Research Article
Managing Outliers in Adolescent Food Frequency Questionnaire Data

https://doi.org/10.1016/j.jneb.2020.08.002Get rights and content

Abstract

Objective

The goal of this study was to explore the impact of 5 decision rules for removing outliers from adolescent food frequency questionnaire (FFQ) data.

Design

This secondary analysis used baseline and 3-month data from a weight loss intervention clinical trial.

Participants

African American adolescents (n = 181) were recruited from outpatient clinics and community health fairs.

Variables Measured

Data collected included self-reported FFQ and mediators of weight (food addiction, depressive symptoms, and relative reinforcing value of food), caregiver-reported executive functioning, and objectively measured weight status (percentage overweight).

Analysis

Descriptive statistics examined patterns in study variables at baseline and follow-up. Correlational analyses explored the relationships between FFQ data and key study variables at baseline and follow-up.

Results

Compared with not removing outliers, using decision rules reduced the number of cases and restricted the range of data. The magnitude of baseline FFQ-mediator relationships was attenuated under all decision rules but varied (increasing, decreasing, and reversing direction) at follow-up. Decision rule use increased the magnitude of change in FFQ estimated energy intake and significantly strengthened its relationship with weight change under 2 fixed range decision rules.

Conclusions and Implications

Results suggest careful evaluation of outliers and testing and reporting the effects of different outlier decision rules through sensitivity analyses.

Section snippets

INTRODUCTION

Childhood obesity is a serious public health problem in the US. The National Health and Nutrition Examination Survey indicates a steady increase in the prevalence of childhood overweight and obesity over the past 2 decades.1 Multiple interventions have been developed in response to this alarming trend. Studies evaluating the effects of these interventions have used a variety of different measures to assess key variables—primarily food intake.

The assessment of food intake has emerged as a major

METHODS

The present study was a secondary analysis of data from a broader behavioral intervention study to promote weight loss among African American adolescents with primary obesity. Full details of the trial methodology and enrollment/recruitment are reported elsewhere.17 In brief, the parent study was the Sequential Multiple Assignment Randomized Trial18 to develop an adaptive intervention for adolescent weight loss. Adolescents were first randomized to either home- or office-based weight-loss

RESULTS

Participants were 181 African American adolescents (Table 2). Summaries of energy intake at both baseline and follow-up, including outliers and under the 5 decision rules, are presented in Table 3. Compared with no removal of outliers, the number of cases removed ranged from 7 (3.9%) to 22 (12.2%) at baseline and 5 (3.2%) to 23 (14.6%) at follow-up. As expected, in all instances, the SD was reduced, and the range of values narrowed when outlier decision rules were applied (compared with no

DISCUSSION

Guidance for identifying outliers and the effect of outlier removal in child and adolescent FFQ data has received little attention in the literature. In this study, the application of outlier decision rules culled from the extant literature on outliers in adult FFQ data had notable effects (compared with no removal of outliers) on the results and the conclusions drawn from an adolescent sample. The use of outlier decision rules altered the number of cases available for analysis but also change

IMPLICATIONS FOR RESEARCH AND PRACTICE

This study suggests a process for the identification and removal of outliers. These results suggest that investigators examine different decision rules to understand how the removal of outliers affects study findings. Currently, there is insufficient information to suggest a single optimal outlier removal strategy. More research is needed to understand how the removal of dietary intake outliers affect weight and psychosocial outcomes. In the meantime, a good strategy is to carefully evaluate

ACKNOWLEDGMENTS

This work was supported by the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant no. U01HL097889; ClinicalTrials.gov Identifier no. NCT01350531. The National Institutes of Health had no role in the study design; data collection, analysis, or interpretation; writing of this manuscript; or the decision to submit the manuscript.

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    Conflict of Interest Disclosure: The authors have not stated any conflicts of interest.

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