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Introdução: O monitoramento do ganho de peso gestacional é de extrema importância nos cuidados pré-natais, pois pode evitar diversos desfechos desfavoráveis tanto para mãe quanto para o bebê. A maioria dos países utiliza algum tipo de referência para o acompanhamento do peso gestacional. Essas referências, muitas vezes, são baseadas em suas próprias populações ou em populações de outros países. Considerando-se que características populacionais variam de acordo com etnia, localização geográfica, hábitos alimentares, medidas antropométricas e condições socioeconômicas, dentre outros fatores, as recomendações baseadas em populações específicas são preferíveis para monitorar o peso gestacional. Várias metodologias são utilizadas para a construção de referências de peso gestacional. A OMS (Organização Mundial da Saúde) recomenda utilizar estudos longitudinais, a partir de populações selecionadas com baixa prevalência de complicações maternas e fetais. No Brasil, as referências utilizadas para peso gestacional são baseadas em duas populações internacionais; essas populações não utilizaram estudos longitudinais para gerar as referências, o que faz com que o Brasil necessite desenvolver sua própria abordagem para o monitoramento do peso gestacional. Objetivo: Construir uma curva de peso gestacional a partir de uma coorte de gestantes brasileiras, utilizando a metodologia estatística recomendada pela OMS para a construção das curvas padrão de crescimento infantil. Método: Dados do Estudo Brasileiro de Diabetes Gestacional (EBDG), estudo multicêntrico que reuniu gestantes de seis capitais brasileiras (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus e Fortaleza), foram utilizados para a construção da curva. Foram selecionadas 2.103 gestantes eutróficas, de acordo com IOM (Institute of Medicine) (2009), com bons desfechos gestacionais, ou seja, foram excluídas gestantes com diabetes mellitus gestacional, com distúrbios hipertensivos, com gestações múltiplas e com partos prematuros (<37 semanas de gestação); excluíram-se também casos de gestantes com recém-nascidos de baixo peso ao nascer (BPN) ou com recém-nascidos grandes para a idade gestacional (GIG) e recém-nascidos pequenos para a idade gestacional (PIG), bem como casos de macrossomia. Dentre essas gestantes eutróficas, foram sorteadas 918, que irão fazer parte da validação da curva. Para a construção da curva, ficaram 1.179 gestantes eutróficas com bons desfechos gestacionais. Para o ajuste, foi utilizado o método GAMLSS (Modelos Aditivos Generalizados de Localização, Escala e Forma) do software R, que estimou os percentis 3, 5, 10, 25, 75, 90, 95 e 97. Após algumas exclusões, a segunda etapa do trabalho utilizou as 918 sorteadas (gestantes eutróficas com bons desfechos) e mais 901 gestantes eutróficas que tinham pelo menos um dos desfechos gestacionais indesejáveis descritos acima (com exceção de gestações múltiplas e diabetes mellitus), totalizando 1.817 gestantes para o grupo de validação. Com os percentis estimados pela curva de peso gestacional, foram definidos pontos de corte que determinaram os fatores de risco para os desfechos de interesse. A influência dos fatores de risco sobre os desfechos foi medida através do risco relativo (RR) e seus respectivos intervalos, com 95% de confiança, estimados através de regressão de Poisson com variância robusta. Os riscos relativos e seus respectivos intervalos de 95% de confiança foram estimados para a exposição em algum momento da gestação e para a exposição em algum momento dentro de cada trimestre. Os percentis foram avaliados sem ajustar para nenhum possível fator de confusão. Resultados: Após testados vários modelos GAMLSS, o que melhor ajustou os dados foi o que utilizou a família de distribuição BCPE (Box Cox de Potência Exponencial), com suavizador pb (B-splines), utilizando dois parâmetros e . O percentil 25 estimado foi capaz de predizer baixo peso ao nascer, prematuridade e PIG; já o percentil 75 pôde ser utilizado como preditor de distúrbios hipertensivos, macrossomia e GIG. Conclusão: o modelo obtido para a construção da curva de peso gestacional indicou que a relação entre peso gestacional e idade gestacional não é linear. A flexibilidade da metodologia estatística utilizada no estudo é suficiente para que possa ser aplicada utilizando-se o Índice de Massa corporal (IMC) em vez de peso gestacional. Essa metodologia também apresenta uma série de vantagens no que diz