Faculty Bibliography

Introduction: Sleep is related to socioeconomic status and impacts health. This study evaluated whether foregoing medical care due to cost impacts sleep and plays a role in sleep disparities and the sleep-obesity relationship.
Method(s): Data from the 2017 Behavioral Risk Factor Surveillance System (N=39,267 from 7 states). Sleep duration was assessed as hours/day. Participants were asked, "Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?" They were also asked for information about age, sex, race/ethnicity, education, income, employment, overall health, and access to health insurance. They were also asked for height/weight, which was used to compute body mass index (BMI).
Result(s): Access to health insurance was not associated with habitual sleep duration. However, foregoing medical care was associated with less sleep (B=-0.26, 95%CI[-0.35,-0.17], p<0.0005). There was an interaction with race/ethnicity; compared to non-Hispanic Whites, the effect was 115% larger among Blacks/African-Americans, 13% larger in Hispanics/Latinos, 101% larger and in the opposite direction for Asians, and non-significant for Multiracial. Race/ethnicity relationships to sleep duration were stratified by foregoing care. Among those who did not (90%), both short and long sleep duration were more likely among Blacks/African-Americans and other minority groups. Among those who did forego care (10%), these effects were dramatically reduced. Further, when sleep duration was evaluated as a predictor of obesity, this relationship was only seen among those who did not forego care.
Conclusion(s): Foregoing medical care due to cost is an independent risk factor for insufficient sleep, irrespective of income, employment, and access to insurance. It disproportionately affects Blacks/ African-Americans and may represent part of the reason why sleep disparities exist even after adjustment for most socioeconomic indices. Further, foregoing medical care may present such health risks that this subsumes the relationship between sleep and obesity

Introduction: Sleep health is associated with many domains of functioning. Yet, changing behaviors linked to improved sleep health is difficult. Beliefs about the health impact of sleep may motivate behavior change. This analysis examined which beliefs about sleep might motivate sleep behavior change.
Method(s): Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study, consisting of N=1007 community-dwelling adults age 22-60. Participants were asked, regarding "the single most important thing you personally could do to improve your sleep," whether participants were in the stage of precontemplation (not considered change), contemplation (considered but not decided), preparation (decided but not acting), and action stages of change from the transtheoretical model. They were also asked items from the Sleep Practices and Attitudes Questionnaire (SPAQ) regarding the degree to which they agree with whether "not getting enough sleep" can cause sleepiness, drowsy driving, weight gain, heart disease, high cholesterol, hypertension, moodiness, lower energy, decreased sex drive, missed days at work, decreased performance, memory/concentration problems, diabetes, and/or tiredness. Ordinal logistic regressions evaluated increased likelihood of stage of change, based on degree of agreement with those statements, adjusted for age, sex, race/ethnicity, and education. Post-hoc analyses also examined sleep duration as an additional covariate.
Result(s): In adjusted analyses, stage of change was associated with degree of agreement that insufficient sleep can cause sleepiness (OR=1.17, p=0.035), weight gain (OR=1.20, p<0.0005), heart disease (OR=1.21, p=0.001), cholesterol (OR=1.13, p=0.047), hypertension (OR=1.16, p=0.014), moodiness (OR=1.42, p<0.0005), decreased energy (OR=1.30, p=0.002), absenteeism (OR=1.13, p=0.007), decreased performance (OR=1.20, p=0.003), concentration/ memory problems (OR=1.23, p=0.004), diabetes (OR=1.14, p=0.042), and feeling tired (OR=1.39, p<0.0005). When sleep duration was added to the model, significant relationships remained for weight, heart, hypertension, moodiness, energy, absenteeism, performance, memory, diabetes, and tiredness.
Conclusion(s): Degree of belief that insufficient sleep can cause outcomes such as moodiness, occupational problems, and health problems may impact whether an individual is contemplating/ attempting to improve their sleep. This may guide education/outreach efforts

STUDY OBJECTIVES/OBJECTIVE:To determine the effect of self-reported clinical diagnosis of Obstructive Sleep Apnea (OSA) on longitudinal changes in brain amyloid-PET and CSF-biomarkers (Aβ42, T-tau and P-tau) in cognitively normal (NL), mild cognitive impairment (MCI) and Alzheimer's Disease (AD) elderly. METHODS:Longitudinal study with mean follow-up time of 2.52±0.51 years. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants included 516 NL, 798 MCI and 325 AD elderly. Main Outcomes were annual rate-of-change in brain amyloid-burden (i.e. longitudinal increases in florbetapir-PET uptake or decreases in CSF-Aβ42 levels); and tau-protein aggregation (i.e. longitudinal increases in CSF total-tau (T-tau) and phosphorylated-tau (P-tau)). Adjusted multi-level mixed effects linear regression models with randomly varying intercepts and slopes was used to test whether the rate-of-biomarker-change differed between participants with and without OSA. RESULTS:In NL and MCI groups, OSA+ subjects experienced faster annual increase in florbetapir uptake (B=.06, 95% CI .02, .11 and B=.08, 95% CI .05, .12 respectively) and decrease in CSF-Aβ42 levels (B=-2.71, 95% CI -3.11, -2.35 and B=-2.62, 95% CI -3.23, -2.03, respectively); as well as increases in CSF T-tau (B=3.68, 95% CI 3.31, 4.07 and B=2.21, 95% CI 1.58, 2.86, respectively) and P-tau (B=1.221, 95% CI, 1.02, 1.42 and, B=1.74, 95% CI 1.22, 2.27, respectively); compared to OSA- participants. No significant variations in the biomarker changes over time were seen in the AD group. CONCLUSIONS:In both NL and MCI, elderly, clinical interventions aimed to treat OSA are needed to test if OSA treatment may affect the progression of cognitive impairment due to AD.

