Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Feasibility of a Dietary Intervention to Limit Gestational Weight Gain and Gestational Diabetes in Overweight and Obese Pregnant Women

Reeta Lamminpää*, Katri Vehviläinen-Julkunen, Tuomas Selander, Sanna Rajapolvi, Ursula Schwab

Department of Nursing Science, University of Eastern Finland, Kuopio, Finland

Abstract: Background: Maternal overweight and obesity rates are rising. Increasingly, pregnant women are gaining weight in excess of gestational weight gain (GWG) recommendations, which is a risk factor for gestational diabetes (GDM). The aim of this study was to test the feasibility of a pilot dietary intervention to limit GWG and GDM and to examine eating behavior and dietary intake in pregnant women with overweight and obesity. Methods: Pregnant women (n=17) were recruited for a dietary intervention during the first trimester at antenatal maternal health clinics. Public health nurses recruited women with overweight and obesity into intervention (n=9) and control groups (n=8). The dietary intervention included printed material on healthy eating for all participants, plus two dietary counseling sessions with a clinical nutritionist for the intervention group. The Three Factor Eating Questionnaire R-18 (TFEQ R-18), Binge Eating Scale (BES), and 4-day food records were used for all participants to assess their eating behaviors and dietary intake during the study. GWG and the results of all oral glucose tolerance tests (OGTT) were collected at the end of pregnancy. Results: GWG during pregnancy was 9.7 ± 4.4 kg (mean ± SD) in the intervention group and 13.0 ± 3.4 kg in the control group (p=0.165). The prevalence of GDM was 42.9% in the intervention group and 33.3% in the control group (p= 0.725). Conclusions: This approach could be applicable in a larger group as a potential intervention to help control GWG and promote the health of pregnant women with overweight and obesity.

Keywords: pregnancy, dietary intervention, Three Factor Eating Questionnaire, Binge Eating Scale, food record, obesity, gestational diabetes mellitus, gestational weight gain

Received November 09, 2020; Revised December 10, 2020; Accepted December 17, 2020

Citation: Reeta Lamminpää, Katri Vehviläinen-Julkunen, Tuomas Selander, Sanna Rajapolvi, Ursula Schwab, Feasibility of a Dietary Intervention to Limit Gestational Weight Gain and Gestational Diabetes in Overweight and Obese Pregnant Women. Journal of Food and Nutrition Research, 8 (12). 739-745. January 2020. doi:10.12691/jfnr-8-12-7

1. Introduction

Rates of maternal obesity and obesity in general are increasing worldwide, which is considered a public health concern. Overweight is defined as body mass index (BMI) of 25 kg/m² or higher, and obesity as BMI ≥ 30 kg/m² [1]. Total gestational weight gain (GWG) is normally calculated as final weight in pregnancy minus pre-pregnancy weight. [2]. The Institute of Medicine’s (IOM’s) recommendations for GWG suggest limiting weight gain to between 5-9 kg for obese women and 7-11.5 kg for overweight women [3]. Gestational diabetes mellitus (GDM) diagnosis is based on glucose concentration results from a 75g oral glucose tolerance test (OGTT).

Excessive GWG and obesity are known to be independent risk factors for maternal and fetal complications of pregnancy, and they have significant consequences later in life for both the mother and the child. With excessive weight gain, the risk for further obesity increases, and may lead to other chronic medical conditions. Obesity during pregnancy is associated with short- and long-term metabolic dysfunction for both the mother and child, as well as the newborn’s increased risk for childhood obesity [4]. It has been shown that the quality or quantity of nutrients consumed by a pregnant woman can influence the child’s risk for noncommunicable diseases in adulthood, including metabolic syndrome and coronary heart diseases [5].

In Finland, public health nurses meet regularly with pregnant women at antenatal maternal health care clinics and are responsible for monitoring pregnancies. Nearly all Finnish women (99.7-99.8%) attend maternity care services during pregnancy [6]. Healthy eating advice is a part of routine antenatal visits, but there are no clear recommendations for certain subgroups, including obese pregnant women, which makes it challenging to give tailored advice. Public health nurses and other health care professionals have also reported a lack of knowledge related to dietary issues that would enable them to offer adequate, individualized counseling at the visits [7,8].