respeito às suas opções de modelagem. As curvas de percentis ajustadas foram eficientes em predizer desfechos gestacionais adversos. A metodologia aplicada nesta tese pode ser replicada para todas as categorias de IMC pré-gestacional. / Introduction: Monitoring gestational weight gain is extremely important in prenatal care, as it can avoid a series of unfavorable outcomes both for the mother and for the baby. Most countries use some kind of reference to follow up gestational weight. These references are often based in their own populations or in populations from other countries. Considering that population characteristics vary according to ethnics, geographical location, eating habits, anthropometric measures and socio-economic conditions, among other factors, recommendations based on specific populations are preferable to measure gestational weight. Several methodologies are used in the construction of references of gestational weight. WHO (World Health Organization) recommends using longitudinal studies based on selected populations with low prevalence of maternal and fetal complications. In Brazil the references used for gestational weight are based in two international populations which did not use longitudinal studies to generate the references, which brings to Brazil the need to develop its own approach to monitor gestational weight. Purpose: Build a gestational weight curve based on a Brazilian pregnant women cohort using the statistical methodology recommended by WHO to build standard curves of child growth. Method: Data from the Brazilian Gestational Diabetes Study, multicentric study which gathered women from six Brazilian capital cities (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus and Fortaleza), was used to build the curve. 2,103 eutrophic pregnant women were selected, according to the IOM (Institute of Medicine) (2009), with good gestational outcomes, that is, there was an exclusion of pregnant women with: gestational diabetes mellitus, hypertensive disorders, multiple pregnancies, preterm deliveries (less than 37 weeks), newborns with low birth weight (LBW), large for gestational age newborns (LGA), small for gestational age newborns (SGA), and macrosomia. From these eutrophic pregnant women, 918 were drawn, who will be part of the validation curve. To build the curve 1,179 eutrophic pregnant women with good gestational outcomes remained. The method GAMLSS (Generalized Additive Models of Location, Scale and Shape) from the software R was used for adjustment, which estimated the percentiles 3, 5, 10, 25, 75, 90, 95 and 97. After some exclusions, the second stage of the work used the 918 drawn eutrophic pregnant women with good outcomes and other 901 eutrophic pregnant women who had at least one unwanted gestational outcomes described above (except for multiple pregnancies and diabetes mellitus), in a total of 1.817 pregnant women for the validation group. With the percentiles estimated by the gestational weight curve, cutoff points were defined which determined the risk factors for the interest outcomes. The influence of risk factors on the outcomes was measured through the relative risk (RR) and its respective intervals with 95% confidence, estimated by Poisson regression with strong variance. The relative risks and their respective intervals of 95% confidence for exhibition at some point during pregnancy and for exhibition at some moment in each trimester. The percentiles were assessed with no adjustment for any possible confounding factor. Results: After testing several GAMLSS methods, the one which best adjusted the data was the one which used the distribution family BCPE (Box Cox of Exponential Power), with pb smoothing (B-splines), using two parameters and . The percentil 25 estimated was able to predict low birth weight, prematurity and SGA, whereas the percentil 75 can be used as a predictor of hypertensive disorders, macrosomia and LGA. Conclusion: the model obtained for the gestational weight curve construction indicated that the relationship between gestational weight and gestational age is not linear. The flexibility of the statistical methodology used in the study is sufficient to be applied using BMI instead of gestational weight. This methodology also presents a series of advantages concerning its modeling options. The adjusted percentile curves were efficient to predict adverse gestational outcomes. The methodology applied in this thesis can be replicated for all pre gestational BMI categories.

