Hostname: page-component-848d4c4894-nmvwc Total loading time: 0 Render date: 2024-06-18T13:19:22.328Z Has data issue: false hasContentIssue false

Implementing weight management during and after pregnancy to reduce diabetes and CVD risk in maternal and child populations

Published online by Cambridge University Press:  01 December 2023

Sharleen L. O'Reilly*
Affiliation:
School of Agriculture and Food Science, University College Dublin College of Health Sciences, Dublin, Ireland UCD Perinatal Research Centre, National Maternity Hospital, University College Dublin School of Medicine, Dublin 2, Ireland
Fionnuala M. McAuliffe
Affiliation:
UCD Perinatal Research Centre, National Maternity Hospital, University College Dublin School of Medicine, Dublin 2, Ireland
Aisling A. Geraghty
Affiliation:
School of Agriculture and Food Science, University College Dublin College of Health Sciences, Dublin, Ireland UCD Perinatal Research Centre, National Maternity Hospital, University College Dublin School of Medicine, Dublin 2, Ireland
Christy Burden
Affiliation:
Academic Women's Health Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
Anna Davies
Affiliation:
Academic Women's Health Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
*
*Corresponding author: Sharleen L. O'Reilly, email sharleen.oreilly@ucd.ie
Rights & Permissions [Opens in a new window]

Abstract

Maintaining a healthy weight during pregnancy is critical for both women's and children's health. Excessive gestational weight gain (GWG) can lead to complications such as gestational diabetes, hypertension and caesarean delivery. Insufficient GWG can cause fetal growth restriction and increase infant mortality risk. Additionally, postpartum weight retention raises risk of obesity, type 2 diabetes and other chronic diseases for both mother and child. This review seeks to identify current obstacles in weight management research during and after pregnancy and explore evidence-based strategies to overcome them. Pregnancy offers a window of opportunity for health behaviour changes as women are more receptive to education and have regular contact with health services. Staying within Institute of Medicine's recommended GWG ranges is associated with better maternal and fetal outcomes. Systematic review evidence supports structured diet and physical activity pregnancy interventions, leading to reduced GWG and fewer complications. Health economic evaluation indicates significant returns from implementation, surpassing investment costs due to decreased perinatal morbidity and adverse events. However, the most effective way to implement interventions within routine antenatal care remains unclear. Challenges increase in the postpartum period due to competing demands on women physically, mentally and socially, hindering intervention reach and retention. Flexible, technology-supported interventions are needed, requiring frameworks such as penetration-implementation-participation-effectiveness and template-for-intervention-description-and-replication for successful implementation. Greater research efforts are necessary to inform practice and investigate fidelity aspects through pragmatic implementation trials during the pregnancy and postpartum periods. Understanding the best ways to deliver interventions will empower women to maintain a healthy weight during their reproductive years.

Type
Conference on ‘Nutrition at key stages of the lifecycle’
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Nutrition Society

Introduction

Global obesity rates are climbing, and women are no exception. Obesity is defined as ‘abnormal or excessive fat accumulation that presents a risk to health’, and BMI is the most common method used to measure prevalence rates(1). At a population level, the female overweight and obesity rates are high for Ireland (24⋅5 % overweight/26 % obesity) and the United Kingdom (31 % overweight/28 % obesity)(1), making obesity-related malnutrition a substantial issue being faced by over a quarter of adult females in these countries(1). Low- and middle-income countries experience a double malnutrition burden with undernutrition remaining prevalent in rural communities but overnutrition forms an increasing challenge for urban settings(Reference Alem, Yeshaw and Liyew2). Overweight- and obesity-related disease accounts for 4 million deaths annually(3).

During pregnancy, women are expected to gain weight. The weight gained for a woman with a normal BMI is 13⋅6 kg, of which 25 % is the baby, 26 % blood and body fluids, 23 % maternal stores of fat, protein and other nutrients, 7 % breast tissue, 7 % uterus, 7 % amniotic fluid and 5 % placenta(4). Weight gained between conception and birth is termed gestational weight gain (GWG) and recommendations by the former US Institute of Medicine (now National Academy of Medicine) outline GWG based on pre-pregnancy BMI categories(5). Weight gained beyond these ranges is termed excessive GWG and linked with poorer health outcomes(Reference Langley-Evans, Pearce and Ellis6). Following birth, the focus shifts to a woman returning to their pre-pregnancy weight and rapid weight loss is discouraged. Intrapartum weight retention refers to the weight retained and gained between pregnancies and is a key long-term negative maternal health indicator. Women have sustained engagement with their healthcare system during their reproductive years and these encounters have the potential to improve longer-term health if they can be optimised with preconception, antenatal and postpartum weight management being connected and using the best available evidence to inform practice.

This review firstly aims to explore the impact of entering pregnancy with overweight/obesity, excessive weight gain and intrapartum weight retention on health outcomes, associated risk and cost. Secondly, it will explore implementation science approaches to bridging the translation of research into practice and related behaviour change frameworks used in this implementation. Finally, the review will look at interventions to support healthy weight management across the antenatal and postpartum periods.

Maternal obesity: prevalence, cost, associated risk

Obesity in pregnancy is a significant contributor to maternal and perinatal morbidity and mortality with a rising global prevalence among reproductive-aged women. The global prevalence of obesity has significantly increased in the past decades, described as a ‘global epidemic’,(Reference James7) with the number of people with overweight and obesity tripling between 1975 and 2016(8). Less than half of pregnant women in the UK have a BMI in the normal range in the UK and 47 % of pregnant women have GWG greater than Institute of Medicine recommendations(Reference Relph9). Moreover, internationally over half of all reproductive-aged women are entering pregnancy overweight and half exceed recommended GWG(Reference Maxwell, Shirley and O'Higgins10).

Gestational diabetes (GDM), new onset of diabetes in pregnant women without a prior history, is one of the most common complications associated with obesity in pregnancy(Reference Adam, McIntyre and Tsoi11). It is defined as high blood sugar levels during pregnancy and is an increasing health problem for both mothers and babies, affecting up to 18 % of pregnancies worldwide. European GDM prevalence varies – north 8⋅9 %, east 31⋅5 %, south 12⋅3 % and west 10⋅7 %(Reference Paulo, Abdo and Bettencourt-Silva12). High BMI (>25 kg/m2) is the most important modifiable risk factor for GDM, with 6⋅8-fold higher risk for BMIs >30 kg/m2 compared to normal BMIs (18⋅5–24⋅9 kg/m2)(Reference Paulo, Abdo and Bettencourt-Silva12). The effects of GWG, GDM, hyperglycaemia and obesity on adverse pregnancy outcomes and on fetal and offspring development are significant. The risk of miscarriage and congenital anomalies has been shown to increase with hyperglycaemia during organogenesis. GDM, hyperglycaemia, obesity and GWG are associated with increased odds of pregnancies affected by neural tube defects (OR, 1⋅87), spina bifida (OR, 2⋅24), cardiovascular anomalies (OR, 1⋅30), septal anomalies (OR, 1⋅20), cleft palate (OR, 1⋅23), cleft lip and palate (OR, 1⋅20), anorectal atresia (OR, 1⋅48), hydrocephaly (OR, 1⋅68), limb reduction anomalies (OR, 1⋅34)(Reference Stothard, Tennant and Bell13) intellectual disability and poorer cognitive development(Reference Torres-Espinola, Berglund and García-Valdés14). GDM is also associated with a long-term tenfold increased maternal risk of type 2 diabetes (T2D)(Reference Vounzoulaki, Khunti and Abner15). Strong evidence exists that health behaviour change can reduce the development of T2D in people at risk(Reference Lindström, Peltonen and Eriksson16,Reference Ratner, Goldberg and Haffner17) , but there are many demands on a new mother and weight gained during pregnancy is frequently not lost afterwards leading to increased risk of obesity, heart disease and diabetes(Reference Li, Song and Li18,Reference Song, Lyu and Li19) . This risk is not confined to just the woman, early fetal programming means that her offspring also have demonstrable increased risk – her children are eight times more likely to develop diabetes or pre-diabetes by early adulthood(Reference Holder, Giannini and Santoro20) with a 59 % increased risk of developing childhood obesity(Reference Patro Golab, Santos and Voerman21).

