Diet and exercise interventions for preventing gestational diabetes mellitus

  • Protocol
  • Intervention

Authors

  • Morven Crane,

    Corresponding author
    1. Lyell McEwin Hospital, Adelaide, South Australia, Australia
    2. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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  • Emily Bain,

    1. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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  • Joanna Tieu,

    1. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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  • Shanshan Han,

    1. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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  • Philippa Middleton,

    1. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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  • Caroline A Crowther

    1. The University of Adelaide, ARCH: Australian Research Centre for Health of Women and Babies, The Robson Institute, Discipline of Obstetrics and Gynaecology, Adelaide, South Australia, Australia
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Abstract

This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the effects of physical exercise in combination with dietary advice for pregnant women for preventing gestational diabetes mellitus (GDM), and associated adverse health consequences for the mother and her infant/child.

Background

Description of the condition

Introduction and definition

Gestational diabetes mellitus (GDM) is a complication of pregnancy that is defined as carbohydrate intolerance resulting in hyperglycaemia (abnormally high blood sugar) of variable severity with onset or first recognition during pregnancy (WHO 1999). GDM defined in this way includes women with undiagnosed pre-existing diabetes, as well as those for whom the first onset is during pregnancy (especially during the third trimester of pregnancy).

Pathophysiology and symptoms

In normal pregnancy, relative maternal insulin resistance develops, beginning in the second trimester, with a progressive decline in insulin sensitivity until term. This physiological change facilitates the transport of glucose across the placenta to stimulate normal fetal growth and development. For women with GDM, a greater degree of maternal insulin resistance may lead to maternal hyperglycaemia, increased glucose transport across the placenta, fetal hyperinsulinaemia and accelerated growth in the fetus (Setji 2005). Usually, pregnancy-induced maternal insulin resistance resolves promptly after the baby is born.

While many women are asymptomatic, symptoms and signs associated with hyperglycaemia, such as polyuria (increased urinary frequency), polydipsia (increased thirst), blurred vision and fatigue, may be seen where GDM is undetected or poorly controlled (Kjos 1999).

Risk factors for GDM

Observational studies have helped to identify a multitude of risk factors for GDM; these include maternal body mass index (BMI) of at least 30 kg/m², physical inactivity (Chasan-Taber 2008), advancing maternal age (Morisset 2010), increasing parity, and ethnicity. Diets low in fibre, with a high glycaemic load have been shown to increase the risk of GDM (Zhang 2006). Women are also at an increased risk of GDM who have had a previous macrocosmic baby (birthweight 4000 grams or more), have had previous GDM (Petry 2010), have a family history or first-degree relative with diabetes, or have polycystic ovarian syndrome (Reece 2010). Weight gain during pregnancy for women who are overweight or obese has also been shown to correlate with GDM risk (Hedderson 2010; Morisset 2010).

Investigations

The prevalence of GDM appears to be increasing worldwide in parallel with increasing rates of type 2 diabetes mellitus and maternal obesity (Bottalico 2007; Dabelea 2005). Depending on the population sampled and diagnostic criteria used, reported prevalences range from 1% to 14% (ADA 2004; Mulla 2010). Diagnostic methods vary and there are currently no uniformly accepted international diagnostic criteria. The World Health Organization (WHO) recommends a 75 gram oral glucose tolerance test (OGTT) at 24 to 28 weeks' gestation. The woman is fasted prior to being given a 75 gram glucose load, with measurement of the blood glucose concentration two hours later (WHO 1999). The diagnosis of GDM is made if the blood glucose concentration at two hours is greater than or equal to 7.8 mmol/L. In some parts of the world a 100 gram three-hour OGTT is used. Universal screening is encouraged due to an absence of pre-identifiable risk factors in up to 50% of cases (Carr 1998). However, in some parts of the world, screening is only performed in 'high-risk' women, following an assessment of risk factors. Due to the lack of consistent screening procedures and diagnostic criteria between and within countries, different populations of women are diagnosed with GDM in different parts of the world.

The Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study addresses the absence of internationally agreed diagnostic criteria for GDM, and was designed to clarify risks of adverse outcomes associated with degrees of maternal glucose intolerance (Coustan 2010). Following this study, a task force of the International Association of Diabetes in Pregnancy Study Group (IADPSG) recommended new criteria for the diagnosis of GDM, which diagnoses GDM if any of the following three 75 gram, two-hour OGTT thresholds are met or exceeded: fasting plasma glucose: 5.1 mmol/L (92 mg/dL), one-hour plasma glucose: 10.0 mmol/L (180 mg/dL) or two-hour plasma glucose: 8.5 mmol/L (153 mg/dL) (IADPSG Consensus Panel 2010). Global adoption of these recommendations would likely have widespread implications, including for some regions/countries, a substantial change in practice, and in most cases, a substantial increase in the diagnosis of GDM, and accordingly significant challenges for healthcare systems. A number of studies have already revealed higher GDM prevalence when using the IADPSG, compared with other (including WHO) criteria (Edwards 2011; Moses 2011; O'Sullivan 2011), yet have also confirmed the increase in adverse pregnancy outcomes for the diagnosed women (O'Sullivan 2011); thus revealing a potential important opportunity the proposed criteria may provide for reducing maternal and infant morbidity. Debate surrounding the potential risks, costs and benefits of global use of these diagnostic criteria is ongoing (Langer 2013).

Health consequences of GDM

GDM is associated with an increased occurrence of a number of complications during pregnancy including pre-eclampsia (Dodd 2007) and the requirement for induction of labour or caesarean section (Dodd 2007; Reece 2010). Fetal consequences may include macrosomia (large baby), which in turn may be associated with adverse maternal outcomes such as uterine rupture, and perineal lacerations (Reece 2010). Women who develop GDM have a significantly increased risk of developing type 2 diabetes later in life (Bellamy 2009); they also are at an increased risk of developing GDM in future pregnancies (Bottalico 2007).

For the infant, GDM is associated with a range of complications. Babies born to mothers with GDM are more likely to be macrosomic or large-for-gestational age (LGA) (Crowther 2005; Metzger 2008; Reece 2009; Reece 2010). LGA infants are at increased risk of birth injury, including perinatal asphyxia, and shoulder dystocia, bone fractures or nerve palsies (Henriksen 2008; Reece 2010). These infants are also at increased risk of developing type 2 diabetes, hypertension, obesity and metabolic syndrome later in life (ADA 2004; Reece 2010; Whincup 2008). In addition, babies born to mothers with GDM are at increased risk of neonatal hypoglycaemia (Dodd 2007), respiratory distress syndrome, polycythaemia (raised red blood cell count), hyperbilirubinaemia, and being born preterm (Metzger 2008; Reece 2009; Reece 2010). Such health consequences together contribute to a need for enhanced neonatal care (Svare 1999). If untreated, GDM may be associated with an increased risk of perinatal mortality.

Importantly, in a recent randomised controlled trial, the treatment of women with mild GDM (dietary intervention, self-monitoring of blood glucose and insulin therapy if needed) was shown to significantly reduce the risk of a number of such complications including fetal overgrowth, shoulder dystocia, caesarean delivery and hypertensive disorders (Landon 2009). The Cochrane review 'Treatments for gestational diabetes' similarly concluded that some specific treatments (including dietary advice and insulin) for mild GDM may reduce the risk of maternal and perinatal morbidity (Alwan 2009).

Maternal hyperglycaemia less severe than that associated with a diagnosis of GDM, may similarly result in clinically important complications for the both mother and her infant (Han 2012a; Metzger 2008). While the risk of adverse maternal and infant pregnancy outcomes appears to increase with increasing levels of glucose impairment (Dodd 2007), the concentration at which pregnancy hyperglycaemia becomes pathological has not been conclusively determined (Metzger 2008; Mulla 2010).

Description of the intervention

Dietary interventions

The aim of dietary advice or related interventions in pregnancy is to optimise glycaemic control, preventing maternal hyperglycaemia and reducing post-prandial glucose concentrations. Dietary advice may be aimed at ensuring women's diets provide sufficient energy and nutrients to allow normal fetal growth while avoiding accelerated fetal growth patterns, and minimising excessive weight gain (Dornhorst 2002). As glucose is the primary source of energy for fetal growth (Moses 2006), excessive fetal growth is most effectively limited by sustaining low post-prandial glucose concentrations (Dornhorst 2002).

