Faculty Bibliography

BACKGROUND:African American youth (aged 8-14 years) do not adhere to national dietary and physical activity guidelines. Nonadherence to these recommendations contributes to disproportionate rates of obesity compared with their white counterparts. Culturally tailored electronic health (eHealth) solutions are needed to communicate nutrition and physical activity messages that resonate with this target population. OBJECTIVE:This study aimed to identify the impact of exposure to a website hosting culturally tailored cartoons to inspire fruit and vegetable uptake and physical activity levels in African American mother-child dyads. METHODS:Statistical analysis included paired sample t tests to evaluate knowledge gains, self-efficacy, and readiness to change. Adapted items from Prochaska's Stages of Change toward the following 4 behaviors were assessed with pre- and posttest surveys: (1) fruit and vegetable selection on my plate, (2) meal preparation, (3) fruit and vegetable selection outside of home, and (4) physical activity. Open-ended comments on videos from mother-child dyads were used to determine user acceptance. Observations of repeated responses during content analysis informed coding and development of key themes. RESULTS:A final sample size of 93 mother-child dyads completed the study. Mothers reported significant improvement from precontemplation or contemplation stages to preparation or action stages for (1) fruit and vegetable selection on her plate (P=.03), (2) meal preparation for her family (P=.01), (3) fruit and vegetable selection outside the home (P<.001), and (4) physical activity (P<.001). Significant improvements were found in knowledge, stage of change, and self-efficacy for the 4 target behaviors of interest (P<.001). Children's open-ended commentary reported vicarious learning and positive character identification with brown-skinned cartoons exhibiting healthful food and exercise behaviors. Mothers commented on the lack of accessible produce in their neighborhoods not depicted in the cartoon videos. CONCLUSIONS:Culturally adapted cartoons that incorporate tailored preferences by African American families, such as race or demography, may help increase adherence to target health behaviors when developing eHealth behavior solutions.

BACKGROUND:Mobile phone and tablet ownership have increased in the United States over the last decade, contributing to the growing use of mobile health (mHealth) interventions to help patients manage chronic health conditions like diabetes. However, few studies have characterized mobile device ownership and the presence of health-related apps on mobile devices in people with a self-reported history of hypertension. OBJECTIVE:This study aimed to describe the prevalence of smartphone, tablet, and basic mobile phone ownership and the presence of health apps by sociodemographic factors and self-reported hypertension status (ie, history) in a nationally representative sample of US adults, and to describe whether mobile devices are associated with health goal achievement, medical decision making, and patient-provider communication. METHODS:Data from 3285 respondents from the 2017 Health Information National Trends Survey were analyzed. Participants were asked if they owned a smartphone, tablet, or basic mobile phone and if they had health apps on a smartphone or tablet. Participants were also asked if their smartphones or tablets helped them achieve a health-related goal like losing weight, make a decision about how to treat an illness, or talk with their health care providers. Chi-square analyses were conducted to test for differences in mobile device ownership, health app presence, and app helpfulness by patient characteristics. RESULTS:Approximately 1460 (37.6% weighted prevalence) participants reported a history of hypertension. Tablet and smartphone ownership were lower in participants with a history of hypertension than in those without a history of hypertension (55% vs 66%, P=.001, and 86% vs 68%, P<.001, respectively). Participants with a history of hypertension were more likely to own a basic mobile phone only as compared to those without a history of hypertension (16% vs 9%, P<.001). Among those with a history of hypertension exclusively, basic mobile phone, smartphone, and tablet ownership were associated with age and education, but not race or sex. Older adults were more likely to report having a basic mobile phone only, whereas those with higher education were more likely to report owning a tablet or smartphone. Compared to those without a history of hypertension, participants with a history of hypertension were less likely to have health-related apps on their smartphones or tablets (45% vs 30%, P<.001) and report that mobile devices helped them achieve a health-related goal (72% vs 63%, P=.01). CONCLUSIONS:Despite the increasing use of smartphones, tablets, and health-related apps, these tools are used less among people with a self-reported history of hypertension. To reach the widest cross-section of patients, a mix of novel mHealth interventions and traditional health communication strategies (eg, print, web based, and in person) are needed to support the diverse needs of people with a history of hypertension.

