Rational Egoism Virtue-Based Ethical Beliefs and Subjective Happiness: An Empirical Investigation.

The fields of positive psychology, cognitive behavioral therapy, mindfulness, and goal-setting have all demonstrated that individuals can modify their beliefs, attitudes, intentions, and behaviors to improve their subjective happiness. But which ethical beliefs affect happiness positively? In comparison to ethical belief systems such as deontology, consequentialism, and altruism, rational egoism appears to be alone in suggesting that an individual's long-term self-interest and subjective happiness is possible, desirable, and moral. Albeit an important theoretical foundation of the rational egoism philosophy, the relationship between rational egoism and subjective happiness has yet to be investigated empirically.

Human rights in patient care: a special collection.

This piece introduces the special Public Health Reviews collection on human rights in patient care (HRPC). Work on HRPC dates back to 2007 and an Open Society Foundations initiative in collaboration with partners in Eastern Europe and Central Asia. We found that for marginalized groups, health care settings often were places of coercion, punishment, and/or violence rather than of treatment or care.

Alaskan Berry Extracts Promote Dermal Wound Repair Through Modulation of Bioenergetics and Integrin Signaling.

Various wild berry species endemic to Alaska and the circumpolar North that exhibit unique medicinal properties have long been appreciated by indigenous Arctic communities. Traditional use of Alaskan berry preparations in the treatment of skin wounds is recorded but has not been scientifically evaluated. Alaskan wild berries feature diverse phytochemical compositions that contain a variety of bioactive polyphenols exhibiting anti-inflammatory, antioxidant, and antimicrobial properties, making them ideal for wound healing interventions and natural anti-aging cosmeceutical formulations.

Changes in the bioactive properties of strawberries caused by the storage in oxygen- and carbon dioxide-enriched atmospheres.

The changes in the antioxidant capacity, anti-inflammatory, and wound healing properties of strawberry fruits as a consequence of the storage in atmospheres enriched in oxygen and carbon dioxide were investigated. Berries were exposed to two different gas compositions: 70% O + 20% CO and 90% O + 10% CO, and stored for up to 20 days at 5°C. The antioxidant capacity, assessed through DPPH and FRAP methods, decreased around 17% in samples exposed to 70% O + 20% CO at day 20.

Berries containing anthocyanins with enhanced methylation profiles are more effective at ameliorating high fat diet-induced metabolic damage.

Driven by the need for alternative whole food options to manage metabolic syndrome, multiple dietary interventions are suggested to achieve a better control of metabolic risk factors and molecular networks that regulate cellular energy metabolism. It is generally accepted that anthocyanin-rich diets are beneficial for maintaining healthy body weight, improving glucose and lipid metabolism, and determining inflammatory status of key metabolic tissues. However, anthocyanins are a structurally diverse group of phenolic compounds and their individual contributions to improving metabolic health are not clear.

Metabolic Effects of Berries with Structurally Diverse Anthocyanins.

Overconsumption of energy dense foods and sedentary lifestyle are considered as major causes of obesity-associated insulin resistance and abnormal glucose metabolism. Results from both cohort studies and randomized trials suggested that anthocyanins from berries may lower metabolic risks, however these reports are equivocal. The present study was designed to examine effects of six berries with structurally diverse anthocyanin profiles (normalized to 400 µg/g total anthocyanin content) on development of metabolic risk factors in the C57BL/6 mouse model of polygenic obesity.

Advancing human rights in patient care through higher education in Eastern Europe and Central Asia.

Background: In Eastern Europe and Central Asia, for society's most marginalized people, health systems are too often places of violations of basic rights, rather than of treatment and care. At the same time, health practitioners are largely unaware of how to incorporate human rights norms in their work. Additionally, they may face abuses themselves, such as unsafe working conditions and sanctions for providing evidence-based care.

The Case for Use of Simple Difference Scores to Test the Significance of Differences in Mean Rates of Change in Controlled Repeated Measurements Designs.