Since 2006, the proportion of pregnant women who are overweight has increased by more than 5% in Finland, while obesity rates in pregnant women have increased by 3%. Overweight has also increased at the population level [9]. In Finland today, the OGTT is routine for nearly all pregnant women. The proportion of Finnish women with GDM has increased rapidly during the past ten years, with 19% diagnosed in 2017 [9]. Moreover, in 2017, more a third (37.5%) of women giving birth in Finland were overweight.

There is a need for individualized pre-conceptional, prenatal, and postpartum care in order to help women avoid excessive weight gain during pregnancy and return to a healthy weight after childbirth [10]. Pregnancy provides an optimal time for health care providers to offer counseling that may decrease maternal obesity and its consequences [11]. In a Finnish intervention study, it was shown that counseling pregnant women at high risk for GDM can improve their diets and help them achieve recommended maternal weight gains [12]. Ideally, interventions encouraging women to make these changes would occur before pregnancy. However, previous studies have demonstrated that the earlier in pregnancy healthy dietary or lifestyle changes are adopted, the better the outcomes [4,13].

The eating behaviors of pregnant women, such as cognitive restraint, uncontrolled eating, and emotional eating, can have important implications for maternal weight, as well as for the eventual weight and eating behaviors of the prospective child. Maternal eating behavior can also impact feeding interactions with the child, affecting both the mother’s and the child’s weight and eating habits [14]. It has been previously stated that obese pregnant women’s diets tend to be of poor quality, characterized by low fruit and vegetable consumption, cravings for sweets, and late daily meal and snack times [15]. In a study by Ainscough et al., (2020) different meal pattern categories were identified in obese pregnant women, which tended to change as pregnancy progressed into more snack-dominant eating patterns. These patterns were associated with higher glycemic index diets and a greater prevalence of fetal macrosomia [16]. To gain better knowledge of the different eating behavior traits in an obstetric population with obesity, several questionnaires are available to help assess these issues, such as the Three Factor Eating Questionnaire Revised (TFEQ R-18) [17] and the Binge Eating Scale (BES) [18]. Both questionnaires were used in this study, to get more detailed information regarding the participants’ eating behaviors during the intervention, and to help explain the potential changes in GWG.

The aim of this study was to test the feasibility of the dietary intervention to limit GWG and GDM in pregnant women with overweight and obesity, and to explore potential changes in eating behaviors and dietary intake.

2. Materials and Methods

2.1. Study Design and Study Population

This was a controlled feasibility study conducted in two antenatal maternal health care clinics in the city of Kuopio, Northern Savo, Finland. There are approximately 1050 births per year in this area. Public health nurses recruited eligible women at these clinics according to the inclusion criteria. Inclusion criteria were age 35 years or less, BMI 25 kg/m² or greater, no chronic medical conditions (except for asthma and stable hypothyroidism), first trimester of pregnancy, and singleton pregnancy. Recruitment was conducted in two phases: from June to September 2017, and from March to December 2018. During the first antenatal visit at the maternal health clinic, the participants were given information about the study. Written informed consent was obtained from those who decided to participate in the study. The ethical statement was provided by the ethical committee of the Hospital District of Northern Savo (192/2017). Permission to conduct the study was obtained from the Chief Medical Officer of the Centre for Social and Health Services, City of Kuopio. The study was registered in Clinicaltrials.gov (NCT03191331).

Power analysis was not conducted because the aim of the study was to test the feasibility of the intervention. We aimed to estimate the means for both groups so we would be able to calculate the proper sample size needed for further intervention studies.

Figure 1. The design of the study

Table 1. Study participants’ background information

Participants were recruited to either an intervention group or a control group, with one antenatal maternal clinic recruiting for the intervention group, and another recruiting for the control group. In total, 17 eligible women were recruited into the study. However, one canceled her participation and three dropped out (Figure 1). Therefore, a total of 13 women received the designated intervention (intervention group n=7, control group n=6). The mean age of the participants was similar in the intervention and control groups (Table 1). BMI at the first antenatal visit did not differ between the groups. More women in the control group were married than in the intervention group, and the number of previous pregnancies was higher in the intervention group. None of the women smoked during pregnancy.