Women with GDM and those living with obesity demonstrate increased insulin resistance in pregnancy and this creates poorer metabolic health, which can even impact placental structure, maternal and cord inflammatory markers as well as endocrine and inflammatory gene expression(Reference Musa, Salazar-Petres and Arowolo22). This common footing between GDM and obesity results in women living with obesity being at increased risk of GDM, preeclampsia, gestational hypertension, fetal macrosomia, caesarean section and postpartum weight retention(Reference Heslehurst, Rankin and Wilkinson23). Furthermore, obesity and GWG in pregnancy increase the risk of complications during labour and birth(Reference Goldstein, Abell and Ranasinha24). Evidence has demonstrated that women with overweight (BMI>25 kg/m2) were more likely to have a slower labour progression, potentially due to the inadequacy of uterine contractions, and fetal distress and ultimately therefore receiving interventions such as labour induction/augmentation or operative birth(Reference Leddy, Power and Schulkin25). Women with obesity who undergo caesarean birth are additionally at increased risk for anaesthesia-related complications (epidural failure, aspiration under general anaesthesia and difficult endotracheal intubation) and post-operative wound infection(Reference Taylor, Dominguez and Habib26). Venous thromboembolism is a further serious risk in pregnant women with obesity. Evidence has demonstrated that up to 57 % of women in the UK who died from venous thromboembolism during pregnancy had BMIs in the obese category(Reference Knight, Bunch and Tuffnell27). Fetal macrosomia is a neonatal complication associated with obesity in pregnancy(Reference Goldstein, Abell and Ranasinha24) and GDM and itself increase the risk for operative delivery and maternal and infant morbidity. It is associated with maternal complications such as genital tract lacerations, and postpartum haemorrhage. Infants have an increased risk of shoulder dystocia, clavicular fractures, brachial plexus injuries and nerve palsies. Importantly, GDM is associated with a high risk of neonatal intensive care unit admission, due to further associated complications such as neonatal hypoglycaemia(Reference Ku, Kim and Im28).

Maternal BMI influences maternal and neonatal morbidity, the number and duration of maternal and neonatal admissions and health service costs(Reference Denison, Norwood and Bhattacharya29). The most recent Mothers and Babies: reducing risk through audits and confidential enquiries across the UK (MBRRACE-UK) report identified maternal obesity as a significant factor in up to 30 % of maternal deaths in the UK and Ireland(Reference Knight, Bunch and Tuffnell27) and this rate is seen in other countries(Reference Mariona30,Reference Poobalan, Marais and Aucott31) . Rising obesity within the obstetric populations will mean that this factor will only continue to grow and impact parents, families and the wider society. The prevalence of stillbirth in the UK is above the European average, affecting almost 1 in 250–300 pregnancies after 28 weeks of pregnancy(Reference Flenady, Wojcieszek and Middleton32). The MBRRACE report concluded that up to 60 % of antepartum stillbirths could have been prevented with improved antenatal care(Reference Knight, Bunch and Tuffnell27).

Even modest increases in maternal BMI are associated with increased risk of fetal death, stillbirth and neonatal, perinatal and infant death. For BMIs of 25 and 30 kg/m2, the absolute risk per 10 000 pregnancies for fetal death are 82 and 102; for stillbirth 48 and 59 and for perinatal death 73 and 86(Reference Aune, Saugstad and Henriksen33). For women who gain four or more BMI units between pregnancies, their risk is 55 % higher for stillbirth and 29 % higher for infant mortality(Reference Cnattingius and Villamor34). We also know that stillbirth risk increases linearly with increased BMI gain and that weight loss prior to a subsequent pregnancy in women with overweight will decrease neonatal mortality(Reference Cnattingius and Villamor34). The risk of late stillbirth is much greater when GDM is not diagnosed – 44 % increase in women at risk of GDM but not screened and women with raised fasting plasma glucose not diagnosed with GDM experienced a fourfold greater risk of late stillbirth than women with normal blood glucose(Reference Stacey, Tennant and McCowan35). The increased focus on detection and management was a key recommendation of the MBRRACE report to decrease stillbirths(Reference Knight, Bunch and Tuffnell27). Eleven per cent of neonatal deaths are attributed to maternal overweight and obesity (Fig. 1)(36).

Fig. 1. Comparison of the percentages of women with a pre-pregnancy BMI ≥30 from 2010 and 2015 (risk ratios and 95 % CI). Pooled random-effects model estimate 1⋅15 (95 % CI 1⋅08, 1⋅22). Adapted from European Perinatal Health Report 2018(36).

Inappropriate GWG is a challenge that weighs heavily on this population but they are also the most nutritionally vulnerable. Suboptimal micronutrient (iron, vitamin D, folate, vitamin B6, magnesium, zinc, potassium and vitamin A) and macronutrient (fibre and carbohydrate) intakes alongside excessive sodium and dietary fat intakes are seen in these women(Reference Dubois, Diasparra and Bedard37). Dietary quality is known to continue to be poor in this population postpartum(Reference Morrison, Koh and Lowe38) and yet it is known that higher dietary quality in pregnancy and lactation are associated with a healthier growth pattern in infants(Reference Tahir, Haapala and Foster39) and better weight maintenance(Reference O'Reilly, Versace and Yelverton40). Maternal nutrition and interventions to reduce maternal complications from suboptimal nutrition are priorities for the WHO(41) and the International Federation of Gynaecology and Obstetrics. The International Federation of Gynaecology and Obstetrics recently established a specific pregnancy non communicable disease (NCD) prevention committee which aims to tackle obesity and GDM as one of its cornerstone aims(Reference Maxwell, Shirley and O'Higgins10,Reference Adam, McIntyre and Tsoi11) . Pregnancy and infant first year of life are a window of opportunity to future health and the long-term effects of obesity in pregnancy needs consideration. Compared to normal-weight women, women with obesity were shown to retain more weight postpartum. More specifically, postpartum weight gain was most strongly associated with weight gain during the first trimester(Reference Walter, Perng and Kleinman42) and intrauterine exposure to maternal obesity can lead to adverse health outcomes in the offspring, including an increased incidence of metabolic syndrome and obesity in the child. Recent studies have shown that childhood obesity can be carried into adulthood, suggesting that fetal overnutrition can adversely affect the health of offspring throughout life(Reference Voerman, Santos and Patro Golab43). Offspring of pregnant women with obesity have 35 % increased all-cause mortality and 29 % increased rates of hospital admission with CVD(Reference Reynolds, Allan and Raja44).

Maternal obesity, GWG and GDM place a substantial economic burden on healthcare systems. Pregnant women with overweight or obesity have a significantly greater number of maternal admissions, longer admissions and higher health service costs than women of normal weight(Reference Denison, Norwood and Bhattacharya29). Excess GWG will significantly increase risks and costs in pregnancy(Reference Broekhuizen, Simmons and Devlieger45). Moreover, infants born to mothers with high BMIs also utilise significantly more health service resources in the first year of life compared to infants born to mothers of healthy weight(Reference Morgan, Rahman and Hill46); they are also at higher risk of developing childhood obesity(Reference Patro Golab, Santos and Voerman21). Pregnant women with elevated BMI are at high risk of developing GDM, which also imposes additional costs independently of BMI(Reference Danyliv, Gillespie and O'Neill47). The cost of managing GDM yielded an economic burden in the United States of $1⋅6 billion in 2017 with $5800 annual burden per case of GDM(Reference Dall, Yang and Gillespie48). In Ireland, the costs of maternity care for women with a diagnosis of GDM are 34 % greater than in women without GDM(Reference Gillespie, Cullinan and O'Neill49). Finally, women who develop GDM have a tenfold increased future risk of developing T2DM(Reference Vounzoulaki, Khunti and Abner15) and an increase in health-related costs is seen postpartum compared with normoglycaemic pregnancies (€680⋅50 in annual healthcare costs 2–5 years after the index pregnancy)(Reference Danyliv, Gillespie and O'Neill50). Health services have seen an almost fourfold increase in GDM incidence caused by a widening of the diagnostic criteria, growing obesity rates and advancing maternal age(Reference Hanna, Duff and Shelley-Hitchen51). In this GDM tsunami, health services are not resourced to manage such numbers and already extended services are further diluted. Currently, health systems around the world do not have sufficient resources to manage the numbers of women at risk of developing GDM to support them reducing their health behaviour-related risks.

Adverse outcomes in pregnancy for both woman and child are socially patterned with greater risk present in low socioeconomic and education, rural and ethnically diverse and minority populations(Reference Knight, Bunch and Tuffnell27). Rates of obesity, T2DM and GDM are also higher in these populations(Reference Collier, Abraham and Armstrong52Reference El-Khoury Lesueur, Sutter-Dallay and Panico54) and intrinsically linked to the higher rates of adverse outcomes seen. Women from disadvantaged communities are currently not engaging or minimally engaging with health services to reduce their GWG and their risks of GDM, T2DM and obesity. Disadvantaged communities will typically not have access to support that fits with their needs owing to linguistic or cultural issues, competing interests of working life and raising a family and lack of financial resources to engage with provided services(Reference Dennison, Fox and Ward55). This systematic discrimination means that the most vulnerable are not able to engage with the care that will positively influence their health. While this will be for a range of reasons, unhealthy lifestyle behaviours promoting weight gain and access to universal screening for GDM is a part of this problem(Reference Knight, Bunch and Tuffnell27). Similarly in the postpartum period, women are not engaging in diabetes screening(Reference Herrick, Keller and Trolard56,Reference Boyle, Versace and Dunbar57) and/or risk reduction programmes(Reference Dasgupta, Terkildsen Maindal and Kragelund Nielsen58,Reference Nielsen, Kapur and Damm59) , both of which will influence their reproductive health. Guidelines for improved health outcomes in both the woman and child are in place nationally and internationally for healthy weight gain, physical activity and eating in pregnancy, health weight management and lifestyle behaviours postpartum and healthy infant feeding practices. Yet the daily challenge that presents in maternity services and public health settings is how to implement these guidelines with fidelity using the few resources available within those settings.

