{"paper_id":"d6fe2300-d243-44b5-9822-2f66dd93e166","body_text":"The Impact of Remote Care on the Quality of Care of Pregnant Women with Diabetes: A Systematic Review and Meta-Analysis Protocol | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Remote Care on the Quality of Care of Pregnant Women with Diabetes: A Systematic Review and Meta-Analysis Protocol Sara Sousi, Geva Greenfield, Benedict W J Hayhoe, Natasha Singh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718344/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Diabetes during pregnancy, whether newly diagnosed gestational diabetes mellitus (GDM) or pre-existing diabetes mellitus (DM), is associated with increased maternal and neonatal risks. Remote care has shown promise in managing diabetes in the general population, however, its impact on the quality of antenatal care for pregnant women with diabetes remains unclear. Considering the growing adoption of remote care globally, it is vital to understand its impact on the quality of care. This review aims to evaluate the impact of remote care on the quality of care for pregnant women with diabetes. Methods MEDLINE, EMBASE, CINAHL, MIDIRS, Scopus, Cochrane, and Global Health databases will be searched to identify studies published from 2005 to 2025. Eligible studies will include original empirical research assessing remote care, including both virtual consultations and remote monitoring, for pregnant women with GDM, Type 1 or 2 DM. Quality of care will be assessed using the Institute of Medicine healthcare quality framework: patient-centredness, effectiveness, safety, efficiency, timeliness, and equity. Screening and data extraction will be conducted by two independent reviewers. The quality of the studies will be assessed using the Cochrane Risk of Bias tools. Subgroup analyses will be undertaken to explore variation by diabetes and intervention type. This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) guideline. Discussion The findings of this review will provide evidence on the impact of remote care on the quality of care for pregnant women with pre-existing DM and GDM. Strengthening the evidence will support the development of evidence-based implementation strategies and inform policy decisions to ensure safe, effective, and patient-centred use of remote care interventions. PROSPERO Registration number: CRD420251024685 gestational diabetes mellitus diabetes in pregnancy remote care virtual care remote monitoring mHealth Background Diabetes in pregnancy, either gestational diabetes mellitus (GDM) or pre-existing diabetes mellitus (DM), increases maternal and neonatal risks. GDM affects around 14% of all pregnancies globally; with an increasing prevalence due to risk factors such as obesity and previous pregnancies with GDM (1). Key risk factors for GDM include advanced maternal age, elevated body mass index (BMI), certain ethnic backgrounds, a history of macrosomic birth (i.e., birth weight > 4kg), prior GDM, and a family history of diabetes (2,3). Given the rising maternal age and the ongoing obesity epidemic, particularly in Western countries, an increasing number of women are at risk of developing GDM or having a pregnancy affected by diabetes, placing both themselves and their babies at heightened risk of adverse health outcomes. Pregnant women with pre-existing DM are at an increased risk of hypoglycaemia, diabetic ketoacidosis, worsening diabetic retinopathy, diabetic nephropathy, and have higher rates of fetal malformations and miscarriage compared to women without DM (4). Furthermore, intrauterine exposure to hyperglycaemia increases the risk of T2DM in later life of the baby (5). All pregnant women with diabetes have an increased risk of pre-eclampsia, premature birth and caesarean delivery (6). Neonatal risks include increased risk of birth defects, macrosomia, and hypoglycaemia, while women with GDM have an increased risk of developing obesity and / or T2DM later in life and post-natal cardiovascular complications (7,8). Diabetes in pregnancy services are increasingly overburdened by the rising numbers of GDM and T2DM pregnancies which are linked with poorer patient experiences and worsening clinical outcomes (9). Furthermore, although screening for GDM is recommended, universal versus risk factor-based screening often is limited by resource capacity rather than risk profiles (10). Virtual care platforms and remote monitoring with integration of continuous glucose monitoring, have the potential for sustainable, scalable, and patient-centred models that can improve quality of care. Remote care, including both virtual consultations and remote monitoring, has been increasingly implemented for the management of non-communicable diseases. The Institute of Medicine (IoM) quality of care framework will be used to map the impact on quality of care; this includes patient-centredness, effectiveness, safety, efficiency, timeliness, and equity (11,12). Previous systematic reviews have applied this framework to evaluate remote care in primary care and mental health settings (13–15). The implementation of virtual care can be an effective alternative model for the control of HbA1c in patients with T2DM, particularly in terms of clinical effectiveness, efficiency, patient-centredness, and timeliness. However, mixed results have been reported for safety and equity, with key determinants including lower digital literacy and the reduced opportunity for physical assessments (16,17). This systematic review aims to summarise the impact of remote care on the quality of care in the management of pregnant women with diabetes. Specific objectives include: 1) to systematically characterise existing initiatives of remote care in this context; and 2) to summarise the evidence on how these interventions affect the overall quality of care, using the six domains of healthcare quality as defined by the IoM. Methodology This protocol has been developed using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) (18). This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO): CRD420251024685 (19). The systematic review will be reported following the PRISMA 2020 guideline (20). Literature Search The electronic databases MEDLINE (via Ovid), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Cochrane, Maternity & Infant Care Database (MIDIRS), and Global Health databases will be searched for studies published over the last 20 years (2005-2025). The reference lists of relevant articles (including systematic reviews), grey literature sources (including PROSPERO, reports of relevant stakeholder organisations), and conference proceedings of related conferences will also be searched to identify possible additional studies meeting the inclusion criteria. The search string will include the following themes, and using MeSH and Boolean operators the search strategy will be created; terms will include “telehealth”, “telemedicine”, “remote care”, “virtual wards”, “gestational diabetes”, and “diabetes in pregnancy”. The search strategy was developed in collaboration with, and optimised by, an experienced librarian; an example is presented in Supplementary Table 1. Study Selection The Population, Intervention, Comparison, Outcomes, Study Design (PICOS) framework was used to define inclusion and inclusion criteria. Studies will be