2.2. Assessments

Background information was collected at the first antenatal visit (Table 1). The information on GWG and the results of the OGTTs, indicating the diagnosis of GDM, were collected by the public health nurses after childbirth from the women’s electronic medical records and forwarded to the researcher. Eating behaviors were assessed during each trimester via two self-reported questionnaires to learn more about the potential effects of the intervention on those behaviors. TFEQ R-18 measures three dimensions of eating behavior: cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE) [17]. BES is used to measure binge eating symptoms [18]. Higher scores in both questionnaires indicate a stronger presence of those behaviors [18,19]. Participants’ dietary intake was measured with 4-day food records obtained during each trimester. Food records were checked by calling the participants after receipt of the completed records. The first cohort of women recruited for the study received all printed materials (questionnaires and food records) from public health nurses at regular antenatal visits each trimester and returned the materials to the nurses. The remaining women received the materials electronically via email (questionnaires) or by mail (food records) and returned them the same way to the researcher. In both groups (intervention and control), compliance with returning the questionnaires or food records was weak, especially during the third trimester, which lead to incomplete data capture of these measurements.

2.3. The Dietary Intervention

The dietary intervention included printed material on healthy eating (two A4 size prints), based on national recommendations provided by the National Nutrition Council and the Institute for Health and Welfare (2016). For both the intervention and control groups, these materials were handed out during recruitment. In addition, the intervention group was offered two face-to-face group dietary counseling sessions led by a clinical nutritionist in the second and third trimesters. The content of the dietary counseling sessions was planned by the research group and the clinical nutritionist, and aimed to discuss relevant issues for that point in the pregnancy. Five women participated in the first dietary counseling session, while only two participated in the second. Public health nurses were also encouraged to emphasize dietary guidelines during every maternal health clinic visit for participants in the intervention group. The intervention began at the first visit, when study recruitment took place, and ended with childbirth.

2.4. Statistical Analyses

The data were handled with Excel and analyzed with SPSS statistical software, versions 25 and 27 (SPSS Inc, Armonk, NY). Statistical analyses are mainly presented as percentages and means (± standard deviation, SD). An Independent samples t-test was used to study the statistical difference in GWG between the groups. Crosstabs and a chi-square test were used to evaluate the statistical difference in GDM between the groups. A paired samples t-test was used to evaluate changes in energy intake within groups between the first and second trimesters. The Mann-Whitney U test was used to evaluate differences in mean scores of three TFEQ R-18 factors and BES between the groups during each trimester. Participants’ weight gain during pregnancy was calculated by subtracting the women’s pre-pregnancy weights from weights at 35-38 weeks’ gestation. Food records were analyzed by AivoDiet nutrient calculation software (AivoFinland, Turku, Finland). A significance level of p<0.05 was used.

3. Results

3.1. Gestational Weight Gain and Gestational Diabetes

Weight gain was 9.7 ± 4.4 kg in the intervention group and 13.0 ± 3.4 kg in the control group (p=0.17) (Table 2). Across BMI groups, the mean GWG in both the intervention and control groups exceeded the IOM’s 2009 recommendations. In the intervention group, 43 % of the participants were diagnosed with GDM, while 33% were diagnosed in the control group (p= 0.73).

3.2. Dietary intake

Total energy intake increased in the intervention group from the first trimester to the second trimester, but the increase was not statistically significant (p=0.13). In the control group, total energy intake remained consistent from the first to second trimester, however, it decreased from the second to third trimester (p= 0.46). No one in the intervention group returned the 4-day food records in the third trimester. Dietary intakes of both groups are presented in Table 3.