Implementation science approaches: frameworks, hybrid designs and health behaviour change

A key issue in the development of effective interventions to support weight management during pregnancy and postpartum is the evidence to implementation (‘know-do’) gap. It has been estimated that it takes 17 years for evidence to be adopted into practice(Reference Morris, Wooding and Grant60). The growing field of implementation science aims to use evidence and theory-informed approaches to address this gap. The UK Medical Research Council (MRC) updated its framework for the development and evaluation of randomised controlled trials for complex interventions to improve health in 2019 and 2021(Reference Craig, Dieppe and Macintyre61,Reference Skivington, Matthews and Simpson62) . Intervention complexity can impact implementation and achieving effect in different settings, due to challenges relating to: standardising intervention design and delivery, sensitivity to local context, the people involved (i.e. staff and patients), organisational context and development of outcome measures and evaluation(Reference Datta and Petticrew63). Intervention complexity has been defined in a number of different ways, including the number of interacting components, groups or organisational levels targeted, variability of outcomes, the degree of tailoring or flexibility of the intervention and whether the intervention has a non-linear causal pathway(Reference Skivington, Matthews and Simpson62,Reference Petticrew64) . There is increased interest in systems thinking and conceptual mapping approaches in healthcare research to understand real-world complexity(Reference Baugh Littlejohns, Near and McKee65), by focusing on ‘people, processes, activities, settings and structures and the dynamic relationships between them’(Reference Wutzke, Morrice and Benton66). A key update within the new MRC framework is a shift towards addressing how interventions interact with their context, and how the intervention interacts with systems change, to identify the conditions needed to achieve intended change mechanisms, and to ensure effectiveness in ‘real-world’ settings(Reference Skivington, Matthews and Simpson62).

The MRC framework specifies that interventions should be systematically developed and evaluated using evidence and theory. Four phases are outlined, which can be addressed iteratively and in any order: (i) development/identification of the intervention, (ii) exploration of feasibility and acceptability, (iii) evaluation, (iv) implementation. Each component should address a number of core elements, relating to contextual considerations, programme theory, stakeholder engagement, identification of key uncertainties, refinement of the intervention and economic considerations(Reference Skivington, Matthews and Simpson62). The authors highlight that early consideration of intervention implementation and investigation of it within each development and evaluation phase can increase potential for its future adoption across settings. The MRC approach aligns with effectiveness-implementation hybrid designs(Reference Curran, Bauer and Mittman67), where evaluation of implementation and effectiveness are undertaken alongside one another. There are three types of hybrid design approaches and the balance between implementation and effectiveness will vary for each type. Hybrid type 1 primarily tests the effects of an intervention while observing and gathering information on implementation. A type 2 hybrid design involves dual testing of the intervention effectiveness and either a secondary aim or co-primary aim of testing implementation strategies. A hybrid type 3 primarily tests the implementation strategy while observing and gathering information on clinical intervention effectiveness(Reference Curran, Bauer and Mittman67).

The MRC framework highlights that interventions should be systematically developed using the best available evidence and appropriate theory. Theoretical frameworks are commonly used in the development stage to develop behaviour change interventions. The behaviour change wheel is commonly used framework to ensure complete and cohesive coverage(Reference Michie, van Stralen and West68). Central to the behaviour change wheel are three core behavioural determinants (capability, opportunity and motivation), which have been organised into the COM-B model of behaviour change(Reference Michie, van Stralen and West68). Used in parallel with the COM-B model, the behaviour change technique (BCT) taxonomy is a consensus-informed transparent and systematic means to specify the content of interventions in terms of 93 distinct BCT(Reference Michie, Richardson and Johnston69). This was derived from the behaviour change wheel development(Reference Michie, van Stralen and West68) and a taxonomy for physical activity and nutrition BCT(Reference Michie, Ashford and Sniehotta70). The BCT taxonomy provides a common language to describe the key ingredients in behaviour change interventions, which enables greater replicability and fidelity of intervention implementation across settings/contexts, and facilitates systematic evidence syntheses that aim to identify the most effective BCT for a given behaviour and/or context.

Implementation theories provide a framework to drive implementation strategies and to explore how or why interventions are successfully or unsuccessfully implemented(Reference Nilsen71). Current implementation theories have differing aims, including guiding the process of applying evidence in practice, exploring what influences implementation, and evaluating intervention implementation(Reference Nilsen71). The exploration, preparation, implementation, sustainment (EPIS) framework(Reference Aarons, Hurlburt and Horwitz72), and normalisation process theory (NPT)(Reference May, Finch and Mair73) fall within the first and second categories, and support exploration of barriers and facilitators to implementation. The EPIS framework(Reference Aarons, Hurlburt and Horwitz72) was developed from the literature on implementation in the public sector and allied health services. It has four implementation phases that describe the process through which an evidence-based practice (EBP) is adopted: exploration (consideration of the health needs of patients/communities and identification of best EBP), preparation (identification of potential contextual barriers and facilitators to implementation), implementation (EBP is adopted), sustainment (context structures, processes and supports are ongoing so that the EBP is delivered to achieve intended impacts). Within the EPIS framework, common and unique factors hypothesised to have a strong influence on implementation of EBP are described, from within the outer system context, the inner organisational context and factors related to the innovation itself(Reference Aarons, Hurlburt and Horwitz72). Outer and inner context, and innovation factors, may be more or less important in the different EPIS phases. A key component of the EPIS framework is the recognition of the interconnectedness and relationships between outer and inner contexts, which are called bridging factors. Systematic reviews show EPIS as a flexible and robust implementation framework suitable for use across low-, middle- and high-income countries(Reference Moullin, Dickson and Stadnick74). NPT(Reference May, Finch and Mair73) is an explanatory model that provides a means to understand what influences implementation, and can be used alongside EPIS to understand some of the contextual- and innovation-related factors that influence it(Reference May, Cummings and Girling75). It was developed within a variety of healthcare systems and looks at individual and collective behaviour shown to be important in empirically studied implementation processes(Reference May, Cummings and Girling75). As an action theory, it describes the mechanisms of social action involved in implementing a new practice. There are four areas to NPT which are coherence (making sense of the new practice/s), cognitive participation (buy-in), collective action (resourcing) and reflexive monitoring (appraisal and feedback)(Reference May, Finch and Ballini76). A systematic review of NPT identified it as a useful theory in a wide range of interventions and that it had a particular benefit in evaluation and understanding implementation as a dynamic process(Reference May, Cummings and Girling75).

Evaluation is critical to understanding implementation and there are several frameworks to inform how evaluation is undertaken. The most commonly used are reach, effectiveness, adoption, implementation, maintenance (RE-AIM)(Reference Glasgow, Vogt and Boles77,Reference Glasgow, Harden and Gaglio78) , template-for-intervention-description-and-replication checklist(Reference Hoffmann, Glasziou and Boutron79) and the penetration-implementation-participation-effectiveness (PIPE) framework(Reference Pronk80). RE-AIM was originally developed to consistently report implementation of innovations, however more recently it has been used to inform programme planning(Reference Glasgow, Harden and Gaglio78). It proposes five steps to translate research into action, each of which can be evaluated to understand intervention impact or targeted with tools and strategies to achieve implementation: reach (number of people willing to participate in the intervention or programme), effectiveness (impact of intervention on key outcomes), adoption (willingness of agents (settings) willing to initiate a programme), implementation (fidelity of delivery an use of intervention strategies) and sustainment (extent to which it becomes part of routine practices and policy). Template-for-intervention-description-and-replication is the template for intervention description and replication, which has twelve items that form a checklist to improve reporting of interventions so they can be replicated and implemented(Reference Hoffmann, Glasziou and Boutron79). The items are brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications, how well (planned) and how well (actual)(Reference Hoffmann, Glasziou and Boutron79). The template-for-intervention-description-and-replication checklist is commonly used in systematic review to deconstruct interventions for implementation strategy analyses and meta-analyses(Reference Lim, Liang and Hill81). Similarly, the PIPE impact metric(Reference Pronk80) provides a framework to assess the implementation of health improvement programmes. The four PIPE elements are: (1) penetration of the programme into the population of interest, (2) implementation of the proposed intervention/programme/services, (3) participation in the programme and (4) effectiveness in generating expected outcomes. PIPE can be used to evaluate implementation and provide feedback about where to focus changes to improve performance of a programme(Reference Aziz, Absetz and Oldroyd82).