included if they have a population of pregnant women with a definitive diagnosis of diabetes, including GDM, T1DM, or T2DM. No restrictions will be placed on the timing of diabetes screening during pregnancy, provided a definitive diagnosis is established. Studies evaluating the use of remote care solutions compared with usual care or paper-based logs for the management of diabetes during pregnancy will be included. Studies focusing on remote care or digital interventions for the management of diabetes in non-pregnant individuals, or those utilising digital tools solely for education or psychological support, will be excluded. The outcomes will focus on the six IoM domains of healthcare quality: effectiveness (e.g., glucose control), safety (e.g., caesarean section rate), efficiency (e.g., cost), timeliness (e.g., time to medication), equity (e.g., variation by ethnicity), and patient-centredness (e.g., patient experiences). The clinical and non-clinical outcomes will be extracted and subsequently mapped to these six domains. Original research articles, including experimental studies, controlled trials, and observational studies, will be included; while reviews, abstracts, case reports, non-full-text articles, and study protocols will be excluded. Purely qualitative studies will be excluded, however, studies with a qualitative component that report relevant outcomes will be included. There will be no restrictions based on country or language. The full PICOS-based inclusion and exclusion criteria are defined in Table 1. Table 1 Inclusion and Exclusion Criteria of the Population, Intervention, Comparison, Outcomes, and Study (PICOS) framework Inclusion Criteria Exclusion Criteria Population Pregnant women with pre-existing diabetes mellitus (type 1 or type 2) Pregnant women with gestational diabetes mellitus Non-pregnant women with diabetes mellitus (type 1 or type 2) Children (age < 16) or men with diabetes mellitus (type 1 or type 2) Post-partum women Intervention Remote care for clinical management of diabetes in pregnancy, this can include: Telemedicine including virtual / remote consultations (telephone, video, online, chat based) Remote monitoring applications and systems, such as mobile applications (mHealth) and web-based systems Remote monitoring devices such as CGMs Virtual wards Teletherapy support interventions Virtual reality interventions Digital tools aimed for post-pregnancy Digital tools aiding in diagnosis and / or screening of pregnant women for gestational diabetes mellitus Digital interventions that are lifestyle, exercise, and education purposed only Comparator Face-to-face monitoring Usual standard of care Non usual care or in-person monitoring Non-comparator (single arm) studies Outcomes IoM Quality of Care Domains: Effectiveness Safety Efficiency Timeliness Equity Patient-centredness N/A Study type Studies with empirical data, original research: randomised control trials, non-randomised control trials, cohort studies, retrospective studies, cross-sectional studies, case-control studies Reviews, abstracts, editorials, book chapters, commentaries, case reports, non-full-text studies, protocols, conference proceedings Study Screening The studies yielded from the selected databases will be screened using Covidence, a web-based systematic review management software (21). The studies will be screened by two reviewers independently. This will include Title and Abstract screening followed by Full-text screening based on the pre-defined inclusion and exclusion criteria (Table 1). Any discrepancies will be solved by consensus and / or a third independent reviewer. Interrater reliability will be reported using Cohen’s kappa (22). Data Extraction Data extraction will be undertaken independently by two reviewers into a pre-populated standardised extraction template (Supplementary Table 2). Study information, including title, first author, publication year, study type, participant group characteristics, sample size, diabetes type, intervention type and description, comparator, clinical and non-clinical outcome measures will be extracted. Any discrepancies will be resolved by consensus. Study Quality The Cochrane Risk of Bias 2 (RoB2) tool will be used for randomised control trials, and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool for non-randomised (23,24). The studies will be scored by two independent reviewers, with any discrepancies being resolved via consensus or a third independent reviewer. The results will be reported using traffic light plots showing each risk of bias domain for each included study. Data Synthesis and Meta-Analysis Initially a narrative synthesis will be conducted for each of the six IoM domains: effectiveness, safety, efficiency, timeliness, equity, and patient-centredness. Study characteristics will be summarised in tables. A meta-analysis will be performed to evaluate clinical outcomes, comparing remote care with usual care. Heterogeneity will be assessed using the I 2 statistic, categorised as low (0% to 25%), moderate (25% to 75%), or high (above 75%). A random-effects model will be used if moderate to high heterogeneity is detected; otherwise, a fixed-effects model will be applied. For continuous outcomes, pooled mean differences (MD) with 95% confidence intervals (CIs) will be calculated. For dichotomous outcomes, pooled risk ratios (RRs) with 95% CIs will be calculated. Subgroup analyses will be conducted based on study design (RCT versus non-RCT), type of remote care, and diabetes type. Given that not all studies are expected to include both types of diabetes, subgroup analyses will help asses variations in the effect of intervention by diabetes type. Additional subgroup analyses may be performed based on risk of bias (high versus low risk) to explore potential sources of heterogeneity. Results will be displayed using forest plots and summary tables, including effect size (MD or RR), 95% CIs, heterogeneity (I 2 ), and overall p-values. Publication bias will be assessed visually via funnel plots, and statistically through Egger’s test. Analyses will be performed using R (25). Strength of evidence The strength of evidence will be evaluated using the ’Grading of Recommendations Assessment, Development, and Evaluation’ (GRADE) criteria (26). Discussion Diabetes in pregnancy is increasing globally, with well-established maternal and neonatal risks, effective management, especially of GDM, can improve outcomes and reduce long-term disease burden and costs ( 27 – 29 ). This systematic literature review aims to bring together the existing evidence, about the role of remote care in managing pregnant women with diabetes. Potential benefits of this review include providing an understanding of the current landscape of remote care within the antenatal pathway for pregnant women with diabetes, evaluating its impact on outcomes compared to standard in-person care, and identifying areas across the six domains of healthcare quality where remote care is effective and where further improvement is needed. Strength and Limitations This systematic review’s protocol has been developed in accordance with the PRISMA-P framework. The search strategy was designed and optimised in collaboration with an experienced librarian, and multiple databases will be systematically searched. No language or