Clinical trials

The prevalence of overweight and obesity remains epidemic in the United States, with some of the highest rates seen in older adults. While this phenomenon is certainly multifactorial, a good deal of evidence suggests that insufficient physical activity (PA) contributes significantly. Pilot data recently collected in a laboratory indicates a strong, inverse relationship between daily step counts and body fatness and cardiometabolic risk (CMR) factors when step counts are expressed relative to fat mass in young adults. This expression of PA may be especially predictive of body composition because it is influenced by factors that influence appetite and energy intake, energy expenditure, and the energy "reservoir" that is represented by body fat stores, all three elements of the "settling point" model of body weight. The strength of this relationship suggests that prescription of step counts that consider current body weight and composition, and weight loss goal, may yield predictable changes in weight and CMR in adults eating ad libitum. The long-term objective of this study is to quantify the relationship between daily step counts and body composition in young, middle aged, and older adults who are overweight/obese and develop a regression model that can be used to prescribe physical activity (daily step counts) for achieving a specific target body weight and predictably improving CMR risk for young, middle-aged, and older adult men and women over eight months while eating ad libitum. To achieve this objective, investigators will undertake two specific aims: 1) quantify the relationship between average steps·kg fat mass-1·day-1 and body composition/CMR profiles in healthy, overweight, and obese adults 20-39 years, 40-59 years, 60-79 years, and 80-plus years old, using inexpensive, widely available triaxial pedometers while eating ad libitum, and 2) quantify the efficacy of employing targeted step counts expressed as steps·kg fat mass-1·day-1 using the model developed in Aim 1 for producing predictable improvements in body composition and CMR factors in overweight and obese adults 20-39, 40-59, 60-79, and 80-plus years old, over 8 months while eating ad libitum. This study will result in a regression model that may significantly improve the way that PA is prescribed for weight management, with vast clinical and public health implications.

Approximately 60% of males and 40% of females do not meet current fluid intake recommendations, which is associated with adverse health consequences such as obesity, diabetes, and chronic kidney disease. Newer technologies have been designed to promote fluid intake. "Smart Water Bottles" use mHealth technology to capture fluid intake behaviors automatically and provide cues to encourage fluid consumption. Studies using Smart Water Bottles have helped some individuals increase fluid intake to help reduce kidney stone formation. However, limited research has assessed the efficacy of this technology on improving fluid intake in college students. College is a time with the potential to form healthy habits that carry into adulthood. Previous work has also identified daily changes in morning urine color, thirst perception, and body mass, as simple, inexpensive indicators of daily fluctuations in water balance. Tracking changes in these metrics has the potential to provide participants with evidence of adequate or inadequate fluid consumption. Thus, the combination of prompting from a smart water bottle, as well as daily self-monitoring changes in hydration status, may encourage college students to increase daily fluid consumption.

The goal of this randomized clinical trial is to learn if imagining fast or slow muscle contractions causes different responses for nervous system excitability and muscle function in young, healthy males and females in. The main questions are: Does imagining fast muscle contractions cause greater nervous system excitability compared to imagining slow muscle contractions? Does imagining fast muscle contractions increase muscle function compared to imagining slow muscle contractions? A control condition (rest) will be compared with two intervention conditions: imagining fast and imagining slow conditions, to determine if the fast and slow increase outcomes more than control and if fast has the greatest response. Participants will: * Attend 4 laboratory visits * Perform 50 imagined contractions fast or slow, but with no physical movement * Physical muscle contractions and non-invasive brain stimulation would be completed before and after each condition.