Different methods and settings for glucose monitoring for gestational diabetes during pregnancy

Puvaneswary Raman; Emily Shepherd; Therese Dowswell; Philippa Middleton; Caroline A Crowther

doi:10.1002/14651858.CD011069.pub2

. 2017 Oct 29;2017(10):CD011069. doi: 10.1002/14651858.CD011069.pub2

Different methods and settings for glucose monitoring for gestational diabetes during pregnancy

Puvaneswary Raman ¹, Emily Shepherd ^2,^✉, Therese Dowswell ³, Philippa Middleton ⁴, Caroline A Crowther ^2,⁵

Editor: Cochrane Pregnancy and Childbirth Group

PMCID: PMC6485695 PMID: 29081069

Abstract

Background

Incidence of gestational diabetes mellitus (GDM) is increasing worldwide. Blood glucose monitoring plays a crucial part in maintaining glycaemic control in women with GDM and is generally recommended by healthcare professionals. There are several different methods for monitoring blood glucose which can be carried out in different settings (e.g. at home versus in hospital).

Objectives

The objective of this review is to compare the effects of different methods and settings for glucose monitoring for women with GDM on maternal and fetal, neonatal, child and adult outcomes, and use and costs of health care.

Search methods

We searched the Cochrane Pregnancy and Childbirth Group Trials Register (30 September 2016) and reference lists of retrieved studies.

Selection criteria

Randomised controlled trials (RCTs) or quasi‐randomised controlled trials (qRCTs) comparing different methods (such as timings and frequencies) or settings, or both, for blood glucose monitoring for women with GDM.

Data collection and analysis

Two authors independently assessed study eligibility, risk of bias, and extracted data. Data were checked for accuracy.

We assessed the quality of the evidence for the main comparisons using GRADE, for:

‐ primary outcomes for mothers: that is, hypertensive disorders of pregnancy; caesarean section; type 2 diabetes; and

‐ primary outcomes for children: that is, large‐for‐gestational age; perinatal mortality; death or serious morbidity composite; childhood/adulthood neurosensory disability;

‐ secondary outcomes for mothers: that is, induction of labour; perineal trauma; postnatal depression; postnatal weight retention or return to pre‐pregnancy weight; and

‐ secondary outcomes for children: that is, neonatal hypoglycaemia; childhood/adulthood adiposity; childhood/adulthood type 2 diabetes.

Main results

We included 11 RCTs (10 RCTs; one qRCT) that randomised 1272 women with GDM in upper‐middle or high‐income countries; we considered these to be at a moderate to high risk of bias. We assessed the RCTs under five comparisons. For outcomes assessed using GRADE, we downgraded for study design limitations, imprecision and inconsistency. Three trials received some support from commercial partners who provided glucose meters or financial support, or both.

Main comparisons

Telemedicine versus standard care for glucose monitoring (five RCTs): we observed no clear differences between the telemedicine and standard care groups for the mother, for:

‐ pre‐eclampsia or pregnancy‐induced hypertension (risk ratio (RR) 1.49, 95% confidence interval (CI) 0.69 to 3.20; 275 participants; four RCTs; very low quality evidence);

‐ caesarean section (average RR 1.05, 95% CI 0.72 to 1.53; 478 participants; 5 RCTs; very low quality evidence); and

‐ induction of labour (RR 1.06, 95% CI 0.63 to 1.77; 47 participants; 1 RCT; very low quality evidence);

or for the child, for:

‐ large‐for‐gestational age (RR 1.41, 95% CI 0.76 to 2.64; 228 participants; 3 RCTs; very low quality evidence);

‐ death or serious morbidity composite (RR 1.06, 95% CI 0.68 to 1.66; 57 participants; 1 RCT; very low quality evidence); and

‐ neonatal hypoglycaemia (RR 1.14, 95% CI 0.48 to 2.72; 198 participants; 3 RCTs; very low quality evidence).

There were no perinatal deaths in two RCTs (131 participants; very low quality evidence).

Self‐monitoring versus periodic glucose monitoring (two RCTs): we observed no clear differences between the self‐monitoring and periodic glucose monitoring groups for the mother, for:

‐ pre‐eclampsia (RR 0.17, 95% CI 0.01 to 3.49; 58 participants; 1 RCT; very low quality evidence); and

‐ caesarean section (average RR 1.18, 95% CI 0.61 to 2.27; 400 participants; 2 RCTs; low quality evidence);

or for the child, for:

‐ perinatal mortality (RR 1.54, 95% CI 0.21 to 11.24; 400 participants; 2 RCTs; very low quality evidence);

‐ large‐for‐gestational age (RR 0.82, 95% CI 0.50 to 1.37; 400 participants; 2 RCTs; low quality evidence); and

‐ neonatal hypoglycaemia (RR 0.64, 95% CI 0.39 to 1.06; 391 participants; 2 RCTs; low quality evidence).

Continuous glucose monitoring system (CGMS) versus self‐monitoring of glucose (two RCTs): we observed no clear differences between the CGMS and self‐monitoring groups for the mother, for:

‐ caesarean section (RR 0.91, 95% CI 0.68 to 1.20; 179 participants; 2 RCTs; very low quality evidence);

or for the child, for:

‐ large‐for‐gestational age (RR 0.67, 95% CI 0.43 to 1.05; 106 participants; 1 RCT; very low quality evidence) and

‐ neonatal hypoglycaemia (RR 0.79, 95% CI 0.35 to 1.78; 179 participants; 2 RCTs; very low quality evidence).

There were no perinatal deaths in the two RCTs (179 participants; very low quality evidence).

Other comparisons

Modem versus telephone transmission for glucose monitoring (one RCT): none of the review's primary outcomes were reported in this trial

Postprandial versus preprandial glucose monitoring (one RCT): we observed no clear differences between the postprandial and preprandial glucose monitoring groups for the mother, for:

‐ pre‐eclampsia (RR 1.00, 95% CI 0.15 to 6.68; 66 participants; 1 RCT);

‐ caesarean section (RR 0.62, 95% CI 0.29 to 1.29; 66 participants; 1 RCT); and

‐ perineal trauma (RR 0.38, 95% CI 0.11 to 1.29; 66 participants; 1 RCT);

or for the child, for:

‐ neonatal hypoglycaemia (RR 0.14, 95% CI 0.02 to 1.10; 66 participants; 1 RCT).

There were fewer large‐for‐gestational‐age infants born to mothers in the postprandial compared with the preprandial glucose monitoring group (RR 0.29, 95% CI 0.11 to 0.78; 66 participants; 1 RCT).

Authors' conclusions

Evidence from 11 RCTs assessing different methods or settings for glucose monitoring for GDM suggests no clear differences for the primary outcomes or other secondary outcomes assessed in this review.

However, current evidence is limited by the small number of RCTs for the comparisons assessed, small sample sizes, and the variable methodological quality of the RCTs. More evidence is needed to assess the effects of different methods and settings for glucose monitoring for GDM on outcomes for mothers and their children, including use and costs of health care. Future RCTs may consider collecting and reporting on the standard outcomes suggested in this review.

Plain language summary

Different methods and settings for glucose monitoring for women with gestational diabetes during pregnancy

What is the issue?

Gestational diabetes mellitus (GDM) is a glucose intolerance leading to high concentrations of glucose (sugar) in the blood (hyperglycaemia) that begins or is first recognised during pregnancy. Monitoring of blood glucose levels is an important way to maintain control of sugar concentrations in the blood. There are several different methods for monitoring blood glucose which can be carried out in different settings (e.g. at home or hospital), however it is not clear which is best for limiting health complications for women and their babies.

Why is this important?

Women with GDM are more likely to develop pre‐eclampsia (a dangerous condition characterised by high blood pressure) during pregnancy, and to have the birth induced, suffer trauma to the perineum during birth, or to give birth by caesarean section. Their babies are more likely to be large for their gestational age at birth, develop low blood sugar (hypoglycaemia), and suffer from complications leading to death. Both the women and their babies are more likely to develop long‐term health complications, including type 2 diabetes.

What evidence did we find?

We searched the medical literature in September 2016 and included 11 randomised controlled trials (RCTs) involving 1272 women with GDM and their babies. Three trials were supported by commercial partners.

We included five different comparisons:

1) telemedicine (transmission of glucose concentrations from home to healthcare professionals for review) versus standard care (face‐to‐face review in a clinic/hospital) (five RCTs);

2) self‐monitoring of glucose (at home) versus periodic monitoring of glucose (less frequently at face‐to‐face visits) (two RCTs);

3) use of a continuous glucose monitoring system (CCMS) versus less frequent self‐monitoring of glucose (two RCTs);

4) modem technology (transmitting glucose concentrations directly from glucose meters to healthcare professionals) versus telephone transmission of glucose concentrations (one RCT);

5) postprandial (after meal) versus preprandial (before meal) monitoring of glucose (one RCT).

Telemedicine versus standard care for glucose monitoring (five RCTs): there were no clear differences between women in the telemedicine and standard care groups for pre‐eclampsia or hypertension, caesarean section or induction of labour; or for their babies being born large‐for‐gestational age, developing a serious morbidity, or having hypoglycaemia. There were no deaths in the two RCTs that reported on deaths of babies.

Self‐monitoring versus periodic glucose monitoring (two RCTs): there were no clear differences between women in the self‐monitoring and periodic glucose monitoring groups for pre‐eclampsia or caesarean section; or for their babies dying, being born large‐for‐gestational age, or developing hypoglycaemia.

CGMS versus self‐monitoring of glucose (two RCTs): there was no clear difference between women in the CGMS and self‐monitoring groups for caesarean section; or for babies being born large‐for‐gestational age, or developing hypoglycaemia. There were no deaths of babies in the two RCTs.

Modem versus telephone transmission for glucose monitoring (one RCT): this RCT reported none of the outcomes we considered most important.

Postprandial versus preprandial glucose monitoring (one RCT): there were no clear differences between women in the postprandial and preprandial glucose monitoring groups for pre‐eclampsia, caesarean section or perineal trauma; or for babies developing hypoglycaemia. Babies born to women in the postprandial glucose monitoring group were less likely to be born large‐for‐gestational age than babies in the preprandial group.

The quality of the evidence for the above findings was low or very low. None of the 11 RCTs reported on postnatal depression, postnatal weight retention, return to pre‐pregnancy weight, or development of type 2 diabetes for the women; or disability, adiposity or development of type 2 diabetes for the babies as children or adults.

What does this mean?

Blood glucose monitoring is an important strategy for managing GDM, however it remains unclear what methods are best. Conclusive evidence from RCTs is not yet available to guide practice, although a range of methods has been investigated. Few RCTs have compared the same or similar interventions, RCTs have been small and have reported limited findings. Further large, well‐designed, RCTs are required to assess the effects of different methods and settings for blood glucose monitoring for women with GDM in order to improve outcomes for women and their babies in the short and long term.

Summary of findings

Background

Description of the condition

Gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is defined as "carbohydrate intolerance of varying degrees of severity with onset or first recognition during pregnancy" (Metzger 1998). Therefore women with unrecognised pre‐existing type 1 or type 2 diabetes, whose first presentation of the disease is during pregnancy, are included in this definition (Hoffman 1998). Many physiological changes occur as a part of normal pregnancy. For example, maternal metabolic changes include the development of relative insulin resistance and reduced glucose sensitivity, particularly during the second trimester of pregnancy (Kuhl 1998); these physiological changes facilitate the transport of glucose across the placenta to stimulate adequate fetal growth and development (Setji 2005). Some women, however, are predisposed to an excessive maternal insulin resistance and are consequently at risk of hyperglycaemia (high blood glucose) and GDM during their pregnancy.

Diagnostic methods for GDM vary, and there are currently no uniformly accepted international diagnostic criteria. The World Health Organization has recommended a 75 g 2‐hour oral glucose tolerance test (OGTT) at 24 to 28 weeks' gestation (WHO 2013), and in some parts of the world, a 100 g 3‐hour OGTT is used. While universal screening has been encouraged, in some countries screening is only performed for 'high‐risk' women, due to the lack of identifiable risk factors in many women subsequently diagnosed with GDM. The effects of different methods of screening (Tieu 2014), and strategies for diagnosing GDM (Farrar 2015), are the topics of other Cochrane Reviews.

