Relationships between use of dietary supplements,caffeine and sensation seeking among college students

Abstract

Objective: Caffeine and dietary supplement (DS) use by college students is not well-documented. Given reported associations between energy drink consumption and sensation seeking, we used the Sensation Seeking Scale Form V (SSS-V) to assess relationships between sensation-seeking, caffeine, and DS use. Participants: Data from 1,248 college students from five US institutions were collected from 2009 to 2011. Methods: Linear regression was used to examine relationships between scores on the SSS-V and caffeine and DS use, demographic, and lifestyle characteristics. Results: Male sex, nonHispanic race-ethnicity, higher family income, tobacco use, consuming caffeinated beverages, more than 400?mg caffeine per day, and energy drinks with alcohol at least 50% of the time, were significantly associated with higher total SSS-V scores (P?<?0.001). Those using protein DSs had higher total, disinhibition, and boredom susceptibility SSS-V scores (Ps?<?0.001). Conclusions: Results demonstrate a positive correlation between sensation-seeking attitudes and habitual caffeine, energy drink, and DS consumption.     

Commentary: Male Osteoporosis—Policy Gaps in Prevention and Treatment

Osteoporosis is a disabling disease affecting 10 million Americans, leading to increased risk of fractures and poor functional ability. Although typically thought of as a women's disease, millions of men are diagnosed with osteoporosis or low bone mass, and the risk for osteoporotic fractures is increasing. To address undetected and untreated osteoporosis among men, public health education should be targeted to males. Insurance programs, including Medicare, should add the heel qualitative ultrasound (QUS) for age-eligible men with follow-up every 2 years. Policy changes should address funding of targeted education and prevention programs for aging males, including coverage of screening tests.     

Health and Medical Services Use: A Matched Case Comparison Between CCRC Residents and National Health and Retirement Study Samples

Little is known about the health status of adults living in continuing care retirement communities (CCRC). Using matched-case control, 458 adults from the Health and Retirement Study (HRS) or a CCRC-based sample were compared on total comorbidity, self-rated health, home health services use, and hospitalizations. At year 2, the CCRC sample reported more comorbidities (96%) but significantly better self-rated health (96% good/excellent) than the HRS sample (93% comorbidity, 73% good/excellent; p < .01). There were no significant differences in frequency of home health use or hospitalization. Living in a CCRC appears to be associated with higher self-ratings of health in this sample.     

Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments

Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.     

Federal Statistical Coordination Today: An Epilogue as Prologue

A common data mining task is the search for associations in large databases. Here we consider the search for “interestingly large” counts in a large frequency table, having millions of cells, most of which have an observed frequency of 0 or 1. We first construct a baseline or null hypothesis expected frequency for each cell, and then suggest and compare screening criteria for ranking the cell deviations of observed from expected count. A criterion based on the results of fitting an empirical Bayes model to the cell counts is recommended. An example compares these criteria for searching the FDA Spontaneous Reporting System database maintained by the Division of Pharmacovigilance and Epidemiology. In the example, each cell count is the number of reports combining one of 1,398 drugs with one of 952 adverse events (total of cell counts = 4.9 million), and the problem is to screen the drug-event combinations for possible further investigation.     

Simultaneous Pitman closeness of progressively type-II right-censored order statistics to population quantiles

In this work, we extend prior results concerning the simultaneous Pitman closeness of order statistics (OS) to population quantiles. By considering progressively type-II right-censored samples, we derive expressions for the simultaneous closeness probabilities of the progressively censored OS to population quantiles. Explicit expressions are deduced for the cases when the underlying distribution has bounded and unbounded supports. Illustrations are provided for the cases of exponential, uniform and normal distributions for various progressive type-II right-censoring schemes and different quantiles. Finally, an extension to the case of generalized OS is outlined.     

A community treatment intervention advancing active treatment in the elderly (ACTIVATE): a pilot study

A growing population of older adults receive treatment for depression but remain symptomatic. We report on a feasibility pilot study of an intervention (ACTIVATE) to improve depression care by encouraging the older person to take a step to intensify the existing treatment. Older adults (N = 43) receiving home-meal service and in depression treatment, but still symptomatic, participated in the ACTIVATE intervention. Assessments were conducted to evaluate change in treatment. Many (66.6%) participants took a step to change their treatment; the rate (88.2%) was higher among individuals with major depression. ACTIVATE may be a useful social work intervention to improve depression care.