD by 69%, and those for non-AD dementia by >  85%, with highest scores in cases with vascular etiologies. Being in the highest mCAIDE risk group improved ability to discriminate dementia from MCI and controls and MCI from controls, with a cut-off of ≥7 points offering the highest sensitivity, specificity, and positive and negative predictive values. Conclusion: mCAIDE is a robust indicator of cognitive impairment in community-dwelling seniors, which can discriminate well between dementia severity including MCI versus controls. The mCAIDE may be a valuable tool for case ascertainment in research studies, helping flag primary care patients for cognitive testing, and identify those in need of lifestyle interventions for symptomatic control.

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