A previous Monte Carlo study examined the relative powers of several simple and more complex procedures for testing the significance of difference in mean rates of change in a controlled, longitudinal, treatment evaluation study. Results revealed that the relative powers depended on the correlation structure of the simulated repeated measurements. Tests on dropout-weighted linear slope coefficients fitted to all of the available measurements for each participant were found to provide superior power in the presence of compound symmetry (CS), but tests of significance applied to simple baseline-to-endpoint difference scores provided superior power in the presence of a strongly autoregressive (AR) correlation structure.

Healthcare workforce development through an international workshop series: nursing care of older adults.

A critical need exists to educate the international healthcare workforce on the care of the older adult. This article describes an interdisciplinary program to address the nursing needs of older adults via a series of workshops in Russia. Strategies to bridge international healthcare and educational cultures are demonstrated.

Determining the efficacy of a resiliency training approach in adults with type 2 diabetes.

Purpose: The purpose of this randomized clinical study was to test the efficacy of a resiliency training approach for people with diabetes who have previously received standard diabetes self-education.

Methods: A single-blinded, randomized design was employed with repeated measures (baseline, 3 months, 6 months) with 67 participants assigned to either treatment as usual (n = 37) or the resiliency classes (n = 30). Outcome variables included physiological measures (glycosylated hemoglobin, waist measurement, eating and exercise habits) and psychosocial measures (self-efficacy, locus of control, social support, and purpose in life).

Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a two-stage analysis of repeated measurements.

Recent contributions to the statistical literature have provided elegant model-based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated error and missing data due to dropouts. However, the mathematical complexity and model specificity of these solutions make them generally inaccessible to most applied researchers who actually design and undertake treatment evaluation research in psychiatry. In contrast, this article relies on a simple two-stage analysis in which dropout-weighted slope coefficients fitted to the available repeated measurements for each subject separately serve as the dependent variable for a familiar ANCOVA test of significance for differences in mean rates of change.

A two-stage analysis of repeated measurements with dropouts and/or intermittent missing data.

This article is about a simple two-stage analysis that utilizes slope coefficients as the dependent variable for testing the significance of difference in mean rates of change in repeated measurement designs with missing data. The ANCOVA test on the doubly weighted slope coefficients provides power comparable to that of more complex maximum likelihood procedures when data are missing completely at random, requires fewer assumptions and is more generally applicable under realistic nonrandom dropout conditions, and most importantly can be readily understood and explained by those who actually do most controlled clinical research.

Lower effectiveness of divalproex versus valproic acid in a prospective, quasi-experimental clinical trial involving 9,260 psychiatric admissions.

Objective: The authors examined clinical differences between divalproex sodium and generic immediate-release valproic acid.

Method: This 6-year prospective, quasi-experimental clinical trial compared the effectiveness and tolerability of divalproex and valproic acid. The dependent variables were length of hospital stay, rehospitalization rate, and adverse drug reactions in 9,260 psychiatric admissions.

Use of resampling to select among alternative error structure specifications for GLMM analyses of repeated measurements.

Autocorrelated error and missing data due to dropouts have fostered interest in the flexible general linear mixed model (GLMM) procedures for analysis of data from controlled clinical trials. The user of these adaptable statistical tools must, however, choose among alternative structural models to represent the correlated repeated measurements. The fit of the error structure model specification is important for validity of tests for differences in patterns of treatment effects across time, particularly when maximum likelihood procedures are relied upon.

Determining the number of clusters by sampling with replacement.

A split-sample replication criterion originally proposed by J. E. Overall and K.

Measuring change in controlled longitudinal studies.

This paper examines the implications of the correlational structure of repeated measurements for three indices of change that can be used to evaluate treatment effects in longitudinal studies with scheduled assessment times and fixed total duration. The generalized least squares (GLS) regression of repeated measurements on time, which is usually reserved for complex mixed model solutions, takes the correlational structure of the repeated measurements into account, whereas simple gain scores and ordinary least squares (OLS) regression calculations do not. Nevertheless, the GLS solution is equivalent to OLS under conditions of compound symmetry and is equivalent to the analysis of simple gain scores in the presence of an autoregressive (order 1) correlational structure.