Introdução: O monitoramento do ganho de peso gestacional é de extrema importância nos cuidados pré-natais, pois pode evitar diversos desfechos desfavoráveis tanto para mãe quanto para o bebê. A maioria dos países utiliza algum tipo de referência para o acompanhamento do peso gestacional. Essas referências, muitas vezes, são baseadas em suas próprias populações ou em populações de outros países. Considerando-se que características populacionais variam de acordo com etnia, localização geográfica, hábitos alimentares, medidas antropométricas e condições socioeconômicas, dentre outros fatores, as recomendações baseadas em populações específicas são preferíveis para monitorar o peso gestacional. Várias metodologias são utilizadas para a construção de referências de peso gestacional. A OMS (Organização Mundial da Saúde) recomenda utilizar estudos longitudinais, a partir de populações selecionadas com baixa prevalência de complicações maternas e fetais. No Brasil, as referências utilizadas para peso gestacional são baseadas em duas populações internacionais; essas populações não utilizaram estudos longitudinais para gerar as referências, o que faz com que o Brasil necessite desenvolver sua própria abordagem para o monitoramento do peso gestacional. Objetivo: Construir uma curva de peso gestacional a partir de uma coorte de gestantes brasileiras, utilizando a metodologia estatística recomendada pela OMS para a construção das curvas padrão de crescimento infantil. Método: Dados do Estudo Brasileiro de Diabetes Gestacional (EBDG), estudo multicêntrico que reuniu gestantes de seis capitais brasileiras (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus e Fortaleza), foram utilizados para a construção da curva. Foram selecionadas 2.103 gestantes eutróficas, de acordo com IOM (Institute of Medicine) (2009), com bons desfechos gestacionais, ou seja, foram excluídas gestantes com diabetes mellitus gestacional, com distúrbios hipertensivos, com gestações múltiplas e com partos prematuros (<37 semanas de gestação); excluíram-se também casos de gestantes com recém-nascidos de baixo peso ao nascer (BPN) ou com recém-nascidos grandes para a idade gestacional (GIG) e recém-nascidos pequenos para a idade gestacional (PIG), bem como casos de macrossomia. Dentre essas gestantes eutróficas, foram sorteadas 918, que irão fazer parte da validação da curva. Para a construção da curva, ficaram 1.179 gestantes eutróficas com bons desfechos gestacionais. Para o ajuste, foi utilizado o método GAMLSS (Modelos Aditivos Generalizados de Localização, Escala e Forma) do software R, que estimou os percentis 3, 5, 10, 25, 75, 90, 95 e 97. Após algumas exclusões, a segunda etapa do trabalho utilizou as 918 sorteadas (gestantes eutróficas com bons desfechos) e mais 901 gestantes eutróficas que tinham pelo menos um dos desfechos gestacionais indesejáveis descritos acima (com exceção de gestações múltiplas e diabetes mellitus), totalizando 1.817 gestantes para o grupo de validação. Com os percentis estimados pela curva de peso gestacional, foram definidos pontos de corte que determinaram os fatores de risco para os desfechos de interesse. A influência dos fatores de risco sobre os desfechos foi medida através do risco relativo (RR) e seus respectivos intervalos, com 95% de confiança, estimados através de regressão de Poisson com variância robusta. Os riscos relativos e seus respectivos intervalos de 95% de confiança foram estimados para a exposição em algum momento da gestação e para a exposição em algum momento dentro de cada trimestre. Os percentis foram avaliados sem ajustar para nenhum possível fator de confusão. Resultados: Após testados vários modelos GAMLSS, o que melhor ajustou os dados foi o que utilizou a família de distribuição BCPE (Box Cox de Potência Exponencial), com suavizador pb (B-splines), utilizando dois parâmetros e . O percentil 25 estimado foi capaz de predizer baixo peso ao nascer, prematuridade e PIG; já o percentil 75 pôde ser utilizado como preditor de distúrbios hipertensivos, macrossomia e GIG. Conclusão: o modelo obtido para a construção da curva de peso gestacional indicou que a relação entre peso gestacional e idade gestacional não é linear. A flexibilidade da metodologia estatística utilizada no estudo é suficiente para que possa ser aplicada utilizando-se o Índice de Massa corporal (IMC) em vez de peso gestacional. Essa metodologia também apresenta uma série de vantagens no que diz respeito às suas opções de modelagem. As curvas de percentis ajustadas foram eficientes em predizer desfechos gestacionais adversos. A metodologia aplicada nesta tese pode ser replicada para todas as categorias de IMC pré-gestacional. / Introduction: Monitoring gestational weight gain is extremely important in prenatal care, as it can avoid a series of unfavorable outcomes both for the mother and for the baby. Most countries use some kind of reference to follow up gestational weight. These references are often based in their own populations or in populations from other countries. Considering that population characteristics vary according to ethnics, geographical location, eating habits, anthropometric measures and socio-economic conditions, among other factors, recommendations based on specific populations are preferable to measure gestational weight. Several methodologies are used in the construction of references of gestational weight. WHO (World Health Organization) recommends using longitudinal studies based on selected populations with low prevalence of maternal and fetal complications. In Brazil the references used for gestational weight are based in two international populations which did not use longitudinal studies to generate the references, which brings to Brazil the need to develop its own approach to monitor gestational weight. Purpose: Build a gestational weight curve based on a Brazilian pregnant women cohort using the statistical methodology recommended by WHO to build standard curves of child growth. Method: Data from the Brazilian Gestational Diabetes Study, multicentric study which gathered women from six Brazilian capital cities (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus and Fortaleza), was used to build the curve. 