country restrictions will be applied, enhancing the global relevance of the findings. Outcomes have been aligned with IoM domains of healthcare quality, providing a structured and internationally recognised framework for evaluating remote care for pregnant women with diabetes. A framework that has been previously used to assess the impact of remote care on specific disease populations ( 13 , 14 ). There are several potential limitations to be considered. Firstly, the amount and heterogeneity of the available evidence may pose challenges for synthesis, particularly given the variation in the types of remote care interventions assessed. Remote care encompasses a range of interventions, including telephone and video consultations, app-based interactions, and remote monitoring via connected devices. This introduces variability and limits the ability to make direct, like-for-like comparisons across studies. In addition, many interventions may be part of a multicomponent approach, which would make it difficult to assess the impact on quality of care due to the remote care alone. Secondly, heterogeneity in the methods used to collect and reported outcomes across studies may limit the ability to conduct a robust meta-analysis and draw definitive conclusions. Thirdly, a wide range of digital and telemedicine interventions are included necessitating subgroup analyses, as these interventions are not directly comparable and cannot be meaningfully aggregated. Thus, a potential limitation is that there may be an insufficient number of studies per each intervention type to support robust subgroup analyses. Implications and Expected Study Impact Given the rising prevalence of diabetes in pregnancy, synthesising the current evidence on remote care is both timely and essential. While there are already published systematic reviews on the effectiveness and safety of telemedicine interventions, they do not capture all six domains of healthcare quality ( 30 – 33 ). This systematic review will synthesise the evidence on how these interventions affect the overall quality of care across the six domains of healthcare quality as defined by the IoM: patient-centredness, effectiveness, safety, efficiency, timeliness, and equity. This structured framework will clarify the benefits and potential risks associated with remote models of care in the antenatal context. Our findings will have implications for research, policy setting, and clinical practice. In research, the review will identify evidence gaps and help define priorities for future studies, including the comparative effectiveness of remote versus in-person antenatal care models. In policy, it will support evidence-based policymaking for the implementation of remote care in antenatal services. In clinical practice, it will help to guide healthcare professionals on the safe and effective integration of remote care models into standard care pathways. Additionally, the review will explore whether certain subgroups, such as women with GDM or T1DM or T2DM, experience different outcomes under remote care. Understanding subgroup variations could facilitate the development of more tailored and equitable remote care pathways. By mapping impacts across the six domains of healthcare quality, the review will also highlight areas where remote care models may need further optimisation, particularly in terms of safety and equity. Ultimately, we anticipate that findings of this review will support the safe, effective, and scalable implementation of remote care models to improve outcomes for all pregnant women living with diabetes. Abbreviations BMI Body Mass Index CI Confidence Interval CINAHL Cumulative Index to Nursing and Allied Health Literature DM Diabetes Mellitus GDM Gestational Diabetes Mellitus IoM Institute of Medicine MD Mean Difference MIDIRS Maternity & Infant Care Database PICOS Population, Intervention, Comparison, Outcomes, and Studies PRISMA Preferred Reporting Items for Systematic Review and Meta-Analysis PRISMA-P Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols PROSPERO Prospective Register of Systematic Reviews RoB2 Risk of Bias 2 ROBINS-I Risk of Bias in Non-randomised Studies of Interventions RR Relative Risk T1DM Type 1 Diabetes Mellitus T2DM Type 2 Diabetes Mellitus Declarations Ethics Approval and Consent to Participate Not applicable Consent for Publication Not applicable Availability of data and materials Not applicable Conflict of Interest BH also works for eConsult Health Ltd, a provider of online consultations for National Health Service primary, secondary, and urgent and emergency care. Funding This study is supported by the National Institute for Health and Care Research North West London Patient Safety Research Collaboration (NIHR NWL PSRC: NIHR204292), with infrastructure support from NIHR Imperial Biomedical Research Centre. GG, BH, AM, and ALN are also funded by the NIHR NWL Applied Health Collaboration (NIHR NWL ARC: NIHR200180). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Authors’ Contributions SS and ALN conceptualised the study. The original draft manuscript was written by SS. The manuscript was reviewed and edited by GG, BH, NS, AM, AD, ALN. All authors read and approved the final manuscript. Acknowledgements We would like to thank Rebecca Jones, Liaison Librarian at Imperial College London, for her support and feedback for improving and optimising the search string strategy. References Sweeting A, Hannah W, Backman H, Catalano P, Feghali M, Herman WH, et al. 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Efficacy and safety of telemedicine for blood glucose and pregnancy outcomes in gestational diabetes mellitus: A systematic review. Chin J Evidence-Based Med. 2019;19(8):960–7. Xie W, Dai P, Qin Y, Wu M, Yang B, Yu X. Effectiveness of telemedicine for pregnant women with gestational diabetes mellitus: an updated meta-analysis of 32 randomized controlled trials with trial sequential analysis. BMC Pregnancy Childbirth. 2020;20(1):198. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Feb, 2026 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 04 Aug, 2025 First submitted to journal 01 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6718344\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":512473094,\"identity\":\"b98cec9e-268b-442b-bb3e-1e14b39c4b38\",\"order_by\":0,\"name\":\"Sara Sousi\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYPACCR5+9sYGEIuHgR0iZEBIi4xkz0GoFmbitDDYGNxIgDIJaZFvYH/4mOePBY/Bzcetmwsq7snwNzMwfvjBcNgYlxaDAzzGxrxtEjyStxPbbs84U8wjcZiBWbKH4bAZTi0MPGzSvA0SPHwgLbxtCTwMhxkYpBkYDtvgcdgzaZ4/EjwMNw8CtfxL4JEH2vIbnxaGAwxm0jxsEjwCNxiBWhoSeAwOM7CBbMHtsMM8xoZzQX7pAfnlWAKP4WHGNsseg3Sc3pdvb3/44M2fOnt+9uPPbhfUJNjLHW8+fONHhbVhAy49zJhsxgYiIhKL9lEwCkbBKBgFcAAAajZMFMmxu44AAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0002-0494-5138\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Sara\",\"middleName\":\"\",\"lastName\":\"Sousi\",\"suffix\":\"\"},{\"id\":512473095,\"identity\":\"e2658326-8f9d-4112-ae67-90fa45f2c70d\",\"order_by\":1,\"name\":\"Geva Greenfield\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Geva\",\"middleName\":\"\",\"lastName\":\"Greenfield\",\"suffix\":\"\"},{\"id\":512473096,\"identity\":\"cac3a303-2b91-4a52-996d-f389bb56423c\",\"order_by\":2,\"name\":\"Benedict W J