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Abstract Objective The objective of this study is to determine whether a sleep and health improvement programme (SHIP) to promote healthy sleep, eating and physical activity would be feasible, acceptable and have a positive impact on lifestyle behaviours for prostate cancer survivors and caregivers. Methods We recruited 50 participants for a single group 3‐month pre‐post pilot study. The SHIP intervention included (1) website about sleep, nutrition and physical activity (≥1 view/week), (2) two email newsletters with goal‐setting exercises and resources and (3) midpoint health coach call. The primary outcome was changes in validated sleep scales; secondary outcomes included changes in diet, physical activity and concentration from baseline to 3 months. Results Of 50 participants enrolled, median age was 65, 30% were Black and 8% were Hispanic. Thirty‐four patients and 7 family caregivers completed the pilot study (82%). Following the intervention, we observed a statistically significant improvement in the Sleep Hygiene Index (pre: 15, post: 13, p

Abstract Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20–50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0–79.5% and precision 77.0–79.5%; Fitbit sensitivity 61.7–78.0% and precision 72.8–73.2%; and Apple sensitivity 50.5–86.1% and precision 72.7–87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.

Abstract The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model’s ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of $$85.3\%$$ 85.3 % on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of $$58.5\%$$ 58.5 % , $$58.7\%$$ 58.7 % , $$72.8\%$$ 72.8 % and $$74.8\%$$ 74.8 % on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

ObjectivesRegional sleep differences may reflect other important indicators of health and well-being. Examining sleep health at the regional level can help inform policies to improve population health. We examined the relationship between neighborhood-level adult sleep health (modeled in this study via adult sleep duration) and other health metrics and multiple indicators of child-relevant opportunity.MethodsData were obtained from the “500 Cities” data collected by the CDC, including the proportion of the adult population in each tract that report obtaining at least 7 h of sleep. The Child Opportunity Index (COI) provides indices for “education,” “health and environment,” and “social and economic” domains, as well as a global score. When data were merged, 27,130 census tracts were included. Linear regression analyses examined COI associated with the proportion of the adult population obtaining 7 h of sleep.ResultsAdult sleep duration was associated with global COI, such that for each additional percent of the population that obtains ≥ 7 h of sleep, COI increases by 3.6 points (95%CI[3.57, 3.64]). Each component of COI was separately related to adult sleep duration. All associations were attenuated but significant in adjusted analyses. In stepwise analyses, sleep health via adult sleep duration emerged as the strongest correlate of global COI, accounting for 57.2% of the variance (p

Abstract Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming.

BackgroundOver the last decade, health mobile apps have become an increasingly popular tool used by clinicians and researchers to track food consumption and exercise. However, many consumer apps lack the technological features for facilitating the capture of critical food timing details. ObjectiveThis study aimed to introduce users to 11 apps from US app stores that recorded both dietary intake and food timing to establish which one would be the most appropriate for clinical research. MethodsTo determine a viable app that recorded both dietary intake and food timing for use in a food timing–related clinical study, we evaluated the time stamp data, usability, privacy policies, the accuracy of nutrient estimates, and general features of 11 mobile apps for dietary assessment that were available on US app stores. The following apps were selected using a keyword search of related terms and reviewed: text entry apps—Cronometer, DiaryNutrition, DietDiary, FoodDiary, Macros, and MyPlate; image entry apps—FoodView and MealLogger; and text plus image entry apps—Bitesnap, myCircadianClock, and MyFitnessPal. ResultsOur primary goal was to identify apps that recorded food time stamps, which 8 (73%) of the 11 reviewed apps did. Of the 11 apps, only 4 (36%) allowed users to edit the time stamps. Next, we sought to evaluate the usability of the apps using the System Usability Scale across 2 days, and 82% (9/11) of the apps received favorable scores for usability. To enable use in research and clinical settings, the privacy policies of each app were systematically reviewed using common criteria, with 1 (9%) Health Insurance Portability and Accountability Act–compliant app (Cronometer). Furthermore, protected health information was collected by 9 (82%) of the 11 apps. Finally, to assess the accuracy of the nutrient estimates generated by these apps, we selected 4 sample food items and a 3-day dietary record to input into each app. The caloric and macronutrient estimates of the apps were compared with the nutrient estimates provided by a registered dietitian using the Nutrition Data System for Research database. In terms of the 3-day food record, the apps were found to consistently underestimate daily calories and macronutrients compared with the Nutrition Data System for Research output. ConclusionsOverall, we found that the Bitesnap app provided flexible dietary and food timing functionality capable of being used in research and clinical settings, whereas most other apps lacked in the necessary food timing functionality or user privacy.