Introduction: Child fatigue has been associated with behavioral outcomes, including aggression, hyperactivity, and conduct problems, which may affect academic performance. We explored whether fatigue was associated with external behavioral health outcomes in a predominantly Black (Afro-Caribbean and African-American) student population (90%). Ratings of parent and teacher agreement of child fatigue was evaluated. This analysis was part of a larger research program, which included a cluster randomized controlled trial in ten public elementary schools in historically disinvested neighborhoods.
Method(s): A total of 804 first-graders (7+/- 0.6 years old) participated in the study focused on child self-regulation, mental health achievement, parenting and parent involvement. Externalizing behaviors (i.e., conduct problems, aggression, and hyperactivity) were reported by teachers using the Behavior System for Children (BASC-2). A composite score of teacher-perceived child fatigue was created based on ratings of child fatigue, morning alertness, and falling asleep in class. Parent perception of child fatigue was assessed using the Children's Sleep Habits Questionnaire. Regression analysis was conducted to determine the association between teacher's reports of child fatigue and externalizing behavior problems. Cohen's kappa coefficient assessed parent and teacher agreement of child fatigue based on categorical classification of presence of child fatigue.
Result(s): Children who were perceived as fatigued (i.e., tiredness and falling asleep in class) by their teacher were more likely to have a high BASC externalizing composite score (T=60 cut off) (beta = -0.24, p<.001). Cohen's kappa of 0.004 (p<0.05) showed a slight discordance in perception of child fatigue comparing reports from teachers and parents, although results were not significant.
Conclusion(s): Teacher perception of child fatigue was significantly associated with teacher BASC T-score of child externalizing behavior outcomes. Future studies should explore longitudinal relationships between fatigue and mental health

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

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: Previous studies have shown that racial/ethnic minorities are more likely to be short and/or long sleepers, which may increase risk for morbidity/mortality. This analysis provides a more recent update from a very large national dataset, including representation of additional groups and examination of the role of poverty.
Method(s): Data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS, collected by the CDC) were used. N=464,671 adults >18yrs from all US states/territories provided data on sleep, demographics, and socioeconomics. Sleep duration was categorized as very short (<=4h), short (5-6h), normal (7-8h as reference), and long (>=9h). Race/ethnicity was self-reported as Non-Hispanic White, Black/ African-American, Hispanic/Latino, American-Indian/Alaskan-Native (AIAN), Native Hawaiian/Pacific-Islander (NHPI), or Multiracial/Other. Covariates included age, sex, relationship status, education, employment, and home ownership. Interactions were explored with poverty (income<$20,000) were explored. Multinomial logistic regressions were weighted using BRFSS-specific weights.
Result(s): A significant race-by-poverty interaction was seen (p<0.0005). Compared to non-poor Non-Hispanic White, increased very short sleep was seen among those who were non-poor Black/African-American (RRR=2.1, p<0.0005), Asian (RRR=1.6, p=0.001), AIAN (RRR=1.4, p=0.001), NHPI (RRR=2.0, p=0.002), and Multiracial/Other (RRR=2.2, p<0.0005), and poor Non-Hispanic White (RRR=1.8, p<0.0005), Black/African-American (RRR=1.8, p<0.0005), AIAN (RRR=1.5, p=0.007), NHPI (RRR=2.4, p=0.005), and Multiracial/Other (RRR=3.4, p<0.0005). Compared to non-poor White, increased short sleep was seen among non-poor Black/African-American (RRR=1.7, p<0.0005), Asian (RRR=1.3, p<0.0005), AIAN (RRR=1.2, p=0.02), NHPI (RRR=1.3, p=0.02), Multiracial/Other (RRR=1.3, p<0.0005), and poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), Asian (RRR=1.3, p=0.04), and Multiracial/Other (RRR=2.2, p<0.0005). Compared to non-poor Non-Hispanic White, increased long sleep was seen for Non-Poor Black/African-American (RRR=1.4, p<0.0005), Poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), and AIAN (RRR=1.3, p<0.05).
Conclusion(s): Established racial/ethnic sleep disparities are supported in this large national sample, with additional information on understudied vulnerable groups including AI/AN and NH/PI. Further, the this study as the contribution of poverty status

Introduction: We determined whether Obstructive Sleep Apnea (OSA) and beta-Amyloid Burden (Abeta) act additively or synergistically to promote conversion from cognitive normal (CN) to mild cognitive impairment (MCI) and from MCI to AD.
Method(s): In this longitudinal observational study, we examined CN (n=298) and MCI (n=418) older adults from the ADNI database (adni.loni.usc.edu). OSA was self-reported during a clinical interview. Brain Abeta was assessed using Florbetapir-PET imaging. The primary outcome of the analysis was conversion from CN to MCI (CN participants) and from MCI to AD (MCI participants). Participants were required to have a baseline and at least one follow-up clinical visit that identified their cognitive status. Logistic mixed-effects models with random intercept and slope were used to assess associations between OSA, Abeta, and risk of conversion from CN to MCI, and MCI to AD. All models included age at baseline, sex, APOE4 status, years of education, and their interactions with time.
Result(s): Of the 716 participants, 329 (46%) were women. The overall mean (SD) age was 74.7 (5.0) years, and the overall mean (SD) follow-up time was 5.5 (1.7) years (Range: 2.7 - 10.9 years). In CN participants at baseline, conversion to MCI was associated with both OSA (beta = 0.418; 95% CI, 0.133 to 0.703; P < .001) and higher Abeta-burden (beta = 0.554; 95% CI, 0.215 to 0.892; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.169, 95% CI, 0.776 to 1.562; P < .001), suggesting a synergistic effect. In MCI participants at baseline, conversion to AD was associated with both OSA (beta = 0.637; 95% CI, 0.291 to 0.982; P < .001) and higher Abeta-burden (beta = 1.061; 95% CI, 0.625 to 1.497; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.312, 95% CI, 0.952 to 1.671; P < .001), suggesting a synergistic effect.
Conclusion(s): In both CN and MCI elderly, Abeta modified the risk of progression to AD in OSA participants. OSA patients maybe more physiologically susceptible as Abeta load becomes increasingly abnormal

