Mapping stakeholders' positioning in the access and delivery partnership value chain: the case of the RTS,S malaria vaccine introduction in Ghana.

Approximately 40% of adults in the U.S. have obesity [], and an estimated 50% of U.S. adults do not meet MVPA requirements []. In fact, nearly 25% of these adults exhibit excessive SB [] and 35% get inadequate sleep time []. This is problematic as each of these lifestyle behaviors is not only linked to obesity, but also associated with type 2 diabetes (T2D) and cardiovascular disease (CVD) risk []. Studies have increasingly pointed to the individual health benefits of meeting MVPA requirements [], decreasing SB [], and achieving adequate sleep []. However, little is known about how the combination of these movement patterns work together to affect health []. The Canadian Society for Exercise Physiology (CSEP) recently published 24-h movement guidelines to optimize health []. These guidelines consider the integration of MVPA, SB, and sleep within a 24-h period. Because the proportion of time spent in each movement pattern is collinear, an increase in time spent in one movement pattern causes a decrease in time spent in another []. However, no clear assessment approach is available to measure this composite of 24-h movement patterns and understand its relation to insulin resistance, obesity, or CVD risk. Thus, the purpose of this study was to identify if a 24-h movement pattern z-score of MVPA, SB, and sleep associates with cardiometabolic health in adults with obesity. Further, the American Heart Association (AHA) recently released an updated version of their construct of cardiovascular health, the Life’s Essential 8 (LE8), which uses physical activity (PA) and sleep, among other clinical risk factors, to create a score which may predict cardiovascular health []. Thus, we secondarily sought to compare the 24-h movement z-score to the LE8 score to understand its utility in relating to CVD risk. Participants ( = 56, Table ) with obesity baseline data who were part of a larger randomized controlled trial (#) were used in this cross-sectional study. Participants were recruited via social media, electronic medical records, and/or newspaper flyers from the local communities. Individuals were included if between the ages of 40 and 70 year, non-smoking, physically inactive (exercise < 150 min/week), weight stable (< 2 kg weight change during last 3 months), not taking medications known to affect glucose metabolism (e.g., biguanides, insulin, etc.), and free of chronic disease (e.g., renal, hepatic, etc.). Female participants were asked to indicate menses status. Participants underwent ECG testing before and during exercise to assess cardiac function. Biochemical analysis as well as a physical examination were also done to confirm eligibility. Biochemical analysis included a fasted lipid profile, comprehensive metabolic panel, and HbA1c blood test to characterize cardiometabolic risk. Blood pressure (BP) was also measured at time of the physical examination using an automatic BP cuff on the upper arm. Heart rate was recorded too, using pulse oximetry. Mean arterial pressure (MAP) was calculated as MAP = (2/3xDBP) + (1/3xSBP). Participants provided written and verbal informed consent prior to participation.