Abstract Background In 2013, PEDV was introduced in the United States (U.S.) and rapidly spread across the country. Here we describe the occurrence of PEDV in the growing pig herd of one large U.S. production system through an active surveillance set in place between October 2019 and November 2020 designed to assess disease status upon placement into the growing pig site, before shipping to the slaughter plant and when diarrhea events were present at the site. We also assessed the impact of preventive procedures implemented in PEDV incidence that comprised site-specific equipment segregation and biosecurity changes regarding personnel movement between sites. Results 36.50% (100/274) of the sites had at least one PEDV introduction event before preventive procedures were implemented, yielding an incidence rate of 2.41 per 100 farm-weeks. Most (63/100) of them occurred in sites where animals were placed negative and PEDV was detected in clinical samples in a median of 8 weeks post placement. After preventive procedures were implemented, the overall PEDV incidence rate dropped to 0.37 per 100 farm-weeks (84.65% reduction, p

Objectives We evaluated an online Sleep Health and Wellness (SHAW) programme paired with dayzz, a personalised sleep training programme deployed via smartphone application (dayzz app) that promotes healthy sleep and treatment for sleep disorders, among employees at a large healthcare organisation.Design Open-label, randomised, parallel-group controlled trial.Setting A healthcare employer in the USA.Participants 1355 daytime workers.Intervention Participants were randomised to intervention (n=794) or control (n=561) on consent. Intervention participants received the SHAW educational programme at baseline plus access to the personalised dayzz app for up to 9 months. The control condition received the intervention at month 10.Primary and secondary outcome measures Our primary outcome measures were sleep-related behavioural changes (eg, consistent sleep schedule); sleep behaviour tracked on an electronic sleep diary and sleep quality. Our secondary outcome measures included employee absenteeism, performance and productivity; stress, mood, alertness and energy; and adverse health and safety outcomes (eg, accidents).Results At follow-up, employees in the intervention condition were more likely to report increased sleep duration on work (7.20 vs 6.99, p=0.01) and on free (8.26 vs 8.04, p=0.03) nights. At follow-up, the prevalence of poor sleep quality was lower in the intervention (n=160 of 321, 50%) compared with control (n=184 of 327, 56%) (p=0.04). The mean total dollars lost per person per month due to reduced workplace performance (presenteeism) was less in the intervention condition (US$1090 vs US$1321, p=0.001). Employees in the intervention reported fewer mental health visits (RR 0.72, 95% CI 0.56 to 0.94, p=0.01) and lower healthcare utilisation over the study interval (RR 0.81, 95% CI 0.67 to 0.98, p=0.03). We did not observe differences in stress (4.7 (95% CI 4.6 to 4.8) vs 4.7 (95% CI 4.6 to 4.8)), mood (4.5 (95% CI 4.4 to 4.6) vs 4.6 (95% CI 4.5 to 4.7)), alertness (4.9 (95% CI 4.8 to 5.0) vs 5.0 (95% CI 4.9 to 5.1)) or adverse health and safety outcomes (motor vehicle crashes: OR 0.82 (95% CI 0.34 to 1.9); near-miss crashes: OR=0.89 (95% CI 0.5 to 1.5) and injuries: 0.9 (95% CI 0.6 to 1.3)); energy was higher at follow-up in the intervention group (4.3 vs 4.5; p=0.03).Conclusions Results from this trial demonstrate that a SHAW programme followed by access to the digital dayzz app can be beneficial to both the employee and employer.Trial registration number NCT04224285

Abstract Background The COVID-19 pandemic resulted in disruptions to medical school training and the transition to residency for new post-graduate year 1 resident-physicians (PGY1s). Therefore, the aim of this study was to understand the perspectives of United States PGY1s regarding the impact of the pandemic on these experiences. Our secondary aims were to understand how desire to practice medicine was impacted by the pandemic and whether PGY1s felt that they were able to meaningfully contribute to the COVID-19 response as students. Method We conducted a national, cross-sectional study of PGY1s who had recently graduated from medical school in 2020. A survey was distributed to PGY1s from across specialties, in programs distributed throughout the United States. It included questions about medical school training during the pandemic, impact on graduation timing and transition to internship, concerns about caring for patients with COVID-19, desire to practice medicine, and ability to meaningfully contribute to the pandemic. Findings are presented using descriptive statistics and univariate logistic regression models. Results 1980 PGY1s consented to participate, 1463 completed the survey (74%), and 713 met criteria for this analysis. 77% of PGY1s reported that the pandemic adversely affected their connection with their medical school communities, and 58% reported that the pandemic impeded their preparation for intern year. 4% of PGY1s reported graduating medical school and practicing as an intern earlier than their expected graduation date. While the majority of PGY1s did not have a change in desire to practice medicine, PGY1s with concerns regarding personal health or medical conditions (OR 4.92 [95% CI 3.20–7.55] p