Introduction: The American Heart Association (AHA) suggests "Life's Simple 7" guidelines to improve overall health. The aim of this study was to test the hypothesis that sleep duration would be associated with each of the Simple 7 metrics and would provide additional information to overall health over and above that of the Simple-7.
Method(s): Data were from the 2011-2014 NHANES (N=8,113). Habitual sleep duration was assessed by self-report and divided into very short(<=4h), short(5-6h), normal(7-8h) and long(>=9h). Overall health was self-rated as "Good" or "Poor." The components of the Simple-7 were assessed as high blood pressure (history, hypertensive medications, or >=140/90mmHg), high cholesterol (history, statin use, or >=240mg/dl), diabetes (history or fasting glucose >=100mg/dl), unhealthy diet (fast food >=7 meals in past week), obesity (BMI>=30kg/m2), inactivity (<30mins/day moderate/vigorous), and smoking (current). Covariates included age, sex, education, race/ethnicity and (in analyses without obesity) BMI.
Result(s): In logistic regression analyses, poor overall health was associated with very short (OR=3.0,P<0.0005), short (OR=1.4,p<0.0005), and long (OR=1.7,p<0.0005) sleep. Variance explained for health by sleep (R-squared=0.02) was of similar magnitude to Simple-7 components (R-squared=0.0001[diet], 0.01[smoking, cholesterol), 0.02[obesity, sedentary], 0.04[hypertension, diabetes]). Sleep duration was associated with six of the Simple-7 (blood pressure, diabetes, diet, obesity, physical activity, and smoking). Sleep duration continues to be associated with overall health after controlling for all of the Simple-7 (very short OR=2.9,p<0.0005; short OR=1.3,p<0.0005; long OR=1.6,p<0.0005). Adding sleep to a model that contained covariates and the Simple-7 explained an additional 0.85% of the variance in health (retaining 46% of the variance explained in the outcome) without the Simple-7.
Conclusion(s): Sleep is related to nearly all of the components of the Simple-7, is related to health even after accounting for the Simple-7, and explains variance in overall health above that explained by the Simple-7. These data/analyses suggest that a sleep assessment should be added to the Simple-7, given the observed increment in predictive value

Introduction: We determined whether the co-occurrence of OSA and hypertension interact synergistically to promote beta-Amyloid burden and cognitive decline in clinically normal older adults Methods: Prospective longitudinal study utilizing NYU cohort of community-dwelling cognitively-normal elderly, with baseline and at least one follow-up of CSF-Abeta42 (measured using ELISA), and neuropsychological visits. OSA was defined using AHI4%. Hypertension diagnosis was according to AHA-guidelines. Cognitive variables assessed included Logic-2, Animal-Fluency [AF], Vegetable-Fluency [VF]), Boston-Naming-Test [BNT], Digit-Symbol-Substitution-Test [DSST], Trails Making Test-A and B [TMT-A and B]). Linear mixed-effects models with random intercept and slope were used to assess associations between OSA, hypertension, and longitudinal changes in CSF-Abeta and cognition, controlling for age-at-baseline, sex, APOE4-status, years-of-education, and their interactions with time.
Result(s): Of the 98 participants, 63 (64.3%) were women. The mean (SD) age was 69.6 (7.3) years and follow-up time was 2.46 (0.64) years. OSA and hypertension were each associated with faster rate-of-change in CSF-Abeta42 (beta = -3.11; 95%CI, -3.71, -2.51; and beta= -2.82, 95% CI -3.29, -2.35, P < .01 for both respectively). The interaction of OSA and hypertension with time was significant (beta= -1.28, 95% CI -1.78 to -0.78, P < .01) suggesting a synergistic effect. No significant associations were seen between annual-changes in CSF-Abeta42 and cognitive-decline. However, faster decline in VF, and DSST were associated with OSA (beta = -0.054; 95%CI, -0.094, -0.013; P = .02; beta = -0.058; 95%CI, -0.084, -0.033; P < .05 for both respectively), and with hypertension (beta = -0.048; 95%CI, -0.079, -0.017; P = .04; beta = -0.078; 95%CI, -0.098, -0.057; P = .002; respectively). The interaction of OSA and hypertension with time was significant for both VF and DSST (beta = -0.033, 95%CI, -0.048, -0.018; P < .001 and beta = -0.040, 95%CI, -0.064, -0.016; P < .001, respectively), suggesting a synergistic effect.
Conclusion(s): In cognitive-normal elderly OSA individuals, vascular risk may complement AD-biomarkers in assessing risk of prospective cognitive-decline in preclinical AD