Epidemiology and 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, and reported incidences vary greatly, and can be as high as 28% (Jiwani 2012). There is a general consensus however, that the incidence of GDM is increasing worldwide, in line with the increasing rates of both type 2 diabetes mellitus and maternal obesity (Dabelea 2005; Getahun 2008; Kim 2010; Lawrence 2008).

The HAPO (Hyperglycaemia and Adverse Pregnancy Outcomes) study explored the risks of adverse outcomes associated with different degrees of maternal hyperglycaemia (Coustan 2010; HAPO 2008). Following this, a task force from the International Association of Diabetes in Pregnancy Study Group (IADPSG) developed new consensus‐based criteria for GDM diagnosis using data from the study ‐ suggesting GDM diagnosis after a 75 g OGTT when any three of the following thresholds are met or exceeded: fasting plasma glucose: 5.1 mmol/L (92 mg/dL), 1‐hour plasma glucose: 10.0 mmol/L (180 mg/dL) or 2‐hour plasma glucose: 8.5 mmol/L (153 mg/dL) (IADPSG Consensus Panel 2010). Some studies published since these recommendations were made have revealed substantial increases in the numbers of women diagnosed with GDM when these criteria were applied (Benhalima 2013; Bodmer‐Roy 2012; Lapolla 2011; O'Sullivan 2011; Morikawa 2010). Accordingly, there is much debate surrounding the implications, including potential costs, risks and benefits of widespread use of these criteria.

In addition to obesity (a maternal body mass index of 30 kg/m² or higher), a range of risk factors for GDM have been identified, which include advanced maternal age, increased parity and certain ethnicities (ACOG 2013), with Indigenous Australian, Polynesian and South Asian (Indian) women being regarded as particularly high‐risk groups (Hoffman 1998). Women who have had GDM in a previous pregnancy are also at an increased risk of GDM in their subsequent pregnancies, as are women who have a family history of diabetes (ADA 2004).

Maternal and fetal complications

Hyperglycaemia has many end‐organ adverse effects and the diagnosis of GDM has implications for both mothers and their infants. The potential maternal complications of GDM include polyhydramnios (too much amniotic fluid) due to increased fetal urine production, pre‐eclampsia and caesarean birth (ACOG 2013). Maternal hyperglycaemia may cause accelerated fetal growth, and result in macrosomic (birthweight of at least 4000 g) or large‐for‐gestational‐age infants (Crowther 2005). While caesarean section is often the preferred mode of birth for a macrosomic infant, helping to avoid maternal perineal trauma and infant injury, this mode of birth may also be associated with increased maternal morbidity (Reece 2010).

There are well documented fetal and neonatal complications of GDM. Large‐for‐gestational‐age infants resulting from GDM can lead to shoulder dystocia (obstructed birth) and birth trauma such as nerve palsies and fractures (Crowther 2005; Dodd 2007; Landon 2009; Metzger 1998). Fetal hyperinsulinaemia (raised insulin levels) that occurs in response to maternal hyperglycaemia, may be associated with neonatal hypoglycaemia (HAPO 2008). Other potential complications for infants include neonatal respiratory distress syndrome, hyperbilirubinaemia (jaundice), polycythaemia (an excess of red blood cells) and hypocalcaemia (low blood calcium levels) (ADA 2004; Crowther 2005; Landon 2009; Metzger 1998).

As GDM is a result of physiological metabolic changes during pregnancy, maternal hyperglycaemia should resolve following birth, and does in the majority of cases. A repeat OGTT is recommended in the postpartum period, however, to confirm resolution of hyperglycaemia. Repeat testing is also recommended every one to two years in women with normalised glucose tolerance (ADA 2012; Hoffman 1998), as an obstetric history of GDM confers an increased risk of type 2 diabetes later in life for both mother and infant (Feig 2008; Kim 2002; O'Sullivan 1991; Pettitt 1985; Silverman 1998).

Description of the intervention

Different methods and settings for glucose monitoring for gestational diabetes mellitus

Treatment of GDM including lifestyle advice, monitoring of blood glucose, insulin therapy and oral hypoglycaemics, has been shown to significantly reduce the risk of maternal and perinatal complications (including perinatal mortality, shoulder dystocia, bone fracture, and nerve palsy) without increasing the risk of caesarean section (Crowther 2005; Landon 2009). Cochrane Reviews have assessed (or plan to assess) alternative management strategies for GDM (Alwan 2009), including lifestyle interventions (Brown 2017), insulin (Brown 2016), oral anti‐diabetic pharmacological therapies (Brown 2017b), exercise (Ceysens 2016), dietary supplementation with myo‐inositol (Brown 2016b), and different intensities of glycaemic control (Martis 2016).

Management of GDM relies on a multi‐disciplinary team approach to inform and educate the woman and to establish glycaemic control. Blood glucose monitoring is a crucial part in maintaining this control and is generally recommended by obstetric healthcare professionals (Gabbe 2004; NICE 2008). The decision to initiate active treatments for the management of GDM, including insulin therapy and oral hypoglycaemic agents, relies on the adequate monitoring of blood glucose. Consensus on the ideal methods (including frequency or timing) and settings for monitoring, however, has yet to be established.

Methods (including frequency and timing) of blood glucose monitoring

There is some evidence that more frequent blood glucose monitoring is associated with improved outcomes (Goldberg 1986: Langer 1994), although the optimal timing and frequency of testing is not known. Guidelines in different countries suggest monitoring three or four times daily (with both fasting and postprandial measurements recorded) (ACOG 2013; Nankervis 2013).

Debate continues about the best time to measure blood glucose concentrations, including whether postprandial monitoring, preprandial monitoring, or both should be conducted. Insulin peaks two to three hours after a meal, although this is likely to vary according to what is eaten and when. If carried out, it is not clear whether postprandial monitoring should take place one, or two, hours after meals (Weisz 2005). Similarly, if carried out, it is not clear when fasting or preprandial monitoring should be conducted (Ben‐Haroush 2004).

The benefits of continuous monitoring are also still in question; glucose monitoring systems can record concentrations at regular intervals over several days and this can give a full picture of changes throughout the day. There have been studies that suggested that such supplementary monitoring improves glycaemic control, which can have an impact on clinical outcomes (Murphy 2008; Yu 2014). As yet, there is insufficient evidence about the harms and benefits for women or babies for it to be generally recommended for GDM, although it may be a component of care for pregnant women with type 1 or 2 diabetes (ADA 2012). There is also a paucity of evidence about the optimal duration of continuous monitoring and the best time in pregnancy for it to be conducted, along with the cost‐effectiveness of such intensive monitoring (Voormolen 2013).

Settings for blood glucose monitoring

With the introduction of home reflectance monitors in the late 1970s, self‐monitoring of blood glucose became possible (Espersen 1985). Some early benefits of self‐monitoring for pregnant women with insulin‐dependent diabetes were observed, including declines in mean blood glucose concentrations and in the numbers of diabetes‐related hospitalisations (Espersen 1985). Self‐monitoring of blood glucose in the management of GDM has since become more widely practised (Gabbe 2004). Much debate, however, still exists surrounding its usefulness (Buchanan 2003; Jovanovic 2003), and particularly regarding the optimal timing and frequency of such self‐monitoring (Buchanan 2003; Gabbe 2004; Jovanovic 2003). Trials investigating treatments for GDM have generally used regular capillary blood glucose testing for monitoring, both in a hospital setting (with weekly testing) and home setting (with frequencies of testing ranging from seven times per day, to five days per week), with monthly glycated haemoglobin (HbA1c) concentrations also monitored in some trials (Alwan 2009).

More recent developments of digital technologies for self‐monitoring allow real‐time transfer of measurements to healthcare providers. Such devices mean that women are able to monitor their blood glucose from home; the results are relayed directly to healthcare providers, who in turn can offer advice or recommend changes in treatment without the women needing to attend a healthcare facility (Mackillop 2014). Electronic monitoring with direct transfer of results reduces the need for women to maintain diaries, and may reduce recording errors (Given 2013). As well as improving monitoring, such devices may enable timely intervention that could improve outcomes. Remote monitoring and feedback may also reduce the need for costly and inconvenient clinic visits, and many women are comfortable using smart‐phone and other interactive internet based technologies (Hirst 2015). However, some women may lack resources (such as internet access) or the confidence to use such devices, and may prefer monitoring to take place at regular clinic visits.

How the intervention might work

A consensus on the ideal method (including frequency and timing) and setting of monitoring is yet to be established. As the number of women affected by GDM increases there is an urgent need to identify the most cost‐effective means of monitoring blood glucose that achieves the best outcomes for women and their infants.

Glucose monitoring at both home and hospital, using a variety of methods, is currently part of the management of GDM, and trials investigating interventions for treating GDM have included testing in both locations (Alwan 2009). Home monitoring may be more likely to be well accepted or tolerated, and may allow more frequent and intensive monitoring. Barriers to home monitoring may include the reliance on women's adherence to the daily regimen, and their ability to use the self‐monitoring equipment appropriately. Glucose monitoring in the hospital setting may be less frequent (for example, weekly or fortnightly), but may encourage increased clinical contact and improved surveillance of measurements.

Non‐randomised studies have provided some support for home care in GDM and the more intensive self‐monitoring of blood glucose concentrations that it allows. In a non‐randomised study of 58 women with GDM, infants born to women who had undertaken home self‐monitoring were found to have lower rates of macrosomia than those born to women who had weekly in‐hospital 2‐hour postprandial capillary glucose monitoring (Goldberg 1986). The reduction of macrosomia was attributed, by the study authors, to the earlier detection of the need for insulin with home monitoring (Goldberg 1986). In a further prospective study of over 2000 women with GDM, intensive glucose self‐monitoring (seven times a day) using memory‐based reflectance meters was found to be associated with lower rates of macrosomia, caesarean birth, shoulder dystocia, stillbirth and neonatal intensive care unit days, when compared with conventional management (Langer 1994).

In addition to the potential health benefits of home care in the management of GDM, home glucose monitoring for women may prove to be more cost‐effective than hospital monitoring, which is an important public health consideration, given the increasing incidence of GDM.

Why it is important to do this review

GDM may be diagnosed through screening processes and potentially managed with adequate monitoring and appropriate initiation of active treatments, so it is vitally important that the most effective and safe monitoring strategies are identified. While blood glucose monitoring for women with GDM is commonly recommended, there is currently no consensus on whether self‐monitoring (which can be carried out at home) has benefits when compared with hospital glucose monitoring. With the increasing incidence of GDM, the optimal method and setting for blood glucose monitoring should be determined, with consideration of the public health and resource implications.

Objectives

Methods

Criteria for considering studies for this review

Types of studies

We planned to include published, unpublished and ongoing randomised or quasi‐randomised controlled trials, however we only identified published trials for inclusion. We planned to include cluster‐randomised trials, however we did not identify any. Cross‐over trials are not eligible for inclusion in this review.

We classified trials that are currently available only as abstracts ‐ for which we could not obtain information about risk of bias and primary or secondary outcomes ‐ as 'awaiting classification'; we will reconsider these trials for inclusion once the full publications are available.

Types of participants

We included women diagnosed with GDM during their current pregnancy, as defined by individual trialists. We included women of any age, gestation and parity, but excluded women with previously diagnosed type 1 or type 2 diabetes.

Types of interventions

We included trials that compared different methods (including timings and frequencies) or settings, or both, for blood glucose monitoring.

This could include, for example, comparisons of ‘home’ (ambulatory or outpatient care) glucose monitoring with ‘hospital’ (acute care) glucose monitoring. ‘Home’ care could include studies where blood glucose self‐monitoring was performed predominately at home by the women (using a variety of methods, frequencies and timings). ‘Hospital’ care could include studies where blood glucose monitoring was performed predominately in the hospital (i.e. at antenatal hospital visits or as an inpatient) using a variety of methods, frequencies and timings. This could also include comparing different methods, frequencies or timings of glucose monitoring in the same setting (e.g. 'home').