Background and Objective Obstructive sleep apnea (OSA) is a highly prevalent disorder that disparately affects racial/ethnic minorities. OSA functional health literacy can contribute to health disparities. Documenting poor OSA functional health literacy is needed to inform research agendas, policy, and advocacy efforts. The objective of this study is to develop a scale for measuring OSA functional health literacy among diverse audiences and a variety of reading levels and to ascertain its reliability and validity. Methods Development of the 18-item Survey of OSA Functional Health Literacy (SOFHL) was guided by literature review and input from experts. A convenience sample of persons enrolled in a clinical trial completed the survey (n = 194). Factor analysis was used to identify the number of dimensions in the SOFHL and their relationship to other domains that are relevant to OSA functional health literacy. Internal consistency reliability (alpha) was estimated for the SOFHL scales and correlations with educational attainment and income computed. Results All respondents were Black and 29% reported average household income less than $10000 USD. Confirmatory factor analysis provided support for two dimensions: OSA general knowledge (alpha = 0.81) and OSA self-management efficacy (alpha = 0.71). OSA general knowledge was significantly correlated with education (r = 0.41) and income (r = 0.21), and OSA self-management efficacy was significantly correlated with education (r = 0.19) and less depression (r = -0.18). Conclusions Higher educational attainment and socioeconomic status were associated with better OSA functional health literacy. These results provide preliminary support for the SOFHL, a measure that can be used to assess OSA functional health literacy.

The COVID-19 pandemic dramatically impacted society and health care on a global scale. To capture the lived experience of patients with prostate cancer and family members/caregivers during the COVID-19 pandemic, we performed a mixed-methods study of posts to two online networks. We compared all 6187 posts to the Inspire Us TOO Prostate Cancer online support and discussion community from December 2019 to April 2020, to 6926 posts from the same interval in 2019, applying a linguistic ethnography method. A similar analysis was performed using data from the Reddit discussion website (246 posts from 2019 and 260 posts from 2020). Manual qualitative analysis was performed for all 207 posts that mentioned COVID, COVID-19, or coronavirus. The computational linguistic ethnography analysis revealed a more collective tone in 2020, with increased concern about death. Our qualitative analysis showed that patients with prostate cancer and caregivers have concern about a variety of COVID-19–related impacts on care, including delays in testing and treatment. There was also substantial concern about the impact of having cancer on COVID-19 risk and access to COVID-19 care. Misinformation was present in 7% of COVID-19–related posts. In conclusion, online networks provide a useful source of real-world data from patients and their families, and analysis of these data highlighted a substantial impact of COVID-19 on prostate cancer care. Patient summary: We performed a study of online posts by patients with prostate cancer and their families on their perspectives about COVID-19. Concerns about the impact of COVID-19 included worry about delays in testing and treatment. Our research also revealed misinformation in COVID-19–related posts.