Migration can be a stressful experience and may lead to poor health and behavioral changes. The immigrant population in Switzerland is disproportionately burdened by several negative health outcomes, chief among these is mental health issues. The aim of the study was to investigate whether sleep disturbances are more prevalent among immigrants compared to non-immigrants and whether emotional distress might explain sleep differences. Based on the Swiss Health Survey 2012 dataset, we analyzed the data of 17,968 people, of which 3406 respondents were immigrants. We examined variables including insomnia symptoms, emotional distress and clinical and socio-demographic data using unadjusted and adjusted generalized linear models. Compared to non-immigrants, immigrants suffer significantly more often from insomnia symptoms. Immigrants also endured higher levels of emotional distress. Higher values of emotional distress are related to other symptoms of sleep disorders. Immigrants with emotional distress were at significant risk of sleep disturbances. Sleep disparities between immigrants and non-immigrants may be influenced by emotional distress. Migration health care should address emotional distress, a more proximal and modifiable factor, as a possible cause of insomnia symptoms in immigrants.

Objective/UNASSIGNED:Stroke survivors generally have problems completing instrumental activities of daily living (IADL; eg, preparing meals, chores, taking a bath, and managing finances). However, it is unclear how stroke survivors might stave off IADL issues. Studies indicating that sleep has restorative neurological effects provide potential mechanisms to address issues with IADL. The aim of this study was to ascertain the association between sleep duration (short or long sleep duration) and IADL among stroke survivors and those without a stroke history. Methods/UNASSIGNED:Data of 486,619 participants were analyzed from the 2000 to 2015 National Health Interview Survey (NHIS), a nationally representative sample. Measures of self-reported stroke, sociodemographic variables, sleep duration, and IADL problems were collected. Binary logistic regression was utilized to analyze the relationship of short (≤6 hours) and long (≥9 hours) sleep duration with limitations to IADL. Results/UNASSIGNED:<0.001) adjusting for age, sex, race, marital status, poverty, and health. Conclusion/UNASSIGNED:Findings from our study indicate that, among stroke survivors, long sleepers were more likely to report IADL problems compared to average sleepers (7-8 hours). Future studies should investigate other potential mediators such as severity of stroke, medication, comorbidities, level of impairment, and whether improving sleep among stroke survivors may improve IADL.

Background/UNASSIGNED:Growing evidence suggests that cancer and diabetes may share common risk factors such as age, race/ethnicity, obesity, insulin resistance, sedentary lifestyle, smoking, and alcohol consumption. However, little is known about how habitual sleep duration (a known cardiometabolic risk factor) may affect the relationship between cancer and diabetes. The aim of this study was to investigate whether sleep duration moderated the relationship between history of cancer and diabetes. Methods/UNASSIGNED:Data were extracted from the National Health Interview Survey dataset from 2004 to 2013 containing demographics, chronic diseases, and sleep duration (N=236,406). Data were analyzed to assess the moderating effect of short and long sleep durations on cancer and diabetes mellitus. Results/UNASSIGNED:<0.05). Conclusion/UNASSIGNED:Our findings indicate that for cancer survivors, short sleep was associated with higher self-reported diabetes and long sleep duration may act as a buffer against diabetes mellitus, as the likelihood of self-reported diabetes was lower among cancer survivors who reported long sleep duration. Impact/UNASSIGNED:Findings from the current study have clinical and public health implications. Clinically, comprehensive sleep assessments and sleep interventions to improve sleep are needed for cancer survivors who have comorbid diabetes. Our findings can also spur public health reform to make sleep an important component of standard cancer survivorship care, as it reduces other chronic disease like diabetes.

OBJECTIVE:To describe behavioral and genetic beliefs about developing hypertension (HTN) by sociodemographic factors and self-reported HTN status, and among those with a history of HTN, evaluate associations between HTN-related causal beliefs and behavior change attempts. METHOD/METHODS:Data from the 2014 Health Information National Trends Survey were evaluated. HTN causal beliefs questions included (a) "How much do you think health behaviors like diet, exercise, and smoking determine whether or not a person will develop high blood pressure/HTN?"; and (b) "How much do you think genetics, that is characteristics passed from one generation to the next, determine whether or not a person will develop high blood pressure/HTN?" Multivariate logistic regressions evaluated associations between HTN causal beliefs and behavior change attempts including diet, exercise, and weight management. RESULTS:Approximately 1,602 out of 3,555 respondents with nonmissing data (33% weighted) reported ever having HTN. In logistic regression models, results show that the more strongly people believed in the impact of behavior on developing HTN, the higher their odds for behavior change attempts. Beliefs about genetic causes of HTN were not associated with behavior change attempts. Women had higher odds of attempts to increase fruit and vegetable intake, reduce soda intake, and lose weight compared to men. Blacks and Hispanics were significantly more likely than Whites to report attempts to lose weight. CONCLUSIONS:Beliefs about behavioral causes of HTN, but not genetic, were associated with behavior change attempts. Health messages that incorporate behavioral beliefs and sociodemographic factors may enhance future prohealth behavior changes. (PsycINFO Database Record (c) 2018 APA, all rights reserved).