Types of outcome measures

Primary outcomes

For the mother

Hypertensive disorders of pregnancy (including pre‐eclampsia, pregnancy‐induced hypertension, eclampsia)
Caesarean section
Development of type 2 diabetes

For the child

Perinatal mortality (stillbirth or neonatal mortality)
Large‐for‐gestational age
Death or serious morbidity composite
Neurosensory disability

Secondary outcomes

For the mother

Perinatal

Induction of labour
Perineal trauma
Placental abruption
Postpartum haemorrhage
Postpartum infection
Gestational weight gain
Adherence to the intervention
Behavioural changes associated with the intervention
Sense of well‐being and quality of life
Views of the intervention
Breastfeeding (e.g. at discharge, six weeks postpartum)
Use of additional pharmacotherapy
Maternal hypoglycaemia
Glycaemic control during or at end of treatment
Mortality

Long‐term

Postnatal depression
Postnatal weight retention or return to pre‐pregnancy weight
Body mass index (BMI)
GDM in a subsequent pregnancy
Type 1 diabetes
Impaired glucose tolerance
Cardiovascular health (e.g. blood pressure, hypertension, cardiovascular disease, metabolic syndrome)

For the child

Fetus/neonate

Stillbirth
Neonatal death
Gestational age at birth
Preterm birth (before 37 weeks' gestation; before 32 weeks' gestation)
Apgar score < 7 at five minutes
Macrosomia
Small‐for‐gestational age
Birthweight and z score
Head circumference and z score
Length and z score
Ponderal index
Adiposity
Shoulder dystocia
Nerve palsies
Bone fractures
Respiratory distress syndrome
Hypoglycaemia
Hyperbilirubinemia or jaundice
Hypocalcaemia
Polycythaemia

Child/adult

Weight and z score
Height and z score
Head circumference and z score
Adiposity (e.g. BMI, skinfold thickness, fat mass)
Cardiovascular health (e.g. blood pressure, hypertension, cardiovascular disease, metabolic syndrome)
Type 1 diabetes
Type 2 diabetes
Impaired glucose tolerance
Employment, education and social status or achievement

Use and costs of health services

Number of antenatal visits or admissions
Number of hospital or health professional visits (e.g. midwife, obstetrician, physician, dietician, diabetic nurse) (unscheduled and scheduled)
Admission to neonatal intensive care unit
Length of antenatal stay
Length of postnatal stay (mother)
Length of postnatal stay (baby)
Cost of maternal care
Cost of offspring care
Costs associated with the intervention
Costs to families associated with the management provided

(We used the standard outcome set agreed by consensus between review authors of Cochrane Pregnancy and Childbirth reviews for prevention and treatment of GDM and pre‐existing diabetes.)

Search methods for identification of studies

The following methods section of this review is based on a standard template used by Cochrane Pregnancy and Childbirth.

Electronic searches

We searched the Cochrane Pregnancy and Childbirth Trials Register by contacting their Information Specialist (30 September 2016).

The Register is a database containing over 21,000 reports of controlled trials in the field of pregnancy and childbirth. For full search methods used to populate the Pregnancy and Childbirth Group Trials Register including the detailed search strategies for CENTRAL, MEDLINE, Embase and CINAHL; the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service, please follow this link to the editorial information about Cochrane Pregnancy and Childbirth in the Cochrane Library and select the ‘Specialized Register ’ section from the options on the left side of the screen.

Briefly, the Cochrane Pregnancy and Childbirth Trials Register is maintained by their Information Specialist and contains trials identified from:

monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);
weekly searches of MEDLINE (Ovid);
weekly searches of Embase (Ovid);
monthly searches of CINAHL (EBSCO);
handsearches of 30 journals and the proceedings of major conferences;
weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.
scoping searches of ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP).

Search results are screened by two people from Cochrane Pregnancy and Childbirth, and the full text of all relevant trial reports identified through the searching activities described above is reviewed. Based on the intervention described, each trial report is assigned a number that corresponds to a specific Pregnancy and Childbirth review topic (or topics), and is then added to the Register. The Information Specialist searches the Register for each review using this topic number rather than keywords. This results in a more specific search set which has been fully accounted for in the relevant review sections (Included studies; Excluded studies; Studies awaiting classification; Ongoing studies).

Searching other resources

We searched reference lists of retrieved articles.

We did not apply any language or date restrictions.

Data collection and analysis

The following methods section of this review is based on a standard template used by Cochrane Pregnancy and Childbirth.

Selection of studies

Independently, two review authors assessed all the potential studies identified as a result of the search strategy for inclusion. We resolved any disagreement through discussion or, if required, we consulted the third review author.

Data extraction and management

At least two review authors extracted the data using the agreed form. We resolved discrepancies through discussion or, if required, we consulted the third review author. Data were entered into Review Manager 5 software (Review Manager 2014), and checked for accuracy.

When information regarding any of the above was unclear, we contacted authors of the original reports to provide further details.

Assessment of risk of bias in included studies

Two review authors independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). Any disagreement was resolved by discussion or by involving a third assessor.

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

For each included study we described the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We assessed the method as being at:

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)

For each included study we described the method used to conceal allocation to interventions prior to assignment and assessed whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.

We assessed the methods as being at:

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)

For each included study we described the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We considered that studies were at low risk of bias if they were blinded, or if we judged that the lack of blinding unlikely to affect results. We assessed blinding separately for different outcomes or classes of outcomes.

We assessed the methods as being at:

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)

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

We assessed methods used to blind outcome assessment as being at:

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)

For each included study, and for each outcome or class of outcomes, we described the completeness of data including attrition and exclusions from the analysis. We stated 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 was reported, or could be supplied by the study authors, we planned to re‐include missing data in the analyses which we undertook.

We assessed methods as being at:

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)

For each included study we described how we investigated the possibility of selective outcome reporting bias and what we found.

We assessed the methods as being at:

low risk of bias (where it was clear that all of the study’s prespecified outcomes and all expected outcomes of interest to the review were reported);
high risk of bias (where not all the study’s prespecified outcomes were reported; one or more reported primary outcomes were not prespecified; outcomes of interest were reported incompletely and so could not be used; study failed 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 (6) above)

For each included study we described any important concerns we had about other possible sources of bias.

(7) Overall risk of bias

We made explicit judgements about whether studies were 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 assessed the likely magnitude and direction of the bias and whether we considered it was likely to have an impact on the findings. We explored the impact of the level of bias through undertaking sensitivity analyses ‐ seeSensitivity analysis.

Assessment of the quality of the evidence using the GRADE approach

We evaluated the quality of the evidence using the GRADE approach as outlined in the GRADE handbook for our three main comparisons.

Telemedicine versus standard care for glucose monitoring
Self‐monitoring versus periodic glucose monitoring
Continuous glucose monitoring system versus self‐monitoring of glucose

The GRADE approach uses five considerations (study limitations, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of the body of evidence for specific outcomes. The evidence can be downgraded from 'high quality' by one level for serious (or by two levels for very serious) limitations, depending on assessments for risk of bias, indirectness of evidence, inconsistency, imprecision of effect estimates or publication bias. In this review we used the GRADE approach to assess the following outcomes, and reported them in 'Summary of findings' tables.

For the mother

Hypertensive disorders of pregnancy (including pre‐eclampsia, pregnancy‐induced hypertension, eclampsia)
Caesarean section
Development of type 2 diabetes
Induction of labour
Perineal trauma
Postnatal depression
Postnatal weight retention or return to pre‐pregnancy weight

For the child

Perinatal mortality (stillbirth or neonatal death)
Large‐for‐gestational age
Death or serious morbidity composite
Neurosensory disability
Hypoglycaemia
Adiposity (e.g. BMI, skinfold thickness, fat mass)
Type 2 diabetes

We used the GRADEpro Guideline Development Tool, GRADEpro 2014, to import data from Review Manager 5 in order to create ’Summary of findings’ tables (Review Manager 2014). A summary of the intervention effect and a measure of quality according to the GRADE approach is presented in the 'Summary of findings' tables for the outcomes listed above.

Measures of treatment effect

Dichotomous data

For dichotomous data, we presented results as summary risk ratios with 95% confidence intervals.

Continuous data

For continuous data, we used the mean difference. We planned to use the standardised mean difference to combine trials that measured the same outcome, but used different methods.

Unit of analysis issues

Cluster‐randomised trials

We did not identify any cluster‐randomised trials for inclusion in this review.

If cluster‐randomised trials are included in future updates of the review, we plan to include them in the analyses along with individually‐randomised trials. Their sample sizes will be adjusted using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), using an estimate of the intra‐cluster correlation co‐efficient (ICC) derived from the trial (if possible), or from another source. If ICCs from other sources are used, 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 plan to synthesise the relevant information. We plan to 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 plan to also acknowledge heterogeneity in the randomisation unit and perform a sensitivity analysis to investigate the effects of the randomisation units.

Cross‐over trials

We excluded trials with cross‐over designs.

Multi‐armed trials

If we had included multi‐armed trials, we planned to record and include all outcome data in the review as two‐arm comparisons. We planned to include the data for the different arms in independent two‐arm comparisons in separate meta‐analyses. In instances where we could not include the data in separate comparisons, we planned to combine them to create a single pair‐wise comparison (Higgins 2011). If the control group was shared by two or more study arms, we planned to divide it between relevant subgroup categories to avoid double‐counting the participants (for dichotomous data, we planned to divide the events and the total population, while for continuous data, we planned to assume the same mean and standard deviation (SD) but planned to divide the total population). We planned to describe the details in the 'Characteristics of included studies' tables.

Other unit of analysis issues

As infants from multiple pregnancies are not independent, we planned to use cluster‐trial methods in the analysis, where the data allowed, and where multiples made up a substantial proportion of the trial population, to account for non‐independence of variables (Gates 2004).

Dealing with missing data

For included studies, we noted levels of attrition. In future updates, if more eligible studies are included, we will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analyses.

For all outcomes, we carried out analyses, as far as possible, on an intention‐to‐treat basis, i.e. we attempted to include all participants randomised to each group in the analyses. The denominator for each outcome in each trial was the number randomised minus any participants whose outcomes were known to be missing.

Assessment of heterogeneity

We assessed statistical heterogeneity in each meta‐analysis using the Tau², I² and Chi² statistics. We regarded heterogeneity as substantial if an I² was greater than 30% and either the Tau² was greater than zero, or there was a low P value (less than 0.10) in the Chi² test for heterogeneity. If we identified substantial heterogeneity (above 30%), we planned to explore it using prespecified subgroup analyses.

Assessment of reporting biases

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

Data synthesis

We carried out statistical analysis using Review Manager 5 software (Review Manager 2014). We used fixed‐effect meta‐analysis for combining data where it was reasonable to assume that studies were estimating the same underlying treatment effect, that is, where studies examined the same intervention, and we judged the studies' populations and methods to be sufficiently similar.

Where there was clinical heterogeneity sufficient to expect that the underlying treatment effects differed between trials, or where substantial statistical heterogeneity was detected, we used random‐effects meta‐analysis to produce an overall summary, if an average treatment effect across trials was considered clinically meaningful. The random‐effects summary has been treated as the average range of possible treatment effects and we have discussed the clinical implications of treatment effects differing between trials. If the average treatment effect was not clinically meaningful, we planned not to combine trials. Where we used random‐effects analyses, the results have been presented as the average treatment effect with 95% confidence intervals, and the estimates of Tau² and I².

Subgroup analysis and investigation of heterogeneity

If we identified substantial heterogeneity, we planned to investigate it using subgroup analyses or sensitivity analyses. We planned to consider whether an overall summary is meaningful, and if it was, to use random‐effects analysis to produce it.

We planned to carry out the following subgroup analyses.

Timing of monitoring (i.e. postprandial versus preprandial)
Frequency of monitoring (i.e. multiple times per day versus daily)
Method of monitoring (i.e. use of glucose meter versus use of continuous glucose monitoring system)
Gestational age at randomisation, and at diagnosis (i.e. first trimester versus second trimester versus third trimester)

We planned to restrict subgroup analyses to the review's primary outcomes.

We planned to assess subgroup differences by interaction tests available within Review Manager 5 (Review Manager 2014). We planned to report the results of subgroup analyses quoting the Chi² statistic and P value, and the interaction test I² value.

Due to paucity of data in the review, however, we were not able to conduct planned subgroup analyses.

Sensitivity analysis

We planned to carry out a sensitivity analysis to explore the effects of trial quality assessed by omitting studies rated as 'high risk of bias' and 'unclear' when considering allocation concealment (selection bias) and incomplete outcome data (attrition bias). We planned to restrict this to the primary outcomes.

