Body image satisfaction in pregnancy may have an important influence on maternal biopsychosocial outcomes. This study aims to examine the mediating influence of trimesters 2 and 3 body image satisfaction on trimesters 2 and 3 depressive symptoms, exercise behavior, and gestational weight gain. Pregnant women (N = 151; mean age = 30) prospectively completed study measures via mail during their 1st, 2nd, and 3rd trimesters. As predicted, trimesters 2 and 3 body image satisfaction mediated the relationship between trimester 1 body image satisfaction and trimester 2 depressive symptoms and the relationship between trimester 2 body image satisfaction and trimester 3 depressive symptoms. In contrast to the hypothesis, no mediation was observed for the relationship between body image satisfaction and exercise behavior or gestational weight gain. These preliminary findings demonstrate that body image satisfaction is an important psychological determinant of depressive symptoms in pregnancy. Promoting healthy body image may be a non-pharmacological strategy that offers protective effects against depressive symptoms during pregnancy.

The 2019 Addendum, in conjunction with the original health and wellness coaching (HWC) Compendium, organizes HWC literature with the aim of assisting researchers while providing a resource for practitioners. The 2019 Addendum to the HWC Compendium extends the initial work by adding HWC-related literature published in the past 2 years. The 2019 Addendum divides articles retrieved into 8 categories, including a new miscellaneous section complementing categories examining HWC effects on cancer, cholesterol, diabetes, heart disease, hypertension, obesity, and wellness. The 2019 Addendum again provides in-depth information about the nature, quality, and results from each article in a detailed spreadsheet provided as an electronic appendix. The 2019 Addendum contributes another 104 peer-reviewed coaching-related articles to the HWC Compendium. This most recent research again describes HWC as a favorable intervention with treatment potential in all categories, though only 3 new cancer articles were included in the 2019 Addendum. Trends in HWC (ie, e-coaching and group coaching) are identified, and there is also discussion of future research needs. In conclusion, the field of HWC continues to grow, as does the research describing this clinical practice; the 2019 Addendum to the Compendium of HWC organizes and assists understanding of this literature.

Objective: Physical activity (PA) declines during the transition to university. Identifying contributing factors is imperative. Participants: Participants were first-year undergraduate students (N = 283). Methods: Participants completed validated surveys. Participants were categorized as meeting PA guidelines or not meeting PA guidelines. MANCOVA’s with Bonferroni correction and Kruskal Wallis tests examined differences in PA motives and self-efficacy to overcome PA barriers across PA groups. Results: Significant multivariate effects were observed across PA groups for PA motives and self-efficacy. Univariate analyses revealed students meeting PA guidelines reported significantly higher PA motives of fitness/health management, appearance/weight management, and stress/mood management compared to students not meeting PA guidelines. Students meeting PA guidelines reported significantly higher self-efficacy for all PA barriers compared to students not meeting PA guidelines. Conclusions: Universities should prioritize education about the physical and psychological benefits of PA and teach first-year students behavioral skills to increase PA motivation, self-efficacy, and PA behavior.

Sedentary behavior increases risk for non-communicable diseases; associations may differ within different contexts (e.g., leisure time, occupational). This study examined associations between different types of sedentary behavior and disease risk factors in women, using objectively measured accelerometer-derived sedentary data. A validation study (n = 20 women) classified sedentary behavior into four categories: lying down; sitting (non-active); sitting (active); standing. A cross-sectional study (n = 348 women) examined associations between these classifications and disease risk factors (body composition, metabolic, inflammatory, blood lipid variables). Participants spent an average of 7 h 42 min per day in sedentary behavior; 58% of that time was classified as non-active sitting and 26% as active sitting. Non-active sitting showed significant (p ≤ 0.001) positive correlations with BMI (r = 0.244), body fat percent (r = 0.216), body mass (r = 0.236), fat mass (r = 0.241), leptin (r = 0.237), and negative correlations with HDL-cholesterol (r = −0.117, p = 0.031). Conversely, active sitting was significantly (p ≤ 0.001) negatively correlated with BMI (r = −0.300), body fat percent (r = −0.249), body mass (r = −0.305), fat mass (r = −0.320), leptin (r = −0.259), and positively correlated with HDL-cholesterol (r = 0.115, p = 0.035). In summary, sedentary behavior can be stratified using objectively measured accelerometer-derived activity data. Subsequently, different types of sedentary behaviors may differentially influence disease risk factors. Public health initiatives should account for sedentary classifications when developing sedentary behavior recommendations.

Limited research has examined American collegiate coaches’ perceptions of collecting data for informing training practices. The purpose of this study was to examine collegiate coaches’ perspectives and needs for collecting data from their athletes using a qualitative approach. Four focus groups were conducted with NCAA Division I coaches ( N = 13) at a mid-sized liberal arts university. Coaches were asked how they could benefit from collecting data from their athletes, how they would use the data to answer questions about athletes’ performance, what data they would want to collect, and what concerns they had regarding data collection. Principles of thematic analysis were used to analyze the interviews. Most coaches (54%) felt that collecting data would provide evidence needed to support training methodologies, and they would benefit (i.e. make improvements to coaching and athlete training) from monitoring progress in their athletes (61%). Regarding performance questions, coaches were interested in utilizing a biomechanist to conduct movement analyses (23%) and assessing athletes’ mental and emotional states (23%). Coaches wanted to monitor athletes’ fatigue, recovery, mental health states, and health behaviors (i.e. sleep and diet; 15%). Coaches expressed concerns over finding an optimal time to test athletes (8%) and effective presentation of data to athletes (23%) while limiting body composition assessments due to athlete discomfort (15%). Findings from these focus groups suggest that coaches would like to collect data to learn more about athletes’ physical and mental states, but several logistical and practical concerns still exist regarding how to efficiently collect and utilize the data.

Background: Discretionary leisure time for health-promoting physical activity (PA) is limited. This study aimed to predict body composition and metabolic health marker changes from PA reallocation using isotemporal substitution analysis. Methods: Healthy New Zealand women (n = 175; 16–45 y) with high BMI (≥25 kg/m²) and high body fat percentage (≥30%) were divided into three groups by ethnicity (Māori n = 37, Pacific n = 54, and New Zealand European n = 84). PA, fat mass, lean mass, and metabolic health were assessed. Isotemporal substitution paradigms reallocated 30 min/day of sedentary behaviour to varying PA intensities. Results: Reallocating sedentary behaviour with moderate intensity, PA predicted Māori women would have improved body fat% (14.83%), android fat% (10.74%), and insulin levels (55.27%) while the model predicted Pacific women would have improved waist-to-hip (6.40%) and android-to-gynoid (19.48%) ratios. Replacing sedentary time with moderate-vigorous PA predicted Māori women to have improved BMI (15.33%), waist circumference (9.98%), body fat% (16.16%), android fat% (12.54%), gynoid fat% (10.04%), insulin (55.58%), and leptin (43.86%) levels; for Pacific women, improvement of waist-to-hip-ratio (5.30%) was predicted. Conclusions: Sedentary behaviour must be substituted with PA of at least moderate intensity to reap benefits. Māori women received the greatest benefits when reallocating PA. PA recommendations to improve health should reflect the needs and current activity levels of specific populations.