Association between screen time and dietary patterns and overweight/obesity among adolescents

Pinho, Maria Gabriela Matias de; Adami, Fernando; Benedet, Jucemar; Vasconcelos, Francisco de Assis Guedes de

doi:10.1590/1678-98652017000300010

Cited by 32 publications

(27 citation statements)

References 39 publications

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“…The components of these patterns are traditional foods eaten in Brazilian meals (“DP I” in lunch and/or dinner; “DP IV” in breakfast and/or snacks between meals). These patterns were also identified in cross-sectional studies conducted with children [ 22 , 24 , 25 ], adolescents [ 21 , 22 , 26 ], and adults [ 55 ] from different regions in Brazil.…”

Section: Discussionmentioning

confidence: 63%

“…Second, as for all other dietary assessment studies, the self-reported food recall may potentially be subject to misreporting. The PDFQ-3 used in this study proved to be a valid instrument [ 28 ] and has been used to assess dietary patterns and behaviors [ 25 , 26 , 58 ]. The PDFQ-3 was designed to avoid the difficulties associated with children’s assessments of portion size, and to simplify the memory task by prompting only the relevant food items eaten on the previous day.…”

Section: Discussionmentioning

confidence: 99%

“…DPs have been identified in some cross-sectional studies in Brazilian children and adolescents [ 21 , 22 , 23 , 24 , 25 , 26 ]. None of these studies has investigated the stability of DPs specifically from childhood to adolescence, and their relationship with changes in body mass index.…”

Section: Introductionmentioning

confidence: 99%

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Changes in Dietary Patterns from Childhood to Adolescence and Associated Body Adiposity Status

et al. 2017

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The aims of this study were to identify cross-sectional dietary patterns (DPs) in a representative sample of 7–10-year-old schoolchildren, to examine how scores for these DPs tracked over a time period of five years (from age 7–10 years to 12–15 years), and to investigate longitudinal associations between changes in DPs scores and changes in BMI (Body Mass Index) z-scores. Children aged 7–10-years were examined in 2007 (n = 1158) and a subset of the sample participated in a follow-up in 2012 (n = 458). Factor analysis (FA) was applied to derive DPs at baseline. The change in DP from childhood to adolescence was analyzed by comparing factor scores using the complete cases, in which factor loadings were the ones evaluated at baseline. Associations of BMI change with DP change were assessed by multivariate linear regression. At baseline, four DP were identified that explained 47.9% of the food intake variance. On average, the factor scores of “DP II” (salty snacks, French fries, fast-food, sugary beverages) decreased in follow-up, while no changes were observed for “DP I” (rice, cooked beans, beef/poultry, leafy vegetables), “DP III” (fruits, cooked and leafy vegetables, fruit juices, pasta, milk, cheese), and “DP IV” (milk, coffee with milk, cheese, breads/biscuits). No significant linear association was shown between changes in BMI z-scores and changes in DP scores from childhood to adolescence. In conclusion, three out of four DP scores identified at baseline tracked slightly in adolescence.

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Section: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

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Changes in Dietary Patterns from Childhood to Adolescence and Associated Body Adiposity Status

et al. 2017

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“…The sampling methods have been described in greater detail elsewhere. 18,19 Based on the prevalence rates of the exposure variables and of each outcome, and considering a study power of 80%, a 95% confidence level, a 10% sample size margin to allow for confounding factors and a design effect of 1.8, this study had sufficient power to: a) detect that prevalence ratios of 0.82 to 0.85 would be protective factors and 1.18 to 1.23 would be risk factors for overweight/obesity; and b) detect that prevalence ratios of 0.50 to 0.60 would be protective factors and 1.68 to 2.01 would be risk factors for abdominal obesity.…”

Section: Study Design and Participantsmentioning

confidence: 99%

Body mass index and association with use of and distance from places for physical activity and active leisure among schoolchildren in Brazil. Cross-sectional study

et al. 2018

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“…In the current study, one more element was added to the postpartum women's lifestyle, namely the average hours of screen time (e.g., TV, smartphone, tablet, etc.) to measure the respondents' social media use [67][68][69]. For measuring average screen time, we asked three questions.…”

Section: Lifestyle (First Mediator)mentioning

confidence: 99%

Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach

Jenatabadi

Radzi

Samsudin

2020

IJERPH

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As postpartum obesity is becoming a global public health challenge, there is a need to apply postpartum obesity modeling to determine the indicators of postpartum obesity using an appropriate statistical technique. This research comprised two phases, namely: i) development of a previously created postpartum obesity modeling; ii) construction of a statistical comparison model and introduction of a better estimator for the research framework. The research model displayed the associations and interactions between the variables that were analyzed using the Structural Equation Modeling (SEM) method to determine the body mass index (BMI) levels related to postpartum obesity. The most significant correlations obtained were between BMI and other substantial variables in the SEM analysis. The research framework included two categories of data related to postpartum women: living in urban and rural areas in Iran. The SEM output with the Bayesian estimator was 81.1%, with variations in the postpartum women’s BMI, which is related to their demographics, lifestyle, food intake, and mental health. Meanwhile, the variation based on SEM with partial least squares estimator was equal to 70.2%, and SEM with a maximum likelihood estimator was equal to 76.8%. On the other hand, the output of the root mean square error (RMSE), mean absolute error (MSE) and mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators. Thus, the predicted values of the SEM with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square. In conclusion, the higher values of R-square and lower values of MPE, RMSE, and MSE will produce better goodness of fit for SEM with Bayesian estimators.

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Association between screen time and dietary patterns and overweight/obesity among adolescents

Cited by 32 publications

References 39 publications

Changes in Dietary Patterns from Childhood to Adolescence and Associated Body Adiposity Status

Changes in Dietary Patterns from Childhood to Adolescence and Associated Body Adiposity Status

Body mass index and association with use of and distance from places for physical activity and active leisure among schoolchildren in Brazil. Cross-sectional study

Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach

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