Due to paucity of data in the review, however, we were not able to conduct our planned sensitivity analyses.

Results

Description of studies

Results of the search

The search of the Cochrane Pregnancy and Childbirth Trials Register retrieved 39 reports, relating to 27 studies. We included 11 studies and excluded five.

Four studies are awaiting further classification (Ding 2012; Paramasivam 2014; Puricel 2014; Rigla 2015), and seven studies are ongoing (Evers 2016; Hanafusa 2015; Kim 2014; Mackillop 2016; Mendez‐Figueroa 2013; Rasekaba 2015a; Rudge 2013) (See Characteristics of studies awaiting classification; Characteristics of ongoing studies).

See Figure 1.

Included studies

We identified 11 trials that met the inclusion criteria for this review (Dalfra 2009; De Veciana 1995; Given 2015; Homko 2002; Homko 2007; Homko 2012; Kestila 2007; Kruger 2003; Perez‐Ferre 2010; Rey 1997; Wei 2016).

Design

One of the 11 included trials was a quasi‐randomised controlled trial (Dalfra 2009), and the other 10 were randomised controlled trials (De Veciana 1995; Given 2015; Homko 2002; Homko 2007; Homko 2012; Kestila 2007; Kruger 2003; Perez‐Ferre 2010; Rey 1997; Wei 2016).

Sample sizes

The 11 included trials randomised a total of 1272 pregnant women with GDM. Sample sizes ranged from 50 women in Given 2015, to 347 women in Rey 1997.

Settings and dates of trials

Five trials were conducted in the USA (De Veciana 1995; Homko 2002; Homko 2007; Homko 2012; Kruger 2003), and one trial was conducted in each of the following countries: Canada (Rey 1997), China (Wei 2016), Finland (Kestila 2007), Ireland (Given 2015), Italy (Dalfra 2009), and Spain (Perez‐Ferre 2010).

Trials ran between 1993 and 2013: Given 2015 took place between January 2012 and May 2013; Homko 2002 between March 1998 and November 1999; Homko 2007 between September 2004 and May 2006; Homko 2012 between September 2007 and November 2009; Perez‐Ferre 2010 between June and December 2007; Rey 1997 between June 1993 and May 1994; and Wei 2016 between September 2011 and December 2012. Four trials did not specify trial dates (Dalfra 2009; De Veciana 1995; Kestila 2007; Kruger 2003).

Participants

All 11 trials included women with gestational diabetes mellitus (GDM). One trial also included women with type 1 diabetes (Dalfra 2009), however, we have only included data related to the women with GDM in the review.

One trial (De Veciana 1995) diagnosed women with GDM according to the O'Sullivan and Mahan 1964 criteria (O'Sullivan 1964), four trials (Dalfra 2009; Homko 2007; Homko 2012; Perez‐Ferre 2010) used the Carpenter and Coustan 1982 criteria (Carpenter 1982), one trial (Given 2015) referenced the National Institute of Clinical Excellence: Diabetes in Pregnancy 2008 guidelines (NICE 2008) (however it did not specify the criteria used), and one trial (Wei 2016) reported that women were diagnosed according to criteria recommended by the American Diabetes Association 2011 (ADA 2012) and IADPSG 2010 (IADPSG Consensus Panel 2010). The Kestila 2007 trial used at least two abnormal values in a 2‐hour 75 g OGTT (fasting > 5.1 mmol/L, 1‐hour > 10 mmol/L, 2‐hour > 8.7 mmol/L). Rey 1997 (using plasma capillary blood) used a 1‐hour 50 g OGCT of ≥ 11.1 mmol/L, or between 8.9 and 11.0 mmol/L plus at least two abnormal values on a three‐hour 100 g OGTT. Abnormal values were dependent on gestation; before 26 weeks (fasting > 5.3 mmol/L, one‐hour > 10 mmol/L, two‐hour > 8.9 mmol/L; 3 hours > 7.8 mmol/L) or during and after 26 weeks (fasting > 5.6 mmol/L, one‐hour > 11.1 mmol/L; two‐hour > 9.2 mmol/L, three‐hour > 8.3 mmol/L). Two trials did not state diagnostic criteria used (Homko 2002; Kruger 2003).

After diagnosis of GDM, inclusion of women with varying gestational ages was reported: between 24 and 28 weeks' gestation (Given 2015; Wei 2016), within a week from diagnosis (mean of 28 weeks' gestation) (Dalfra 2009), before 28 weeks' gestation (Perez‐Ferre 2010), at or before 30 weeks' gestation (De Veciana 1995), at or before 33 weeks' gestation (Homko 2002; Homko 2007; Homko 2012), between 22 and 34 weeks' gestation (Kestila 2007), between 22 and 38 weeks' gestation (Rey 1997); Kruger 2003 did not specify gestational age.

Additional eligibility criteria varied across the trials, with Given 2015 also including women with impaired glucose tolerance (and referencing the National Institute of Clinical Excellence: Diabetes in Pregnancy 2008 guidelines (NICE 2008), however not specifying the criteria used); Homko 2002 required women to have a fasting glucose value of ≤ 5.3 mmol/L at the OGTT; and De Veciana 1995 included only women who required insulin (i.e. those with elevated fasting blood glucose values at the time of a 3‐hour OGTT or with weekly fasting and 1‐hour postprandial blood glucose values exceeding 5.8 mmol/L or 7.8 mmol/L respectively).

Six trials specified that only women with singleton pregnancies were included (De Veciana 1995; Kestila 2007; Wei 2016), or that women with multiple pregnancies were excluded (Homko 2007; Homko 2012; Rey 1997). Six trials detailed exclusion of women with a history of diabetes (type 1 or 2) (De Veciana 1995; Given 2015; Kruger 2003; Wei 2016) and/or prior glucose intolerance (Homko 2007; Homko 2012). Additional exclusion criteria reported included: pre‐existing hypertension, renal disease or autoimmune disorders (De Veciana 1995), receipt of oral steroid therapy (Given 2015), congential malformations, or current diet or insulin therapy (Rey 1997), and previous treatment for GDM, presence of infection, or other severe metabolic, endocrine, medical or psychological co‐morbidities (Wei 2016). Four trials did not specify exclusion criteria (Dalfra 2009; Homko 2002; Kestila 2007; Perez‐Ferre 2010).

Interventions and comparisons

We assessed the 11 included trials under five different comparisons.

Telemedicine versus standard care for glucose monitoring

Five trials compared the use of telemedicine versus standard care in glucose monitoring (Dalfra 2009; Given 2015; Homko 2007; Homko 2012; Perez‐Ferre 2010). Women in the telemedicine groups of these trials transmitted their blood glucose measurements weekly (Dalfra 2009; Given 2015; Homko 2012; Perez‐Ferre 2010), or at least three times per week (Homko 2007). The blood glucose measurements were sent to healthcare practitioners for review, using varying technologies, including: an interfacing device that converted values recorded by a blood glucose meter into audio tones which were sent via a normal telephone receiver to an Internet‐based server (Dalfra 2009); a cellular telephone (with an interfacing device) that converted values recorded by a blood glucose meter to messages sent via a short message service (SMS) to an Internet‐based application (Perez‐Ferre 2010); a telemedicine hub (with a small screen and three buttons to collect and transmit data) which sent stored values recorded by a blood glucose meter to a central server (Given 2015); and a computer using a web‐based disease management interactive healthcare delivery system composed of a secure Internet server and a database (Homko 2007; Homko 2012 (with the option of telephone communication (Homko 2012)). Following review of the information, women received feedback from the healthcare practitioners via telephone voice messages (Dalfra 2009), cellular telephone text messages (Rey 1997), telephone calls (Given 2015), or written messages on a web‐based system (Homko 2007; Homko 2012).

Regimens varied between trials: in the Perez‐Ferre 2010 trial, women in both groups were asked to self‐monitor their blood glucose six times daily during the first week, and then (if glycaemic control was achieved) three times daily or every other day. The Dalfra 2009, Homko 2007, and Homko 2012 trials requested self‐monitoring four times daily, while in Given 2015 it was required up to seven times a day. Medical examinations or specialist diabetes clinic visits occurred at least every two weeks in the Given 2015, Homko 2007 and Homko 2012 trials, and once a month in the Perez‐Ferre 2010 trial. In Dalfra 2009, women in the standard care group had visits every two weeks, while women in the telemedicine group had monthly visits.

Self‐monitoring versus periodic glucose monitoring

Two trials compared self‐monitoring of blood glucose with periodic (outpatient) monitoring (Homko 2002; Rey 1997). Women in the self‐monitoring groups were instructed to measure their blood glucose every day, alternating between three times daily (one hour after each meal) and four times daily (before each meal and at bed time) (Rey 1997); or four times daily (fasting and one hour after each meal) four times per week (Homko 2002). Women in the periodic monitoring groups had their blood glucose (fasting or one hour post breakfast, or both (Rey 1997), or fasting and one hour post meal (Homko 2002)) measured at each antenatal visit (Homko 2002) or at outpatient clinic visits every two weeks (Rey 1997).

Continuous glucose monitoring system versus self‐monitoring of glucose

Two trials compared the use of continuous glucose monitoring system (CGMS) with self‐monitoring of blood glucose, to determine subsequent management (i.e. need for anti‐diabetic drug therapy) within a week after initiating monitoring (Kestila 2007; Wei 2016). One of the trials assessed both early (24 to 28 weeks' gestation) and late (28 to 36 weeks' gestation) CGMS (Wei 2016). All women in both groups were taught to perform self‐monitoring of blood glucose, and were instructed to measure their blood glucose four times (Wei 2016), or five times (Kestila 2007), daily.

Modem versus telephone transmission for glucose monitoring

One trial compared the transmission of blood glucose data from a meter to an Endocrinology and Metabolism Clinic via a modem, with the transmission of data via telephone calls directly to clinic personnel (Kruger 2003). In both groups, women were instructed to measure their blood glucose five times daily (before breakfast, one hour after each meal (three meals), and before bed), and to report their concentrations daily for the first two weeks, and then weekly thereafter; the data were reviewed by clinic personnel who provided feedback and guidance to the women via the telephone (Kruger 2003).

Postprandial versus preprandial glucose monitoring

One trial compared daily monitoring of blood glucose before breakfast (fasting) and one hour after each meal (postprandial monitoring), with daily monitoring of fasting, before meal and bed time blood glucose (preprandial monitoring) (De Veciana 1995).

Funding and declarations of interest

Seven trials received funding support from non‐commercial organisations:

Given 2015: the Department for Employment and Learning for Northern Ireland and the Derry City Council, Ireland;
Homko 2002: the General Clinical Research Center Branch of the National Center for Research Resources, USA;
Homko 2007: the National Institute of Nursing Research, National Institutes of Health, USA;
Homko 2012: the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health USA;
Kestila 2007 the Turku University Central Hospital Research Fund and the Foundation of Gynaecologists and Obstetricians, Finland;
Perez‐Ferre 2010: 'Fundacion para Estudios Metabolicos'; and
Wei 2016: the Social Development Project of JiangSu Province, China.

Three trials received some support from commercial partners: Homko 2002 was provided with glucose meters by LifeScan Inc; Kruger 2003 was provided with a grant, and glucose meters from Roche Diagnostics; and Rey 1997 was supported by Lilly Canada.

Two trials did not report any funding sources (Dalfra 2009; De Veciana 1995).

Given 2015 reported that one author had received research funding from Nova Biomedical (a manufacturer of glucose meters); and Homko 2012 reported that one author had stock ownership in, and another was a consultant for, Insight Telehealth Systems. Perez‐Ferre 2010 and Wei 2016 reported that the authors had no conflicts of interest. The other seven trials did not report on declarations of interest (Dalfra 2009; De Veciana 1995; Homko 2002; Homko 2007; Kestila 2007; Kruger 2003; Perez‐Ferre 2010).

For further details, see Characteristics of included studies.

Excluded studies

We excluded five studies (Bancroft 2000; Bartholomew 2015; Clarke 2005; Elnour 2008; Fung 1996). Two assessed treatment strategies for women with GDM (Bancroft 2000; Elnour 2008), two were cross‐over trials (Bartholomew 2015; Clarke 2005), and one included pregnant women (but not specifically women with GDM) (Fung 1996).

For further details, see Characteristics of excluded studies.