Hayhoe\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Benedict\",\"middleName\":\"W J\",\"lastName\":\"Hayhoe\",\"suffix\":\"\"},{\"id\":512473097,\"identity\":\"c7b3aaa0-ec8b-4587-a10e-ea76fc98b02c\",\"order_by\":3,\"name\":\"Natasha Singh\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chelsea and Westminster Hospital NHS Foundation Trust\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Natasha\",\"middleName\":\"\",\"lastName\":\"Singh\",\"suffix\":\"\"},{\"id\":512473098,\"identity\":\"6b80d0fa-0590-4f3c-b989-6505972fd25f\",\"order_by\":4,\"name\":\"Azeem Majeed\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Azeem\",\"middleName\":\"\",\"lastName\":\"Majeed\",\"suffix\":\"\"},{\"id\":512473099,\"identity\":\"134d260c-35f9-48c1-b9aa-fbccc0be4381\",\"order_by\":5,\"name\":\"Ara Darzi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ara\",\"middleName\":\"\",\"lastName\":\"Darzi\",\"suffix\":\"\"},{\"id\":512473100,\"identity\":\"d16f517c-a455-4a32-b213-643b0ebc45f8\",\"order_by\":6,\"name\":\"Ana Luísa Neves\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ana\",\"middleName\":\"Luísa\",\"lastName\":\"Neves\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-21 16:28:58\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6718344/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6718344/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":91159451,\"identity\":\"2234800e-a316-482b-9eb4-313af2943a74\",\"added_by\":\"auto\",\"created_at\":\"2025-09-12 08:42:50\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":571940,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6718344/v1/fda89aea-2cd1-4358-81d6-63c457e80c27.pdf\"},{\"id\":91158178,\"identity\":\"6fbb3385-28b3-4171-9a7e-5c042e5027b9\",\"added_by\":\"auto\",\"created_at\":\"2025-09-12 08:26:50\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":20424,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6718344/v1/62635cc261f5ebeb8968ab2e.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"The Impact of Remote Care on the Quality of Care of Pregnant Women with Diabetes: A Systematic Review and Meta-Analysis Protocol\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eDiabetes in pregnancy, either gestational diabetes mellitus (GDM) or pre-existing diabetes mellitus (DM), increases maternal and neonatal risks. GDM affects around 14% of all pregnancies globally; with an increasing prevalence due to risk factors such as obesity and previous pregnancies with GDM (1). Key risk factors for GDM include advanced maternal age, elevated body mass index (BMI), certain ethnic backgrounds, a history of macrosomic birth (i.e., birth weight \\u0026gt; 4kg), prior GDM, and a family history of diabetes (2,3). Given the rising maternal age and the ongoing obesity epidemic, particularly in Western countries, an increasing number of women are at risk of developing GDM or having a pregnancy affected by diabetes, placing both themselves and their babies at heightened risk of adverse health outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003ePregnant women with pre-existing DM are at an increased risk of hypoglycaemia, diabetic ketoacidosis, worsening diabetic retinopathy, diabetic nephropathy, and have higher rates of fetal malformations and miscarriage compared to women without DM (4). Furthermore, intrauterine exposure to hyperglycaemia increases the risk of T2DM in later life of the baby (5). All pregnant women with diabetes have an increased risk of pre-eclampsia, premature birth and caesarean delivery (6). Neonatal risks include increased risk of birth defects, macrosomia, and hypoglycaemia, while women with GDM have an increased risk of developing obesity and / or T2DM later in life and post-natal cardiovascular complications (7,8).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eDiabetes in pregnancy services are increasingly overburdened by the rising numbers of GDM and T2DM pregnancies which are linked with poorer patient experiences and worsening clinical outcomes (9). Furthermore, although screening for GDM is recommended, universal versus risk factor-based screening often is limited by resource capacity rather than risk profiles (10). Virtual care platforms and remote monitoring with integration of continuous glucose monitoring, have the potential for sustainable, scalable, and patient-centred models that can improve quality of care.\\u003c/p\\u003e\\n\\u003cp\\u003eRemote care, including both virtual consultations and remote monitoring, has been increasingly implemented for the management of non-communicable diseases. The Institute of Medicine (IoM) quality of care framework will be used to map the impact on quality of care; this includes patient-centredness, effectiveness, safety, efficiency, timeliness, and equity (11,12). Previous systematic reviews have applied this framework to evaluate remote care in primary care and mental health settings (13–15). The implementation of virtual care can be an effective alternative model for the control of HbA1c in patients with T2DM, particularly in terms of clinical effectiveness, efficiency, patient-centredness, and timeliness. However, mixed results have been reported for safety and equity, with key determinants including lower digital literacy and the reduced opportunity for physical assessments (16,17).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThis systematic review aims to summarise the impact of remote care on the quality of care in the management of pregnant women with diabetes. Specific objectives include: 1) to systematically characterise existing initiatives of remote care in this context; and 2) to summarise the evidence on how these interventions affect the overall quality of care, using the six domains of healthcare quality as defined by the IoM.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Methodology\",\"content\":\"\\u003cp\\u003eThis protocol has been developed using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) (18). This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO): CRD420251024685 (19). The systematic review will be reported following the PRISMA 2020 guideline (20).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLiterature Search\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe electronic databases MEDLINE (via Ovid), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Cochrane, Maternity \\u0026amp; Infant Care Database (MIDIRS), and Global Health databases will be searched for studies published over the last 20 years (2005-2025). The reference lists of relevant articles (including systematic reviews), grey literature sources (including PROSPERO, reports of relevant stakeholder organisations), and conference proceedings of related conferences will also be searched to identify possible additional studies meeting the inclusion criteria. The search string will include the following themes, and using MeSH and Boolean operators the search strategy will be created; terms will include “telehealth”, “telemedicine”, “remote care”, “virtual wards”, “gestational diabetes”, and “diabetes in pregnancy”. The search strategy was developed in collaboration with, and optimised by, an experienced librarian; an example is presented in Supplementary Table 1.