Interventions to reduce excessive gestational weight gain and intrapartum weight retention: what works and what needs consideration for implementation

There is a need to take a holistic view of the best time to intervene for reducing excessive GWG and intrapartum weight retention and the evidence is conflicting. On one hand, addressing maternal BMI was shown to be the only preventative strategy that reduces childhood obesity(Reference Patro Golab, Santos and Voerman21) and recent systematic review found no evidence that maternal dietary and/or health behaviour change intervention during pregnancy alone modifies early childhood obesity risk(Reference Louise, Poprzeczny and Deussen83). Interpregnancy weight gain is also associated with increased rates of subsequent large for gestational age infants and higher rates of subsequent GDM, further emphasising the importance of the interpregnancy and postpartum periods in weight management(Reference Maxwell, Shirley and O'Higgins10,Reference Crosby, Walsh and Segurado84) . Conversely, there is a clear need to target and achieve engagement in women with the most risk when they are most motivated to manage their health risk during pregnancy. Health behaviour change interventions during this time show improved dietary patterns, exercise habits and GWG with improved pregnancy outcomes. Level 1 evidence clearly demonstrates that antenatal health behaviour change interventions are effective in reducing GWG, adverse maternal and neonatal birth outcomes and reduced subsequent development of T2D(Reference Teede, Bailey and Moran85,Reference Cantor, Jungbauer and McDonagh86) . A recent systematic review and meta-analysis of 117 randomised clinical trials with over 34 000 pregnancies showed that antenatal diet and physical activity-based interventions were associated with less GWG(Reference Teede, Bailey and Moran85). They reduced GWG (−1⋅15 kg; 95 % CI −1⋅40, −0⋅91), GDM risk (OR, 0⋅79; 95 % CI 0⋅70, 0⋅89) and total adverse maternal outcomes (OR, 0⋅89; 95 % CI 0⋅84, 0⋅94) when compared to routine antenatal care(Reference Teede, Bailey and Moran85). Interventions that combined diet and physical activity and behaviour change had the greatest impact on GWG(Reference Teede, Bailey and Moran85). Rates of women exceeding GWG recommendations (based on IOM criteria) were significantly lower after exercise-only and combined interventions during pregnancy. A separate systematic review also showed that when behavioural therapy supported combined diet and physical activity interventions, GWG was significantly reduced (standardised mean difference −0⋅16 kg; 95 % CI −0⋅28, −0⋅04, four trials, n = 2132)(Reference Behnam, Timmesfeld and Arabin87).

From an implementation perspective, the evaluation of existing pregnancy health behaviour change interventions (n = 117) using PIPE and template-for-intervention-description-and-replication frameworks leads to some interesting findings. Only 14 % interventions provided sufficient data to calculate penetration and where available the level of population penetration was low(Reference Bahri Khomami, Teede and Enticott88). Implementation was reported with moderate fidelity and participation was variable(Reference Bahri Khomami, Teede and Enticott88). The effectiveness analysis showed −1⋅15 kg GWG (95 % CI −1⋅4, −0⋅91)(Reference Bahri Khomami, Teede and Enticott88). Allied healthcare professionals were the most common and most effective delivery agents (−1⋅36 kg GWG)(Reference Harrison, Bahri Khomami and Enticott89). Structured diet health behaviour change delivered individually was the most effective format (−3⋅91 kg GWG) and having a moderate number of sessions was significant in reducing GWG (−4⋅91 kg)(Reference Harrison, Bahri Khomami and Enticott89). Further cost-effectiveness analysis of systematic review data showed that structured diet and physical activity intervention would see $4⋅75 Australian dollars returned for every dollar invested by health funders, with cost offsets from reduced perinatal morbidity and adverse events exceeding intervention costs(Reference Lloyd, Morton and Teede90). For the postpartum period, the penetration remained low and participation variable. The implementation fidelity was low and effectiveness analysis showed −2⋅3 kg weight loss with intervention(Reference Lim, Liang and Hill81). Healthcare professional delivery remained more effective (−3⋅22 kg) compared with −0⋅99 kg non-healthcare professional delivery(Reference Lim, Liang and Hill81). Diet and physical activity combined was more effective than physical activity intervention alone but the intensity (duration or number of sessions) and setting (group or individual) did not affect weight loss(Reference Lim, Liang and Hill81). The most effective BCT strategies for greater reductions in energy intake were problem solving, goal setting, feedback on behaviour, self-monitoring, credible source, behavioural substitution and reviewing goal outcomes(Reference Lim, Hill and Teede91).

While we have evidence for what works for the antenatal and postpartum periods separately, the challenge remains for bridging the gap between pregnancy and postpartum care. Women with GDM in particular report feeling like they have been dropped by the hospital system once they deliver their baby and are left to struggle on trying to manage their future chronic risk with the additional stressors of rearing a young family(Reference Dennison, Fox and Ward55,Reference Pennington, O'Reilly and Young92) . Women place a large amount of trust in the hospital and their staff to support them during pregnancy(Reference Lie, Hayes and Lewis-Barned93) but continuity of care is not sustained beyond the immediate postpartum period. Research suggested that interventions postpartum can be successful in management postpartum weight, however most interventions to date have been short in duration and had a relatively short follow-up period (Reference Christiansen, Skjøth and Rothmann94). Systematic review shows that only five interventions have been conducted that cross the pregnancy to postpartum divide for GWG or postpartum weight management and that the postpartum contact was minimal(Reference Vincze, Rollo and Hutchesson95). All the identified interventions were also not integrated into routine care and as such, fail to shed any light on their implementation potential. The other aspect to this gap is that the interventions all focus on behaviour change in either the woman or the infant but not both, yet there are clear opportunities to leverage the impact of change in this instance by creating a more holistic approach to behaviour change at the family unit level. Research also indicates that the guidance available to inform translating the evidence into practice is lacking in terms of health service implementation or optimal programme delivery(Reference Bahri Khomami, Teede and Enticott88,Reference Makama, Skouteris and Moran96,Reference Lim, Chen and Makama97) . There is a clear need to use implementation science methods to inform the integration of effective lifestyle interventions into routine antenatal and postpartum care if we are to reduce the burdens of overweight and obesity and its impacts. In addition to understanding how to integrate and implement effective programmes, we need to have programmes that can be delivered with fidelity across multiple contexts in a scalable, sustainable way.

Digitally delivered health behaviour change interventions can successfully influence inequality by increasing availability and access for users. Smartphones have near ubiquitous coverage in all socioeconomic groups. For example, almost 90 % of people in the UK and Spain own a smartphone(Reference Poushter, Bishop and Chwe98) and mothers aged 18–49 years spend over 21 h/week on their smartphones(99). Apps can provide ‘around the clock’ high-quality information as well as tailored support at low cost(Reference Becker, Miron-Shatz and Schumacher100). Women with low socioeconomic status commonly use apps during pregnancy but not postpartum because of the lack of quality apps, with a postnatal app gap(Reference Guerra-Reyes, Christie and Prabhakar101). Maternal wellbeing and education level have also been identified as an enabler for engagement with mhealth-based health behaviour change interventions in pregnancy(Reference Roche, Rafferty and Holden102,Reference O'Brien, Segurado and Geraghty103) . Higher mhealth engagement occurs when a health care professional (HCP) does the referral, a credible source co-designs the app with user-tailored content and the app usage starts during pregnancy(Reference Lim, Liang and Hill81,Reference Hussain, Smith and Yee104Reference Taki, Lymer and Russell107) . Reviews of nutritional information on smartphone apps for pregnancy found that although the volume of apps was high, the overall quality was low, they did not consistently provide accurate nutritional information and only a few used BCT(Reference Brown, Bucher and Collins105,Reference Bland, Dalrymple and White108) . While there is evidence that mobile smart phone applications may have a role to play(Reference Greene, O'Brien and Kennelly109), the literature evaluating pregnancy or postpartum apps effectiveness and long-term engagement is sparse. Reviews indicate little to no literature exists on how to deliver digital health coaching programmes most effectively, and that digital tools to support women are ‘of low quality, had minimal behaviour change potential, and were potentially unsafe, with minimal linkage to evidence-based information or partnership with health care’(Reference Brammall, Garad and Boyle110). A qualitative systematic review identified digital health interventions were highly acceptable to pregnant women with obesity or GDM both in pregnancy and postpartum(Reference Kelly-Whyte, McNulty and O'Reilly111). Smartphone technology holds the potential to provide more advanced methods of delivering personalised health behaviour change messages, while reaching a greater number of women for significantly lower costs than would be possible with traditional methods. Implementation research around app-based interventions that cover pregnancy and postpartum periods presents a major gap that has still to be addressed.

