The Role of Ethnicity in School‐Based Obesity Intervention for School‐Aged Children: A Pilot Evaluation

Karczewski, Sabrina; Carter, Jocelyn S.; DeCator, Draycen D.

doi:10.1111/josh.12433

Cited by 7 publications

(2 citation statements)

References 56 publications

(142 reference statements)

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“…The heterogeneity in terminology used to describe the study designs that we found is consistent with previous literature describing the diverse and ambiguous study design labels for nonrandomized intervention studies including cluster-allocated and population health studies (Reeves et al 2017). To bridge the differences in language 2017, Weiss et al (2015) No description 4 (17) (Bhave et al 2016;Bielec et al 2013;Karczewski et al 2016;Vinck et al 2016) Generalized estimation equations 2 (9) (Bolton et al 2017;Yang et al 2017) Difference-in-difference 2 (9) (Sadeghi et al 2017;Schwartz et al 2016) Analyzed graphically…”

Section: Discussionsupporting

confidence: 62%

“…Distribution of study characteristics (n = 23) Characteristic Number of studies (%) Reference Statistical Analysis Method for group comparisons a GLM and/or ANOVA, ANCOVA, MANOVA 8 (35) Bielec et al(2013),Eichner et al (2016),Ermetici et al (2016),Reed et al (2013),Ronsley et al (2013),Treu et al (2017),Weiss et al (2015),Zhou et al (2014) Hierarchical multilevel or mixed effects regression models 7 (30)Karczewski et al (2016),Klakk et al (2013),Lazorick et al (2016),Mårild et al (2015),Sadeghi et al (2017),Swinburn et al (2014),Yang et al (2017) Linear or logistic regression models7 (30) Adab et al (2014), Bhave et al (2016), Bolton et al (2017), Economos et al (2013), Erfle and Gamble (2015), Schwartz et al (2016), Slusser et al Adab et al (2014), Bolton et al (2017), Erfle and Gamble (2015), Ermetici et al (2016), Karczewski et al (2016), Klakk et al (2013), Mårild et al (2015), Schwartz et al (2016), Slusser et al (2013), Swinburn et al (2014), Treu et al (2017), Weiss et al (2015), Yang et al (2017) Not conducted or no description 10 (48) Adab et al (2014), Bhave et al (2016), Erfle and Gamble (2015), Karczewski et al (2016), Reed et al (2013), Ronsley et al (2013), Vinck et al (2016), Weiss et al (2015), Yang et al (2017), Zhou et al (2014) Accounted for clustering effect in models 7 (33) Bolton et al (2017), Economos et al (2013), Lazorick et al (2016), Mårild et al (2015), Sadeghi et al (2017), Swinburn et al (2014), Treu et al Adab et al (2014), Bolton et al (2017), Economos et al (2013), Eichner et al (2016), Karczewski et al (2016), Klakk et al (2013), Lazorick et al (2016), Ronsley et al (2013), Slusser et al (2013), Swinburn et al (2014), Treu et al (2017), Vinck et al (2016), Yang et al (2017), Zhou et al (2014) Matched on demographic variables 8 (35) Adab et al (2014), Economos et al (2013), Ermetici et al (2016), Karczewski et al (2016), Klakk et al (2013), Weiss et al (2015), Yang et al (2017), Zhou et al Adab e...…”

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confidence: 99%

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Methodological approaches to the design and analysis of nonrandomized intervention studies for the prevention of child and adolescent obesity