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy Selection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Population, Intervention, Comparison, Outcomes, Study Design (PICOS) framework was used to define inclusion and inclusion criteria. Studies will be included if they have a population of pregnant women with a definitive diagnosis of diabetes, including GDM, T1DM, or T2DM. No restrictions will be placed on the timing of diabetes screening during pregnancy, provided a definitive diagnosis is established.\\u003c/p\\u003e\\n\\u003cp\\u003eStudies evaluating the use of remote care solutions compared with usual care or paper-based logs for the management of diabetes during pregnancy will be included. Studies focusing on remote care or digital interventions for the management of diabetes in non-pregnant individuals, or those utilising digital tools solely for education or psychological support, will be excluded.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe outcomes will focus on the six IoM domains of healthcare quality: effectiveness (e.g., glucose control), safety (e.g., caesarean section rate), efficiency (e.g., cost), timeliness (e.g., time to medication), equity (e.g., variation by ethnicity), and patient-centredness (e.g., patient experiences). The clinical and non-clinical outcomes will be extracted and subsequently mapped to these six domains.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOriginal research articles, including experimental studies, controlled trials, and observational studies, will be included; while reviews, abstracts, case reports, non-full-text articles, and study protocols will be excluded. Purely qualitative studies will be excluded, however, studies with a qualitative component that report relevant outcomes will be included. There will be no restrictions based on country or language.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe full PICOS-based inclusion and exclusion criteria are defined in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e Inclusion and Exclusion Criteria of the Population, Intervention, Comparison, Outcomes, and Study (PICOS) framework\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"604\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInclusion Criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eExclusion Criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePopulation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003ePregnant women with pre-existing diabetes mellitus (type 1 or type 2)\\u003c/li\\u003e\\n \\u003cli\\u003ePregnant women with gestational diabetes mellitus\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eNon-pregnant women with diabetes mellitus (type 1 or type 2)\\u003c/li\\u003e\\n \\u003cli\\u003eChildren (age \\u0026lt; 16) or men with diabetes mellitus (type 1 or type 2)\\u003c/li\\u003e\\n \\u003cli\\u003ePost-partum women\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIntervention\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eRemote care for clinical management of diabetes in pregnancy, this can include:\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eTelemedicine including virtual / remote consultations (telephone, video, online, chat based)\\u003c/li\\u003e\\n \\u003cli\\u003eRemote monitoring applications and systems, such as mobile applications (mHealth) and web-based systems\\u003c/li\\u003e\\n \\u003cli\\u003eRemote monitoring devices such as CGMs\\u003c/li\\u003e\\n \\u003cli\\u003eVirtual wards\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eTeletherapy support interventions\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eVirtual reality interventions\\u003c/li\\u003e\\n \\u003cli\\u003eDigital tools aimed for post-pregnancy\\u003c/li\\u003e\\n \\u003cli\\u003eDigital tools aiding in diagnosis and / or screening of pregnant women for gestational diabetes mellitus\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eDigital interventions that are lifestyle, exercise, and education purposed only\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComparator\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFace-to-face monitoring\\u003c/p\\u003e\\n \\u003cp\\u003eUsual standard of care\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNon usual care or in-person monitoring\\u003c/p\\u003e\\n \\u003cp\\u003eNon-comparator (single arm) studies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOutcomes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIoM Quality of Care Domains:\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eEffectiveness\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eSafety\\u003c/li\\u003e\\n \\u003cli\\u003eEfficiency\\u003c/li\\u003e\\n \\u003cli\\u003eTimeliness\\u003c/li\\u003e\\n \\u003cli\\u003eEquity\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003ePatient-centredness\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN/A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStudy type\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eStudies with empirical data, original research: randomised control trials, non-randomised control trials, cohort studies, retrospective studies, cross-sectional studies, case-control studies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eReviews, abstracts, editorials, book chapters, commentaries, case reports, non-full-text studies, protocols, conference proceedings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy Screening\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe studies yielded from the selected databases will be screened using Covidence, a web-based systematic review management software (21). The studies will be screened by two reviewers independently. This will include Title and Abstract\\u0026nbsp;screening followed by Full-text screening based on the pre-defined inclusion and exclusion criteria (Table 1). Any discrepancies will be solved by consensus and / or a third independent reviewer. Interrater reliability will be reported using Cohen’s kappa (22).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Extraction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData extraction will be undertaken independently by two reviewers into a pre-populated standardised extraction template (Supplementary Table 2). Study information, including title, first author, publication year, study type, participant group characteristics, sample size, diabetes type, intervention type and description, comparator, clinical and non-clinical outcome measures will be extracted. Any discrepancies will be resolved by consensus.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy Quality\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Cochrane Risk of Bias 2 (RoB2) tool will be used for randomised control trials, and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool for non-randomised (23,24). The studies will be scored by two independent reviewers, with any discrepancies being resolved via consensus or a third independent reviewer. The results will be reported using traffic light plots showing each risk of bias domain for each included study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Synthesis and Meta-Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInitially a narrative synthesis will be conducted for each of the six IoM domains: effectiveness, safety, efficiency, timeliness, equity, and patient-centredness. Study characteristics will be summarised in tables.