Longer-term engagement is required for pregnancy and postpartum interventions to understand impact, yet the average duration of interventions is 6 months(Reference Lim, Liang and Hill81,Reference Bahri Khomami, Teede and Enticott88) . The LIFE-Moms consortium lifestyle interventions in women with overweight or obesity (n = 1150; interventions included variable types of diet, physical activity and behaviour change; three were for pregnancy only and four continued postpartum) analysis showed lower postpartum weight retention at 12 months (2⋅2 ± 7⋅0 v. control 0⋅7 ± 6⋅2 kg, respectively; −1⋅6 kg difference (95 % CI −2⋅5, −0⋅7; P = 0⋅0003))(Reference Phelan, Clifton and Haire-Joshu112). The odds (OR = 1⋅68 (95 % CI 1⋅26, 2⋅24)) of women returning to their pre-pregnancy weight at 1-year postpartum were significantly increased but the intervention did not impact infant anthropometry and it is very difficult to extract meaningful implementation-relevant information from these different interventions(Reference Phelan, Clifton and Haire-Joshu112). However, lifestyle interventions in pregnant and postnatal women do demonstrate improved dietary patterns, exercise habits, and GWG with improved pregnancy outcomes(Reference Teede, Bailey and Moran85,Reference Flynn, Dalrymple and Barr113,Reference Lim, O'Reilly and Behrens114) . The challenge remains that most are intensive, have low penetration or uptake rates, low adherence or participation rates, are not co-designed or implementation informed, require costly personnel and resources or lack long-term follow-up(Reference Cantor, Jungbauer and McDonagh86,Reference Bahri Khomami, Teede and Enticott88,Reference Makama, Skouteris and Moran96,Reference Lim, Chen and Makama97) . They also do not generate evidence to address the most significant evidence practice gap – how to implement evidence-based interventions into routine care. Although evidence shows that health behaviour change can reduce GWG and intrapartum weight management, there is a large research translation gap about achieving implementable interventions with adequate population penetration and participation. Evidence to support the capacity for implementation of antenatal health behaviour change interventions in maternity care settings remains limited(Reference Bahri Khomami, Teede and Enticott88).

Implementation-focused interventions

There are several ongoing studies that are seeking to move the implementation of interventions forward into more real-world settings and whom their primary results are due in 2024. The first study is called Face-it(Reference Maindal, Timm and Dahl-Petersen115) and this is a effectiveness-implementation hybrid type 1 design. The intervention seeks to change health behaviours around physical activity, diet and breastfeeding in the first postpartum year for women with previous GDM and their families. Face-it is a health system-based intervention with three core components that will be undertaken in Denmark(Reference Nielsen, Dahl-Petersen and Jensen116). The central intervention component is additional visits from the public health nurse covering the health behaviour changes required and these visits will see the public health nurse work with the family unit, rather than just the mother. The second component is mhealth coaching delivered by a healthcare professional using an app. The mhealth coaching is offered to both the woman and her partner. The final component is a structured communication system that sends the public health nurse the woman's maternity discharge letter (normally only provided to general practitioner) and provides the ability to send reminders to either the public health nurse, health coaches or the woman through the secure platform. The intervention used the MRC complex intervention design(Reference Skivington, Matthews and Simpson62) to underpin the study and seeks to recruit 460 women into the randomised controlled trial in a ratio of two intervention to one usual care participants. The primary outcome is reduction in BMI from baseline to 12 months postpartum and implementation data will be collected to explore fidelity and PIPE metrics in detail.

The second implementation study is Optimal Me(Reference Harrison, Brammall and Garad117), which is a hybrid type 3 design and will be conducted in Australia. The study seeks to primarily test whether the implementation strategy of the intervention's health coaching is better delivered via telephone or online videoconferencing. Women will be randomised to either arm and both will have a mhealth app that will provide health behaviour change and text message support. Extended implementation data will be collected using the RE-AIM framework(Reference Glasgow, Vogt and Boles77) to ensure the study penetration, reach, feasibility, acceptability, adoption and fidelity can be evaluated. Optimal Me aims to recruit 300 women of childbearing age who are not pregnant but wish to conceive in the next 12 months. This selective criterion is normally very challenging to recruit as one in two pregnancies is unplanned but it can be achieved because the women will be recruited through a private health insurance company and women are required to upgrade their policy if they wish to include maternity care in the following year. The study has been designed to use an alternative pathway to usual maternity care and as a result will have a higher socioeconomic position population, which will need to be accounted for in the analysis.

The final study is Bump2Baby and Me(Reference O'Reilly, Burden and Campoy118), which is a hybrid type 2 design and will recruit women during pregnancy in four countries (Ireland, England, Spain and Australia). Bump2Baby and Me will provide mhealth coaching from a healthcare professional via a smartphone app that will also support self-monitoring of diet, physical activity and weight as well as a private social media community and a tailored information library. The intervention is designed to sit alongside usual care in each country and facilitates communication between the maternity service provider and the health coaches. The study is a randomised controlled trial that seeks to recruit 800 women (200 per country) in their first trimester of pregnancy at-risk of developing GDM to either usual care or usual care and the intervention. The intervention will provide health coaching and app access for the duration of the woman's pregnancy and the first postpartum year. It will expand the health coaching provided during the first postpartum year to include healthy infant feeding and active play alongside healthy family meals. The study is underpinned by the EPIS framework and will use NPT to achieve better understanding of the factors that influenced normalisation of the intervention in both the healthcare setting and in the women(Reference O'Reilly, Laws and Maindal119). The primary outcome of the randomised controlled trial is maternal weight at 12 months. The RE-AIM evaluation framework will be used to explore implementation and fidelity aspects as secondary outcomes(Reference O'Reilly, Laws and Maindal119).

Conclusion

Addressing weight management in pregnancy and the postpartum period is a well-recognised health and healthcare issue. To effectively tackle this issue, we need to understand the prevalence and impact of excessive GWG and intrapartum weight retention and how it varies within countries and different population sub-groups. Excessive GWG and intrapartum weight retention are linked to adverse short- and long-term health outcomes, including GDM, obesity and T2D. The management of weight requires more longer-term, flexibly delivered and family-focused approaches, which are commonly not included for a variety of reasons, but they are what is needed to support long-term weight management. All of this means that there is insufficient evidence available to implement health behaviour change interventions within everyday care and health service delivery.

Implementation science presents an opportunity to develop and deliver interventions that are suitable for use within healthcare services and settings. Ongoing research illustrates different implementation science approaches to achieving improved maternal health during the peripartum window and providing personalised support at the right time and place for each woman leveraging technology and digital tools to facilitate self-monitoring and behavioural interventions. In conclusion, by addressing implementation challenges and adopting evidence-based approaches, more effective weight management in pregnancy and the postpartum period will be possible and ultimately improve maternal and child health outcomes worldwide.

Financial Support

This work was supported by the European Union Commission Horizon 2020 (grant agreement 847984), with collaborative National Health and Medical Research Council, Australia co-funding (grant number APP1194234).

Conflict of Interest

None.

Authorship

S. L. O’. R. proposed the original concept and developed the first draft of the manuscript. All other authors contributed to the final draft. All authors have been involved in revising the manuscript, have given final approval of the version to be published and agree to be accountable for all aspects of the work. All authors read and approved the final manuscript.