2,103 eutrophic pregnant women were selected, according to the IOM (Institute of Medicine) (2009), with good gestational outcomes, that is, there was an exclusion of pregnant women with: gestational diabetes mellitus, hypertensive disorders, multiple pregnancies, preterm deliveries (less than 37 weeks), newborns with low birth weight (LBW), large for gestational age newborns (LGA), small for gestational age newborns (SGA), and macrosomia. From these eutrophic pregnant women, 918 were drawn, who will be part of the validation curve. To build the curve 1,179 eutrophic pregnant women with good gestational outcomes remained. The method GAMLSS (Generalized Additive Models of Location, Scale and Shape) from the software R was used for adjustment, which estimated the percentiles 3, 5, 10, 25, 75, 90, 95 and 97. After some exclusions, the second stage of the work used the 918 drawn eutrophic pregnant women with good outcomes and other 901 eutrophic pregnant women who had at least one unwanted gestational outcomes described above (except for multiple pregnancies and diabetes mellitus), in a total of 1.817 pregnant women for the validation group. With the percentiles estimated by the gestational weight curve, cutoff points were defined which determined the risk factors for the interest outcomes. The influence of risk factors on the outcomes was measured through the relative risk (RR) and its respective intervals with 95% confidence, estimated by Poisson regression with strong variance. The relative risks and their respective intervals of 95% confidence for exhibition at some point during pregnancy and for exhibition at some moment in each trimester. The percentiles were assessed with no adjustment for any possible confounding factor. Results: After testing several GAMLSS methods, the one which best adjusted the data was the one which used the distribution family BCPE (Box Cox of Exponential Power), with pb smoothing (B-splines), using two parameters and . The percentil 25 estimated was able to predict low birth weight, prematurity and SGA, whereas the percentil 75 can be used as a predictor of hypertensive disorders, macrosomia and LGA. Conclusion: the model obtained for the gestational weight curve construction indicated that the relationship between gestational weight and gestational age is not linear. The flexibility of the statistical methodology used in the study is sufficient to be applied using BMI instead of gestational weight. This methodology also presents a series of advantages concerning its modeling options. The adjusted percentile curves were efficient to predict adverse gestational outcomes. The methodology applied in this thesis can be replicated for all pre gestational BMI categories.

Introdução: O monitoramento do ganho de peso gestacional é de extrema importância nos cuidados pré-natais, pois pode evitar diversos desfechos desfavoráveis tanto para mãe quanto para o bebê. A maioria dos países utiliza algum tipo de referência para o acompanhamento do peso gestacional. Essas referências, muitas vezes, são baseadas em suas próprias populações ou em populações de outros países. Considerando-se que características populacionais variam de acordo com etnia, localização geográfica, hábitos alimentares, medidas antropométricas e condições socioeconômicas, dentre outros fatores, as recomendações baseadas em populações específicas são preferíveis para monitorar o peso gestacional. Várias metodologias são utilizadas para a construção de referências de peso gestacional. A OMS (Organização Mundial da Saúde) recomenda utilizar estudos longitudinais, a partir de populações selecionadas com baixa prevalência de complicações maternas e fetais. No Brasil, as referências utilizadas para peso gestacional são baseadas em duas populações internacionais; essas populações não utilizaram estudos longitudinais para gerar as referências, o que faz com que o Brasil necessite desenvolver sua própria abordagem para o monitoramento do peso gestacional. Objetivo: Construir uma curva de peso gestacional a partir de uma coorte de gestantes brasileiras, utilizando a metodologia estatística recomendada pela OMS para a construção das curvas padrão de crescimento infantil. Método: Dados do Estudo Brasileiro de Diabetes Gestacional (EBDG), estudo multicêntrico que reuniu gestantes de seis capitais brasileiras (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus e Fortaleza), foram utilizados para a construção da curva. Foram selecionadas 2.103 gestantes eutróficas, de acordo com IOM (Institute of Medicine) (2009), com bons desfechos gestacionais, ou seja, foram excluídas gestantes com diabetes mellitus gestacional, com distúrbios hipertensivos, com gestações múltiplas e com partos prematuros (<37 semanas de gestação); excluíram-se também casos de gestantes com recém-nascidos de baixo peso ao nascer (BPN) ou com recém-nascidos grandes para a idade gestacional (GIG) e recém-nascidos pequenos para a idade gestacional (PIG), bem como casos de macrossomia. Dentre essas gestantes eutróficas, foram sorteadas 918, que irão fazer parte da validação da curva. Para a construção da curva, ficaram 1.179 gestantes eutróficas com bons desfechos gestacionais. Para o ajuste, foi utilizado o método GAMLSS (Modelos Aditivos Generalizados de Localização, Escala e Forma) do software R, que estimou os percentis 3, 5, 10, 25, 75, 90, 95 e 97. Após algumas exclusões, a segunda etapa do trabalho utilizou as 918 sorteadas (gestantes eutróficas com bons desfechos) e mais 901 gestantes eutróficas que tinham pelo menos um dos desfechos gestacionais indesejáveis descritos acima (com exceção de gestações múltiplas e diabetes mellitus), totalizando 1.817 gestantes para o grupo de