Introduction COVID-19 disrupted traditional research infrastructures and processes most notably in-person community recruitment, especially in underrepresented populations like racial ethnic minorities. To find creative and effective strategies, our group implemented and tested the efficacy of a culturally tailored community outreach plan (COP) developed during the US COVID-19 pandemic. Methods In February 2021, we developed an 11 step culturally-tailored community outreach program to support the implementation of three NIH funded community-based sleep studies. The following steps include: (1) description of the situation statement, 2) definition of goals, 3) engagement of audience/stakeholders, 4) tailoring message, 5) defining incentives, 6) choice of outreach methods, 7) identification of spokesperson, 8) choice of tools to assess progress, 9) identification of media outlets, 10) creation of study timeline, and 11) implementation of the plan. The studies leveraged several recruitment channels: 1) community settings (Place of worship, “community recruiter”, health fairs, word of mouth, & healthcare providers/doctors’ clinics), 2) online platforms (Facebook, Twitter, LinkedIn and Research Match), and 3) preexisting datasets in NYC. Results All three studies successfully met recruitment goals. ESSENTIAL [n= 224, 69% females, mean age= 36], MOSAIC [n=109, 61% females; mean age= 64] and Latinx/Hispanics: DORMIR[n=260, 61.3% of female; 32.4]. Among the three NYC cohorts, the most common recruitment channels were: preexisting datasets (74%), community settings (19%), & online platform (7%) for ESSENTIAL; preexisting datasets (85%) & community settings (15%) for MOSAIC, and (71.7%) online platform for DORMIR. However, the Miami cohorts came mostly from community settings 90% for Essential and 97% for MOSAIC. Conclusion Overall, the TSCS community outreach plan seems to be an effective tool to engage minoritized populations in greater NY and Miami. Our current field experience indicates that recruitment channels must be adapted to age, and community resources. Limited access to technology, particularly among older Blacks seem to be a major barrier for field staff to successfully engage the disenfranchised communities. Support (If Any) NIH R01HL142066-04; R01HL152453-01 R01HL142066, R01HL095799, RO1MD004113
Introduction Social determinants of health (SDOH) have been linked to well-being, quality-of-life and health disparities. We aim to investigate, 1) To classify adults based on their SDOH characteristics, and 2) To examine association between SDOH classes and sleep health. Methods This study used 2020 National Health Interview Survey data (n=31,568). SDOH was captured by health insurance, well-visits, delayed medical care, neighborhood walkability, social support, food insecurity, food stamp, poverty, and education. Sleep health was captured by: 1) Sleep quantity (hours): very short (< 5), short (5-6), normal (7-8), and long (≥9); 2) Sleep quality: trouble falling asleep or staying asleep; 3) Sleep medication, 4) Feeling well-rested, and 5) Fatigue. Latent class analysis generated a 3-class model with a gradient on all SDOH characteristics. Generalized structural equation modeling was used to examine associations between adverse’ SDOH classes and sleep health, with Class 1 (lowest probability of adverse’ SDOH), as reference category. Results Adults in Class 3 were 36% more likely to have short sleep than normal sleep. Classes 2 and 3 were 16% and 24% more likely to have trouble falling asleep on “some days”; 26% and 40% more likely on “most days”; and 28% and 77% more likely every day than never. Classes 2 and 3 were 22% and 23% more likely to have trouble staying asleep on “some days”; 36% and 35% more likely on “most days”; and 48% and 77% more likely every day than never. Class 2 was 19% more likely to take sleep medications on “some days” and 25% more likely every day than never. Classes 2 and 3 were 26% and 86% more likely to never feel well-rested; 30% and 60% more likely to feel well-rested on “some days” than every day. Classes 2 and 3 were 17% and 49% more likely to feel fatigued on “some days”; 43% and 99% more likely on “most days”; 55% and twice as likely to feel fatigued every day than never. Conclusion Adverse SDOH are associated with worsened unhealthy sleep. Further research is needed for the implementation of interventions to improve sleep health among marginalized groups more impacted by adverse SDOH. Support (if any) G1-5524901
Introduction In 2020, 55.4 million Americans sought medical attention due to nonfatal, preventable injuries. Injury-related death rate was 15.7% higher than in 2019. Poor sleep health is associated with increased risk of injuries (e.g., falls, sports, and motor vehicle-related injuries). This study examined the associations between sleep health and injuries among adults (≥18 years). Methods This study utilized the 2020 National Health Interview Survey (n=31,568). The primary outcome encompassed sustained bodily injuries in the past 3 months. Secondary outcomes were fall-related, sports-related, and motor vehicle (MV)-related injuries. Sleep health in the past month was measured via 1) sleep quantity: very short (≤ 4 hours), short (5-6 hours), healthy (7-8 hours), or long (≥9 hours); 2) sleep quality: trouble falling asleep and trouble staying asleep; 3) feeling well-rested upon waking-up; and 4) sleep medications. Response categories included never, some days, most days, or every day. Adjusted multivariable logistic regression was used to examine associations between injuries and these four domains of sleep health. Results Overall, 9.1% of respondents sustained an injury. Among injured adults, 47.4% had fall-related injuries, 29.1% had sport-related injuries, and 6% had a MV-related injury. Adults with very short, short, and long sleep were 37%, 15%, and 22% more likely to be injured than adults with healthy sleep (p< 0.05). Adults with trouble staying asleep were 36% more likely to be injured than adults who never had trouble staying asleep (p< 0.01). Adults who woke up never rested or rested on some days were 49% and 36% more likely to be injured (p< 0.01) Adults who took medications for sleep on some days or every day were 24% and 36% more likely to be injured (p< 0.05; p< 0.001). Adults who had trouble staying asleep some days were 22% more likely to have a fall-related injury (p< 0.05). Respondents with long sleep were 43% less likely to have sports-related injuries (p< 0.05). Those who had trouble staying asleep were 3.5 times more likely to have experienced a MV-related injury (p< 0.01). Conclusion Sleep health is strongly associated with injuries among adults. Further studies are needed to determine causality in the observed associations. Support (if any)
