Suchergebnisse

The early 21st century has witnessed a steady push by scientists, industry leaders, and government officials to make medicine more personalized. To date, the concept of personalized medicine has referred largely to the field of pharmacogenomics. In contrast, relatively few data exist regarding the application of preventive strategies such as physical exercise in the context of personalized medicine. Within this review, we highlight the extant literature and propose five strategies for scientists that may propel the exercise and sports science fields toward this global goal. Notably, these approaches are in addition to methods to maintain adherence to training – a well-known factor in determining exercise responsiveness. Briefly, these strategies include (1) evaluating participant responses to training at the individual as well as group level; (2) identifying sources of variability in responsiveness to training; (3) optimizing exercise dosing strategies to maximize benefits while minimizing barriers to participation; (4) evaluating the efficacy of multimodal interventions for relevant population subgroups; and (5) increasing the clinical relevance of study populations and outcomes in exercise trials. We look forward to seeing these strategies considered in trials of preventive health interventions such as exercise. Extensive future research in this area is needed for the vision of exercise as a personalized form of medicine to become a reality.

Despite theoretical evidence that the model commonly referred to as the 3500 kcal rule grossly overestimates actual weight loss, widespread application of the 3500 kcal formula continues to appear in textbooks, on respected government and health related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500 kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft® Excel and Java which simulates a rigorously validated, dynamic model of weight change. The first two tools available at www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool which can be downloaded from the URL, www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500 kcal rule.

The Coalition for Clinical Research—Self-Monitoring of Blood Glucose Scientific Board, a group of nine academic clinicians and scientists from the United States and Europe, convened in San Francisco, California, on June 11–12, 2008, to discuss the appropriate uses of self-monitoring of blood glucose (SMBG) and the measures necessary to accurately assess the potential benefit of this practice in noninsulin-treated type 2 diabetes mellitus (T2DM). Thirteen consultants from the United States, Europe, and Canada from academia, practice, and government also participated and contributed based on their fields of expertise. These experts represent a range of disciplines that include adult endocrinology, pediatric endocrinology, health education, mathematics, statistics, psychology, nutrition, exercise physiology, and nursing. This coalition was organized by Diabetes Technology Management, Inc. Among the participants, there was consensus that: protocols assessing the performance of SMBG in noninsulin treated T2DM must provide the SMBG intervention subjects with blood glucose (BG) goals and instructions on how to respond to BG data in randomized controlled trials (RCTs);intervention subjects in clinical trials of SMBG-driven interventions must aggressively titrate their therapeutic responses or lifestyle changes in response to hyperglycemia;control subjects in clinical trials of SMBG must be isolated from SMBG-driven interventions and not be contaminated by physician experience with study subjects receiving a SMBG intervention;the best endpoints to measure in a clinical trial of SMBG in T2DM include delta Hemoglobin A1c levels, hyperglycemic events, hypoglycemic events, time to titrate noninsulin therapy to a maximum necessary dosage, and quality of life indices;either individual randomization or cluster randomization may be appropriate methods for separating control subjects from SMBG intervention subjects, provided that precautions are taken to avoid bias and that the sample size is adequate;treatment algorithms for ...