Measures for Children at High Risk for Obesity



Children and their families at high risk for obesity are often under-represented in instrument validation studies that measure obesity and related psychosocial, behavioral, and environmental factors. Culturally and linguistically appropriate assessments are important for effective interventions and research. NCCOR’s Measures Registry hosts nearly 1,400 articles on instrument validation, with more than 100 discrete measures related to diet, physical activity and their environments. Over the past eight years, two reviews1,2 have assessed the number of articles in the Registry that focus on validation of high-risk populations. Results showed that fewer than 1 in 5 articles were used in high-risk populations. While the recent review identified more measures used in high-risk populations, especially among African American and Hispanic individuals and communities, even fewer were used among Asians, American Indians/Alaska Natives, or Hawaiian/Pacific Islanders, despite the high prevalence of overweight and obesity among American Indian and Hawaiian/Pacific Islander children. It is evident that more measures are needed.

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There are several challenges related to measurement in high-risk populations. One challenge related to measurement in high-risk populations is to promote the use of best practices for adaptation and standard validation procedures. The literature suggests there are three ways to use measures in high-risk populations: (1) apply (2) adapt an existing instrument or (3) develop a new instrument. However, little guidance exists on when each approach is best. To address this gap and assist researchers and practitioners in accurately measuring high-risk populations, NCCOR created this resource, “Measures for children at high risk for obesity: Choosing whether to apply, adapt, or develop a measure.”

This resource includes a decision tree that walks researchers and practitioners through a series of questions regarding whether to develop, adapt, or apply an instrument for obesity measures in high-risk populations. The decision tree also provides five real-world case scenarios that describe the rationale for choosing one of the three measurement approaches. Examples of case study topics include how to involve community stakeholders, how to determine whether populations are meaningfully different, and how to assess whether modifications to instruments require revalidation. Lastly, there is a resource list of guides, articles, tools, and compendia related to adapting measures.

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