The benefits of low glycaemic index (GI) diets have been shown for individuals being treated for type 2 diabetes (Brand-Miller 2003), and some evidence exists to suggest similar benefits may be conferred for women with GDM (Cheung 2009). GI quantitatively defines the effect of carbohydrate-based foods on blood glucose concentration (Jenkins 1981). The GI value for a food is determined by comparing the blood glucose response to that food to the response to an equivalent amount of standard glucose (Foster-Powell 2002). Foods with a 'low' GI (less than 55) induce a gradual increase in blood glucose due to slow digestion and absorption, whereas foods that produce a rapid rise in blood glucose concentration are referred to as 'high' GI (greater than 70). Examples of low GI foods are whole-grain bread and dairy foods. High GI foods include potatoes, highly processed carbohydrate foods such as white bread and some breakfast cereals (Atkinson 2008; Jenkins 1981).

Other suggested dietary recommendations for GDM prevention have included the consumption of a high fibre diet (Fraser 1983), and changing the proportion of each macronutrient that makes up the woman’s overall intake, for example increasing the proportion of fat in the diet to compensate for carbohydrate proportion changes (Dornhorst 2002). While high fat diets may have a low GI, they are generally contraindicated due to known associated cardiovascular health risks.

Exercise interventions

Benefits of exercise during pregnancy are now recognised, and thus women are encouraged to engage in 'light-to-moderate' exercise in the absence of any known pregnancy or medical complications (ACOG 2002; Davies 2003; Dempsey 2005). The Royal College of Obstetricians and Gynaecologists recommend that all women participate in aerobic and strength-conditioning exercise, with the goal to maintain a good fitness level, as part of a healthy lifestyle during their pregnancy (RCOG 2006). Women often reduce their levels of physical activity during pregnancy (Pereira 2007), many due to a perceived risk to maternal or fetal health (Clarke 2004) and the impact of early pregnancy symptoms such as nausea and fatigue (Pereira 2007).

Regular aerobic exercise may lead to lower fasting and postprandial blood glucose concentrations in previously sedentary individuals. Exercise may decrease circulating glucose and insulin during, and for a period of time after, an exercise session (Clapp 1991; Clapp 1998). It has been shown outside of pregnancy that exercise may reduce the risk and delay the onset of the development of type 2 diabetes mellitus (Jeon 2007). Exercise has been shown to reduce insulin resistance in non-pregnant women and men, leading to effective prevention and management of type 2 diabetes (Clapp 2006Knowler 2002Redden 2011).

Suggested benefits of exercise during pregnancy include a reduction in lower back pain, fluid retention and cardiovascular stress (Schlüssel 2008). Exercise is believed to play a role in reducing the risk of complications such as preterm birth and pre-eclampsia (Dempsey 2005; Schlüssel 2008), and may help prevent excess pregnancy weight gain and post-partum weight retention (Schlüssel 2008). There is increasing evidence from observational studies indicating that pre-pregnancy exercise and exercise in early pregnancy is associated with a reduction in insulin resistance (Reece 2009), and consequently a reduced risk of developing GDM (Jeon 2007; Redden 2011).

How the intervention might work

Combined diet and exercise interventions

Whille dietary advice and exercise interventions alone for the prevention of type 2 diabetes and GDM have been widely assessed, more recently a shift towards combining such interventions in what may be regarded as 'lifestyle' interventions has been observed.

Several randomised controlled trials have established that the progression to type 2 diabetes can be prevented or postponed with lifestyle interventions in individuals with impaired glucose tolerance ('high-risk' individuals) (Knowler 2002; Li 2008; Ratner 2008; Tuomilehto 2001). Such studies have focused strongly on combining increased physical activity and dietary modification, along with weight reduction for overweight participants. Long-term follow-up studies of such lifestyle interventions (that lasted for a limited time), have shown sustained beneficial effects on risk factors and diabetes incidence (Tuomilehto 2011). It has been suggested that a key factor in the success of such interventions has been the comprehensive approach, addressing and working to correct several lifestyle-related risk factors simultaneously (Tuomilehto 2011).