validação. Com os percentis estimados pela curva de peso gestacional, foram definidos pontos de corte que determinaram os fatores de risco para os desfechos de interesse. A influência dos fatores de risco sobre os desfechos foi medida através do risco relativo (RR) e seus respectivos intervalos, com 95% de confiança, estimados através de regressão de Poisson com variância robusta. Os riscos relativos e seus respectivos intervalos de 95% de confiança foram estimados para a exposição em algum momento da gestação e para a exposição em algum momento dentro de cada trimestre. Os percentis foram avaliados sem ajustar para nenhum possível fator de confusão. Resultados: Após testados vários modelos GAMLSS, o que melhor ajustou os dados foi o que utilizou a família de distribuição BCPE (Box Cox de Potência Exponencial), com suavizador pb (B-splines), utilizando dois parâmetros e . O percentil 25 estimado foi capaz de predizer baixo peso ao nascer, prematuridade e PIG; já o percentil 75 pôde ser utilizado como preditor de distúrbios hipertensivos, macrossomia e GIG. Conclusão: o modelo obtido para a construção da curva de peso gestacional indicou que a relação entre peso gestacional e idade gestacional não é linear. A flexibilidade da metodologia estatística utilizada no estudo é suficiente para que possa ser aplicada utilizando-se o Índice de Massa corporal (IMC) em vez de peso gestacional. Essa metodologia também apresenta uma série de vantagens no que diz respeito às suas opções de modelagem. As curvas de percentis ajustadas foram eficientes em predizer desfechos gestacionais adversos. A metodologia aplicada nesta tese pode ser replicada para todas as categorias de IMC pré-gestacional. / Introduction: Monitoring gestational weight gain is extremely important in prenatal care, as it can avoid a series of unfavorable outcomes both for the mother and for the baby. Most countries use some kind of reference to follow up gestational weight. These references are often based in their own populations or in populations from other countries. Considering that population characteristics vary according to ethnics, geographical location, eating habits, anthropometric measures and socio-economic conditions, among other factors, recommendations based on specific populations are preferable to measure gestational weight. Several methodologies are used in the construction of references of gestational weight. WHO (World Health Organization) recommends using longitudinal studies based on selected populations with low prevalence of maternal and fetal complications. In Brazil the references used for gestational weight are based in two international populations which did not use longitudinal studies to generate the references, which brings to Brazil the need to develop its own approach to monitor gestational weight. Purpose: Build a gestational weight curve based on a Brazilian pregnant women cohort using the statistical methodology recommended by WHO to build standard curves of child growth. Method: Data from the Brazilian Gestational Diabetes Study, multicentric study which gathered women from six Brazilian capital cities (Porto Alegre, Rio de Janeiro, São Paulo, Salvador, Manaus and Fortaleza), was used to build the curve. 2,103 eutrophic pregnant women were selected, according to the IOM (Institute of Medicine) (2009), with good gestational outcomes, that is, there was an exclusion of pregnant women with: gestational diabetes mellitus, hypertensive disorders, multiple pregnancies, preterm deliveries (less than 37 weeks), newborns with low birth weight (LBW), large for gestational age newborns (LGA), small for gestational age newborns (SGA), and macrosomia. From these eutrophic pregnant women, 918 were drawn, who will be part of the validation curve. To build the curve 1,179 eutrophic pregnant women with good gestational outcomes remained. The method GAMLSS (Generalized Additive Models of Location, Scale and Shape) from the software R was used for adjustment, which estimated the percentiles 3, 5, 10, 25, 75, 90, 95 and 97. After some exclusions, the second stage of the work used the 918 drawn eutrophic pregnant women with good outcomes and other 901 eutrophic pregnant women who had at least one unwanted gestational outcomes described above (except for multiple pregnancies and diabetes mellitus), in a total of 1.817 pregnant women for the validation group. With the percentiles estimated by the gestational weight curve, cutoff points were defined which determined the risk factors for the interest outcomes. The influence of risk factors on the outcomes was measured through the relative risk (RR) and its respective intervals with 95% confidence, estimated by Poisson regression with strong variance. The relative risks and their respective intervals of 95% confidence for exhibition at some point during pregnancy and for exhibition at some moment in each trimester. The percentiles were assessed with no adjustment for any possible confounding factor. Results: After testing several GAMLSS methods, the one which best adjusted the data was the one which used the distribution family BCPE (Box Cox of Exponential Power), with pb smoothing (B-splines), using two parameters and . The percentil 25 estimated was able to predict low birth weight, prematurity and SGA, whereas the percentil 75 can be used as a predictor of hypertensive disorders, macrosomia and LGA. Conclusion: the model obtained for the gestational weight curve construction indicated that the relationship between gestational weight and gestational age is not linear. The flexibility of the statistical methodology used in the study is sufficient to be applied using BMI instead of gestational weight. This methodology also presents a series of advantages concerning its modeling options. The adjusted percentile curves were efficient to predict adverse gestational outcomes. The methodology applied in this thesis can be replicated for all pre gestational BMI categories.