Risk of bias in included studies

Overall, we judged the trials to be at moderate to high risk of bias; lack of methodological detail led to 'unclear' risk of bias judgements across many of the domains (See Figure 2; Figure 3).

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies

Risk of bias summary: review authors' judgements about each risk of bias item for each included study

Allocation

We judged one of the trials to be at low risk of selection bias, because it used an adequate method for sequence generation (random allocation software) and allocation concealment (an independent research secretary) (Given 2015). While two further trials detailed adequate methods for sequence generation (use of computer‐generated tables of random numbers), they did not detail methods for concealment of allocation, and thus we judged them to be at an unclear risk of selection bias (Rey 1997; Wei 2016).

Six of the trials did not provide sufficient detail regarding sequence generation or allocation concealment and therefore we judged them to be at an unclear risk of selection bias (De Veciana 1995; Homko 2002; Homko 2007; Homko 2012; Kestila 2007; Kruger 2003). The final two trials were judged to be at high risk of selection bias, with one trial using alternate allocation (Dalfra 2009), and another allocating a subgroup of women (those most likely to require treatment) to the intervention group (Perez‐Ferre 2010).

Blinding

We considered all of the 11 trials to be at a high risk of performance bias as, due to the nature of the interventions, it was not considered feasible for women or study personnel to be blinded (Dalfra 2009; De Veciana 1995; Given 2015; Homko 2002; Homko 2007; Homko 2012; Kestila 2007; Kruger 2003; Perez‐Ferre 2010; Rey 1997; Wei 2016).

Only one of the trials specifically detailed that it was unblinded, and this was also judged to be at high risk of detection bias (De Veciana 1995). In the other 10 trials, risk of detection bias was considered unclear, as no details were provided regarding whether outcome assessors could be blinded (Dalfra 2009; Given 2015; Homko 2002; Homko 2007; Homko 2012; Kestila 2007; Kruger 2003; Perez‐Ferre 2010; Rey 1997; Wei 2016).

Incomplete outcome data

We judged six of the trials to be at low risk of attrition bias, with either no missing outcome data (De Veciana 1995), or missing outcome data balanced in numbers across groups and/or similar reasons for missing data across groups (Homko 2007; Homko 2012; Perez‐Ferre 2010; Rey 1997; Wei 2016). We judged three trials to be at unclear risk of attrition bias, with insufficient reporting of attrition or exclusions to permit clear judgements (Given 2015; Homko 2002; Kestila 2007). We judged the other two trials to be at a high risk of attrition bias, with an imbalance in numbers or reasons for missing data across groups (Dalfra 2009), or a high proportion of missing outcome data (Kruger 2003).

Selective reporting

We judged nine of the trials to be at an unclear risk of reporting bias (Dalfra 2009; De Veciana 1995; Given 2015; Homko 2002; Homko 2007; Homko 2012; Perez‐Ferre 2010; Rey 1997; Wei 2016), as no trial protocols were available to help us assess whether the published reports included all prespecified outcomes. We judged the other two trials to be at a high risk of reporting bias, as they reported outcomes of interest incompletely (providing only narrative summaries in text; or P values), which meant they could not be entered in meta‐analyses (Kestila 2007; Kruger 2003), or did not present all of the prespecified outcomes (as per the 'Methods' section of the published report) (Kestila 2007).

Other potential sources of bias

In eight of the trials, there were no obvious sources of other bias (De Veciana 1995; Given 2015; Homko 2002; Homko 2007; Homko 2012; Perez‐Ferre 2010; Rey 1997; Wei 2016). We judged risk of other bias to be unclear in three of the trials, due to the lack of methodological detail provided (Dalfra 2009; Kestila 2007), or lack of information provided regarding the baseline characteristics of the women (Kruger 2003), or both.

Effects of interventions

See: Table 1; Table 2; Table 3; Table 4; Table 5; Table 6

Summary of findings for the main comparison. Telemedicine versus standard care for glucose monitoring for gestational diabetes during pregnancy (effect on mother).

Telemedicine versus standard care for glucose monitoring for gestational diabetes during pregnancy (effect on mother)
Patient or population: women with gestational diabetes mellitus   Setting: 2 RCTs in USA; 1 RCT each in Italy, Ireland and Spain set in clinics or hospitals  Intervention: telemedicine  Comparison: standard care
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Quality of the evidence  (GRADE)	Comments
	Risk with standard care	Risk with telemedicine
Hypertensive disorders of pregnancy including pre‐eclampsia, gestational hypertension and eclampsia	Study population		RR 1.49  (0.69 to 3.20)	275  (4 RCTs)	⊕⊝⊝⊝  VERY LOW^1,2
	58 per 1000	87 per 1000  (40 to 187)
Caesarean section	Study population		RR 1.05  (0.72 to 1.53)	478  (5 RCTs)	⊕⊝⊝⊝  VERY LOW^3,4,5
	444 per 1000	467 per 1000  (320 to 680)
Development of type 2 diabetes	Study population		not estimable	(0 RCTs)	‐	None of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Induction of labour	Study population		RR 1.06  (0.63 to 1.77)	47  (1 RCT)	⊕⊝⊝⊝  VERY LOW ^2,6
	538 per 1000	571 per 1000  (339 to 953)
Perineal trauma	Study population		Not estimable	(0 RCTs)	‐	None of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Postnatal depression	Study population		Not estimable	(0 RCTs)	‐	None of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Postnatal weight retention or return to pre‐pregnancy weight	Study population		Not estimable	(0 RCTs)	‐	None of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).  CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio
GRADE Working Group grades of evidence   High quality: we are very confident that the true effect lies close to that of the estimate of the effect  Moderate quality: we are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different  Low quality: our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect  Very low quality: we have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect

Self‐monitoring versus periodic for glucose monitoring for gestation diabetes during pregnancy (effect on child)
Patient or population: women with gestational diabetes mellitus  Setting: 1 RCT in Canda, 1 RCT in USA set in clinics or hospitals Intervention: self‐monitoring of glucose  Comparison: periodic glucose monitoring
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Quality of the evidence  (GRADE)	Comments
	Risk with periodic glucose monitoring	Risk with self‐monitoring of glucose
Perinatal mortality (stillbirth or neonatal death)	Study population		RR 1.54  (0.21 to 11.24)	400  (2 RCTs)	⊕⊝⊝⊝  VERY LOW^1,2
	5 per 1000	8 per 1000  (1 to 57)
Large‐for‐gestational age	Study population		RR 0.82  (0.50 to 1.37)	400  (2 RCTs)	⊕⊕⊝⊝  LOW^1,3
	142 per 1000	117 per 1000  (71 to 195)
Death or serious morbidity composite	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Neurosensory disability	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Hypoglycaemia	Study population		RR 0.64  (0.39 to 1.06)	391  (2 RCTs)	⊕⊕⊝⊝  LOW^1,3
	173 per 1000	111 per 1000  (67 to 183)
Adiposity (e.g. BMI, skinfold thickness, fat mass)	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Type 2 diabetes	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).  BMI: body mass index;CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio
GRADE Working Group grades of evidence   High quality: we are very confident that the true effect lies close to that of the estimate of the effect  Moderate quality: we are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different  Low quality: our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect  Very low quality: we have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect

Continuous glucose monitoring system versus self‐monitoring of glucose for gestational diabetes during pregnancy (effect on mother)
Patient or population: women with gestational diabetes mellitus  Setting: 1 RCT in Finland, 1 RCT in China set in clinics or hospitals Intervention: continuous glucose monitoring system  Comparison: self‐monitoring of glucose
Outcomes	*Anticipated absolute effects^ (95% CI)**		Relative effect  (95% CI)	№ of participants  (studies)	Quality of the evidence  (GRADE)	Comments
	Risk with self‐monitoring of glucose	Risk with continuous glucose monitoring system
Hypertensive disorders of pregnancy	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Caesarean section	Study population		RR 0.91  (0.68 to 1.20)	179  (2 RCTs)	⊕⊕⊝⊝  VERY LOW^1,2
	500 per 1000	455 per 1000  (340 to 600)
Development of type 2 diabetes	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Induction of labour	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Perineal trauma	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Postnatal depression	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
Postnatal weight retention or return to pre‐pregnancy weight	Study population		Not estimable	(0 RCTs)	‐	Neither of the included RCTs reported this outcome
	0 per 1000	0 per 1000  (0 to 0)
*The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).  CI: confidence interval; RCT: randomised controlled trial; RR: risk ratio
GRADE Working Group grades of evidence   High quality: we are very confident that the true effect lies close to that of the estimate of the effect  Moderate quality: we are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different  Low quality: our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect  Very low quality: we have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Hypertensive disorders of pregnancy	4	275	Risk Ratio (M‐H, Fixed, 95% CI)	1.49 [0.69, 3.20]
1.1 Pre‐eclampsia, pregnancy‐induced hypertension	3	178	Risk Ratio (M‐H, Fixed, 95% CI)	1.29 [0.58, 2.89]
1.2 Pregnancy‐induced hypertension	1	97	Risk Ratio (M‐H, Fixed, 95% CI)	4.9 [0.24, 99.48]
2 Caesarean section	5	478	Risk Ratio (M‐H, Random, 95% CI)	1.05 [0.72, 1.53]
3 Perinatal mortality	2	131	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
4 Large‐for‐gestational age	3	228	Risk Ratio (M‐H, Fixed, 95% CI)	1.41 [0.76, 2.64]
5 Death or serious morbidity composite	1	57	Risk Ratio (M‐H, Fixed, 95% CI)	1.06 [0.68, 1.66]
6 Operative vaginal birth (not a prespecified outcome)	1	47	Risk Ratio (M‐H, Fixed, 95% CI)	0.50 [0.11, 2.30]
7 Induction of labour	1	47	Risk Ratio (M‐H, Fixed, 95% CI)	1.06 [0.63, 1.77]
8 Placental abruption	2	154	Risk Ratio (M‐H, Fixed, 95% CI)	0.88 [0.12, 6.42]
9 Gestational weight gain (kg)	2	300	Mean Difference (IV, Fixed, 95% CI)	‐0.47 [‐1.50, 0.55]
10 Weight at 36 weeks (kg)	1	44	Mean Difference (IV, Fixed, 95% CI)	5.5 [‐5.69, 16.69]
11 Adherence to the intervention	3		Mean Difference (IV, Fixed, 95% CI)	Subtotals only
11.1 Appointments attended (%)	1	47	Mean Difference (IV, Fixed, 95% CI)	5.20 [‐2.27, 12.67]
11.2 Average daily self‐monitoring of blood glucose frequency: meter memory	1	44	Mean Difference (IV, Fixed, 95% CI)	0.5 [‐0.42, 1.42]
11.3 Average daily self‐monitoring of blood glucose frequency: diary	1	45	Mean Difference (IV, Fixed, 95% CI)	0.10 [‐0.66, 0.86]
11.4 Frequency of monitoring (number of data points)	1	57	Mean Difference (IV, Fixed, 95% CI)	21.10 [‐9.33, 51.53]
11.5 Frequency of monitoring (number of data sets)	1	74	Mean Difference (IV, Fixed, 95% CI)	1.20 [‐12.32, 14.72]
12 Sense of well‐being and quality of life: DES: Diabetes Empowerment Scale	1		Mean Difference (IV, Fixed, 95% CI)	Subtotals only
12.1 Total	1	57	Mean Difference (IV, Fixed, 95% CI)	0.40 [0.14, 0.66]
12.2 Subscale 1: managing the psychosocial aspects of diabetes	1	57	Mean Difference (IV, Fixed, 95% CI)	0.5 [0.21, 0.79]
12.3 Subscale 2: assessing dissatisfaction and readiness to change	1	57	Mean Difference (IV, Fixed, 95% CI)	0.40 [0.14, 0.66]
12.4 Subscale 3: setting and achieving diabetes goals	1	57	Mean Difference (IV, Fixed, 95% CI)	0.30 [‐0.04, 0.64]
13 Use of additional pharmacotherapy	5		Risk Ratio (M‐H, Fixed, 95% CI)	Subtotals only
13.1 Insulin	5	484	Risk Ratio (M‐H, Fixed, 95% CI)	1.52 [1.18, 1.96]
13.2 Oral agents	3	184	Risk Ratio (M‐H, Fixed, 95% CI)	0.85 [0.50, 1.42]
13.3 Insulin and oral agents	1	47	Risk Ratio (M‐H, Fixed, 95% CI)	1.24 [0.19, 8.06]
14 Maternal hypoglycaemia	1	203	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
15 Maternal hypoglycaemia: self‐monitored blood glucose episodes hypoglycaemic (< 3.9 mmol/L) (%)	1	44	Mean Difference (IV, Fixed, 95% CI)	‐0.10 [‐1.64, 1.44]
16 Glycaemic control: HbA1c (%)	3	357	Mean Difference (IV, Fixed, 95% CI)	‐0.15 [‐0.26, ‐0.04]
17 Glycaemic control: HbA1c < 5.8%	1	97	Risk Ratio (M‐H, Fixed, 95% CI)	1.0 [0.96, 1.04]
18 Glycaemic control: HbA1c at 36 weeks (mmol/mol)	1	30	Mean Difference (IV, Fixed, 95% CI)	0.20 [‐2.03, 2.43]
19 Glycaemic control: self‐monitored blood glucose (mmol/L)	1	44	Mean Difference (IV, Fixed, 95% CI)	0.0 [‐0.30, 0.30]
20 Glycaemic control: fasting and 2‐hour post‐prandial blood glucose (mg/dL)	2		Mean Difference (IV, Random, 95% CI)	Subtotals only
20.1 Fasting blood glucose (mg/dL)	2	131	Mean Difference (IV, Random, 95% CI)	‐0.50 [‐5.38, 4.38]
20.2 2‐hour post‐prandial blood glucose (mg/dL)	2	131	Mean Difference (IV, Random, 95% CI)	‐0.21 [‐5.09, 4.67]
21 Stillbirth	3	178	Risk Ratio (M‐H, Fixed, 95% CI)	0.41 [0.02, 9.55]
22 Neonatal death	2	131	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
23 Gestational age at birth (weeks)	5	478	Mean Difference (IV, Fixed, 95% CI)	0.10 [‐0.18, 0.37]
24 Preterm birth < 37 weeks	4	275	Risk Ratio (M‐H, Fixed, 95% CI)	0.66 [0.31, 1.39]
25 Macrosomia	2	249	Risk Ratio (M‐H, Random, 95% CI)	1.43 [0.27, 7.52]
26 Small‐for‐gestational age	1	97	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
27 Birthweight (g)	5	477	Mean Difference (IV, Fixed, 95% CI)	63.13 [‐32.32, 158.59]
28 Head circumference (cm)	1	45	Mean Difference (IV, Fixed, 95% CI)	0.70 [0.02, 1.38]
29 Length (cm)	1	42	Mean Difference (IV, Fixed, 95% CI)	0.20 [‐1.34, 1.74]
30 Shoulder dystocia	2	142	Risk Ratio (M‐H, Fixed, 95% CI)	0.33 [0.01, 7.83]
31 Respiratory distress syndrome	3	176	Risk Ratio (M‐H, Fixed, 95% CI)	0.63 [0.26, 1.49]
32 Neonatal hypoglycaemia	3	198	Risk Ratio (M‐H, Fixed, 95% CI)	1.14 [0.48, 2.72]
33 Hyperbilirubinaemia or jaundice	3	176	Risk Ratio (M‐H, Fixed, 95% CI)	1.09 [0.59, 2.01]
34 Hypocalcaemia	1	97	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
35 Polycythaemia	1	97	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
36 Number of hospital or health professional visits: face‐to‐face visits	1	97	Mean Difference (IV, Fixed, 95% CI)	‐0.36 [‐0.92, 0.20]
37 Number of hospital or health professional visits: unscheduled face‐to‐face visits	1	97	Mean Difference (IV, Fixed, 95% CI)	‐0.62 [‐1.05, ‐0.19]
38 Neonatal intensive care unit admission	3	176	Risk Ratio (M‐H, Fixed, 95% CI)	1.05 [0.62, 1.79]
39 'Neonatal morbidity' (neonatal complications: e.g. hypoglycaemia, hyperbilirubinaemia, respiratory distress syndrome, shoulder dystocia, malformations) (not a prespecified outcome)	1	203	Risk Ratio (M‐H, Fixed, 95% CI)	1.52 [0.53, 4.38]
40 'Maternal morbidity' (maternal complications: gestational hypertension, pre‐eclampsia, eclampsia, hypoglycaemic episodes) (not a prespecified outcome)	1	203	Risk Ratio (M‐H, Fixed, 95% CI)	0.49 [0.13, 1.79]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Hypertensive disorders of pregnancy: pre‐eclampsia	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	0.18 [0.01, 3.49]
2 Caesarean section	2	400	Risk Ratio (M‐H, Random, 95% CI)	1.18 [0.61, 2.27]
3 Perinatal mortality	2	400	Risk Ratio (M‐H, Fixed, 95% CI)	1.54 [0.21, 11.24]
4 Large‐for‐gestational age	2	400	Risk Ratio (M‐H, Fixed, 95% CI)	0.82 [0.50, 1.37]
5 Placental abruption	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	2.63 [0.11, 61.88]
6 Postpartum haemorrhage	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	2.63 [0.11, 61.88]
7 Gestational weight gain (kg/week)	1	342	Mean Difference (IV, Fixed, 95% CI)	‐0.1 [‐0.15, ‐0.05]
7.1 1‐hour post‐breakfast glucose < 7.8 mmol/L	1	227	Mean Difference (IV, Fixed, 95% CI)	‐0.10 [‐0.17, ‐0.03]
7.2 1‐hour post‐breakfast glucose ≥ 7.8 mmol/L	1	115	Mean Difference (IV, Fixed, 95% CI)	‐0.10 [‐0.17, ‐0.03]
8 Gestational weight gain (lb)	1	58	Mean Difference (IV, Fixed, 95% CI)	‐5.5 [‐13.57, 2.57]
9 Adherence to the intervention: < 70% adherence to home blood glucose measurements or diabetes outpatient clinic appointments	1	342	Risk Ratio (M‐H, Fixed, 95% CI)	0.74 [0.32, 1.71]
10 Adherence to the intervention: Dietary Compliance Questionnaire	1		Mean Difference (IV, Fixed, 95% CI)	Subtotals only
10.1 Total compliance score	1	58	Mean Difference (IV, Fixed, 95% CI)	1.5 [‐0.47, 3.47]
10.2 Mean compliance score	1	58	Mean Difference (IV, Fixed, 95% CI)	0.0 [‐0.40, 0.40]
11 Sense of well‐being and quality of life: Diabetes Empowerment Scale delta scores	1		Mean Difference (IV, Fixed, 95% CI)	Subtotals only
11.1 Overall	1	47	Mean Difference (IV, Fixed, 95% CI)	3.70 [‐2.08, 9.48]
11.2 Setting goals	1	47	Mean Difference (IV, Fixed, 95% CI)	0.65 [‐1.10, 2.40]
11.3 Solving problems	1	47	Mean Difference (IV, Fixed, 95% CI)	1.35 [‐0.37, 3.07]
11.4 Motivating oneself	1	47	Mean Difference (IV, Fixed, 95% CI)	0.63 [‐0.89, 2.15]
11.5 Obtaining support	1	47	Mean Difference (IV, Fixed, 95% CI)	0.94 [‐0.09, 1.97]
11.6 Making decisions	1	47	Mean Difference (IV, Fixed, 95% CI)	0.01 [‐1.39, 1.41]
12 Sense of well‐being and quality of life: emotional adjustment: Appraisal of Diabetes Scale delta scores	1	47	Mean Difference (IV, Fixed, 95% CI)	1.20 [‐0.88, 3.28]
13 Use of additional pharmacotherapy: insulin	2	400	Risk Ratio (M‐H, Fixed, 95% CI)	1.31 [0.69, 2.48]
14 Glycaemic control: pre‐prandial blood glucose (mmol/L)	2	360	Mean Difference (IV, Random, 95% CI)	0.06 [‐0.08, 0.19]
14.1 Breakfast glucose < 7.8 mmol/L	1	192	Mean Difference (IV, Random, 95% CI)	0.10 [‐0.03, 0.23]
14.2 Breakfast glucose ≥ 7.8 mmol/L	1	110	Mean Difference (IV, Random, 95% CI)	0.10 [‐0.07, 0.27]
14.3 All women	1	58	Mean Difference (IV, Random, 95% CI)	‐0.21 [‐0.54, 0.12]
15 Glycaemic control: 1‐hour post‐prandial blood glucose (mmol/L)	2	395	Mean Difference (IV, Random, 95% CI)	‐0.09 [‐0.60, 0.42]
15.1 1‐hour post‐breakfast glucose > 7.8 mmol/L	1	222	Mean Difference (IV, Random, 95% CI)	0.0 [‐0.19, 0.19]
15.2 1‐hour post‐breakfast glucose ≥ 7.8 mmol/L	1	115	Mean Difference (IV, Random, 95% CI)	‐0.60 [‐0.90, ‐0.30]
15.3 All women	1	58	Mean Difference (IV, Random, 95% CI)	0.47 [‐0.12, 1.06]
16 Stillbirth	2	400	Risk Ratio (M‐H, Fixed, 95% CI)	1.54 [0.21, 11.24]
17 Neonatal death	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
18 Gestational age at birth (weeks)	2	400	Mean Difference (IV, Fixed, 95% CI)	‐0.03 [‐0.32, 0.27]
18.1 1‐hour post‐breakfast glucose < 7.8 mmol/L	1	227	Mean Difference (IV, Fixed, 95% CI)	‐0.20 [‐0.59, 0.19]
18.2 1‐hour post‐breakfast glucose ≥ 7.8 mmol/L	1	115	Mean Difference (IV, Fixed, 95% CI)	0.20 [‐0.31, 0.71]
18.3 All neonates	1	58	Mean Difference (IV, Fixed, 95% CI)	0.30 [‐0.78, 1.38]
19 Macrosomia	1	342	Risk Ratio (M‐H, Fixed, 95% CI)	0.94 [0.53, 1.67]
20 Small‐for‐gestational age	1	342	Risk Ratio (M‐H, Fixed, 95% CI)	1.19 [0.53, 2.67]
21 Birthweight (kg)	2	400	Mean Difference (IV, Fixed, 95% CI)	‐40.22 [‐148.37, 67.93]
21.1 1‐hour post‐breakfast glucose < 7.8 mmol/L	1	227	Mean Difference (IV, Fixed, 95% CI)	‐10.0 [‐145.47, 125.47]
21.2 1‐hour post‐breakfast glucose ≥ 7.8 mmol/L	1	115	Mean Difference (IV, Fixed, 95% CI)	‐70.0 [‐283.34, 143.34]
21.3 All neonates	1	58	Mean Difference (IV, Fixed, 95% CI)	‐150.0 [‐482.61, 182.61]
22 Birthweight (percentile)	1	342	Mean Difference (IV, Fixed, 95% CI)	‐0.67 [‐6.75, 5.42]
22.1 1‐hour post‐breakfast glucose < 7.8 mmol/L	1	227	Mean Difference (IV, Fixed, 95% CI)	1.5 [‐5.71, 8.71]
22.2 1‐hour post‐breakfast glucose ≥ 7.8 mmol/L	1	115	Mean Difference (IV, Fixed, 95% CI)	‐6.00 [‐17.32, 5.32]
23 Shoulder dystocia	1	342	Risk Ratio (M‐H, Fixed, 95% CI)	0.25 [0.03, 2.19]
24 Neonatal hypoglycaemia	2	391	Risk Ratio (M‐H, Fixed, 95% CI)	0.64 [0.39, 1.06]
25 Hyperbilirubinaemia or jaundice	2	370	Risk Ratio (M‐H, Fixed, 95% CI)	0.63 [0.39, 1.04]
26 Number of antenatal visits or admissions: prenatal visits with the diabetes team	1	58	Mean Difference (IV, Fixed, 95% CI)	0.20 [‐1.09, 1.49]
27 Neonatal intensive care unit admission	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	0.87 [0.13, 5.77]
28 'Birth trauma' (not a prespecified outcome)	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	0.87 [0.06, 13.27]
29 'Respiratory complications' (not a prespecified outcome)	1	58	Risk Ratio (M‐H, Fixed, 95% CI)	0.87 [0.06, 13.27]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Caesarean section	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.91 [0.68, 1.20]
2 Perinatal mortality	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
3 Large‐for‐gestational age	1	106	Risk Ratio (M‐H, Fixed, 95% CI)	0.67 [0.43, 1.05]
4 Gestational weight gain (kg)	2	179	Mean Difference (IV, Fixed, 95% CI)	‐1.26 [‐2.28, ‐0.24]
5 Use of additional pharmacotherapy	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	2.86 [1.47, 5.56]
6 Glycaemic control: HbA1c at 32 to 36 weeks (%)	1	106	Mean Difference (IV, Fixed, 95% CI)	‐0.10 [‐0.24, 0.04]
7 Stillbirth	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
8 Neonatal death	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.0 [0.0, 0.0]
9 Gestational age at birth (weeks)	2	179	Mean Difference (IV, Fixed, 95% CI)	‐0.17 [‐0.52, 0.19]
10 Preterm birth < 37 weeks	1		Risk Ratio (M‐H, Fixed, 95% CI)	Subtotals only
11 Macrosomia	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.85 [0.35, 2.05]
12 Small‐for‐gestational age	1	106	Risk Ratio (M‐H, Fixed, 95% CI)	1.08 [0.16, 7.37]
13 Birthweight (g)	2	179	Mean Difference (IV, Fixed, 95% CI)	‐110.17 [‐264.73, 44.39]
14 Neonatal hypoglycaemia	2	179	Risk Ratio (M‐H, Fixed, 95% CI)	0.79 [0.35, 1.78]
15 Hyperbilirubinaemia or jaundice	1	73	Risk Ratio (M‐H, Fixed, 95% CI)	1.03 [0.28, 3.80]
16 Neonatal intensive care unit admission	1	73	Risk Ratio (M‐H, Fixed, 95% CI)	0.65 [0.29, 1.50]
17 Length of postnatal stay (baby): length of stay in neonatal intensive care unit (days)	1	18	Mean Difference (IV, Fixed, 95% CI)	‐0.83 [‐2.35, 0.69]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Views of the intervention	1		Risk Ratio (M‐H, Fixed, 95% CI)	Subtotals only
1.1 Overall, I am satisfied with how easy it is to use Accu‐Chek Complete, Acculink	1	38	Risk Ratio (M‐H, Fixed, 95% CI)	1.11 [0.90, 1.38]
1.2 I feel comfortable using the Accu‐Chek Complete, Acculink	1	38	Risk Ratio (M‐H, Fixed, 95% CI)	0.96 [0.66, 1.41]
1.3 Whenever I made a mistake using the Accu‐Chek Complete, Acculink, I could recover easily and quickly	1	38	Risk Ratio (M‐H, Fixed, 95% CI)	0.92 [0.67, 1.25]
1.4 It was easy to learn to use the Accu‐Chek Complete, Acculink	1	38	Risk Ratio (M‐H, Fixed, 95% CI)	1.05 [0.82, 1.34]
1.5 The written material provided for the Accu‐Chek Complete was easy to understand	1	38	Risk Ratio (M‐H, Fixed, 95% CI)	1.18 [0.92, 1.51]

