OBJECTIVES: Cardiovascular disease prevention should begin in childhood. However, the influence of physical activity on cardiovascular health in early childhood is unknown. Our purpose in this study was to determine the effect of physical activity on trajectories of cardiovascular health indicators during early childhood. METHODS: This prospective, observational cohort study (Health Outcomes and Physical Activity in Preschoolers) enrolled 418 3- to 5-year-olds with annual assessments for 3 years. Total physical activity (TPA) and moderate-to-vigorous physical activity (MVPA) were measured over 7 days via accelerometry. Cardiovascular health indicators included cardiovascular fitness (exercise time on a maximal treadmill test [treadmill time] and 1-minute heart rate recovery), resting arterial stiffness (whole-body pulse wave velocity and carotid β stiffness index), and seated systolic blood pressure. Data were analyzed by using linear mixed-effects modeling; effects are reported as unstandardized estimates (Est). RESULTS: There were main effects of TPA and MVPA on treadmill time (Est = 0.004 [P = .005] and 0.008 [P = .001], respectively) and heart rate recovery (Est = 0.05 [P < .001] and 0.08 [P < .001], respectively). There was a main effect of TPA on pulse wave velocity (Est = −0.001; P = .02) and an MVPA × time interaction (Est = −0.002; P = .01). For carotid β stiffness index, the effect of a TPA × time interaction was not significant (Est = −0.002; P = .051); however, there was a significant MVPA × time interaction (Est = −0.003; P = .03). MVPA was associated with a slower rate of change in systolic blood pressure for girls (Est = 0.06; P = .009). CONCLUSIONS: Children who engage in higher levels of physical activity during early childhood have better cardiovascular health indicators, with more intense physical activity (ie, MVPA) attenuating the stiffening of arteries.

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BackgroundIt is well established that drastic declines in physical activity (PA) occur during young adults’ transition into university; however, our understanding of contextual and environmental factors as it relates to young adults’ PA is limited.ObjectiveThe purpose of our study was to examine the feasibility of using wrist-worn accelerometers and the use of ecological momentary assessment (EMA) to assess the context and momentary correlates of PA on multiple occasions each day during first-year university.MethodsFirst-year university students were asked to participate in the study. The participants completed a brief questionnaire and were subsequently asked to wear an ActiGraph GT9X-Link accelerometer and respond to a series of EMA prompts (7/day) via their phones for 5 consecutive days.ResultsA total of 96 first-year university students with smartphones agreed to participate in the study (mean age 18.3 [SD 0.51]; n=45 females). Overall, there was good compliance for wearing the accelerometers, with 91% (78/86) of the participants having ≥2 days of ≥10 hours of wear time (mean=3.53 valid days). Students were generally active, averaging 10,895 steps/day (SD 3413) or 1123.23 activity counts/min (SD 356.10). Compliance to EMA prompts was less desirable, with 64% (55/86) of the participants having usable EMA data (responding to a minimum of ≥3 days of 3 prompts/day or ≥4 days of 2 prompts/day), and only 47% (26/55) of these participants were considered to have excellent EMA compliance (responding to ≥5 days of 4 prompts/day or ≥ 4 days of 5 prompts/day).ConclusionsThis study represents one of the first studies to use an intensive real-time data capture strategy to examine time-varying correlates of PA among first-year university students. These data will aim to describe the physical and social contexts in which PA occurs and examine the relationships between momentary correlates of PA among the first-year university students. Overall, current results suggest that wrist-worn accelerometers and EMA are feasible methods for data collection among the young adult population; however, more work is needed to understand how to improve upon compliance to a real-time data capture method such as EMA.

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