Table 2. Gestational weight gains and gestational diabetes diagnoses

Table 3. Dietary intake of pregnant women with overweight and obesity

Figure 2. The mean scores of Binge Eating Scale (BES) at every trimester in intervention (blue line) and control (red line) groups. Intervention group: 1st trimester n=7, 2nd trimester n=6, 3rd trimester n=3; Control group 1st trimester n=6, 2nd trimester n=6, 3rd trimester n=2

3.3. Eating Behavior

The intervention group’s mean BES score was lower in first trimester (5.3 ± 3.9) versus the control group (8.2 ± 6.5, p=0.53) (Figure 2). In the second and third trimesters, mean scores were higher in the intervention group (7.3±7.3 and 7.0 ± 3.5, respectively) versus the control group (6.3 ± 6.6, p=0.59 and 4.0 ± 1.4, p=0.40, respectively).

The mean CR scores were higher in every trimester in the intervention group (45.2 ± 18.8, 43.5 ±19.4, 57.4 ±17.0, respectively) versus the control group (37.9 ±22.4, p = 0.53, 38.9 ±10.6, p=0.59, 36.0 ±11.8, p=0.40, respectively). The mean UE scores were highest in the third trimester in both groups (intervention group 44.4 ± 3.7 and control group 33.3 ±5.2, p=0.20). The mean EE scores were similar in both groups in the first trimester (intervention group 33.3 ± 27.2 and control group 33.3 ±21.1, p=0.84), and lower in the second trimester in the intervention group (31.5 ±26.7) versus the control group (35.1 ±23.7, p=0.70). In the third trimester, the mean EE score in the intervention group was 41.0 ±12.8, and 50.0 ±7.8 (p=0.40) in the control group (Figure 3).

Figure 3. The mean scores of eating behavior traits assessed with the Three Factor Eating Questionnaire Revised (TFEQ R-18) in each trimester in intervention (blue line) and control (red line) groups. Intervention group: 1st trimester n=7, 2nd trimester n=6, 3rd trimester n=3; Control group: 1st trimester n=6, 2nd trimester n=6, 3rd trimester n=2

4. Discussion

This study aimed to test a dietary intervention for pregnant women with overweight and obesity to prevent excessive GWG and GDM. Due to the small sample size, the interpretation of the results is limited, but based on the results we can assume that the dietary intervention tested in this study could be effective in preventing excessive GWG. There was no impact on GDM, with similar results noted in previous research [12,20,21].

The intensity of our intervention, which lasted throughout the pregnancy, is supported by previous studies that highlighted the importance of the continuity and duration of interventions such as ours [22]. We also engaged public health nurses to support the intervention; the dietary recommendations offered were simple, and were based on national recommendations that can be easily transferred into everyday practice in antenatal care settings. In a review by Bennett et al. (2018) that explored interventions related to GWG, no optimal duration, frequency, intensity, setting, or diet type was identified that might enhance the effectiveness of an intervention [22]. However, the authors suggested that face-to-face interventions, as well as the use of the internet or related technologies, may be successful. Reflecting on that observation, in our study it also seemed that electronically-delivered material was more suitable, and that the return rate was higher than with paper materials.

A review by Lamminpää et al. (2018) showed that there is no particular type of dietary intervention that would automatically be effective, and there is a great variability in the content and delivery method of these interventions [23]. Effective elements of these interventions are difficult to define, as it has been said that any dietary improvement is better than none [22]. Therefore, given that obesity is increasing all over the world, advice on healthy eating should be routine for all women receiving maternity care, and not just for pregnant women with overweight or obesity. Adoption of healthy eating habits by pregnant women will also improve the diets of their families and reduce the risks associated with obesity. It has been stated that even small amounts of education based on printed guidelines can have a significant effect on dietary habits, which makes these efforts important for improving the health of the whole society [24]. Studying the eating patterns of pregnant women with obesity will also help aid the development of future dietary interventions, with greater specificity for this group of women [15].

