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BACKGROUND: Women are increasingly turning to mobile health platforms to receive health information and support in pregnancy, yet the content of these platforms vary. Although there is great potential to influence health behaviors, little research has assessed the quality of these platforms or their ability to change behavior. In recent years, validated tools to assess app quality have become available. OBJECTIVE: To identify and assess the quality and ongoing popularity of the top 10 freely available pregnancy apps in Australia using validated tools. METHODS: A systematic search on app stores to identify apps was performed. A Google Play search used subject terms pregnancy, parenting, and childbirth; the iTunes search used alternative categories medical and health and fitness. The top 250 apps from each store were cross-referenced, and the top 100 found in both Google Play and iTunes were screened for eligibility. Apps that provided health information or advice for pregnancy were included. Excluded apps focused on nonhealth information (eg, baby names). The top 10 pregnancy apps were assessed using the Mobile App Rating Scale (MARS). A comparative analysis was conducted at 2 time points over 2 years to assess the ongoing popularity of the apps. The MARS score was compared to the download and star rating data collected from iTunes and Google Play in 2017 and 2019. Health behaviors including breastfeeding, healthy pregnancy weight, and maternal awareness of fetal movements were reviewed for apparent impact on the user's knowledge, attitudes, and behavior change intentions using the MARS perceived impact section and the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy. RESULTS: A total of 2052 free apps were screened for eligibility, 1397 were excluded, and 655 were reviewed and scored. The top 10 apps were selected using download numbers and star ratings. All 10 apps were suboptimal in quality, practicality, and functionality. It was not possible to identify a primary purpose for all apps, and there was overlap in purpose for many. The mean overall MARS app quality score across all 10 apps was 3.01 (range 1.97-4.40) in 2017 and 3.40 (range 2.27-4.44) in 2019. A minority of apps scored well for perceived impact on health behavior using the MARS tool. Using the CALO-RE 40 item taxonomy, the number of behavior change techniques used was low. The mean number of behavior change techniques for breastfeeding was 5 (range 2-11), for pregnancy weight was 4 (range 2-12), and for maternal awareness of fetal movements was 5 (range 2-8). CONCLUSIONS: This review provides valuable information to clinicians and consumers about the quality of apps currently available for pregnancy in Australia. Consideration is needed regarding the regulation of information and the potential opportunity to incorporate behavior change techniques to improve maternal and fetal outcomes.

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BACKGROUND: Many of the adverse outcomes experienced by mothers and babies are directly related to the health of the woman prior to pregnancy. This preconception period is a unique window of opportunity when women are often more motivated to optimise health and change their lifestyle in preparation for pregnancy. Several risk factors in the preconception period can contribute to adverse perinatal outcomes. These risk factors can be divided into three broad areas: biomedical, social and environmental. Mobile phone applications as a behaviour change intervention have the potential to address these risks through supporting the provision of information, healthier lifestyles and informed decision-making. The aim of this systematic review is to assess the effectiveness of mobile phone applications in promoting behaviour change and improving long-term outcomes for mother and babies, in women of reproductive age. METHODS: This review will include trials that assess any mobile phone application (app) that assist women of reproductive age to optimise health behaviours. Randomised controlled trials, quasi-randomised controlled trials and cluster-randomised trials will be included. The search strategy will use both MeSH and keyword combinations to search databases including the WHO Global Health Library, CINHAL, The Cochrane Library, Embase and MEDLINE for relevant studies. Retrieved citations will be screened independently by two authors to assess eligibility. Studies will be selected only if the intervention was commenced prior to pregnancy. Comparisons will be made including mobile phone applications versus text messaging-based communications or paper-based, face-to-face or telephone conversations and standard care or no specific intervention. The Cochrane Handbook for Systematic Reviews of Interventions will be utilised to assess the quality of included randomised studies. Primary and secondary outcomes will be compared and analysed. Results of the review will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. DISCUSSION: This systematic review is the first to assess the effects of preconception mobile phone app behaviour change and educational interventions in improving future pregnancy and maternal and child outcomes, in women of reproductive age. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2017: CRD42017065903 .

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BACKGROUND: Preconception care (PCC) defines health interventions prior to conception aimed at improving pregnancy and infant outcomes. AIM: To explore the understanding and provision of PCC by general practitioners (GPs) within the Sydney Local Health District. MATERIALS AND METHODS: A questionnaire developed with GPs assessed structure and content of PCC provided, attitudes toward PCC and perceived barriers and facilitators. RESULTS: One hundred and ten GPs completed the survey: 84% reported that GPs should be the main providers of PCC; however, only 53% were aware of PCC guidelines. Seventy-five percent of responders initiated PCC discussion with women of reproductive age, 56% provided PCC to women at higher risk of adverse outcomes and 16% waited for the discussion to be initiated by the patient. Smoking, vaccination, alcohol and supplements/medication use were the most discussed PCC components, while serology, full blood count and blood pressure were the most performed assessments. Most respondents stated that PCC is essential for women with pre-existing diabetes, previous pregnancy complications or chronic illness. However, only 45% stated PCC was essential for women >35 years and 39% for women who were overweight. Importantly, weight and mental health were among the least discussed PCC components. CONCLUSION: General practitioners are key providers of PCC; however, only half are aware of PCC guidelines and most do not recognise overweight to be a significant preconception issue. The most common barriers to PCC delivery were time constraints, lack of knowledge and lack of resources for patients. Improved resources and education are required to support adequate PCC provision.