References

World Health Organisation (2022) WHO European Regional Obesity Report 2022. Copenhagen: WHO Regional Office for Europe.Google Scholar
Alem, AZ, Yeshaw, Y, Liyew, AM et al. (2023) Double burden of malnutrition and its associated factors among women in low and middle income countries: findings from 52 nationally representative data. BMC Public Health 23, 1479.CrossRefGoogle ScholarPubMed
Food and Agriculture Organization of the United Nations (2019) The State of Food Security and Nutrition in the World 2019: Safeguarding Against Economic Slowdowns and Downturns. Rome: FAO.Google Scholar
American College of Obstetricians and Gynecologists (2000) Planning your pregnancy and birth 3rd edition American College of Obstetricians and Gynecologists.Google Scholar
Institute of Medicine & National Research Council (2009) Weight Gain During Pregnancy: Reexamining the Guidelines. Washington, DC: The National Academies Press.Google Scholar
Langley-Evans, SC, Pearce, J & Ellis, S (2022) Overweight, obesity and excessive weight gain in pregnancy as risk factors for adverse pregnancy outcomes: a narrative review. J Hum Nutr Diet 35, 250264.CrossRefGoogle ScholarPubMed
James, WP (2008) WHO recognition of the global obesity epidemic. Int J Obes 32(Suppl. 7), S120S126.CrossRefGoogle ScholarPubMed
World Health Organisation (2021) Obesity and overweight. In Fact Sheets. Geneva: World Health Organisation.Google Scholar
Relph, S & National Maternity and Perinatal Audit Project Team (2021) NHS maternity care for women with a body mass index of 30 kg/m2 or above: births between 1 April 2015 and 31 March 2017 in England, Wales and Scotland. London: RCOG.Google Scholar
Maxwell, CV, Shirley, R, O'Higgins, AC et al. (2023) Management of obesity across women's life course: FIGO best practice advice. Int J Gynecol Obstet 160, 3549.CrossRefGoogle ScholarPubMed
Adam, S, McIntyre, HD, Tsoi, KY et al. (2023) Pregnancy as an opportunity to prevent type 2 diabetes mellitus: FIGO best practice advice. Int J Gynecol Obstet 160, 5667.CrossRefGoogle ScholarPubMed
Paulo, MS, Abdo, NM, Bettencourt-Silva, R et al. (2021) Gestational diabetes mellitus in Europe: a systematic review and meta-analysis of prevalence studies. Front Endocrinol 12, 691033.CrossRefGoogle ScholarPubMed
Stothard, KJ, Tennant, PW, Bell, R et al. (2009) Maternal overweight and obesity and the risk of congenital anomalies: a systematic review and meta-analysis. JAMA 301, 636650.CrossRefGoogle ScholarPubMed
Torres-Espinola, FJ, Berglund, SK, García-Valdés, LM et al. (2015) Maternal obesity, overweight and gestational diabetes affect the offspring neurodevelopment at 6 and 18 months of age – a follow up from the PREOBE cohort. PLoS ONE 10, e0133010.CrossRefGoogle ScholarPubMed
Vounzoulaki, E, Khunti, K, Abner, SC et al. (2020) Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. Br Med J 369, m1361.CrossRefGoogle ScholarPubMed
Lindström, J, Peltonen, M, Eriksson, JG et al. (2006) High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish diabetes prevention study. Diabetologia 49, 912920.CrossRefGoogle ScholarPubMed
Ratner, R, Goldberg, R, Haffner, S et al. (2005) Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program. Diabetes Care 28, 888894.Google Scholar
Li, J, Song, C, Li, C et al. (2018) Increased risk of cardiovascular disease in women with prior gestational diabetes: a systematic review and meta-analysis. Diabetes Res Clin Pract 140, 324338.CrossRefGoogle ScholarPubMed
Song, C, Lyu, Y, Li, C et al. (2018) Long-term risk of diabetes in women at varying durations after gestational diabetes: a systematic review and meta-analysis with more than 2 million women. Obes Rev 19, 421429.CrossRefGoogle ScholarPubMed
Holder, T, Giannini, C, Santoro, N et al. (2014) A low disposition index in adolescent offspring of mothers with gestational diabetes: a risk marker for the development of impaired glucose tolerance in youth. Diabetologia 57, 24132420.CrossRefGoogle ScholarPubMed
Patro Golab, B, Santos, S, Voerman, E et al. (2018) Influence of maternal obesity on the association between common pregnancy complications and risk of childhood obesity: an individual participant data meta-analysis. Lancet Child Adolesc Health 2, 812821.CrossRefGoogle ScholarPubMed
Musa, E, Salazar-Petres, E, Arowolo, A et al. (2023) Obesity and gestational diabetes independently and collectively induce specific effects on placental structure, inflammation and endocrine function in a cohort of South African women. J Physiol 601, 12871306.CrossRefGoogle Scholar
Heslehurst, N, Rankin, J, Wilkinson, JR et al. (2010) A nationally representative study of maternal obesity in England, UK: trends in incidence and demographic inequalities in 619 323 births, 1989–2007. Int J Obes 34, 420428.CrossRefGoogle ScholarPubMed
Goldstein, RF, Abell, SK, Ranasinha, S et al. (2017) Association of gestational weight gain with maternal and infant outcomes: a systematic review and meta-analysis. JAMA 317, 22072225.CrossRefGoogle ScholarPubMed
Leddy, MA, Power, ML & Schulkin, J (2008) The impact of maternal obesity on maternal and fetal health. Rev Obstet Gynecol 1, 170178.Google ScholarPubMed
Taylor, CR, Dominguez, JE & Habib, AS (2019) Obesity and obstetric anesthesia: current insights. Local Reg Anesth 12, 111124.CrossRefGoogle ScholarPubMed
Knight, M, Bunch, K, Tuffnell, D et al. (2018) Saving Lives, Improving Mothers’ Care – Lessons Learned to Inform Maternity Care From the UK and Ireland Confidential Enquiries Into Maternal Deaths and Morbidity 2014–16. Oxford: National Perinatal Epidemiology Unit, University of Oxford.Google Scholar
Ku, TI, Kim, YN, Im, DH et al. (2021) Maternal and fetal risk factors associated with neonatal intensive care unit admission in term neonates. Perinatology 32, 184192.CrossRefGoogle Scholar
Denison, FC, Norwood, P, Bhattacharya, S et al. (2014) Association between maternal body mass index during pregnancy, short-term morbidity, and increased health service costs: a population-based study. Br J Obstet Gynaecol 121, 7281, discussion 82.CrossRefGoogle ScholarPubMed
Mariona, FG (2017) Does maternal obesity impact pregnancy-related deaths? Michigan experience. J Matern Fetal Neonatal Med 30, 10601065.CrossRefGoogle ScholarPubMed
Poobalan, AS, Marais, D, Aucott, L et al. (2016) Maternal obesity in Africa: a systematic review and meta-analysis. J Public Health 38, e218e231.Google Scholar
Flenady, V, Wojcieszek, AM, Middleton, P et al. (2016) Stillbirths: recall to action in high-income countries. Lancet 387, 691702.CrossRefGoogle ScholarPubMed
Aune, D, Saugstad, OD, Henriksen, T et al. (2014) Maternal body mass index and the risk of fetal death, stillbirth, and infant death: a systematic review and meta-analysis. JAMA 311, 15361546.CrossRefGoogle ScholarPubMed
Cnattingius, S & Villamor, E (2016) Weight change between successive pregnancies and risks of stillbirth and infant mortality: a nationwide cohort study. Lancet 387, 558565.CrossRefGoogle ScholarPubMed
Stacey, T, Tennant, P, McCowan, L et al. (2019) Gestational diabetes and the risk of late stillbirth: a case–control study from England, UK. BJOG 126, 973982.CrossRefGoogle ScholarPubMed
Euro-Peristat Project (2018) European Perinatal Health Report: core indicators of the health and care of pregnant women and babies in Europe in 2015. Paris: Euro-peristat Project.Google Scholar
Dubois, L, Diasparra, M, Bedard, B et al. (2018) Adequacy of nutritional intake during pregnancy in relation to prepregnancy BMI: results from the 3D cohort study. Br J Nutr 120, 335344.CrossRefGoogle ScholarPubMed
Morrison, MK, Koh, D, Lowe, JM et al. (2012) Postpartum diet quality in Australian women following a gestational diabetes pregnancy. Eur J Clin Nutr 66, 11601165.CrossRefGoogle ScholarPubMed
Tahir, MJ, Haapala, JL, Foster, LP et al. (2019) Higher maternal diet quality during pregnancy and lactation is associated with lower infant weight-for-length, body fat percent, and fat mass in early postnatal life. Nutrients 11, 632.CrossRefGoogle ScholarPubMed
O'Reilly, SL, Versace, V, Yelverton, C et al. (2018) Improved dietary quality in women with previous gestational diabetes: secondary analysis of a randomised trial. Proc Nutr Soc 77, E53.CrossRefGoogle Scholar
World Health Organization. Regional Office for Europe (2016) Good maternal nutrition: the best start in life. World Health Organization. Regional Office for Europe.Google Scholar
Walter, JR, Perng, W, Kleinman, KP et al. (2015) Associations of trimester-specific gestational weight gain with maternal adiposity and systolic blood pressure at 3 and 7 years postpartum. Am J Obstet Gynecol 212, 499.e491–412.CrossRefGoogle ScholarPubMed