As it is accepted that a multitude of risk factors may increase the risk of type 2 diabetes, these randomised trials focused on a number of lifestyle-related factors concurrently. In the Finnish Diabetes Prevention Study five lifestyle targets were predefined, including: weight loss greater than 5%, intake of fat lower than 30% energy, intake of saturated fats lower than 10% energy, intake of dietary fibre greater than 15 g/1000 kcal, and an increase of physical activity to at least four hours per week (Tuomilehto 2001). These targets were perceived as relatively modest, and it was believed that such lifestyle changes would be feasible to maintain in the long term (Tuomilehto 2011). No 'high-risk' individual with impaired glucose tolerance developed diabetes during the trial if they achieved at least four of the five lifestyle targets (Tuomilehto 2001). This trial was the first of a number to show that type 2 diabetes may be prevented with lifestyle interventions, and highlighted the importance of addressing multiple lifestyle-related risk factors for optimal benefit (Knowler 2002; Li 2008; Tuomilehto 2001).

Whilst such trials included 'high-risk' individuals, were not focused on pregnant women, and considered type 2 diabetes, they certainly offer some support for the use of lifestyle interventions in pregnant women for the prevention of GDM. Furthermore, the Cochrane reviews assessing dietary or exercise interventions alone (not lifestyle interventions) for the prevention of GDM have revealed inconclusive findings. The review 'Dietary advice in pregnancy for preventing gestational diabetes mellitus' (Tieu 2008) included three small trials, and concluded that while a low GI diet was beneficial for some outcomes for the mother (lower maternal fasting glucose concentration) and child (reduction in LGA infants, lower ponderal indexes) (Clapp 2006; Moses 2006), the evidence is limited (Tieu 2008). Similarly, the review 'Exercise for pregnant women for preventing gestational diabetes mellitus' (Han 2012b) concluded that there is limited evidence to currently support exercise during pregnancy for the prevention of glucose intolerance or GDM. This review included five trials and revealed no improvement in GDM incidence, nor any improvements for the reported infant outcomes (Han 2012b).

As it is widely acknowledged that a multitude of risk factors are associated with GDM, it is considered plausible that lifestyle interventions for women during pregnancy, aimed at correcting multiple lifestyle-related risk factors, may be effective in reducing the incidence of GDM. Such lifestyle interventions may combine, for example, dietary advice or modifications with exercise interventions.

Why it is important to do this review

There is an increased risk of perinatal complications associated with GDM (Han 2012a). Effective strategies are required to reduce the incidence of GDM and the associated health consequences for both the mother and her infant. This review will complement the existing reviews titled 'Dietary advice in pregnancy for preventing gestational diabetes mellitus' (Tieu 2008) and 'Exercise for pregnant women for preventing gestational diabetes mellitus' (Han 2012b), to assess combined diet and exercise interventions for preventing GDM.

Objectives

To assess the effects of physical exercise in combination with dietary advice for pregnant women for preventing gestational diabetes mellitus (GDM), and associated adverse health consequences for the mother and her infant/child.

Methods

Criteria for considering studies for this review

Types of studies

All published randomised controlled trials assessing the effects of diet and exercise interventions in combination for preventing gestational diabetes mellitus (GDM) will be included. We will include cluster-randomised trials, and studies published as abstracts only. We will exclude quasi-randomised controlled trials and cross-over trials.

Types of participants

Pregnant women regardless of age, gestation, parity or plurality. We will exclude women with pre-existing type 1 or type 2 diabetes.

Types of interventions

Interventions will include any type of exercise intervention (i.e. exercise advice, providing exercise sessions), when combined with any type of dietary advice. We will include studies where such interventions are compared with no intervention (i.e. routine antenatal care), or with an alternative exercise and/or dietary advice intervention.