Few studies have measured the changes of postpartum weight retention (PWR), and none of them have assessed the effect of pregnancy on waist circumference (WC) in Taiwanese women. The primary aims of this longitudinal study were to explore the changes in body weight and WC during the first six months postpartum and to identify the explanatory factors of PWR and of WC. A theoretical framework that incorporated Bandura's social learning theory and the results of a literature review was used to guide this study. Structured questionnaires were used for data collection. Postpartum body weight and WC were measured. Data were collected from May 2011 to January 2013 and analyzed using the SPSS 19.0 and Mplus 6.12. A sample of 200 healthy postpartum women was recruited from three clinics in Tainan City, Taiwan. The mean age of the women was 31.19 years, and the majority of them were married (98.0%), primiparas (56%), had a bachelor's degree (52.5%), and planned to have this pregnancy (62.5%). The mean prepregnancy body weight was 55.84 kg, and the mean GWG was 13.76 kg. About one third of the sample gained weight exceeding the GWG recommendations of the IOM. The mean PWR decreased over time from 9.13 kg at hospitalization to 2.73 kg at 6 months postpartum. Approximately 24% of the participants still retained 5 kg or more at 6 months postpartum, and about 44% of the women had at least one kind of weight-related risk--substantial PWR, overweight, or central obesity. Age, prepregnancy BMI, parity, GWG, and place for doing the month significantly affected PWR. The final latent growth curve (LGC) model of PWR explained 91.5% and 33.9% of the variance in initial status and overall change rate in PWR. Age, prepregnancy BMI, parity, GWG, and cesarean delivery significantly affected WC, which explained 84.1% and 38.1% of the variance in initial status and change rate in WC. GWG was the most influential factor in the change rate of PWR and WC. Establishing tailored recommendations for GWG for Taiwanese women is warranted. / text

Fetal exposure to an intrauterine environment affected by maternal obesity and excessive gestational weight gain (GWG) pose several adverse short- and long-term health risks to infants. Excessive GWG and maternal obesity are of high priority to public health across many nations. Improving maternal and child health can be achieved by encouraging women to meet Institute of Medicine (IOM) weight gain recommendations, sound clinical guidance, and other forms of support. Another means of helping women adhere to weight gain guidelines is by making use of the near ubiquitous nature of mobile technology and promoting healthy pregnancies through reliable mobile health (mHealth) applications (apps). The objective of the first study of this thesis was to examine the associations between psychosocial factors and achieving IOM-recommended weight gain during pregnancy. Cross-sectional data were collected from pregnant and postpartum women who responded to a validated questionnaire, the Electronic Maternal health survey. Multiple linear logistic regression analyses were used to determine correlates associated with meeting IOM guidelines. The objective of the second study was to conduct a preliminary exploration of women’s attitudes towards an evidence-based, mHealth app, SmartMoms Canada, as a valid source of pregnancy-related information and its ability to offer physical activity, nutrition, and lifestyle support. Focus groups were organized to assess women’s attitudes towards the app and inductive thematic content analysis was utilized to interpret focus group data. It was found that self-efficacy and perceived controllability of behaviours are important factors contributing to whether women meet IOM weight gain recommendations. Further, pregnant women are quite receptive to mHealth technology and positively viewed the future prospective of SmartMoms Canada as a means of promoting overall maternal health. Combined, these findings will contribute to our understanding of how to best improve maternal-fetal health outcomes in the near future.