\\u003c/p\\u003e\\n\\u003cp\\u003eA meta-analysis will be performed to evaluate clinical outcomes, comparing remote care with usual care. Heterogeneity will be assessed using the I\\u003csup\\u003e2\\u0026nbsp;\\u003c/sup\\u003estatistic, categorised as low (0% to 25%), moderate (25% to 75%), or high (above 75%). A random-effects model will be used if moderate to high heterogeneity is detected; otherwise, a fixed-effects model will be applied. For continuous outcomes, pooled mean differences (MD) with 95% confidence intervals (CIs) will be calculated. For dichotomous outcomes, pooled risk ratios (RRs) with 95% CIs will be calculated.\\u003c/p\\u003e\\n\\u003cp\\u003eSubgroup analyses will be conducted based on study design (RCT versus non-RCT), type of remote care, and diabetes type. Given that not all studies are expected to include both types of diabetes, subgroup analyses will help asses variations in the effect of intervention by diabetes type. Additional subgroup analyses may be performed based on risk of bias (high versus low risk) to explore potential sources of heterogeneity.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eResults will be displayed using forest plots and summary tables, including effect size (MD or RR), 95% CIs, heterogeneity (I\\u003csup\\u003e2\\u003c/sup\\u003e), and overall p-values. Publication bias will be assessed visually via funnel plots, and statistically through Egger’s test. Analyses will be performed using R (25).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStrength of evidence\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe strength of evidence will be evaluated using the ’Grading of Recommendations Assessment, Development, and Evaluation’ (GRADE) criteria\\u0026nbsp;(26).\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eDiabetes in pregnancy is increasing globally, with well-established maternal and neonatal risks, effective management, especially of GDM, can improve outcomes and reduce long-term disease burden and costs (\\u003cspan additionalcitationids=\\\"CR28\\\" citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e). This systematic literature review aims to bring together the existing evidence, about the role of remote care in managing pregnant women with diabetes.\\u003c/p\\u003e\\u003cp\\u003ePotential benefits of this review include providing an understanding of the current landscape of remote care within the antenatal pathway for pregnant women with diabetes, evaluating its impact on outcomes compared to standard in-person care, and identifying areas across the six domains of healthcare quality where remote care is effective and where further improvement is needed.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStrength and Limitations\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis systematic review\\u0026rsquo;s protocol has been developed in accordance with the PRISMA-P framework. The search strategy was designed and optimised in collaboration with an experienced librarian, and multiple databases will be systematically searched. No language or country restrictions will be applied, enhancing the global relevance of the findings. Outcomes have been aligned with IoM domains of healthcare quality, providing a structured and internationally recognised framework for evaluating remote care for pregnant women with diabetes. A framework that has been previously used to assess the impact of remote care on specific disease populations (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThere are several potential limitations to be considered. Firstly, the amount and heterogeneity of the available evidence may pose challenges for synthesis, particularly given the variation in the types of remote care interventions assessed. Remote care encompasses a range of interventions, including telephone and video consultations, app-based interactions, and remote monitoring via connected devices. This introduces variability and limits the ability to make direct, like-for-like comparisons across studies. In addition, many interventions may be part of a multicomponent approach, which would make it difficult to assess the impact on quality of care due to the remote care alone. Secondly, heterogeneity in the methods used to collect and reported outcomes across studies may limit the ability to conduct a robust meta-analysis and draw definitive conclusions. Thirdly, a wide range of digital and telemedicine interventions are included necessitating subgroup analyses, as these interventions are not directly comparable and cannot be meaningfully aggregated. Thus, a potential limitation is that there may be an insufficient number of studies per each intervention type to support robust subgroup analyses.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eImplications and Expected Study Impact\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven the rising prevalence of diabetes in pregnancy, synthesising the current evidence on remote care is both timely and essential. While there are already published systematic reviews on the effectiveness and safety of telemedicine interventions, they do not capture all six domains of healthcare quality (\\u003cspan additionalcitationids=\\\"CR31 CR32\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). This systematic review will synthesise the evidence on how these interventions affect the overall quality of care across the six domains of healthcare quality as defined by the IoM: patient-centredness, effectiveness, safety, efficiency, timeliness, and equity. This structured framework will clarify the benefits and potential risks associated with remote models of care in the antenatal context.\\u003c/p\\u003e\\u003cp\\u003eOur findings will have implications for research, policy setting, and clinical practice. In research, the review will identify evidence gaps and help define priorities for future studies, including the comparative effectiveness of remote versus in-person antenatal care models. In policy, it will support evidence-based policymaking for the implementation of remote care in antenatal services. In clinical practice, it will help to guide healthcare professionals on the safe and effective integration of remote care models into standard care pathways.\\u003c/p\\u003e\\u003cp\\u003eAdditionally, the review will explore whether certain subgroups, such as women with GDM or T1DM or T2DM, experience different outcomes under remote care. Understanding subgroup variations could facilitate the development of more tailored and equitable remote care pathways. By mapping impacts across the six domains of healthcare quality, the review will also highlight areas where remote care models may need further optimisation, particularly in terms of safety and equity. Ultimately, we anticipate that findings of this review will support the safe, effective, and scalable implementation of remote care models to improve outcomes for all pregnant women living with diabetes.