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Nutrition therapy is considered a key component of diabetes management, yet evidence around the ideal macronutrient composition of the diet remains inconclusive. A systematic review and meta-analysis was performed to assess the effects of carbohydrate-restricted diets (≤45% of total energy) compared to high carbohydrate diets (>45% of total energy) on glycemic control in adults with diabetes mellitus. Six databases were searched for articles published between January 1980 and August 2016. Primary outcome was between-group difference in HbA1c change. Individual effect sizes were standardized, and a meta-analysis performed to calculate pooled effect size using random effects. 25 RCTs involving 2412 participants were included. Carbohydrate-restricted diets, in particular those that restrict carbohydrate to <26% of total energy, produced greater reductions in HbA1c at 3 months (WMD -0.47%, 95% CI: -0.71, -0.23) and 6 months (WMD -0.36%, 95% CI: -0.62, -0.09), with no significant difference at 12 or 24 months. There was no difference between moderately restricted (26-45% of total energy) and high carbohydrate diets at any time point. Although there are issues with the quality of the evidence, this review suggests that carbohydrate-restricted diets could be offered to people living with diabetes as part of an individualised management plan.

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OBJECTIVE: Maternal glycemia plays a key role in fetal growth. We hypothesized that lower glycemic load (GL) meals (lower glycemic index, modestly lower carbohydrate) would substantially reduce day-long glucose variability in women at risk of gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS: A crossover study of 17 women (mean±SD age 34.8±4 years; gestational weeks 29.3±1.3; body mass index 23.8±4.7 kg/m2) who consumed a low GL or a high GL diet in random order, 1-day each, over 2 consecutive days. Diets were energy-matched and fiber-matched with 5 meals per 24 hours. All food was provided. Continuous glucose monitoring was used to assess diurnal glycemia. RESULTS: Maternal glucose levels were 51% lower on the low GL day with lower incremental area under the curve (iAUC±SEM 549±109 vs 1120±198 mmol/L min, p=0.015). Glycemic variability was significantly lower on the low GL day, as demonstrated by a lower average SD (0.7±0.1 vs 0.9±0.1, p<0.001) and lower mean amplitude of glycemic excursions (2.1±0.2 vs 2.7±0.2 mmol/L, p<0.001). CONCLUSIONS: A lower GL meal plan in pregnancy acutely halves day-long maternal glucose levels and reduces glucose variability, providing further evidence to support the utility of a low GL diet in pregnancy.

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The influence of maternal macronutrient balance and dietary glycemic index (GI) on neonatal body composition has received little study. We hypothesized that the overall quantity and quality of macronutrients, particularly carbohydrate, in the maternal diet could have trimester-specific effects on neonatal growth and body composition in women at risk of gestational diabetes. Maternal diet was assessed using 3-day food records in mid (n = 96) and late (n = 88) pregnancy as part of the GI Baby 3 study. Neonatal body composition was assessed by air-displacement plethysmography within 48 h of birth, adjusted for length, and expressed as fat mass index (FMI) and fat-free mass index (FFMI). In mid pregnancy, higher maternal intake of carbohydrate energy was negatively correlated with infant FFMI (p = 0.037). In late pregnancy, higher dietary GI was associated with lower FFMI (p = 0.010) and higher carbohydrate energy predicted lower FMI (p = 0.034). Higher fat intake (%E) and saturated fat, but not protein, also predicted neonatal body composition (higher FFMI in mid pregnancy and higher FMI in late pregnancy). Depending on pregnancy stage, a high carbohydrate-low fat diet, particularly from high glycemic sources, may reduce neonatal indices of both lean mass and adiposity.

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BACKGROUND: Elevated maternal blood glucose concentrations may contribute to macrosomia, adiposity, and poorer vascular health in the offspring. OBJECTIVE: The aim was to explore the effect of a low-glycemic index (low-GI) diet during pregnancy on offspring growth, adiposity, and arterial wall thickness during infancy. DESIGN: This was a longitudinal follow-up study in a self-selected subgroup of mother-infant pairs (n= 59) participating in a larger randomized trial comparing the effects on perinatal outcomes of a low-GI diet and a conventional high-fiber (HF) diet during pregnancy. Infant anthropometric measurements were taken every month for 6 mo and then at 9 and 12 mo of age. Adiposity was assessed at birth and at 3 mo by air-displacement plethysmography by using the Pea Pod system (Cosmed) and at 6 and 12 mo by bioimpedance analysis (Bodystat). Aortic intima-media thickness was assessed at 12 mo by high-resolution ultrasound (Philips). RESULTS: Maternal dietary GI was lower in the low-GI group than in the HF group (51 ± 1 compared with 57 ± 1;P< 0.001). No differences in neonatal outcomes were observed in the main trial. In the self-selected subsample, birth weight and length z scores were lower in the low-GI group than in the HF group (birth weight z score: 0.2 ± 0.2 compared with 0.7 ± 0.2, respectively;P= 0.04; birth length z score: 0.3 ± 0.2 compared with 0.9 ± 0.2, respectively;P= 0.04), but adiposity from birth to 12 mo of age and growth trajectories from 1 to 12 mo of age were similar. Aortic intima-media thickness was lower in the low-GI group than in the HF group (657 ±12 compared with 696 ± 12 µm, respectively;P= 0.02), which was partly mediated by differences in birth weight. CONCLUSION: In women at risk of gestational diabetes mellitus, a low-GI diet influences offspring birth weight, birth length, and arterial wall thickness in early childhood, but not adiposity or growth trajectory during the first year of life. This trial was registered at anzctr.org.au as ACTRN12610000681055.

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