Outcome or subgroup title	No. of studies	No. of participants	Statistical method	Effect size
1 Hypertensive disorders of pregnancy: pre‐eclampsia	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	1.0 [0.15, 6.68]
2 Caesarean section	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.62 [0.29, 1.29]
3 Large‐for‐gestational age	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.29 [0.11, 0.78]
4 Perineal trauma	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.38 [0.11, 1.29]
5 Gestational weight gain (kg)	1	66	Mean Difference (IV, Fixed, 95% CI)	‐0.20 [‐2.81, 2.41]
6 Adherence to the intervention: compliance with schedule (%)	1	66	Mean Difference (IV, Fixed, 95% CI)	‐3.0 [‐3.99, ‐2.01]
7 Use of additional pharmacotherapy	1		Mean Difference (IV, Fixed, 95% CI)	Subtotals only
7.1 Insulin dose (during the last 4 weeks of pregnancy, including regular and intermediate acting) (units/day)	1	66	Mean Difference (IV, Fixed, 95% CI)	23.60 [11.17, 36.03]
7.2 Insulin dose (during the last 4 weeks of pregnancy, including regular and intermediate acting) (units/kg)	1	66	Mean Difference (IV, Fixed, 95% CI)	0.20 [0.12, 0.28]
8 Glycaemic control: change in HbA1c (%)	1	66	Mean Difference (IV, Fixed, 95% CI)	‐2.4 [‐3.33, ‐1.47]
9 Glycaemic control: hospitalisation for glycaemic control	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	1.33 [0.32, 5.50]
10 Glycaemic control: success in glycaemic control (%)	1	66	Mean Difference (IV, Fixed, 95% CI)	2.0 [‐0.26, 4.26]
11 Stillbirth	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.33 [0.01, 7.90]
12 Gestational age at birth (weeks)	1	66	Mean Difference (IV, Fixed, 95% CI)	0.30 [‐1.08, 1.68]
13 Apgar score < 7 at 5 minutes	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.33 [0.04, 3.04]
14 Macrosomia	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.25 [0.08, 0.81]
15 Small‐for‐gestational age	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	3.0 [0.13, 71.07]
16 Birthweight (g)	1	66	Mean Difference (IV, Fixed, 95% CI)	‐379.0 [‐650.79, ‐107.21]
17 Shoulder dystocia	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.17 [0.02, 1.31]
18 Nerve palsies	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.5 [0.05, 5.25]
19 Bone fractures	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	1.0 [0.07, 15.33]
20 Neonatal hypoglycaemia	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.14 [0.02, 1.10]
21 Hyperbilirubinaemia or jaundice	1	66	Risk Ratio (M‐H, Fixed, 95% CI)	0.75 [0.18, 3.09]

Methods	Quasi‐randomised controlled trial
Participants	276 pregnant women enrolled – 240 had GDM and 36 had type 1 diabetes. Setting: 12 Italian diabetes clinics Inclusion criteria: 240 women with GDM were included within a week of diagnosis of GDM (Carpenter and Coustan 1982 criteria); mean of 28 weeks’ gestation. Exclusion criteria: not stated.
Interventions	Telemedicine (n = unclear; 88 followed up) Women received standard care plus telemedicine. They were given training on the use of the equipment and were asked to submit their glycaemic data every week, and more often if necessary, and had a medical examination at the diabetes clinic once a month. Women were asked to report their glucose concentrations (as recorded by the glucometer) using an interfacing device that converted the values into audio tones which could be transmitted via a normal telephone receiver. Women dialled the Glucobeep server’s number and identified themselves by a code – the system received their glycaemic data; they could also record a voice message containing any details they deemed useful to help the physician interpret their glycaemic values. Physicians logged in to the server to download the women’s glucose values and any messages; they analysed the data and recorded prescriptions in a message on the server; women then called the server to hear the message containing any new prescriptions. Both women and physicians received a text message immediately when their messages were received by the other party. Standard care (n = not clear; 115 followed up) Women received standard care (see below for details). All women Women were given standard care according to the recommendations of the American Diabetes Association; women with GDM were placed on a diet and trained to monitor their blood glucose using a home monitor. Women were asked to measure their blood glucose 4 times per day. Insulin was provided when glucose exceeded 95 mg/dl (5.3 mmol/L) fasting, or 130 mg/dl (7.2 mmol/L) 1 hour after meals. Women had a medical examination every 2 weeks. All women could contact the physician whenever they wished.
Outcomes	Review outcomes reported: maternal morbidity (including gestational hypertension, pre‐eclampsia, eclampsia, hypoglycaemic episodes); caesarean section; use of additional pharmacotherapy (insulin therapy); glycaemic control (HbA1c in third trimester); maternal hypoglycaemia; gestational weight gain; adherence to intervention; quality of life; views of intervention; neonatal morbidity (including hypoglycaemic, hyperbilirubinaemia, respiratory distress syndrome, shoulder dystocia, malformations); macrosomia; gestational age at birth; birthweight; medical examinations and visits to diabetic clinic
Notes	Funding: not reported Declarations of interest: not reported Dates: not specified
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	High risk	Quote: "Women were sequentially assigned to the two groups: one patient was followed up using the telemedicine approach, and the next using the conventional approach (usual care)."
Allocation concealment (selection bias)	High risk	As above
Blinding of participants and personnel (performance bias)   All outcomes	High risk	Not feasible to blind participants and personnel
Blinding of outcome assessment (detection bias)   All outcomes	Unclear risk	No detail provided; unclear how lack of blinding would have affected outcomes
Incomplete outcome data (attrition bias)   All outcomes	High risk	270 pregnant women were enrolled (240 with GDM, 36 with type 1 diabetes); 203/240 women with GDM and 32/36 women with type 1 diabetes were analysed; the others were excluded as they did not complete the questionnaires at the end of the study. While the authors reported "The demographic, clinical and metabolic characteristics of the women excluded from the study were no different from those of the women who completed the study," there were unbalanced numbers of women with GDM included in the analysis (88 in intervention; 115 in control) indicating a higher rate of exclusion from the intervention group.
Selective reporting (reporting bias)	Unclear risk	No access to trial protocol to permit confident assessment of selective reporting. Some results (particularly surrounding adherence and views) reported incompletely in text.
Other bias	Unclear risk	Lack of methodological detail provided to determine risks of other bias.

Methods	Randomised controlled trial
Participants	66 women randomised Setting: University of California, Irvine and Long Beach Memorial Medical Centre, USA Inclusion criteria: women with GDM who required insulin at or before 30 weeks’ gestation, with a singleton fetus. Women with elevated fasting values at the time of a 3‐hour OGTT received insulin; for others, insulin therapy was initiated if weekly fasting and post‐prandial (1 hour after breakfast) values exceeded 105 mg/dL or 140 mg/dL, respectively. Women were screened for GDM at 24 to 28 weeks' gestation with a 50 g 1‐hour OGCT; if the value was ≥ 140 mg/dL (7.8 mmol/L) but < 190 mg/dL* (10.6 mmol/L) a 3‐hour OGTT was performed, with GDM diagnosed with any 2 of the following abnormal values: fasting > 105 mg/dL (5.9 mmol/L); 1‐hour > 190 mg/dL (10.6 mmol/L); 2‐hour > 165 mg/dL (9.2 mmol/L); or 3‐hour > 145 mg/dL (8.1 mmol/L). (O'Sullivan and Mahan 1964 criteria; O'Sullivan 1964).  Women with a value ≥ 190 mg/dL on the initial screening test were also diagnosed with GDM. Exclusion criteria:* women with a history of diabetes before pregnancy, with pre‐existing hypertension, renal disease or autoimmune disorders
Interventions	Postprandial monitoring plan (n = 33) Women were required to undertake daily monitoring of blood glucose concentrations before breakfast (fasting), and 1 hour after each meal for the duration of the pregnancy. preprandialmonitoring plan (n = 33) Women were required to undertake daily monitoring of fasting, preprandial and bedtime capillary blood glucose concentrations for the duration of the pregnancy. All women Women were evaluated weekly by the perinatal diabetes team (obstetrician, dietitian, nurse educator, counsellor) unless pregnancy complications (including poor glycaemic control, preterm labour or hypertension) made hospitalisation necessary. Women had a diet prescribed with 30 kcal to 35 kcal per kg of ideal body weight, divided into 3 meals and 1 to 3 snacks (with 40% to 45% of the energy provided by carbohydrates); calorie intake and food choices were adjusted at the weekly visits according to weight gain and blood glucose. All women received split‐dose therapy, with short‐ and intermediate‐acting human insulin, adjusted to achieve fasting blood glucose of 60 mg/dL to 90 mg/dL (3.3 mmol/L to 5 mmol/L) and preprandial values of 60 mg/dL to 105 mg/dL (3.3 mmol/L to 5.9 mmol/L) or post‐prandial values < 140 mg/dL. Women used memory‐based reflectance glucometers to measure their blood glucose; adjustments to insulin doses were made if any of the values were consistently higher than the target concentrations (with efforts made to normalise fasting glucose first).
Outcomes	Review outcomes reported: hypertensive disorders of pregnancy (pre‐eclampsia); caesarean section; perineal trauma (3rd or 4th degree lacerations); gestational weight gain; adherence to intervention (compliance with schedule); use of pharmacotherapy (insulin dose); glycaemic control (change in HbA1c; hospitalisation for glycaemic control; success in glycaemic control); large‐for‐gestational age; stillbirth; gestational age at birth; Apgar score < 7 at 5 minutes; macrosomia; small‐for‐gestational age; birthweight; shoulder dystocia; nerve palsy (Erb’s palsy); bone fracture; hypoglycaemia (requiring glucagon or dextrose infusion); hyperbilirubinaemia
Notes	Funding: not reported Declarations of interest: not reported Dates: not specified
*Risk of bias*
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Unclear risk	Quote: "permuted‐block randomization was used;" no further details provided.
Allocation concealment (selection bias)	Unclear risk	No further details provided.
Blinding of participants and personnel (performance bias)   All outcomes	High risk	Not feasible to blind participants and personnel.
Blinding of outcome assessment (detection bias)   All outcomes	High risk	As above; and described in Discussion as a "non‐blinded study".
Incomplete outcome data (attrition bias)   All outcomes	Low risk	No losses to follow up or missing data.
Selective reporting (reporting bias)	Unclear risk	Reported all outcomes as per manuscript methods; however no access to trial protocol/registration to assess selective reporting further.
Other bias	Low risk	No other obvious risk of bias identified.