Sandhu

Mbuagbaw

Tarride

et al. 2020

Can J Public Health

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Objectives Interventions for child obesity prevention are needed and it is unclear whether evidence from nonrandomized intervention studies is adequate. The objective of this research was to review the methods for the design, analysis and reporting of nonrandomized intervention studies for child obesity prevention and to assess potential for bias. Methods We conducted a review of nonrandomized intervention studies, including population health interventions, quasiexperimental studies and natural experiments, published from 2013 to 2017 that were identified in a recent systematic review. Data on study design, intervention and control groups, outcome measures, and statistical analyses, were extracted. Risk of bias was evaluated using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Results All identified studies (n = 23) included a school or community-based intervention and had a concurrent control group. Participants were 3-18 years and sample sizes were 100 to > 1 million. Study designs were described inconsistently, and interventions ranged from 14 weeks to 5 years. Obesity was compared between control and intervention groups using logistic or linear regression, analysis of variance and mixed effects regression. Only 48% of studies accounted for clustering, and methods to control for confounding and repeated measures varied substantially. Overall risk of bias was moderate to serious for all studies. Conclusion There are substantial opportunities to improve the methods for nonrandomized intervention studies and reduce bias. Future studies should use advanced statistical and causal epidemiology methods, including better control for confounding and clustering, to generate higher quality evidence and certainty regarding which obesity prevention interventions are effective.

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Section: Discussionsupporting

confidence: 62%

mentioning

confidence: 99%

Methodological approaches to the design and analysis of nonrandomized intervention studies for the prevention of child and adolescent obesity

Sandhu

Mbuagbaw

Tarride

et al. 2020

Can J Public Health

View full text Add to dashboard Cite

show abstract

Demographics and anthropometrics impact benefits of health intervention: data from the Reduce Obesity and Diabetes Project

Ostrowski

Speiser

Accacha

et al. 2019

Obesity Science & Practice

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Summary Objective To determine the efficacy of a 4‐month school‐based health, nutrition and exercise intervention on body fatness and examine possible effects of demographic and anthropometric covariates. Methods Height, weight, waist circumference and body composition were measured in a diverse population of 644 NYC middle school students (mean ± SD age 12.7 ± 0.9 years; 46% male; 38% Hispanic, 17% East Asian, 15% South Asian, 13.5% African American, 8.5% Caucasian, 8% other) during the fall and spring semesters. Year 1 participants ( n = 322) were controls. Experimental participants (year 2, n = 469) received a 12‐session classroom‐based health and nutrition educational programme with an optional exercise intervention. Results Groups were demographically and anthropometrically similar. The intervention resulted in significant reductions in indices of adiposity (ΔBMI z ‐scores [−0.035 ± 0.014; p = 0.01], Δ% body fat [−0.5 ± 0.2; p < 0.0001] and Δwaist circumference [−0.73 ± 0.30 cm; p < 0.0001]). Intervention effects were greater ( p = 0.01) in men (ΔBMI z ‐score = −0.052 ± 0.015) versus women (0.022 ± 0.018), participants who were obese (ΔBMI z ‐score −0.083 ± 0.022 kg m −2 ) versus lean (−0.0097 ± 0.020 kg m −2 ) and South Asians (Δ% body fat −1.03 ± 0.35) versus total (−0.49 ± 0.20%) participants ( p = 0.005). Conclusion A 4‐month school‐based health intervention was effective in decreasing measures of adiposity in middle school students, particularly in men, participants who were obese and South Asians.

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Interventions to prevent global childhood overweight and obesity: a systematic review

Bleich

Vercammen

Zatz

et al. 2018

The Lancet Diabetes & Endocrinology

306

301

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The Role of Ethnicity in School‐Based Obesity Intervention for School‐Aged Children: A Pilot Evaluation

Abstract: The study demonstrates the intervention is effective, but that the effectiveness varies across ethnicity. Interventions can be made more efficient and cost-effective by targeting youth of a common ethnicity that has shown empirical responsiveness to certain program elements.

Cited by 7 publications

References 56 publications

Methodological approaches to the design and analysis of nonrandomized intervention studies for the prevention of child and adolescent obesity

Methodological approaches to the design and analysis of nonrandomized intervention studies for the prevention of child and adolescent obesity

Demographics and anthropometrics impact benefits of health intervention: data from the Reduce Obesity and Diabetes Project

Interventions to prevent global childhood overweight and obesity: a systematic review

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