Types of outcome measures

Primary outcomes
Maternal outcomes
  1. Incidence of GDM (diagnostic criteria as defined in individual trials)

  2. Mode of birth (normal vaginal birth, operative vaginal birth, caesarean section)

Neonatal outcomes
  1. Large-for-gestational age (LGA) (as defined in individual trials)

  2. Perinatal mortality (fetal and neonatal mortality)

Secondary outcomes
Perinatal outcomes
  1. Incidence of pregnancy hyperglycaemia not meeting GDM diagnostic criteria (diagnostic criteria as defined in individual trials)

  2. Induction of labour

  3. Augmentation of labour

  4. Perineal trauma

  5. Pre-eclampsia

  6. Weight gain during pregnancy

  7. Gestational age at screening for GDM

  8. Postpartum haemorrhage

  9. Postpartum infection

  10. Placental abruption

  11. Adherence with intervention

  12. Women’s sense of well-being and quality of life (as defined in individual trials)

  13. Women’s view of intervention

Long term
  1. Postnatal weight retention

  2. Body mass index (BMI)

  3. Gestational diabetes in subsequent pregnancy

  4. Development of type 2 diabetes mellitus

  5. Development of type 1 diabetes mellitus

  6. Impaired glucose tolerance (defined by author(s))

  7. Insulin sensitivity (defined by author(s))

Fetal/neonatal outcomes
  1. Macrosomia (birthweight greater than 4000 grams)

  2. Birthweight

  3. Small-for-gestational age (as defined in individual trials)

  4. Neonatal hypoglycaemia requiring treatment (variously defined by authors of individual trials)

  5. Gestational age at birth

  6. Preterm birth (less than 37 weeks’ gestation)

  7. Shoulder dystocia

  8. Bone fracture

  9. Nerve palsy

  10. Respiratory distress syndrome

  11. Hyperbilirubinaemia requiring treatment (variously defined by authors of individual trials)

  12. Apgar scores (less than seven at five minutes)

  13. Ponderal index

  14. Skinfold thickness measurements

  15. Neonatal glucose concentration

Childhood outcomes
  1. Weight

  2. Height

  3. BMI

  4. Fat mass/fat free mass

  5. Skinfold thickness measurement

  6. Blood pressure

  7. Impaired glucose tolerance (as defined by authors)

  8. Development of type 1 diabetes mellitus

  9. Development of type 2 diabetes mellitus

  10. Insulin sensitivity

  11. Dyslipidaemia or metabolic syndrome

  12. Neurodisability

  13. Educational achievement

Adulthood outcomes
  1. Weight

  2. Height

  3. BMI

  4. Fat mass/fat-free mass

  5. Skinfold thickness measurements

  6. Blood pressure

  7. Impaired glucose tolerance (defined by author(s))

  8. Development of type 1 diabetes

  9. Development of type 2 diabetes

  10. Insulin sensitivity (defined by author(s))

  11. Dyslipidaemia or metabolic syndrome

  12. Educational achievement

Health services cost
  1. Number of hospital visits or health professional visits (e.g. physiotherapist) or antenatal visits for mother

  2. Medical physician visits

  3. Costs to families in relation to the management provided

  4. Length of postnatal stay (mother)

  5. Admission to neonatal ward

  6. Length of postnatal stay (baby)

  7. Cost of maternal care

  8. Cost of offspring care

Search methods for identification of studies

Electronic searches

We will contact the Trials Search Co-ordinator to search the Cochrane Pregnancy and Childbirth Group’s Trials Register. 

The Cochrane Pregnancy and Childbirth Group’s Trials Register is maintained by the Trials Search Co-ordinator and contains trials identified from:

  1. monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);

  2. weekly searches of MEDLINE;

  3. weekly searches of EMBASE;

  4. handsearches of 30 journals and the proceedings of major conferences;

  5. weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.

Details of the search strategies for CENTRAL, MEDLINE and EMBASE, the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service can be found in the ‘Specialized Register’ section within the editorial information about the Cochrane Pregnancy and Childbirth Group.

Trials identified through the searching activities described above are each assigned to a review topic (or topics). The Trials Search Co-ordinator searches the register for each review using the topic list rather than keywords. 

We will not apply any language restrictions.

Data collection and analysis

Selection of studies

Two review authors will independently assess for inclusion all the potential studies we identify as a result of the search strategy. We will resolve any disagreement through discussion or, if required, we will consult a third review author.