Introduction Circadian disruptions are associated with increased risk for morbidity and mortality. However, it is unclear whether these associations vary by race/ethnicity. We aim to explore, 1) Examine whether rest-activity rhythm (RAR) patterns vary across race/ethnicity among a representative sample of the US adult population, and 2) Examine the interaction of race/ethnicity and sex in RAR patterns. Methods The study was based on the National Health and Nutrition Examination Survey (2013–2014) with data from participants who wore a physical activity monitor (PAM ActiGraph accelerometer model GT3X+) continuously for seven consecutive days. After excluding pregnant participants and those with sleep minutes < 120 and >1200, the analytic sample included was 4,722 adults. The cosinor method was used based on PAM minutes time-series data to RAR outcome variables: mesor, amplitude, acrophase. and robustness. The main predictor of interest was race/ethnicity (Asian, Black, Hispanic, multi/other, and White). A sex-race/ethnicity interaction was tested to assess if racial/ethnic differences in RAR patterns differ between males and females. Other covariates included were age, marital status education federal poverty level, and employment status. Eight adjusted Generalized linear models (GLM) : four with race/ethnicity as the main predictors and four multiplicative interaction models with the product term of race/ethnicity and sex. An adjusted Wald test was used to test for interaction. Results Compared with White adults, Hispanic adults had increased mesor levels (ß=0.10; 95% CI:0.07;0.13), increased RAR amplitude (ß=0.10; 95% CI:0.06;0.13), and increased robustness (ß=0.07; 95% CI:0.03;0.11) whereas Black adults had decreased amplitude (ß=-0.11; 95% CI:-0.16;-0.07) and decreased robustness ( (ß= -0.14; 95% CI:-0.20; -0.09) compared to White adults. Similarly, Asians had decreased amplitude (ß=-0.06; 95% CI:-0.10;-0.01) and decreased robustness (ß=-0.11; 95% CI:-0.16; -0.06). A significant sex-race/ethnicity interaction was found for amplitude F(4,12)=3.94;p=0.029 and robustness (4,12)=6.02;p< 0.001. Conclusion RAR is associated with race/ethnicity, and this association varies by sex. Notably, Hispanic adults had increased mesor, amplitude, and robustness compared to Whites. Conversely, Black and Asian populations shared decreased amplitude and robustness compared to Whites. Future studies may consider further investigation of circadian health by race/ethnicity and sex for community intervention. Support (if any) K01HL135452, K07AG052685, R01AG072644, R01HL152453, R01MD007716, R01HL142066, R01AG067523, R01AG056031, and R01AG075007
Introduction The COVID-19 pandemic has deteriorated sleep health in the United States (U.S.) and worldwide. Most studies that have examined the association between COVID-19 and sleep outcomes have used a non-probability sampling with potential sampling bias and limited generalizability. We examined the association between diagnosed COVID-19 and sleep health in a large representative sample of civilian adults aged ≥18 years in the U.S. Methods This study was based on data from the 2020 National Health Interview Survey (NHIS) of adults (n=17,636). Sleep health was captured by self-reported sleep quantity [(very short (≤ 4 hours), short (5-6 hours), healthy (7-8 hours), or long (≥9 hours)] and sleep complaints (trouble falling and staying asleep; with responses ranging from never to every day) in the past 30 days. To account for correlated residuals among the endogenous sleep outcomes, generalized structural equation modeling (GSEM) was conducted with COVID-19 diagnosis as the predictor of interest. Other covariates (age, sex, race/ethnicity, education, employment, poverty level, marital status, birthplace, health insurance, region of residence, metropolitan areas, number of children and adults in the household, obesity, and sleep medication) were included in the models. NHIS complex probability sampling design was accounted for in descriptive and GSEM analyses. Results About 4.2% of adults had a positive COVID-19 diagnosis. Among them, 3.1% had very short sleep, 24.2% had short sleep, 59.9% had healthy sleep, and 12.8% had long sleep; 37.0% had trouble falling some days, 10.9% most days, and 6.5% every day; and 33.7% had trouble staying asleep some days, 13.9% most days, and 6.6% every day. Findings from GSEM revealed that a history of COVID-19 almost doubled the odds of having short sleep (OR: 1.9; 95% CI: 1.1-3.4; p=0.032). No significant associations were found between COVID-19 and the other sleep outcomes. Conclusion Individuals with a COVID-19 diagnosis were more likely to report very short sleep, although they did not exhibit a greater likelihood of reporting more sleep complaints. Further research using longitudinal national data and examining environmental factors are needed to determine causality. Support (If Any) NIH R01HL142066, R01HL095799, RO1MD004113, R01HL152453, R25HL105444
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