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Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the “Lifespan and Life Course Research: integrating strategies” “Un-Meeting” to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.
Few studies have derived data-driven dietary patterns in youth in the USA. This study examined data-driven dietary patterns and their associations with BMI measures in predominantly low-income, racial/ethnic minority US youth. Data were from baseline assessments of the four Childhood Obesity Prevention and Treatment Research (COPTR) Consortium trials: NET-Works (534 2–4-year-olds), GROW (610 3–5-year-olds), GOALS (241 7–11-year-olds) and IMPACT (360 10–13-year-olds). Weight and height were measured. Children/adult proxies completed three 24-h dietary recalls. Dietary patterns were derived for each site from twenty-four food/beverage groups using k-means cluster analysis. Multivariable linear regression models examined associations of dietary patterns with BMI and percentage of the 95th BMI percentile. Healthy (produce and whole grains) and Unhealthy (fried food, savoury snacks and desserts) patterns were found in NET-Works and GROW. GROW additionally had a dairy- and sugar-sweetened beverage-based pattern. GOALS had a similar Healthy pattern and a pattern resembling a traditional Mexican diet. Associations between dietary patterns and BMI were only observed in IMPACT. In IMPACT, youth in the Sandwich (cold cuts, refined grains, cheese and miscellaneous) compared with Mixed (whole grains and desserts) cluster had significantly higher BMI (β = 0·99 (95 % CI 0·01, 1·97)) and percentage of the 95th BMI percentile (β = 4·17 (95 % CI 0·11, 8·24)). Healthy and Unhealthy patterns were the most common dietary patterns in COPTR youth, but diets may differ according to age, race/ethnicity or geographic location. Public health messages focused on healthy dietary substitutions may help youth mimic a dietary pattern associated with lower BMI.
To describe snacking characteristics and patterns in children and examine associations with diet quality and BMI.
Children’s weight and height were measured. Participants/adult proxies completed multiple 24 h dietary recalls. Snack occasions were self-identified. Snack patterns were derived for each sample using exploratory factor analysis. Associations of snacking characteristics and patterns with Healthy Eating Index-2010 (HEI-2010) score and BMI were examined using multivariable linear regression models.
Childhood Obesity Prevention and Treatment Research (COPTR) Consortium, USA: NET-Works, GROW, GOALS and IMPACT studies.
Two snack patterns were derived for three studies: a meal-like pattern and a beverage pattern. The IMPACT study had a similar meal-like pattern and a dairy/grains pattern. A positive association was observed between meal-like pattern adherence and HEI-2010 score (P for trend < 0⋅01) and snack occasion frequency and HEI-2010 score (β coefficient (95 % CI): NET-Works, 0⋅14 (0⋅04, 0⋅23); GROW, 0⋅12 (0⋅02, 0⋅21)) among younger children. A preference for snacking while using a screen was inversely associated with HEI-2010 score in all studies except IMPACT (β coefficient (95 % CI): NET-Works, −3⋅15 (−5⋅37, −0⋅92); GROW, −2⋅44 (−4⋅27, −0⋅61); GOALS, −5⋅80 (−8⋅74, −2⋅86)). Associations with BMI were almost all null.
Meal-like and beverage patterns described most children’s snack intake, although patterns for non-Hispanic Blacks or adolescents may differ. Diets of 2–5-year-olds may benefit from frequent meal-like pattern snack consumption and diets of all children may benefit from decreasing screen use during eating occasions.
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