Data extraction and management

We will design a form to extract data. For eligible studies, two review authors will extract the data using the agreed form. We will resolve discrepancies through discussion or, if required, we will consult the third review author. We will enter data into Review Manager software (RevMan 2011) and check for accuracy.

When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to provide further details.

Assessment of risk of bias in included studies

Two review authors will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third assessor.

(1) Random sequence generation (checking for possible selection bias)

We will describe for each included study the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We will assess the method as:

  • low risk of bias (any truly random process, e.g. random number table; computer random number generator);

  • high risk of bias (any non-random process, e.g. odd or even date of birth; hospital or clinic record number);

  • unclear risk of bias.   

 (2) Allocation concealment (checking for possible selection bias)

We will describe for each included study the method used to conceal allocation to interventions prior to assignment and will assess whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.

We will assess the methods as:

  • low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);

  • high risk of bias (open random allocation; unsealed or non-opaque envelopes, alternation; date of birth);

  • unclear risk of bias.   

(3.1) Blinding of participants and personnel (checking for possible performance bias)

We will describe for each included study the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We will consider that studies are at low risk of bias if they were blinded, or if we judge that the lack of blinding would be unlikely to affect results. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess the methods as:

  • low, high or unclear risk of bias for participants;

  • low, high or unclear risk of bias for personnel.

(3.2) Blinding of outcome assessment (checking for possible detection bias)

We will describe for each included study the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess methods used to blind outcome assessment as:

  • low, high or unclear risk of bias.

(4) Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data)

We will describe for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported and the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information is reported, or can be supplied by the trial authors, we will re-include missing data in the analyses which we undertake.

We will assess methods as:

  • low risk of bias (e.g. no missing outcome data; missing outcome data balanced across groups);

  • high risk of bias (e.g. numbers or reasons for missing data imbalanced across groups; ‘as treated’ analysis done with substantial departure of intervention received from that assigned at randomisation);

  • unclear risk of bias.

(5) Selective reporting (checking for reporting bias)

We will describe for each included study how we investigated the possibility of selective outcome reporting bias and what we found.

We will assess the methods as:

  • low risk of bias (where it is clear that all of the study’s pre-specified outcomes and all expected outcomes of interest to the review have been reported);

  • high risk of bias (where not all the study’s pre-specified outcomes have been reported; one or more reported primary outcomes were not pre-specified; outcomes of interest are reported incompletely and so cannot be used; study fails to include results of a key outcome that would have been expected to have been reported);

  • unclear risk of bias.

(6) Other bias (checking for bias due to problems not covered by (1) to (5) above)

We will describe for each included study any important concerns we have about other possible sources of bias.

We will assess whether each study was free of other problems that could put it at risk of bias:

  • low risk of other bias;

  • high risk of other bias;

  • unclear whether there is risk of other bias.

(7) Overall risk of bias

We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). With reference to (1) to (6) above, we will assess the likely magnitude and direction of the bias and whether we consider it is likely to impact on the findings. We will explore the impact of the level of bias through undertaking sensitivity analyses - see Sensitivity analysis

Measures of treatment effect

Dichotomous data

For dichotomous data, we will present results as summary risk ratio with 95% confidence intervals. 

Continuous data

For continuous data, we will use the mean difference if outcomes are measured in the same way between trials. We will use the standardised mean difference to combine trials that measure the same outcome, but use different methods.  

Unit of analysis issues

Cluster-randomised trials

We will include cluster-randomised trials in the analyses along with individually-randomised trials. We will adjust their sample sizes using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) using an estimate of the intracluster correlation coefficient (ICC) derived from the trial (if possible), from a similar trial, or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster-randomised trials and individually-randomised trials, we will synthesise the relevant information. We will consider it reasonable to combine the results from both if there is little heterogeneity between the study designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be unlikely.

We will acknowledge heterogeneity in the randomisation unit and perform a sensitivity analysis to investigate the effects of the randomisation unit.

Cross-over trials

We consider cross-over designs inappropriate for this research question.

Multi-arm studies

For multi-arm studies, we will use methods as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) to overcome possible unit-of analysis errors, by combining groups to make a single pair-wise comparison (where appropriate), or by splitting the 'shared' group into two (or more) groups with smaller sample sizes, and including the two (or more) comparisons.