Background: The number of women considered overweight (OW) and obese (OB) in Canada has steadily increased over the past thirty years. In addition, there has also been a rise in the amount of weight women gain during pregnancy. Many adverse pregnancy outcomes are associated with maternal overweight, obesity and/or excessive gestational weight gain (GWG), which have been widely studied and reported. In 2009, the Institute of Medicine (IOM) developed healthy GWG guidelines, based on trial and observational evidence, that provide BMI-related weight gain targets. This evidence has shown that weight gain within the guidelines results in better health outcomes for the mother and baby, during pregnancy, as well as postpartum. Objectives: (1) To address diet quality and patterns using data collected from the Maternal Obesity Management (MOM) Intervention Trial, and (2) Develop and validate a comprehensive web-based questionnaire that can be used in a future study to examine women’s knowledge of the IOM GWG guidelines, dietary recommendations, physical activity (PA) practices, as well as other lifestyle habits. Methods: (1) Exploratory pooled analysis of dietary data from Maternal Obesity Management (MOM) trial - To identify diet quality of women who exceeded (EX) versus did not exceed (NEX) the 2009 IOM pregnancy weight gain targets. Participants (n=50) completed 7-day food records at 3 points during pregnancy (baseline (V1: 12-20 weeks), between 26-28 weeks (V2) and between, 36-40 weeks (V3). Data were analyzed in ESHA Food Processor Program and SPSS (version 13) to see if there was any difference found in diet between EX and NEX women. (2) Development and validation of a comprehensive maternal health questionnaire aimed to establish gaps in women’s behaviours and perceptions of the IOM GWG guidelines - An expert panel was consulted in the development of questionnaire constructs and items to gain content validity of the questionnaire. After multiple phases of questionnaire development and revisions, a 14-day test re-test validation pilot study was conducted to establish test re-test validity. Results: (1) In the EX and NEX analysis, significant decreases were found in total energy intake, including fat and protein, across pregnancy in the NEX GWG group. Significant group-by-time interaction was also found for energy intake and protein. (2) Most constructs included in the electronic maternal (EMat) Health questionnaire all proved to have sufficient test re-test validity via correlation analysis. Conclusion: In order to address the knowledge gaps regarding excess weight and changes in dietary habits during pregnancy, it is beneficial to explore pregnant women's knowledge and behaviours regarding these issues and collect information on what women report as barriers and facilitators to gestational weight management. The conclusions drawn from both of these studies may inform future interventions, as well as indicate where further education strategies are needed.

A healthy in utero environment is essential for achieving optimal outcomes for women and their children. Gestational weight gain (GWG) has been shown to impact current and future maternal-infant health outcomes. Suboptimal weight gain during pregnancy (defined by the Institute of Medicine GWG guidelines) has been linked to several complications and is implicated in the inter-general cycle of obesity. Understanding contributors to GWG and intervening during pregnancy with healthy behaviour strategies may have a multi-generational effect for chronic disease prevention. The objective of the first study of this thesis was to examine the association between i) eating habits during pregnancy, ii) advice from family or friends about GWG, and iii) personal effort to stay within weight gain limits, and meeting GWG recommendations. Cross-sectional data were collected from pregnant and postpartum women who responded to the validated electronic maternal (EMat) health survey. Regardless of receiving advice about GWG, women self-reporting less healthy eating habits in pregnancy than before pregnancy, receiving advice from family/friends about GWG, and lower personal effort to stay within guidelines, had an increased odds of weight gain discordant with recommendations. The objective of the second study was to assess the short-term effect of the SmartMoms Canada application (app) usage on promoting adequate GWG and healthy behaviours. SmartMoms Canada is an app-based intervention designed to help pregnant women adhere to GWG guidelines and improve healthful behaviours. Pregnant women using the SmartMoms Canada app more frequently had a higher moderate-to-vigorous physical activity daily average when compared with women with a lower usage. Together, the EMat and SmartMoms results from this thesis contribute to identifying and mitigating potential factors associated with discordant GWG and healthy behaviours.