Abstract Background Governments worldwide recommended unprecedented measures to contain the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As pressure mounted to scale back measures, understanding public priorities was critical. We assessed initial public adherence with and support for stay-at-home orders in nations and cities with different SARS-CoV-2 infection and COVID-19 death rates. Methods Cross-sectional surveys were administered to representative samples of adults aged ≥18 years from regions with different SARS-CoV-2 prevalences from April 2–8, 2020. Regions included two nations [the United States (US—high prevalence) and Australia (AU—low prevalence)] and two US cities [New York City (NY—high prevalence) and Los Angeles (LA—low prevalence)]. Regional SARS-CoV-2 and COVID-19 prevalence (cumulative SARS-CoV-2 infections, COVID-19 deaths) as of April 8, 2020: US (363,321, 10,845), AU (5956, 45), NY (81,803, 4571), LA (7530, 198). Of 8718 eligible potential respondents, 5573 (response rate, 63.9%) completed surveys. Median age was 47 years (range, 18–89); 3039 (54.5%) were female. Results Of 5573 total respondents, 4560 (81.8%) reported adherence with recommended quarantine or stay-at-home policies (range of samples, 75.5–88.2%). Additionally, 29.1% of respondents screened positive for anxiety or depression symptoms (range of samples, 28.6–32.0%), with higher prevalences among those of younger age, female gender, and those in quarantine or staying at home most of the time versus those who did not report these behaviours. Despite elevated prevalences of adverse mental health symptoms and significant life disruptions, 5022 respondents (90.1%) supported government-imposed stay-at-home orders (range of samples, 88.9–93.1%). Of these, 90.8% believed orders should last at least three more weeks or until public health or government officials recommended, with support spanning the political spectrum. Conclusions Public adherence with COVID-19 mitigation policies was highly prevalent, in both highly-affected (US, NY) and minimally-affected regions (AU, LA). Despite disruption of respondents’ lives, the vast majority supported continuation of extended stay-at-home orders. Despite common support, these two countries diverged in stringent mitigation implementation, which may have contributed to subsequent outcomes. These results reveal the importance of surveillance of public support for and adherence with such policies during the COVID-19 pandemic and for future infectious disease outbreaks.

Sleep deficiency is a hidden cost of our 24–7 society, with 70% of adults in the US admitting that they routinely obtain insufficient sleep. Further, it is estimated that 50–70 million adults in the US have a sleep disorder. Undiagnosed and untreated sleep disorders are associated with diminished health for the individual and increased costs for the employer. Research has shown that adverse impacts on employees and employers can be mitigated through sleep health education and sleep disorder screening and treatment programs. Smartphone applications (app) are increasingly commonplace and represent promising, scalable modalities for such programs. The dayzz app is a personalized sleep training program that incorporates assessment of sleep disorders and offers a personalized comprehensive sleep improvement solution. Using a sample of day workers affiliated with a large institution of higher education, we will conduct a single-site, parallel-group, randomized, waitlist control trial. Participants will be randomly assigned to either use the dayzz app throughout the study or receive the dayzz app at the end of the study. We will collect data on feasibility and acceptability of the dayzz app; employee sleep, including sleep behavioral changes, sleep duration, regularity, and quality; employee presenteeism, absenteeism, and performance; employee mood; adverse and safety outcomes; and healthcare utilization on a monthly basis throughout the study, as well as collect more granular daily data from the employee during pre-specified intervals. Our results will illuminate whether a personalized smartphone app is a viable approach for improving employee sleep, health, and productivity. Trial registration: ClinicalTrials.gov Identifier: NCT04224285.

Study objectives: Clinical and population health recommendations are derived from studies that include self-report. Differences in question wording and response scales may significantly affect responses. We conducted a methodological review assessing variation in event definition(s), context (i.e., work- versus free-day), and timeframe (e.g., “in the past 4 weeks”) of sleep timing/duration questions. Methods: We queried databases of sleep, medicine, epidemiology, and psychology studies for survey-based research and/or publications with sleep duration/timing questions. The text of these questions was thematically analyzed. Results: We identified 53 surveys with sample sizes ranging from 93 to 1,185,106. For sleep duration questions, participants reported nocturnal sleep (24 of 44 questions), sleep in the past 24-hours (14/44), their major sleep episode (3/44), or answered unaided (3/44). For bedtime questions, participants reported time into bed (19/47), first attempt to sleep (16/40), or fall-asleep time (12/47). For wake-time questions, participants reported wake-up time (30/43), the time they “get up” (7/43), or their out-of-bed time (6/43). Context guidance appeared in 18/44 major sleep duration, 35/47 bedtime, and 34/43 wake-time questions. Timeframe was provided in 8/44 major sleep episode duration, 16/47 bedtime, and 10/43 wake-time questions. One question queried the method of awakening (e.g., by alarm clock), 18 questions assessed sleep latency, and 12 measured napping. Conclusion: There is variability in the event definition(s), context, and timeframe of questions relating to sleep. This work informs efforts at data harmonization for meta-analyses, provides options for question wording, and identifies potential questions for future surveys.

Using mosaic analysis with double markers to label genetically-distinct clones in established tumors, the authors studied the effects of p53 loss in lung and pancreatic cancers. They find that loss of p53 enhances progression in both models but only influences initiation in the pancreas.