Dealing with missing data

For included studies, we will note levels of attrition. We will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysis.

For all outcomes, we will carry out analyses, as far as possible, on an intention-to-treat basis, i.e. we will attempt to include all participants randomised to each group in the analyses, and all participants will be analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial will be the number randomised minus any participants whose outcomes are known to be missing.

Assessment of heterogeneity

We will assess statistical heterogeneity in each meta-analysis using the T², I² and Chi² statistics. We will regard heterogeneity as substantial if the I² is greater than 30% and either the T² is greater than zero, or there is a low P value (less than 0.10) in the Chi² test for heterogeneity. 

Assessment of reporting biases

If there are 10 or more studies in the meta-analysis we will investigate reporting biases (such as publication bias) using funnel plots. We will assess
funnel plot asymmetry visually. If asymmetry is suggested by a visual assessment, we will perform exploratory analyses to investigate it.

Data synthesis

We will carry out statistical analysis using the Review Manager software (RevMan 2011). We will use fixed-effect meta-analysis for combining data where it is reasonable to assume that studies are estimating the same underlying treatment effect: i.e. where trials are examining the same intervention, and the trials’ populations and methods are judged sufficiently similar. If there is clinical heterogeneity sufficient to expect that the underlying treatment effects differ between trials, or if substantial statistical heterogeneity is detected, we will use random-effects meta-analysis to produce an overall summary if an average treatment effect across trials is considered clinically meaningful. The random-effects summary will be treated as the average range of possible treatment effects and we will discuss the clinical implications of treatment effects differing between trials. If the average treatment effect is not clinically meaningful, we will not combine trials.

If we use random-effects analyses, the results will be presented as the average treatment effect with 95% confidence intervals, and the estimates of  T² and I².

Subgroup analysis and investigation of heterogeneity

If we identify substantial heterogeneity, we will investigate it using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, use random-effects analysis to produce it.

Maternal characteristics, and characteristics of the dietary advice or exercise interventions are likely to affect health outcomes.

We plan to carry out the following subgroup analyses.

  • Maternal age (35 years of age or more versus less than 35 years of age).

  • Maternal BMI (at or before trial entry) (BMI of 18.5 to 24.9 kg/m² versus BMI of less than 18.5 kg/m²; versus BMI of 25 to 29.9 kg/m²; versus BMI of 30 kg/m² to 39.9 kg/m²; and versus BMI of 40 kg/m² or more).

  • Ethnicity (high-risk ethnic groups versus low-risk ethnic groups).

  • Parity (parity of 0 versus 1-2; and versus 3 or more).

  • Nature of the exercise intervention (e.g. frequent versus infrequent advice/sessions; short versus long duration of advice/sessions; high intensity verus low intensity of advice/sessions; advice only versus interactive sessions).

  • Nature of the dietary intervention (e.g. frequent versus infrequent intervention; short versus long duration of intervention; advice only versus more intensive support).

We will use primary outcomes in subgroup analyses.

We will assess subgroup differences by interaction tests available within RevMan (RevMan 2011). We will report the results of subgroup analyses quoting the χ2 statistic and P value, and the interaction test I² value.

Sensitivity analysis

We will carry out sensitivity analysis to explore the effects of trial quality assessed by sequence generation and allocation concealment, by omitting studies rated as 'high risk of bias' or 'unclear risk of bias' for these components. We will restrict this to the primary outcomes.

Acknowledgements

As part of the pre-publication editorial process, this protocol has been commented on by three peers (an editor and two referees who are external to the editorial team) and the Group's Statistical Adviser.

Contributions of authors

Morven Crane wrote the first draft of the protocol, with all review authors (Emily Bain, Joanna Tieu, Shanshan Han, Philippa Middleton, Caroline Crowther) making comments and contributing to subsequent drafts.

Declarations of interest

None known.

Sources of support

Internal sources

  • ARCH, Robinson Institute, Discipline of Obstetrics and Gynaecology, The University of Adelaide, Australia.

External sources

  • National Health and Medical Research Council, Australia.

Ancillary