Voerman, E, Santos, S, Patro Golab, B et al. (2019) Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: an individual participant data meta-analysis. PLoS Med 16, e1002744.CrossRefGoogle ScholarPubMed
Reynolds, RM, Allan, KM, Raja, EA et al. (2013) Maternal obesity during pregnancy and premature mortality from cardiovascular event in adult offspring: follow-up of 1 323 275 person years. Br Med J 347, f4539.CrossRefGoogle ScholarPubMed
Broekhuizen, K, Simmons, D, Devlieger, R et al. (2018) Cost-effectiveness of healthy eating and/or physical activity promotion in pregnant women at increased risk of gestational diabetes mellitus: economic evaluation alongside the DALI study, a European multicenter randomized controlled trial. Int J Behav Nutr Phys Act 15, 23.CrossRefGoogle ScholarPubMed
Morgan, KL, Rahman, MA, Hill, RA et al. (2015) Obesity in pregnancy: infant health service utilisation and costs on the NHS. BMJ Open 5, e008357.CrossRefGoogle ScholarPubMed
Danyliv, A, Gillespie, P, O'Neill, C et al. (2016) The cost-effectiveness of screening for gestational diabetes mellitus in primary and secondary care in the Republic of Ireland. Diabetologia 59, 436444.CrossRefGoogle ScholarPubMed
Dall, TM, Yang, W, Gillespie, K et al. (2019) The economic burden of elevated blood glucose levels in 2017: diagnosed and undiagnosed diabetes, gestational diabetes mellitus, and prediabetes. Diabetes Care 42, 16611668.CrossRefGoogle ScholarPubMed
Gillespie, P, Cullinan, J, O'Neill, C et al. (2013) Modeling the independent effects of gestational diabetes mellitus on maternity care and costs. Diabetes Care 36, 11111116.CrossRefGoogle ScholarPubMed
Danyliv, A, Gillespie, P, O'Neill, C et al. (2015) Short- and long-term effects of gestational diabetes mellitus on healthcare cost: a cross-sectional comparative study in the ATLANTIC DIP cohort. Diabet Med 32, 467476.CrossRefGoogle Scholar
Hanna, FW, Duff, CJ, Shelley-Hitchen, A et al. (2017) Diagnosing gestational diabetes mellitus: implications of recent changes in diagnostic criteria and role of glycated haemoglobin (HbA1c). Clin Med 17, 108113.CrossRefGoogle ScholarPubMed
Collier, A, Abraham, EC, Armstrong, J et al. (2017) Reported prevalence of gestational diabetes in Scotland: the relationship with obesity, age, socioeconomic status, smoking and macrosomia, and how many are we missing? J Diabetes Investig 8, 161167.CrossRefGoogle ScholarPubMed
Boudet-Berquier, J, Salanave, B, Desenclos, JC et al. (2017) Sociodemographic factors and pregnancy outcomes associated with prepregnancy obesity: effect modification of parity in the nationwide Epifane birth-cohort. BMC Pregnancy Childbirth 17, 273.CrossRefGoogle ScholarPubMed
El-Khoury Lesueur, F, Sutter-Dallay, A-L, Panico, L et al. (2018) The perinatal health of immigrant women in France: a nationally representative study. Int J Public Health 63, 10271036.CrossRefGoogle Scholar
Dennison, RA, Fox, RA, Ward, RJ et al. (2020) Women's views on screening for type 2 diabetes after gestational diabetes: a systematic review, qualitative synthesis and recommendations for increasing uptake. Diabetic Med 37, 2943.CrossRefGoogle ScholarPubMed
Herrick, CJ, Keller, MR, Trolard, AM et al. (2019) Postpartum diabetes screening among low income women with gestational diabetes in Missouri 2010–2015. BMC Public Health 19, 148.CrossRefGoogle ScholarPubMed
Boyle, DIR, Versace, VL, Dunbar, JA et al. (2018) Results of the first recorded evaluation of a national gestational diabetes mellitus register: challenges in screening, registration, and follow-up for diabetes risk. PLoS ONE 13, e0200832.CrossRefGoogle ScholarPubMed
Dasgupta, K, Terkildsen Maindal, H, Kragelund Nielsen, K et al. (2018) Achieving penetration and participation in diabetes after pregnancy prevention interventions following gestational diabetes: a health promotion challenge. Diabetes Res Clin Pract 145, 200213.CrossRefGoogle ScholarPubMed
Nielsen, KK, Kapur, A, Damm, P et al. (2014) From screening to postpartum follow-up – the determinants and barriers for gestational diabetes mellitus (GDM) services, a systematic review. BMC Pregnancy Childbirth 14, 41.CrossRefGoogle ScholarPubMed
Morris, ZS, Wooding, S & Grant, J (2011) The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med 104, 510520.CrossRefGoogle ScholarPubMed
Craig, P, Dieppe, P, Macintyre, S et al. (2019) Developing and Evaluating Complex Interventions. London, UK: Medical Research Council.Google Scholar
Skivington, K, Matthews, L, Simpson, SA et al. (2021) A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. Br Med J 374, n2061.CrossRefGoogle ScholarPubMed
Datta, J & Petticrew, M (2013) Challenges to evaluating complex interventions: a content analysis of published papers. BMC Public Health 13, 568.CrossRefGoogle Scholar
Petticrew, M (2011) When are complex interventions ‘complex’? When are simple interventions ‘simple’? Eur J Public Health 21, 397398.CrossRefGoogle ScholarPubMed
Baugh Littlejohns, L, Near, E, McKee, G et al. (2023) A scoping review of complex systems methods used in population physical activity research: do they align with attributes of a whole system approach? Health Research Policy and Systems 21, 18.CrossRefGoogle ScholarPubMed
Wutzke, S, Morrice, E, Benton, M et al. (2016) Systems approaches for chronic disease prevention: sound logic and empirical evidence, but is this view shared outside of academia? Public Health Res Pract 26, e2631632.CrossRefGoogle ScholarPubMed
Curran, GM, Bauer, M, Mittman, B et al. (2012) Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care 50, 217226.CrossRefGoogle ScholarPubMed
Michie, S, van Stralen, M & West, R (2011) The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci 6, 42.CrossRefGoogle ScholarPubMed
Michie, S, Richardson, M, Johnston, M et al. (2013) The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 46, 8195.CrossRefGoogle ScholarPubMed
Michie, S, Ashford, S, Sniehotta, FF et al. (2011) A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health 26, 14791498.CrossRefGoogle ScholarPubMed
Nilsen, P (2015) Making sense of implementation theories, models and frameworks. Implement Sci 10, 113.CrossRefGoogle ScholarPubMed
Aarons, GA, Hurlburt, M & Horwitz, SM (2011) Advancing a conceptual model of evidence-based practice implementation in public service sectors. Adm Policy Ment Health 38, 423.CrossRefGoogle ScholarPubMed
May, C, Finch, T, Mair, F et al. (2007) Understanding the implementation of complex interventions in health care: the normalization process model. BMC Health Serv Res 7, 148.CrossRefGoogle ScholarPubMed
Moullin, JC, Dickson, KS, Stadnick, NA et al. (2019) Systematic review of the exploration, preparation, implementation, sustainment (EPIS) framework. Implement Sci 14, 1.CrossRefGoogle ScholarPubMed
May, CR, Cummings, A, Girling, M et al. (2018) Using normalization process theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review. Implement Sci 13, 80.CrossRefGoogle ScholarPubMed
May, CR, Finch, T, Ballini, L et al. (2011) Evaluating complex interventions and health technologies using normalization process theory: development of a simplified approach and web-enabled toolkit. BMC Health Serv Res 11, 245.CrossRefGoogle ScholarPubMed
Glasgow, RE, Vogt, TM & Boles, SM (1999) Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 89, 13221327.CrossRefGoogle ScholarPubMed
Glasgow, RE, Harden, SM, Gaglio, B et al. (2019) RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review. Front Public Health 7, 64.CrossRefGoogle ScholarPubMed
Hoffmann, TC, Glasziou, PP, Boutron, I et al. (2014) Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. Br Med J 348, g1687.CrossRefGoogle ScholarPubMed
Pronk, NP (2003) Designing and evaluating health promotion programs. Dis Manag Health Out 11, 149157.CrossRefGoogle Scholar
Lim, S, Liang, X, Hill, B et al. (2019) A systematic review and meta-analysis of intervention characteristics in postpartum weight management using the TIDieR framework: a summary of evidence to inform implementation. Obes Rev 20, 10451056.CrossRefGoogle ScholarPubMed
Aziz, Z, Absetz, P, Oldroyd, J et al. (2015) A systematic review of real-world diabetes prevention programs: learnings from the last 15 years. Implement Sci 10, 117.CrossRefGoogle ScholarPubMed
Louise, J, Poprzeczny, AJ, Deussen, AR et al. (2021) The effects of dietary and lifestyle interventions among pregnant women with overweight or obesity on early childhood outcomes: an individual participant data meta-analysis from randomised trials. BMC Med 19, 128.CrossRefGoogle ScholarPubMed
Crosby, DA, Walsh, JM, Segurado, R et al. (2017) Interpregnancy weight changes and impact on pregnancy outcome in a cohort of women with a macrosomic first delivery: a prospective longitudinal study. BMJ Open 7, e016193.CrossRefGoogle Scholar
