24 research outputs found

    Performance of Risk-Based Criteria for Targeting Acute HIV Screening in San Francisco

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    Federal guidelines now recommend supplemental HIV RNA testing for persons at high risk for acute HIV infection. However, many rapid HIV testing sites do not include HIV RNA or p24 antigen testing due to concerns about cost, the need for results follow-up, and the impact of expanded venipuncture on clinic flow. We developed criteria to identify patients in a municipal STD clinic in San Francisco who are asymptomatic but may still be likely to have acute infection.Data were from patients tested with serial HIV antibody and HIV RNA tests to identify acute HIV infection. BED-CEIA results were used to classify non-acute cases as recent or longstanding. Demographics and self-reported risk behaviors were collected at time of testing. Multivariate models were developed and preliminarily evaluated using predictors associated with recent infection in bivariate analyses as a proxy for acute HIV infection. Multivariate models demonstrating ≥70% sensitivity for recent infection while testing ≤60% of patients in this development dataset were then validated by determining their performance in identifying acute infections.From 2004-2007, 137 of 12,622 testers had recent and 36 had acute infections. A model limiting acute HIV screening to MSM plus any one of a series of other predictors resulted in a sensitivity of 83.3% and only 47.6% of patients requiring testing. A single-factor model testing only patients reporting any receptive anal intercourse resulted in 88.9% sensitivity with only 55.2% of patients requiring testing.In similar high risk HIV testing sites, acute screening using "supplemental" HIV p24 antigen or RNA tests can be rationally targeted to testers who report particular HIV risk behaviors. By improving the efficiency of acute HIV testing, such criteria could facilitate expanded acute case identification

    Negative Mood and Food Craving Strength Among Women with Overweight: Implications for Targeting Mechanisms Using a Mindful Eating Intervention

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    ObjectivesWhen experiencing negative mood, people often eat to improve their mood. A learned association between mood and eating may cultivate frequent food cravings, detracting from health goals. Training in mindful eating may target this cycle of emotion-craving-eating by teaching individuals to manage urges when experiencing negative mood. We examined the impact of a mobile mindful eating intervention on the link between negative mood and food cravings among overweight women.MethodsIn a single-arm trial, participants (n = 64, M age = 46.1 years, M BMI = 31.5 kg/m2) completed ecological momentary assessments of negative mood and food cravings 3 times/day for 3 days pre- and post-intervention, as well as 1-month post-intervention. Using multilevel linear regression, we compared associations between negative mood and food craving strength at pre- vs. post-intervention (model 1) and post-intervention vs. 1-month follow-up (model 2).ResultsIn model 1, negative mood interacted with time point (β =  - .20, SE = .09, p = .02, 95% CI [- .38, - .03]) to predict craving strength, indicating that the within-person association between negative mood and craving strength was significantly weaker at post-intervention (β = 0.18) relative to pre-intervention (β = 0.38). In model 2, negative mood did not interact with time point to predict craving strength (β = .13, SE = .09, p = .10, 95% CI - .03, .31]); the association did not significantly differ between post-intervention and 1-month follow-up.ConclusionsTraining in mindful eating weakened the mood-craving association from pre- to post-intervention. The weakened association remained at follow-up. Our findings highlight the mood-craving link as a target-worthy mechanism of mindful eating that should be assessed in clinical trials.Trial registrationNCT02694731.Supplementary informationThe online version contains supplementary material available at 10.1007/s12671-021-01760-z

    Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

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    BackgroundDaily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.ObjectiveUse data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.MethodsWe used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.ResultsAlthough the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.ConclusionWeb-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection

    Correction to: Lipid findings from the Diabetes Education to Lower Insulin, Sugars, and Hunger (DELISH) Study

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    Following publication of the original article [1], the author reported that the co-author’s name was missing in the original article.http://deepblue.lib.umich.edu/bitstream/2027.42/173891/1/12986_2019_Article_416.pd

    Protocol for a randomized controlled trial comparing a very low-carbohydrate diet or moderate-carbohydrate plate-method diet for type 2 diabetes: the LEGEND (Lifestyle Education about Nutrition for Diabetes) trial

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    Abstract Background Optimal carbohydrate intake is an important and controversial area in the nutritional management of type 2 diabetes. Some evidence indicates that reducing overall carbohydrate intake with a low- or very low-carbohydrate eating plan can improve glycemic control compared to following eating plans that involve greater carbohydrate intake. However, critical knowledge gaps currently prevent clear recommendations about carbohydrate intake levels. Methods The LEGEND (Lifestyle Education about Nutrition for Diabetes) Trial aims to compare a very low-carbohydrate diet to a moderate-carbohydrate plate-method diet for glycemic control in adults with type 2 diabetes. This two-site trial plans to recruit 180 adults with type 2 diabetes. We will randomize participants to either a 20-session group-based diet and lifestyle intervention that teaches either a very low-carbohydrate diet or a moderate-carbohydrate plate-method diet. We will assess participants at study entry and 4 and 12 months later. The primary outcome is HbA1c, and secondary outcomes include inflammation (high sensitivity C-reactive protein), body weight, changes in diabetes medications, lipids (small particle LDL, HDL, triglycerides), skeletal metabolism (bone mineral density from dual-energy x-ray absorptiometry and bone turnover markers serum procollagen type I N propeptide and serum C-terminal telopeptide of type I collagen), and body composition (percent body fat, percent lean body mass). Discussion The LEGEND trial is a randomized controlled trial to assess optimal carbohydrate intake in type 2 diabetes by evaluating the effects of a very low-carbohydrate diet vs. a moderate-carbohydrate plate-method diet over a year-long period. The research addresses important gaps in the evidence base for the nutritional management of type 2 diabetes by providing data on potential benefits and adverse effects of different levels of carbohydrate intake. Trial registration ClinicalTrials.gov NCT05237128. Registered on February 11, 202
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