Study	Reason for exclusion
Bancroft 2000	This randomised trial is included in the Alwan 2009 Cochrane Review that assessed treatments for women with GDM. The monitored group were given standard dietary advice, glucose metabolism was monitored by capillary glucose series 5 days a week, HbA1c was measured monthly (insulin was introduced if 5 or more capillary measurements > 7.0 mmol/L in 1 week), serial ultrasound for growth and amniotic fluid, Doppler studies, CTG monitoring. The unmonitored group received dietary advice, and HbA1c monthly, but no capillary glucose measurements.
Bartholomew 2015	This was a cross‐over randomised trial, in women with type 2 diabetes or GDM. The trial compared a traditional method of blood sugar reporting using telephone and voicemail and a novel method using cell phone/internet technology.
Clarke 2005	This was a cross‐over randomised trial, in women with type 2 diabetes or GDM. The trial compared the use of Softsense and Optium (MediSense Products) meters.
Elnour 2008	This randomised controlled trial assessed a structured pharmaceutical care service (including education and introduction of intensive self‐monitoring) for women with GDM. Women in the pharmaceutical care intervention group received care by a clinical pharmacist at baseline, and reinforced at monthly clinic visits, with education about GDM and its management (including advice on diet, exercise, blood glucose control, self‐monitoring of glucose, and adjustment of treatment if required; a printed educational booklet; and instructions for self‐monitoring of blood glucose). Control women received traditional care (which included monthly clinic visits and self‐monitoring of plasma glucose using diary cards, but did not include patient education or counselling by the clinical pharmacist, or liaison between the clinical pharmacist and the prescribing doctor). This trial is likely to be eligible for the Brown 2017 Cochrane Review.
Fung 1996	Participants were pregnant women, not specifically women with GDM. All women received a 50 g 3‐hour and 75 g 2‐hour OGTT in a random order, 7 days apart between 28 and 32 weeks' gestation. Women were compared according to whether they received the 50 g test first or 75 g test first.

Trial name or title	GlucoMOMS trial
Methods	Randomised controlled trial
Participants	Pregnant women aged 18 and over with either diabetes mellitus type 1 or 2 on insulin therapy or with GDM requiring insulin therapy before 30 weeks of gestation.
Interventions	Intervention group: women will use continuous glucose monitoring for 5 to 7 days every 6 weeks; based on their profiles, they will receive dietary advice and insulin therapy adjustments if necessary. Control group: women will receive usual care. All women will determine their glycaemic control by self‐monitoring of blood glucose levels and HbA1c.
Outcomes	Primary outcome: macrosomia (birthweight > 90th centile) Secondary outcomes: maternal: pre‐eclampsia; caesarean section; hypoglycaemia; HbA1c levels; and glucose variability; neonatal: birthweight; preterm birth; perinatal death; birth trauma; hypoglycaemia; respiratory distress syndrome; bronchopulmonary dysplasia; intraventricular haemorrhage; necrotising enterocolitis; and sepsis
Starting date	Planned start date: 1 July 2011
Contact information	Daphne N Voormolen: d.p.vanmunster‐2@umcutrecht.nl  Department of Obstetrics and Gynaecology, University Medical Centre, Utrecht, The Netherlands
Notes	Recruitment target: 300 women 2016 Abstract reported that as of Septenber 2015, 300 pregnant women were included (N = 108 with GDM).

Trial name or title	Evaluation of the efficacy of self monitoring blood glucose for GDM with 1 point abnormality
Methods	Randomised controlled trial
Participants	Pregnant women with normal glucose tolerance GDM with 1 point abnormality on 75 g OGTT in second trimester GDM with 2 or 3 point abnormality in second trimester
Interventions	Intervention group: self‐blood glucose monitoring
Outcomes	Outcomes: body weight; HbA1c; glycoalbumin plasma glucose; insrinogenic index; continuous glucose monitoring; neonatal complications; and complications of pregnancy
Starting date	Anticipated start date: 5 January 2016
Contact information	Toshiaki Hanafusa: hanafusa@poh.osaka‐med.ac.jp Department of Internal Medicine, Osaka Medical College, Osaka, Japan
Notes	Recruitment target: 60 women

Trial name or title	Self‐blood glucose monitoring and real‐time continuous glucose monitoring in patients with GDM
Methods	Randomised controlled trial
Participants	Women with newly diagnosed GDM who meet 'two‐step' approach (Carpenter and Coustan criteria) at 24‐28 weeks' gestation.
Interventions	Intervention group: as per control group, plus real‐time continuous glucose monitoring Control group: women receive education every 1 to 2 weeks, about glucose controlling and diet, according to their self‐monitored glucose levels.
Outcomes	Primary outcome: composite maternal and neonatal outcome consisting of: pregnancy‐induced pre‐eclampsia; preterm birth; macrosomia/large‐for‐gestational age/small‐for‐gestational age; and obstetric trauma Secondary outcomes: caesarean birth; eclampsia, gestational hypertension; intrauterine fetal death; gestational age at birth; birthweight, birthweight percentile; neonatal hypoglycaemia; hyperbilirubinaemia; respiratory distress syndrome
Starting date	Anticipated date of first enrolment: 29 May 2015
Contact information	Jae Hyeon KIm: jaehyeon@skku.edu Samsung Medical Center, Seoul, South Korea
Notes	Recruitment target: 178 women

Trial name or title	Trial of remote evaluation and treatment of GDM (TREAT‐GDM)
Methods	Randomised controlled trial
Participants	Women with abnormal glucose tolerance test in this pregnancy (as defined by IADPSG recommendations); not requiring pharmacological treatment at recruitment; started on oral hypoglycaemic therapy at recruitment; with a singleton pregnancy; able to travel to hospital independently
Interventions	Intervention group: women will receive the GDM‐health system and half the normal clinic visits. Control group: women will receive normal clinic care.
Outcomes	Primary outcome: mean blood glucose from recruitment to delivery calculated, with adjustments made for number of measurements, proportion of preprandial and post‐prandial readings and length of time in study Secondary outcomes: compliance; maternal and neonatal outcomes; glycaemic control using HbA1c and other blood glucose metrics; attitudes to care; resource use
Starting date	September 2013
Contact information	Lucy Mackillop; lucy.mackillop@ouh.nhs.uk Nuffield Department of Obstetrics and Gynaecology, John Radcliffe Hospital, Oxford, UK
Notes	Recruitment target: 203 women

Trial name or title	Frequency of blood glucose monitoring in patients with GDM (GLIMPSE)
Methods	Randomised controlled trial
Participants	Women with GDM diagnosed using ACOG criteria; between 20‐32 weeks gestation; singleton pregnancies; not requiring medical therapy after the first weeks of blood glucose monitoring
Interventions	Intervention group: blood glucose monitoring every other day as per below Control group: blood glucose monitoring done every day (during a fasting state and 2 hours after breakfast, lunch and dinner) continued throughout gestation.
Outcomes	Primary outcome: neonatal weight Secondary outcome: macrosomia
Starting date	May 2013
Contact information	Hector Mendez‐Figueroa: Hector.R.Mendezfigueroa@uth.tmc.edu The University of Texas Health Science Center, Houston, USA
Notes	Recruitment target: 286 women

Trial name or title	Telemedicine for insulin treated GDM (TeleGDM)
Methods	Randomised controlled trial
Participants	IADPSG criteria based clinical diagnosis of GDM confirmed by OGTT; 24 to 33 weeks' gestation, or earlier diagnosis if in high risk group; management of hyperglycaemia with insulin; smart phone/tablet with internet access and/or internet connected personal computer; not requiring an interpreter to navigate through the healthcare system
Interventions	Intervention group: telemedicine as an add‐on (adjunct) to usual care Control group: usual care
Outcomes	Primary outcome: patient service utilisation: assessed as a composite of scheduled face‐to‐face consultations, unscheduled face‐to‐face consultations, and telephone consultations. Secondary outcomes: glycaemic control; diabetes self efficacy; patient satisfaction; clinician satisfaction; service provision costs; technology capability and capacity; insulin adjustments; type of delivery (normal vaginal delivery, caesarean delivery or instrument deliveries); large‐for‐gestational age; macrosomia; and neonate admissions to special care nursery
Starting date	Anticipated date of first enrolment: 2 September 2014
Contact information	Tshepo Rasekaba: tshepo.rasekaba@unimelb.edu.au School of Medicine,The University of Melbourne, Australia
Notes	Recruitment target: 100 women

Trial name or title	Home versus hospital care in glucose monitoring of GDM and mild gestational hyperglycemia
Methods	Randomised controlled trial
Participants	Women with GDM, pre‐GDM or mild gestational hyperglycaemia
Interventions	Intervention group: home care, 'ambulatory care' or 'outpatient' care; blood glucose self‐monitoring by the women at home Control group: hospital care, 'acute care'; control of diabetes at hospitals by admission to hospital
Outcomes	Primary outcomes: maternal mortality and morbidity; perinatal mortality and morbidity Secondary outcomes: glucose control; maternal hospitalisation for any cause and prolonged hospitalisation; maternal prenatal and postnatal acute care visits; length of stay for delivery; postpartum repeated hospitalisation; biophysical profile tests; preterm birth; birthweight; infant repeated hospitalisation; infant acute care visits; costs
Starting date	May 2010
Contact information	Marilza Rudge
Notes	Recruitment target: 80 women

PERMALINK

Different methods and settings for glucose monitoring for gestational diabetes during pregnancy

Puvaneswary Raman

Emily Shepherd

Therese Dowswell

Philippa Middleton

Caroline A Crowther

Abstract

Background

Objectives

Search methods

Selection criteria

Data collection and analysis

Main results

Authors' conclusions

Plain language summary

Summary of findings

Background

Description of the condition

Gestational diabetes mellitus

Epidemiology and risk factors

Maternal and fetal complications

Description of the intervention

Different methods and settings for glucose monitoring for gestational diabetes mellitus

Methods (including frequency and timing) of blood glucose monitoring

Settings for blood glucose monitoring

How the intervention might work

Why it is important to do this review

Objectives

Methods

Criteria for considering studies for this review

Types of studies

Types of participants

Types of interventions

Types of outcome measures

Primary outcomes

For the mother

For the child

Secondary outcomes

For the mother

Perinatal

Long‐term

For the child

Fetus/neonate

Child/adult

Use and costs of health services

Search methods for identification of studies

Electronic searches

Searching other resources

Data collection and analysis

Selection of studies

Data extraction and management

Assessment of risk of bias in included studies

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

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

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

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

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

(5) Selective reporting (checking for reporting bias)

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

(7) Overall risk of bias

Assessment of the quality of the evidence using the GRADE approach

Measures of treatment effect

Dichotomous data

Continuous data

Unit of analysis issues

Cluster‐randomised trials

Cross‐over trials

Multi‐armed trials

Other unit of analysis issues

Dealing with missing data

Assessment of heterogeneity

Assessment of reporting biases

Data synthesis

Subgroup analysis and investigation of heterogeneity

Sensitivity analysis

Results

Description of studies

Results of the search

1.