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eBMI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eBody Mass Index\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eCI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eConfidence Interval\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eCINAHL\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eCumulative Index to Nursing and Allied Health Literature\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eDM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eDiabetes Mellitus\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eGDM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eGestational Diabetes Mellitus\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eIoM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eInstitute of Medicine\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eMD\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eMean Difference\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eMIDIRS\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eMaternity \\u0026amp; Infant Care Database\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePICOS\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePopulation, Intervention, Comparison, Outcomes, and Studies\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePRISMA\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePreferred Reporting Items for Systematic Review and Meta-Analysis\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePRISMA-P\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePreferred Reporting Items for Systematic Review and Meta-Analysis Protocols\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePROSPERO\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eProspective Register of Systematic Reviews\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eRoB2\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eRisk of Bias 2\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eROBINS-I\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eRisk of Bias in Non-randomised Studies of Interventions\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eRR\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eRelative Risk\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eT1DM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eType 1 Diabetes Mellitus\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eT2DM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eType 2 Diabetes Mellitus\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBH also works for eConsult Health Ltd, a provider of online consultations for National Health Service primary, secondary, and urgent and emergency care.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study is supported by the National Institute for Health and Care Research North West London Patient Safety Research Collaboration (NIHR NWL PSRC: NIHR204292), with infrastructure support from NIHR Imperial Biomedical Research Centre. GG, BH, AM, and ALN are also funded by the NIHR NWL Applied Health Collaboration (NIHR NWL ARC: NIHR200180). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors’ Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSS and ALN conceptualised the study. The original draft manuscript was written by SS. The manuscript was reviewed and edited by GG, BH, NS, AM, AD, ALN. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank Rebecca Jones, Liaison Librarian at Imperial College London, for her support and feedback for improving and optimising the search string strategy.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSweeting A, Hannah W, Backman H, Catalano P, Feghali M, Herman WH, et al. Epidemiology and management of gestational diabetes. Lancet. 2024;404(10448):175\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePlows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci. 2018;19(11).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhang Y, Xiao CM, Zhang Y, Chen Q, Zhang XQ, Li XF et al. 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Rev Obstet Gynecol. 2010;3(3):92.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDabelea D, Mayer-Davis EJ, Lamichhane AP, D\\u0026rsquo;Agostino RB, Liese AD, Vehik KS, et al. Association of intrauterine exposure to maternal diabetes and obesity with type 2 diabetes in youth: the SEARCH Case-Control Study. Diabetes Care. 2008;31(7):1422\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYang Y, Wu N. Gestational Diabetes Mellitus and Preeclampsia: Correlation and Influencing Factors. Front Cardiovasc Med. 2022;9:831297.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMora-Ortiz M, Rivas-Garc\\u0026iacute;a L. Gestational Diabetes Mellitus: Unveiling Maternal Health Dynamics from Pregnancy Through Postpartum Perspectives. Open Res Europe. 2024;4:164.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSullivan SD, Umans JG, Ratner R. Gestational diabetes: implications for cardiovascular health. Curr Diab Rep. 2012;12(1):43\\u0026ndash;52.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eIkomi A, Mannan S. The GooD Pregnancy Network: An Alternative Approach for Gestational Diabetes. BioMed 2022, Vol 2, Pages 37\\u0026ndash;49. 2022;2(1):37\\u0026ndash;49.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGriffin ME, Coffey M, Johnson H, Scanlon P, Foley M, Stronge J, et al. Universal vs. risk factor-based screening for gestational diabetes mellitus: Detection rates, gestation at diagnosis and outcome. Diabet Med. 2000;17(1):26\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNeves AL, Carter AW, Freise L, Laranjo L, Darzi A, Mayer EK. Impact of sharing electronic health records with patients on the quality and safety of care: a systematic review and narrative synthesis protocol. BMJ Open. 2018;8(8):e020387.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWolfe A. Crossing the Quality Chasm. Washington, D.C.: National Academies; 2001.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCampbell K, Greenfield G, Li E, O\\u0026rsquo;Brien N, Hayhoe B, Beaney T, et al. The Impact of Virtual Consultations on the Quality of Primary Care: Systematic Review. J Med Internet Res. 2023;25(1):e48920.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSockalingam S, Kirvan A, Pereira C, Rajaratnam T, Elzein Y, Serhal E et al. The Role of Tele-Education in Advancing Mental Health Quality of Care: A Content Analysis of Project ECHO Recommendations. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://home.liebertpub.com/tmj\\u003c/span\\u003e\\u003cspan address=\\\"https://home.liebertpub.com/tmj\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. 2021;27(8):939\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNeves AL, Freise L, Laranjo L, Carter AW, Darzi A, Mayer E. Impact of providing patients access to electronic health records on quality and safety of care: a systematic review and meta-analysis. BMJ Qual Saf. 2020;29(12):1019\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAldakhil R, Greenfield G, Lammila-Escalera E, Laranjo L, Hayhoe BWJ, Majeed A et al. The Impact of Virtual Consultations on Quality of Care for Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis. J Diabetes Sci Technol. 2025.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRavi S, Meyerowitz-Katz G, Yung C, Ayre J, McCaffery K, Maberly G, et al. Effect of virtual care in type 2 diabetes management \\u0026ndash; a systematic umbrella review of systematic reviews and meta-analysis. BMC Health Serv Res. 2025;25(1):348.