However, there are several limitations in this study. First, the number of participants was low despite the relatively long recruiting time. Also, participants’ engagement with the intervention was weak, although the women who were recruited for the second phase received email reminders to return study materials (TFEQ R-18 and BES questionnaires) after completion. Printed 4-day food records were handed out directly to participants in the first phase, and sent by mail in the second phase. None of the women received reminders to return the food records. This may have resulted in incomplete data capture via eating behavior and diet questionnaires, though these were not the main outcome measures in this study. For women in the intervention group, participation in dietary counseling sessions given by a clinical nutritionist was also poor. Due to recruitment-related challenges, we identified the need for an assistant to manage the process, which could potentially increase participants’ motivation to engage in the intervention.

Interestingly, based on the data available, the food records indicate that energy intake decreased during pregnancy, and was lower in the control group compared to the intervention group, though the control group’s GWG was still higher. This may reflect the phenomenon of misreporting on self-reported dietary assessment tools [25], as the study’s topic was a sensitive one, relating to obesity and weight gain. It has been previously shown that fatty foods especially are underreported by people with obesity [26]. Dietary records can also be affected by error, and the tendency of participants to report food consumption close to the general ideal. Other problems are related to the high burden posed on participants. [27]. However, it has been suggested that the TFEQ R-18 is a valid measure of eating behaviors in Finland, both in the general population, and in those with obesity [28].

5. Conclusions

The results of this feasibility study will help define further dietary interventions and procedures. The dietary intervention itself seems practical, and electronic delivery of study materials will be preferred in the future.

Statement of Competing Interests

The authors have no competing interests.

References

[1]   | WHO 2014. http://www.who.int/topics/obesity/en/.
[2]   | Gilmore LA, Redman LM. Weight gain in pregnancy and application of the 2009 IOM guidelines: toward a uniform approach. Obesity (Silver Spring). 2015; 23(3): 507-511.
[3]   | IOM 2009. Weight-gain during pregnancy. http://iom.edu/~/media/Files/Report%20Files/2009/Weight- GainDuring-Pregnancy-Reexamining-the- Guidelines/Report%20Brief%20%20Weight%20Gain%20During%20Pregnancy.pdf.
[4]   | Catalano P, deMouzon SH. Maternal obesity and metabolic risk to the offspring: why lifestyle interventions may have not achieved the desired outcomes. Int J Obes (Lond). 2015; 39(4): 642-649.
[5]   | Langley-Evans SC, Moran VH. Childhood obesity: risk factors, prevention and management. Matern Child Nutr. 2014; 10(4): 453-455.
[6]   | Klemetti, R. & Hakulinen-Viitanen, T. (toim.). 2013. Äitiysneuvolaopas, suosituksia äitiysneuvolatoimintaan. Opas 29. Helsinki: Terveyden ja hyvinvoinnin laitos.
[7]   | Piirainen T, Isolauri E, Huurre A, et al. Ravitsemus- ja terveysneuvonta äitiys- ja lastenneuvolassa. Suomen Lääkärilehti. 2004; 59(19): 2047-2053.
[8]   | Heslehurst N, Crowe L, Robalino S et al. Interventions to change maternity healthcare professionals' behaviours to promote weight-related support for obese pregnant women: a systematic review. Implementation Science 2014; 9(97).
[9]   | The National Institute for Health and Welfare (THL) 2019. Perinatal statistics - parturients, delivers and newborns 2018. http://www.julkari.fi/handle/10024/138998. Published 2019. Accessed July 20, 2020.
[10]   | Rasmussen KM, Abrams B, Bodnar LM, Butte NF, Catalano PM, Maria Siega-Riz A. Recommendations for weight gain during pregnancy in the context of the obesity epidemic. Obstet Gynecol. 2010; 116(5): 1191-1195.
[11]   | Artal R, Lockwood CJ, Brown HL. Weight gain recommendations in pregnancy and the obesity epidemic. Obstet Gynecol. 2010 Jan; 115(1): 152-5.

[12]  
Korpi-Hyövälti E. Elämäntapaohjauksen merkitys raskausdiabeteksen riskiryhmään kuuluvilla naisilla: syö yhden, liiku kahden puolesta. In: Dissertations in Health Sciences, no 141. Kuopio, Finland: University of Eastern Finland; 2012. https://core.ac.uk/download/pdf/15169993.pdf. Accessed July 20, 2020.

