9 research outputs found
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Psychiatric Illness, Substance Use, and Viral Suppression Among HIV-Positive Men of Color Who Have Sex with Men in Los Angeles.
For individuals living with human immunodeficiency virus (HIV), viral suppression positively affects quality and length of life and reduces risks for HIV transmission. Men of color who have sex with men (MoCSM) who have been diagnosed with HIV have disproportionately low rates of viral suppression, with concomitant increases in incidence. We identified specific social, structural, and psychiatric factors associated with viral suppression among a sample of 155 HIV-positive MoCSM. Cigarette smoking and biological markers of recent drug use were significantly associated with detectable viral load. In contrast, individuals reporting a history of psychiatric illness during medical examination were more likely to be virally suppressed. Further analyses demonstrated that psychiatric illness may affect virologic outcomes through increased probability of being prescribed HIV medications. Alternatively, cigarette smoking and drug use appear to negatively affect subsequent HIV Care Continuum milestones such as medication adherence. Findings provide support for comprehensive intervention programs that emphasize prevention and treatment of cigarette, methamphetamine, and other drug use, and promote improved connection to psychiatric care. Continual achievement of this goal may be a crucial step to increase rates of viral suppression and slow HIV incidence in communities of MoCSM in Los Angeles and other urban areas
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Non-Pharmaceutical Interventions and COVID-19 Burden in the United States.
BACKGROUND: Non-pharmaceutical interventions (NPIs) are mitigation strategies used to reduce the spread of transmissible diseases. The relative effectiveness of specific NPIs remains uncertain. METHODS: We used state-level Coronavirus disease 2019 (COVID-19) case and mortality data between January 19, 2020 and March 7, 2021 to model NPI policy effectiveness. Empirically derived breakpoints in case and mortality velocities were used to identify periods of stable, decreasing, or increasing COVID-19 burden. The associations between NPI adoption and subsequent decreases in case or death velocities were estimated using generalized linear models accounting for weekly variability shared across states. State-level NPI policies included: stay at home order, indoor public gathering ban (mild >10 or severe ≤10), indoor restaurant dining ban, and public mask mandate. RESULTS: 28,602,830 cases and 511,899 deaths were recorded. The odds of a decrease in COVID-19 case velocity were significantly elevated for stay at home (OR 2.02, 95% CI 1.63-2.52), indoor dining ban (OR 1.62, 95% CI 1.25-2.10), public mask mandate (OR 2.18, 95% CI 1.47-3.23), and severe gathering ban (OR 1.68, 95% CI 1.31-2.16). In mutually adjusted models, odds remained elevated for stay at home (AOR 1.47, 95% CI 1.04-2.07) and public mask mandate (AOR = 2.27, 95% CI 1.51-3.41). Stay at home (OR 2.00, 95% CI 1.53-2.62; AOR 1.89, 95% CI 1.25-2.87) was also associated with greater likelihood of decrease in death velocity in unadjusted and adjusted models. CONCLUSIONS: NPIs employed in the U.S. during the COVID-19 pandemic, most significantly stay at home orders, were associated with decreased COVID-19 burden
Utility of the Actiheart Accelerometer for Estimating Exercise Energy Expenditure in Female Adolescent Runners
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors' purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 +/- 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner's training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal . kg(-1) min(-1) during recovery, tempo, and race pace, respectively (p < .0001). Bland Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner's recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal . kg(-1) . min(-1). Using the manufacturer's equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.2.230 JCR (2010) Q2, 33/70 Nutrition & dietetics, 20/80 Sport scienceUE
Utility of the Actiheart Accelerometer for Estimating Exercise Energy Expenditure in Female Adolescent Runners
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors' purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 +/- 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner's training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal . kg(-1) min(-1) during recovery, tempo, and race pace, respectively (p < .0001). Bland Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner's recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal . kg(-1) . min(-1). Using the manufacturer's equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.2.230 JCR (2010) Q2, 33/70 Nutrition & dietetics, 20/80 Sport scienceUE