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMoher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSousi S, Neves AL. The Impact of Remote Care on the Quality of Care for Pregnant Women with Diabetes: A Systematic Review and Meta-Analysis. PROSPERO 2025 CRD420251024685. Available from \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251024685\\u003c/span\\u003e\\u003cspan address=\\\"https://www.crd.york.ac.uk/PROSPERO/view/CRD420251024685\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med. 2021;18(3):e1003583.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCovidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.covidence.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMcHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276\\u0026ndash;82.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSterne JA, Hern\\u0026aacute;n MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHiggins JPT, Altman DG, Gotzsche PC, Juni P, Moher D, Oxman AD, et al. The Cochrane Collaboration\\u0026rsquo;s tool for assessing risk of bias in randomised trials. BMJ. 2011;343(oct18 2):d5928\\u0026ndash;5928.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eR Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024 [cited 2025 May 29]. Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.R-project.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.R-project.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGuyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMcMicking J, Lam AYR, Lim W, Tan APLK, Pasupathy PD. Epidemiology and Classification of Diabetes in Pregnancy. The Global Library of Women\\u0026rsquo;s Medicine; 2021.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNICE Guideline No 3. Diabetes in pregnancy: management from preconception to the postnatal period. 2020 Dec 16 [cited 2025 Apr 26]; Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK555331/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/books/NBK555331/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLloyd M, Morton J, Teede H, Marquina C, Abushanab D, Magliano DJ, et al. Long-term cost-effectiveness of implementing a lifestyle intervention during pregnancy to reduce the incidence of gestational diabetes and type 2 diabetes. Diabetologia. 2023;66(7):1223.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang F, Yu K, Li Z. Effectiveness of telemedicine for glucose control and pregnancy outcomes in gestational diabetes mellitus: systematic review and meta-analysis. 中华健康管理学杂志. 2020;14(06):571\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHangaard S, Laursen SH, Udsen FW, Vestergaard P, Hejlesen O. Telemedicine Interventions for the Management of Diabetes: A Systematic Review and Meta-Analysis. Studies in Health Technology and Informatics. IOS; 2020. pp. 1403\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHuang N, Chen S, Zhou Y, Xing W, Wang K, Zhong J, et al. Efficacy and safety of telemedicine for blood glucose and pregnancy outcomes in gestational diabetes mellitus: A systematic review. Chin J Evidence-Based Med. 2019;19(8):960\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eXie W, Dai P, Qin Y, Wu M, Yang B, Yu X. Effectiveness of telemedicine for pregnant women with gestational diabetes mellitus: an updated meta-analysis of 32 randomized controlled trials with trial sequential analysis. BMC Pregnancy Childbirth. 2020;20(1):198.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":true,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"systematic-reviews\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sysr\",\"sideBox\":\"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/sysr/default.aspx\",\"title\":\"Systematic Reviews\",\"twitterHandle\":\"@MedicalEvidence\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"gestational diabetes mellitus, diabetes in pregnancy, remote care, virtual care, remote monitoring, mHealth\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6718344/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6718344/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eDiabetes during pregnancy, whether newly diagnosed gestational diabetes mellitus (GDM) or pre-existing diabetes mellitus (DM), is associated with increased maternal and neonatal risks. Remote care has shown promise in managing diabetes in the general population, however, its impact on the quality of antenatal care for pregnant women with diabetes remains unclear. Considering the growing adoption of remote care globally, it is vital to understand its impact on the quality of care. This review aims to evaluate the impact of remote care on the quality of care for pregnant women with diabetes.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eMEDLINE, EMBASE, CINAHL, MIDIRS, Scopus, Cochrane, and Global Health databases will be searched to identify studies published from 2005 to 2025. Eligible studies will include original empirical research assessing remote care, including both virtual consultations and remote monitoring, for pregnant women with GDM, Type 1 or 2 DM. Quality of care will be assessed using the Institute of Medicine healthcare quality framework: patient-centredness, effectiveness, safety, efficiency, timeliness, and equity. Screening and data extraction will be conducted by two independent reviewers. The quality of the studies will be assessed using the Cochrane Risk of Bias tools. Subgroup analyses will be undertaken to explore variation by diabetes and intervention type. This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) guideline.\\u003c/p\\u003e\\u003ch2\\u003eDiscussion\\u003c/h2\\u003e\\u003cp\\u003eThe findings of this review will provide evidence on the impact of remote care on the quality of care for pregnant women with pre-existing DM and GDM. Strengthening the evidence will support the development of evidence-based implementation strategies and inform policy decisions to ensure safe, effective, and patient-centred use of remote care interventions.\\u003c/p\\u003e\\u003ch2\\u003ePROSPERO Registration number:\\u003c/h2\\u003e\\u003cp\\u003eCRD420251024685\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Impact of Remote Care on the Quality of Care of Pregnant Women with Diabetes: A Systematic Review and Meta-Analysis Protocol\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-12 08:26:46\",\"doi\":\"10.21203/rs.3.rs-6718344/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2026-02-03T13:55:12+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-09T11:25:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-08-04T06:20:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Systematic Reviews\",\"date\":\"2025-08-01T17:38:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"systematic-reviews\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sysr\",\"sideBox\":\"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/sysr/default.aspx\",\"title\":\"Systematic Reviews\",\"twitterHandle\":\"@MedicalEvidence\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d3caee6d-869a-40a0-ae71-313807b1cf00\",\"owner\":[],\"postedDate\":\"September 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-02T00:19:58+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-12 08:26:46\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6718344\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6718344\",\"identity\":\"rs-6718344\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}