[13]   | Walker JL, Ardouin S, Burrows T. The validity of dietary assessment methods to accurately measure energy intake in children and adolescents who are overweight or obese: a systematic review. Eur J Clin Nutr. 2018 Feb;72(2):185-197.
[14]   | Korani M, Rea DM, King PF, Brown AE. Maternal eating behaviour differs between ethnic groups: Considerations for research and practice. Matern Child Nutr. 2018; 14(4): e12630.
[15]   | Most J, Rebello CJ, Altazan AD, Martin CK, Amant MS, Redman LM. Behavioral Determinants of Objectively Assessed Diet Quality in Obese Pregnancy. Nutrients. 2019; 11(7): 1446. Published 2019 Jun 26.
[16]   | Ainscough, Kate M et al. “Nutrition, Behavior Change and Physical Activity Outcomes From the PEARS RCT-An mHealth-Supported, Lifestyle Intervention Among Pregnant Women With Overweight and Obesity.” Frontiers in endocrinology vol. 10 938. 4 Feb. 2020.
[17]   | Stunkard A & Messick S. The three factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research 1985; 29(1):71-83.
[18]   | Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47-55.

[19]  
de Lauzon, B., Romon, M., Deschamps, V., Lafay, L., Borys, J.-M., Karlsson, J., Ducimetière, P., Charles, M. A., & Group, F. L. V. S. (FLVS) S. (2004). The Three-Factor Eating Questionnaire-R18 Is Able to Distinguish among Different Eating Patterns in a General Population. The Journal of Nutrition, 134(9), 2372-2380.

[20]  
Markovic TP, Muirhead R, Overs S, Ross GP, Louie JC, Kizirian N, Denyer G, Petocz P, Hyett J, Brand-Miller JC. Randomized Controlled Trial Investigating the Effects of a Low-Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study. Diabetes Care. 2016 Jan; 39(1): 31-8.

[21]   | Poston, L., Bell, R., Croker, H. et al. 2015. Effect of a behavioural intervention in obese pregnant women (the UPBEAT study): a multicentre, randomised controlled trial. The Lancet 3 (10): 767-777.

[22]  
Bennett CJ, Walker RE, Blumfield ML, Gwini SM, Ma J, Wang F, Wan Y, Dickinson H, Truby H. Interventions designed to reduce excessive gestational weight gain can reduce the incidence of gestational diabetes mellitus: A systematic review and meta-analysis of randomised controlled trials. Diabetes Res Clin Pract. 2018 Jul; 141: 69-79.

[23]   | Lamminpää R, Vehviläinen-Julkunen K, Schwab U. A systematic review of dietary interventions for gestational weight gain and gestational diabetes in overweight and obese pregnant women. Eur J Nutr. 2018 Aug; 57(5): 1721-1736.
[24]   | Tanha FD, Mohseni M, Ghajarzadeh M, Shariat M. The effects of healthy diet in pregnancy. J Family Reprod Health. 2013; 7(3): 121-125.

[25]  
Yikyung Park, Kevin W Dodd, Victor Kipnis, Frances E Thompson, Nancy Potischman, Dale A Schoeller, David J Baer, Douglas Midthune, Richard P Troiano, Heather Bowles, Amy F Subar, Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers, The American Journal of Clinical Nutrition, Volume 107, Issue 1, January 2018, Pages 80-93.

[26]   | Heitmann BL, Lissner L. Dietary underreporting by obese individuals--is it specific or non-specific? BMJ. 1995; 311(7011): 986-989.
[27]   | Ortega RM, Pérez-Rodrigo C, López-Sobaler AM. Dietary assessment methods: dietary records. Nutr Hosp. 2015;31 Suppl 3: 38-45. Published 2015 Feb 26.
[28]   | Anglé S, Engblom J, Eriksson T, et al. Three factor eating questionnaire-R18 as a measure of cognitive restraint, uncontrolled eating and emotional eating in a sample of young Finnish females. Int J Behav Nutr Phys Act. 2009;6:41. Published 2009 Jul 17.