Teede, HJ, Bailey, C, Moran, LJ et al. (2022) Association of antenatal diet and physical activity-based interventions with gestational weight gain and pregnancy outcomes: a systematic review and meta-analysis. JAMA Intern Med 182, 106114.CrossRefGoogle ScholarPubMed
Cantor, AG, Jungbauer, RM, McDonagh, M et al. (2021) Counseling and behavioral interventions for healthy weight and weight gain in pregnancy: evidence report and systematic review for the US preventive services task force. JAMA 325, 20942109.CrossRefGoogle ScholarPubMed
Behnam, S, Timmesfeld, N & Arabin, B (2022) Lifestyle interventions to improve pregnancy outcomes: a systematic review and specified meta-analyses. Geburtshilfe Frauenheilkd 82, 12491264.Google ScholarPubMed
Bahri Khomami, M, Teede, HJ, Enticott, J et al. (2022) Implementation of antenatal lifestyle interventions into routine care: secondary analysis of a systematic review. JAMA Network Open 5, e2234870.CrossRefGoogle ScholarPubMed
Harrison, CL, Bahri Khomami, M, Enticott, J et al. (2023) Key components of antenatal lifestyle interventions to optimize gestational weight gain: secondary analysis of a systematic review. JAMA Network Open 6, e2318031.CrossRefGoogle ScholarPubMed
Lloyd, M, Morton, J, Teede, H et al. (2023) Long-term cost-effectiveness of implementing a lifestyle intervention during pregnancy to reduce the incidence of gestational diabetes and type 2 diabetes. Diabetologia 66, 12231234.CrossRefGoogle ScholarPubMed
Lim, S, Hill, B, Teede, HJ et al. (2020) An evaluation of the impact of lifestyle interventions on body weight in postpartum women: a systematic review and meta-analysis. Obes Rev 21, e12990.CrossRefGoogle ScholarPubMed
Pennington, A, O'Reilly, SL, Young, D et al. (2016) Improving follow-up care for women with a history of gestational diabetes: perspectives of GPs and patients. Aust J Prim Health 23, 6674.CrossRefGoogle Scholar
Lie, MLS, Hayes, L, Lewis-Barned, NJ et al. (2013) Preventing type 2 diabetes after gestational diabetes: women's experiences and implications for diabetes prevention interventions. Diabetic Med 30, 986993.CrossRefGoogle ScholarPubMed
Christiansen, PK, Skjøth, MM, Rothmann, MJ et al. (2019) Lifestyle interventions to maternal weight loss after birth: a systematic review. Syst Rev 8, 327.CrossRefGoogle ScholarPubMed
Vincze, L, Rollo, M, Hutchesson, M et al. (2019) Interventions including a nutrition component aimed at managing gestational weight gain or postpartum weight retention: a systematic review and meta-analysis. JBI Database System Rev Implement Rep 17, 297364.CrossRefGoogle ScholarPubMed
Makama, M, Skouteris, H, Moran, LJ et al. (2021) Reducing postpartum weight retention: a review of the implementation challenges of postpartum lifestyle interventions. J Clin Med 10, 1891.CrossRefGoogle ScholarPubMed
Lim, S, Chen, M, Makama, M et al. (2020) Preventing type 2 diabetes in women with previous gestational diabetes: reviewing the implementation gaps for health behavior change programs. Semin Reprod Med 38, 377383.Google ScholarPubMed
Poushter, J, Bishop, C & Chwe, H (2018) Social media use continues to rise in developing countries but plateaus across developed ones: digital divides remain, both within and across countries. http://www.pewglobal.org/2018/06/19/social-media-use-continues-to-rise-in-developing-countries-but-plateaus-across-developed-ones/#table (accessed September 18).Google Scholar
Nielson (2017) Mom genes: looking at the media DNA of working and stay-at-home mothers. https://www.nielsen.com/us/en/insights/article/2017/mom-genes-looking-at-the-media-dna-of-working-and-stay-at-home-moms/ (accessed September 2020).Google Scholar
Becker, S, Miron-Shatz, T, Schumacher, N et al. (2014) mHealth 2⋅0: experiences, possibilities, and perspectives. JMIR Mhealth Uhealth 2, e24.CrossRefGoogle Scholar
Guerra-Reyes, L, Christie, VM, Prabhakar, A et al. (2016) Postpartum health information seeking using mobile phones: experiences of low-income mothers. Matern Child Health J 20, 1321.CrossRefGoogle ScholarPubMed
Roche, D, Rafferty, A, Holden, S et al. (2022) Maternal well-being and stage of behaviour change during pregnancy: a secondary analysis of the PEARS randomised controlled trial. Int J Environ Res Public Health 20, 34.CrossRefGoogle ScholarPubMed
O'Brien, EC, Segurado, R, Geraghty, AA et al. (2019) Impact of maternal education on response to lifestyle interventions to reduce gestational weight gain: individual participant data meta-analysis. BMJ Open 9, e025620.CrossRefGoogle ScholarPubMed
Hussain, T, Smith, P & Yee, LM (2020) Mobile phone-based behavioral interventions in pregnancy to promote maternal and fetal health in high-income countries: systematic review. JMIR Mhealth Uhealth 8, e15111.CrossRefGoogle ScholarPubMed
Brown, HM, Bucher, T, Collins, CE et al. (2019) A review of pregnancy iPhone apps assessing their quality, inclusion of behaviour change techniques, and nutrition information. Matern Child Nutr 0, e12768.CrossRefGoogle Scholar
Litterbach, E-K, Russell, CG, Taki, S et al. (2017) Factors influencing engagement and behavioral determinants of infant feeding in an mHealth program: qualitative evaluation of the growing healthy program. JMIR Mhealth Uhealth 5, e196.CrossRefGoogle Scholar
Taki, S, Lymer, S, Russell, CG et al. (2017) Assessing user engagement of an mHealth intervention: development and implementation of the growing healthy app engagement Index. JMIR Mhealth Uhealth 5, e89.CrossRefGoogle ScholarPubMed
Bland, C, Dalrymple, KV, White, SL et al. (2020) Smartphone applications available to pregnant women in the United Kingdom: an assessment of nutritional information. Matern Child Nutr 16, e12918.CrossRefGoogle ScholarPubMed
Greene, EM, O'Brien, EC, Kennelly, MA et al. (2021) Acceptability of the pregnancy, exercise, and nutrition research study with smartphone app support (PEARS) and the use of mobile health in a mixed lifestyle intervention by pregnant obese and overweight women: secondary analysis of a randomized controlled trial. JMIR Mhealth Uhealth 9, e17189.CrossRefGoogle Scholar
Brammall, BR, Garad, RM, Boyle, JA et al. (2022) Assessing the content and quality of digital tools for managing gestational weight gain: systematic search and evaluation. J Med Internet Res 24, e37552.CrossRefGoogle ScholarPubMed
Kelly-Whyte, N, McNulty, C & O'Reilly, S (2021) Perspectives on mHealth interventions during and after gestational diabetes. Curr Dev Nutr 5, 768.CrossRefGoogle Scholar
Phelan, S, Clifton, RG, Haire-Joshu, D et al. (2020) One-year postpartum anthropometric outcomes in mothers and children in the LIFE-Moms lifestyle intervention clinical trials. Int J Obes 44, 5768.CrossRefGoogle ScholarPubMed
Flynn, AC, Dalrymple, K, Barr, S et al. (2016) Dietary interventions in overweight and obese pregnant women: a systematic review of the content, delivery, and outcomes of randomized controlled trials. Nutr Rev 74, 312328.CrossRefGoogle ScholarPubMed
Lim, S, O'Reilly, S, Behrens, H et al. (2015) Effective strategies for weight loss in post-partum women: a systematic review and meta-analysis. Obes Rev 16, 972987.CrossRefGoogle ScholarPubMed
Maindal, HT, Timm, A, Dahl-Petersen, IK et al. (2021) Systematically developing a family-based health promotion intervention for women with prior gestational diabetes based on evidence, theory and co-production: the Face-it study. BMC Public Health 21, 1616.CrossRefGoogle ScholarPubMed
Nielsen, KK, Dahl-Petersen, IK, Jensen, DM et al. (2020) Protocol for a randomised controlled trial of a co-produced, complex, health promotion intervention for women with prior gestational diabetes and their families: the Face-it study. Trials 21, 146.CrossRefGoogle ScholarPubMed
Harrison, CL, Brammall, BR, Garad, R et al. (2022) OptimalMe intervention for healthy preconception, pregnancy, and postpartum lifestyles: protocol for a randomized controlled implementation effectiveness feasibility trial. JMIR Res Protoc 11, e33625.CrossRefGoogle ScholarPubMed
O'Reilly, SL, Burden, C, Campoy, C et al. (2021) Bump2Baby and Me: protocol for a randomised trial of mHealth coaching for healthy gestational weight gain and improved postnatal outcomes in high-risk women and their children. Trials 22, 963.CrossRefGoogle ScholarPubMed
O'Reilly, SL, Laws, R, Maindal, HT et al. (2023) A complex mHealth coaching intervention to prevent overweight, obesity, and diabetes in high-risk women in antenatal care: protocol for a hybrid type 2 effectiveness-implementation study. JMIR Res Protoc 12, e51431.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Comparison of the percentages of women with a pre-pregnancy BMI ≥30 from 2010 and 2015 (risk ratios and 95 % CI). Pooled random-effects model estimate 1⋅15 (95 % CI 1⋅08, 1⋅22). Adapted from European Perinatal Health Report 2018(36).