Background. Approximately 35% of men in the United States are obese, though little theoretical work examining predictors correlates of obesity exists for this population. The purpose of this study was to test the capacity of Bandura’s social cognitive model of health behavior to account for variance in fruit and vegetable consumption, moderate physical activity, and sleep behavior in overweight and obese men. Methods. Data were collected from overweight and obese men using previously validated questionnaires. Structural equation models were built to examine the direct and indirect effects the social cognitive theory constructs of self-efficacy, outcome expectation, socio-structural factors, and goals on the behaviors under investigation. Results. A total of 305 men participated in this study (Mage=44.52; SD=6.95). Overall fit for the social cognitive models of health behavior were adequate, accounting for 35.0%, 31.2%, and 21.1% of the variance in the fruit and vegetable consumption, moderate physical activity, and sleep behavior correlates, respectively. Self-efficacy had the greatest total effect on fruit and vegetable consumption (βtotal=.500) and sleep behavior (βtotal=.406), while goals has the greatest total effect on moderate physical activity (βtotal=.495). The indirect effects of self-efficacy on the three behaviors demonstrates the relative importance of self-efficacy as a mediator for health behavior change. Discussion. Men are underrepresented in behavioral obesity prevention and treatment research. This study provides support for the social cognitive model of health behavior as a theoretical framework for predicting behaviors hypothesized to protect against and treat obesity in men. Additionally, this study represents the first attempt to model correlates social cognitive constructs on sleep behavior.

STUDY OBJECTIVES:Sleep apnea is associated with hypertension, and treatment may improve outcomes. We examine national burden of sleep apnea, rates of sleep apnea treatment, and whether racial/ethnic disparities exist among patients with hypertension. METHODS:Data from the National Ambulatory Medical Care Survey/National Hospital Ambulatory Medical Care Survey (NAMCS/NHAMCS), 2005-2012, were analyzed (N = 417,950). We identified hypertension patient visits where sleep apnea diagnosis or complaint was recorded. Primary outcome measures were sleep study, medication, or behavioral therapy (diet, weight loss, or exercise counseling). We used multivariate logistic regression to examine treatment by demographic/clinical factors. RESULTS:Among patients with hypertension, sleep apnea was identified in 11.2-per-1,000 visits. Overall, patients with hypertension and a sleep disorder were referred for sleep study in 14.4% of visits, prescribed sleep medication in 11.2% of visits, and offered behavioral therapy in 34.8% of visits. Adjusted analyses show behavioral therapy more likely to be provided to obese patients than normal/overweight (OR = 4.96, 95%CI[2.93-8.38]), but less likely to be provided to smokers than nonsmokers (OR = 0.54, 95%CI[0.32-0.93]). Non-Hispanic blacks were less likely to receive medications than non-Hispanic whites (OR = 0.19, 95% CI[0.06-0.65]). CONCLUSIONS:In the U.S., sleep apnea were observed in a small proportion of hypertension visits, a population at high-risk for the disorder. One explanation for the low prevalence of sleep apnea observed in this patient population at high risk for the disorder is under-diagnosis of sleep related breathing disorders. Behavioral therapy was underutilized, and non-Hispanic Blacks were less likely to receive medications than non-Hispanic Whites.

Introduction: The proportion of university/college students (UCS) consuming alcohol is similar to the number of those reporting poor sleep, at approximately 30%, the proportion being greater in student athletes (SA). What remains to be understood is if poor sleep potentiates risky behaviors. Objective: Our aim was to examine the association among sleep difficulties, insomnia symptoms, and insufficient sleep on the risk of driving under the influence of alcohol in a sample of UCS and whether these associations were more pertinent in SA. Methods: Data from the National University/College Health Assessment was used from the years 2011⁻2014. Questions on number of drinks consumed and behaviors such as driving after drinking alcohol were related to answers to questions pertaining to sleep difficulties, insufficient sleep, and insomnia symptoms. Results: Mean alcohol intake was of about 3 drinks; SA consumed significantly more than student non-athletes (SNA). Binge-drinking episodes were significantly higher among SA than SNA. Difficulty sleeping was associated with an increased likelihood of driving after any drinks and after 5 or more drinks in both groups, effects being stronger among SA. Insomnia was associated with an increased likelihood of driving after any drinks and after 5 or more drinks in SA and after 5 or more drinks in SNA. These effects were stronger among athletes. Conclusion: The present study found that self-reported difficulties sleeping, insomnia symptoms, and insufficient sleep are associated with driving after drinking alcohol. This relationship applied to driving after drinking any alcohol or binge drinking and was again stronger among SA than SNA.