10 research outputs found
Systems Maintenance Automated Repair Tasks (SMART)
SMART is a uniform automated discrepancy analysis and repair-authoring platform that improves technical accuracy and timely delivery of repair procedures for a given discrepancy (see figure a). SMART will minimize data errors, create uniform repair processes, and enhance the existing knowledge base of engineering repair processes. This innovation is the first tool developed that links the hardware specification requirements with the actual repair methods, sequences, and required equipment. SMART is flexibly designed to be useable by multiple engineering groups requiring decision analysis, and by any work authorization and disposition platform (see figure b). The organizational logic creates the link between specification requirements of the hardware, and specific procedures required to repair discrepancies. The first segment in the SMART process uses a decision analysis tree to define all the permutations between component/ subcomponent/discrepancy/repair on the hardware. The second segment uses a repair matrix to define what the steps and sequences are for any repair defined in the decision tree. This segment also allows for the selection of specific steps from multivariable steps. SMART will also be able to interface with outside databases and to store information from them to be inserted into the repair-procedure document. Some of the steps will be identified as optional, and would only be used based on the location and the current configuration of the hardware. The output from this analysis would be sent to a work authoring system in the form of a predefined sequence of steps containing required actions, tools, parts, materials, certifications, and specific requirements controlling quality, functional requirements, and limitations
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0613 COVID-19 Is Associated with Shorter Sleep Duration among American Adults
Abstract
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, R25HL10544
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0620 Is SVI a Risk Factor for Sleep and Cardiometabolic Health Among Blacks?
Abstract
Introduction
The Social Vulnerability Index (SVI) is a novel metric that incorporates a multitude of population factors to predict the susceptibility of communities to deleterious effects of disaster, natural hazards, and environmental insult. Studies show socioeconomic status (SES), an important component of SVI, is a risk factor for cardiometabolic disease and sleep quality. Objectives: This study examined the effect of SVI on cardiometabolic and sleep health among Blacks.
Methods
We utilized harmonized data extracted from two NIH-funded studies enrolling Blacks (i.e., MetSO and PEERS-ED registries). Participants (N=1,497) included New York residents; 65% were male, with a mean(SD) age of 55(±16.2). Data were collected via self-reports (e.g., ARES questionnaire) for sleep quality/duration and cardiometabolic factors (e.g., weight and diet). SVI components included SES, household composition, minority status, and housing type. Mixed-effect logistic regression models were applied, which assessed the effect of SVI and its many subcomponents on each health-related variable of interest. The model was adjusted for age, sex, and education to account for the effects of these factors overlapping in the SVI subcomponents.
Results
Approximately 81% of the sample population was obese, 37.9% were diabetic, 62.3% had a history of hypertension, and 18.4% with a heart disease. Regarding sleep health, 7.7% suffered from sleep apnea, 66.6% were short sleepers, 6.64% were long sleepers, and 14.2% reported insomnia. They had a mean(SD) sleep time of 5.92(±2.05) hours. “Overall SVI” was associated with hypertension (OR=3.98) and “housing type & transport” was correlated with heart disease (OR=4.44) prior to adjusting the model. Applying the adjusted model, “minority status & language” predicts obesity (OR=5.32). Also, “overall SVI” and “SES” were associated with diabetes (OR=3.26; OR=2.71) and hypertension (OR=4.00; OR=3.95). “Household composition” approaches significance as a predictor for sleep apnea (unadjusted - OR=0.26; adjusted - OR=0.26) despite the relatively low case proportion.
Conclusion
SVI seems to be a good indicator of cardiometabolic health among Blacks. However, it is likely a poor marker for sleep health in that population, although trends were observed suggesting that it might play an important role. Further studies are necessary to elucidate the role of SVI on sleep health among Blacks.
Support (If Any)
R01HL142066, R01HL095799, RO1MD004113, R01HL15245
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0858 Associations between Sleep Duration and Cholesterol Levels among Hispanics: Findings from the National Health Interview Survey
Abstract Introduction Short sleep (< 7 hours of sleep/24 hr. period) duration is associated with unhealthy cholesterol levels, a significant cardiovascular risk marker. Precious epidemiological studies indicate that Hispanics are at increased risk for hypercholesteremia. However, little is known about whether sleep duration contributes to high cholesterol levels, among Hispanics. We sought to investigate the following: 1) Examine whether sleep duration predicts cholesterol levels; 2) Examine whether this relationship differs in Hispanics in comparison to non-Hispanics. Methods This study was based on the 2020 National Health Interview Survey. Cholesterol, the outcome, is defined as whether an individual had high cholesterol during the last 12 months. Sleep quantity was categorized as follows: short sleep (< 7 hours), healthy sleep (7-8 hours), and long sleep (≥9 hours). For stratified analyses, we investigated whether the relationship between sleep duration and cholesterol differed between Hispanics and non-Hispanics. We performed unadjusted and fully adjusted binary logistic regression analyses, with age, sex, education, income, and BMI as covariates. Results In our unadjusted models, Hispanic short sleepers had increased odds of high cholesterol (OR: 1.39 p<.01 ), while long sleepers (OR: 1.25, n.s. p=.129) did not, compared to individuals who slept 7-8 hours. Non-Hispanic short (OR: 1.14 p<.01) and long (OR: 1.47, p< .01) sleepers had greater odds of high cholesterol, compared to individuals who slept 7-8 hours. For our fully adjusted models, Hispanic short sleepers had increased odds of high cholesterol (OR: 1.29, p<.05), while long sleepers did not (OR: 1.15, n.s. p=.55), compared to individuals who slept 7-8 hours. Non-Hispanics short sleepers had greater odds of high cholesterol (OR: 1.13 p<.01), while among long sleepers, no significant relationship was observed (OR: 1.05, p=.46), compared to individuals who slept 7-8 hours. Overall, Hispanic short sleepers had greater odds of high cholesterol compared to their non-Hispanic counterparts. Conclusion Hispanic short sleepers have an increased risk for high cholesterol. Further studies are needed to elucidate the pathophysiological mechanisms that affect the relationship between short sleep and cholesterol levels among Hispanics. This will enable tailored risk and protective profiling of Hispanic individuals to reduce their risk for high cholesterol. Support (if any) K01HL135452, K07AG052685, R01AG072644, and R01HL15245
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0615 Racial/Ethnic and Sex Differences in Circadian Rest-Activity Rhythm Patterns: Findings from a Sample of the US Population
Abstract 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 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 R01AG07500
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