The Alzheimer's disease activities of daily living international scale (ADL-IS).

Background: Activities of daily living (ADL) deficits are integral components of dementia disorders, and ADL measures are among the most robust markers of the course of Alzheimer's disease (AD). Despite this acknowledged importance, no clearly useful ADL instrument for cross-cultural application in pharmacologic trials in the early stages of AD had been available.

Method: An international effort was launched to develop an ADL scale for pharmacologic trials in early AD.

Sample size and power calculations in repeated measurement analysis.

Controlled clinical trials in neuropsychopharmacology, as in numerous other clinical research domains, tend to employ a conventional parallel-groups design with repeated measurements. The hypothesis of primary interest in the relatively short-term, double-blind trials, concerns the difference between patterns or magnitudes of change from baseline. A simple two-stage approach to the analysis of such data involves calculation of an index or coefficient of change in stage 1 and testing the significance of difference between group means on the derived measure of change in stage 2.

Testing differences in response trends across a normalized time domain.

A two-stage mixed model analysis of repeated measurement calculates participant-specific regression slopes relating change in available measurements to associated assessment times, and then the difference between mean regression slopes in two or more treatment groups is tested for significance against the within-groups variability of the participant-specific regression slopes. It is not necessary that all participants have the same schedule or number of repeated measurements. However, when dropouts are included in an "intent to treat" analysis, the shortened treatment exposures for the dropouts substantially increase variability and reduce power of tests for differences in rates of change.

Issues in use of SAS PROC.MIXED to test the significance of treatment effects in controlled clinical trials.

A project that originated with the aim of documenting the implications of dropouts for tests of significance based on general linear mixed model procedures resulted in recognition of problems in the use of SAS PROC.MIXED for this purpose. In responding to suggestions and criticisms, we have further analyzed simulated clinical trial data with realistic autoregressive structure, using alternative error model formulations, different approaches to the use of covariates to model dropout patterns, and different ways to include the critical time variable in the mixed model.

Power of univariate and multivariate analyses of repeated measurements in controlled clinical trials.

The power of univariate and multivariate tests of significance is compared in relation to linear and nonlinear patterns of treatment effects in a repeated measurement design. Bonferroni correction was used to control the experiment-wise error rate in combining results from univariate tests of significance accomplished separately on average level, linear, quadratic, and cubic trend components. Multivariate tests on these same components of the overall treatment effect, as well as a multivariate test for between-groups difference on the original repeated measurements, were also evaluated for power against the same representative patterns of treatment effects.

Problematic formulations of SAS PROC.MIXED models for repeated measurements.

The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with drop-outs. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important.

Adjusting sample size for anticipated dropouts in clinical trials.

Statistical models for calculating sample sizes for controlled clinical trials often fail to take into account the negative impact that dropouts have on the power of intent-to-treat analyses. Empirically defined dropout correction coefficients are proposed to adjust sample sizes for endpoint analysis of variance (ANOVA) and analysis of covariance (ANCOVA) that have been initially calculated assuming complete data. The implications of type of analysis (change-score ANOVA or ANCOVA), correlational structure of the repeated measurements (compound symmetry or autoregressive), and percentage of dropouts (20% or 30%) are considered, together with other less influential design and data parameters.

Comparative evaluation of two models for estimating sample sizes for tests on trends across repeated measurements.

Two equations for calculating sample sizes that are required for power in testing differences in rates of change in repeated measurement designs have been presented by different authors. One equation provides support for the conclusion that increased frequency of measurements across a treatment period of fixed duration enhances power of the tests. The other equation supports the counterintuitive conclusion that increased frequency of measurements actually tends to decrease power in the presence of realistic serial dependencies in the data.