What Psychological, Physical, Lifestyle, and Knowledge Factors Are Associated With Excess or Inadequate Weight Gain During Pregnancy? A Cross-Sectional Survey

McDonald, Sarah D.; Park, Christina K.; Timm, Valerie; Neupane, Binod; Beyene, Joseph

doi:10.1016/s1701-2163(15)30757-x

Cited by 26 publications

(45 citation statements)

References 30 publications

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“…In the analysis of excessive versus appropriate weight gain, not only were the conclusions about the significance of the predictors the same, but the estimates of effect sizes and P-values were also very similar in the multinomial analysis we performed here and in separate binomial analyses reported earlier, 13 although the multinomial approach produced a smaller P-value (or narrower CI interval) for one predictor. However, the set of predictors differed, and the estimated effect sizes and P-values for the common predictors also changed from the previous analysis for inadequate weight gain, where the subgroup size for separate binomial analysis was small and we derived the final set of predictors by variable selection.…”

Section: Discussionsupporting

confidence: 65%

“…As previously reported, 13 response data were available from 312 women, of whom 68(21.8%), 54(17.3%), and 190 (60.9%) inadequately, appropriately, or excessively gained weight, respectively. Descriptive statistics, count (%) for categorical and mean (standard deviation) for continuous exposure variables of interest in each of the three GWG response categories and their joint association P-values with trinomial GWG responses obtained using univariate multinomial approach are displayed in Table 1.…”

Section: Resultsmentioning

confidence: 98%

“…The estimates of OR and their 95% CIs and P-values of associations for the final set of predictors with excessive and inadequate weight gain from the multinomial analysis and separate binomial analyses (copied from a previously reported paper 13 with permission from the publisher) are presented in Table 2. In multinomial analysis, the predictors of excessive weight gain were: the neurotic score per unit increase (OR 1.24, 95% CI 1.10, 1.41; P-value =0.001), excessively planning weight gain compared with appropriately planning (OR 12.47, 95% CI 4.57, 34.03; P-value ,0.001), and watching television compared with not watching television (OR 2.38, 95% CI 1.11, 5.11; P-value =0.027).…”

Section: Resultsmentioning

confidence: 99%

“…For each woman, the response variable was created as three categories of inadequate, appropriate, or excessive GWG if her average GWG per week was below, within, or above, respectively, the range of GWG recommended for her BMI category by the guidelines. The details of the study design and list of outcomes and potential exposure variables and their categorization schemes can be found in McDonald et al 13 …”

Section: Multinomial Logistic Regression Modelmentioning

confidence: 99%

“…Nevertheless, there have been limited data 6,7 generated from a multinomial approach in the gestational weight gain (GWG) literature, because typically GWG is examined as excess ("above") or inadequate ("below") weight gain compared with adequate ("within" the recommended range of the clinical guidelines) in a binomial fashion. [8][9][10][11][12] In a study designed to assess the associations between clinical, psychological, lifestyle knowledge, and demographic factors and excess or inadequate GWG compared with appropriate gain, 13 we used the binomial logistic regression approach for each of the binomial outcomes of excess versus appropriate, and inadequate versus appropriate weight gain, separately. In this study, our objective was to identify the determinants of the trinomial response categories of inadequate, appropriate, and excessive weight gain simultaneously by fitting a single multinomial model to the data.…”

Section: Introductionmentioning

confidence: 99%

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Identifying determinants and estimating the risk of inadequate and excess gestational weight gain using a multinomial logistic regression model

Neupane¹,

McDonald²,

Beyene

2014

OAMS

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When there are three or more nominal categories of a response variable, the binomial logistic regression approach is widely used to model the relationships of exposure variables with different binomial responses one at a time. However, some of the separate binomial comparisons would be redundant. This approach is also suboptimal because of the loss of information that will result when only a subset of the data is analyzed at a time and the multiple testing problems arising from analysis of several pairs of categories. These drawbacks of fitting separate binomial regression models to a multicategory nominal outcome variable can be overcome using a single multinomial regression modeling framework. In this study, we compared the results using a multinomial regression with the separate two binomial regressions to determine factors associated with excess and inadequate weight gain during pregnancy in a data set from a gestational weight gain study involving a cross-sectional survey of 312 women with singleton pregnancies. We found that both approaches identified the same set of predictors, ie, higher neuroticism, planning to gain more weight than the recommended level, and bedtime television watching, with P-values #0.05 of the excessive (versus appropriate) weight gain, for which the subgroup size was moderate. The final list of significant predictors of inadequate (versus appropriate) weight gain identified by multinomial regression were planned weight gain below the recommended range, overweight or obese women, and bedtime television watching, while those by a separate binomial approach were self-efficacy towards achieving healthy weight, lack of weight satisfaction, and bedtime television watching, which differed between the two approaches where the final set of predictors were identified by a variable selection process and the comparisons were made in a small subgroup. A multinomial approach is a useful analytical framework that researchers may consider when they have multinomial response categories because this approach allows nonredundant comparisons to be made, avoiding the need to analyze a subset of the data one at a time and also allows for risk prediction of multinomial categories from a well validated multinomial model, and will not lead to multiple testing problems.

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Section: Discussionsupporting

confidence: 65%

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Multinomial Logistic Regression Modelmentioning

confidence: 99%