Background: In the pregnant population, being physical active and meeting gestational weight gain (GWG) guidelines have numerous health benefits for both mother and infant. Markers of lipid metabolism are known to be influenced by these two variables in the non-pregnant population. However, the relationship between physical activity (PA) and GWG on lipid markers has yet to be assessed during pregnancy. My thesis aims to address this gap in the literature. Methods: The first objective of my thesis was to examine the relationship between maternal PA and GWG on gross measurements of fetal and placental development (n=40). Specifically, three markers of placental efficiency (Pl-E) were examined (birthweight [BW], BW-to-placenta weight ratio, and residual BW). The second objective of my thesis was to analyze maternal serum lipid and glucose markers (n=40), in mid (24-28 weeks) and late (34-38 weeks) gestation as well as from the umbilical cord (UC) as they relate to both PA and GWG. The third objective of my thesis was to explore how PA level and GWG status affect markers of lipid metabolism in term placenta (n=31). Markers of placental lipid transport (FATP1, FABP4, FAT/CD36) were assessed at the protein level, and enzymatic activity of placental lipoprotein lipase was also measured. Lastly, placental lipid storage was assessed by examining triglyceride content, paired with lipid droplet staining. Results: There was no relationship between PA independently or in combination with GWG on any Pl-E markers. A significant association was found between GWG and BW in women who gained weight excessively compared to insufficiently. Neither PA nor GWG categorization was associated with maternal lipid and glucose markers. Total cholesterol levels measured in UC serum were significantly lower in women categorized as active throughout pregnancy (p<0.0001) or whose activity dropped in late gestation (p<0.0001) compared to those who were inactive v throughout gestation. Glucose levels were lower in UC blood of women who gained weight appropriately in mid-gestation compared to those who gained insufficient (p=0.040) or excessive (p=0.021) weight. In terms of placental fatty acid transport, there was a significant interaction between PA status and GWG categorization and placental FATP1 protein expression (F=14.62, p<0.0001). Finally, while no differences were found in placental lipid droplet staining, the droplets were more likely to be clustered within the syncytiotrophoblast border. Conclusion: In conclusion, maternal PA had no association with Pl-E, while GWG was only associated with BW. My thesis work found that while maternal serum lipid markers were not associated with PA and GWG, both maternal PA and GWG status were related to changes in UC and placental lipid markers throughout pregnancy. In combination with previous research from our lab, it is suggested that women who are physically active during pregnancy, and gain weight appropriately may be transporting fewer nutrients (i.e. fatty acid, glucose, cholesterol) to the placenta than those who are inactive, yet simultaneously increasing metabolization. Future research should further investigate these findings by performing functional experiments.

Prolonged second stage of labor, excessive gestational weight gain and cesarean delivery has been associated with adverse maternal and fetal outcomes. Physical activity during pregnancy is a modifiable risk factor which has never been studied among Hispanic women. Gestational weight gain, another modifiable risk factor has only been evaluated as a risk factor for cesarean delivery in two studies among women induced for labor. To date, no study has examined the effect of duration of second stage of labor on intra-ventricular hemorrhage in very preterm births. We examined these maternal risk factors for prolonged second stage of labor, rate of cesarean delivery and fetal outcomes. The first study evaluated the association between physical activity and duration of second stage of labor. Prior studies regarding physical activity and duration of second stage of labor have been conflicting and none have examined the Hispanic population. During pregnancy, activities such as household chores, childcare, sports and women's occupation constitute a significant proportion of physical activity but have not been considered in prior studies. We examined the association between total physical activity (occupational, sport/exercise, household/care giving, and active living) during pre, early and mid-pregnancy and duration of second stage of labor in a prospective cohort of 1,231 Hispanic participants. Physical activity was quantified using the Kaiser Physical Activity Survey administered during pregnancy. Using multivariate linear regression we did not find statistically significant association between pre, early and mid-pregnancy physical activity and duration of second stage of labor. The second study focused on the effect of gestational weight gain on the cesarean delivery rate after induction of labor. The rate of induction of labor (IOL) has more than doubled from 9.5% in 1990 to 22.5% in 2006. Cesarean delivery usually follows a failed IOL and is associated with maternal and fetal morbidity. One of the two studies evaluating the effect of gestational weight gain on the rate of cesarean section in patients undergoing IOL was restricted to women with normal Body Mass Index (BMI) and the other was subjected to bias because more than half of the patients were missing BMI data. Therefore, we evaluated the effect of gestational weight gain on the rate of cesarean delivery after labor induction. In a retrospective cohort study design, using data from May 2005 to June 2008 and a multivariate logistic regression we found a 13% increase in risk of cesarean delivery with 5 kg increase in gestational weight gain. Finally, we evaluated the effect of mode of delivery and duration of second stage of labor on intra-ventricular hemorrhage (IVH) among early preterm births. IVH is a serious complication associated with preterm birth and important predictors of cerebral palsy and neurodevelopmental delays. Prior studies on this relationship in early preterm births are sparse. In a retrospective cohort study of newborns born less than 30 weeks or less than 1500 g between May 2003 and August 2008, we found an increase in risk of IVH after vaginal delivery. However, duration of second stage of labor had no significant effect on risk of IVH.