scholarly journals Protocol for a qualitative study exploring the perception of need, importance and acceptability of a digital diabetes prevention intervention for women with gestational diabetes mellitus during and after pregnancy in Malaysia (Explore-MYGODDESS)

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e044878
Author(s):  
Nur Hafizah Mahamad Sobri ◽  
Irmi Zarina Ismail ◽  
Faezah Hassan ◽  
Iliatha Papachristou Nadal ◽  
Angus Forbes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Behaviour Change ◽  
Diabetes Prevention ◽  
Mobile App ◽  
Expert Committee ◽  
Prevention Intervention ◽  
Behaviour Changes ◽  
Healthy Behaviour

IntroductionWomen who develop gestational diabetes mellitus (GDM) have an increased risk of developing type 2 diabetes, and to reduce this risk the women have to adopt healthy behaviour changes. Although previous studies have explored the challenges and facilitators to initiate behaviour change among women with GDM, there is limited data from Malaysian women. Thus, this study will explore the factors affecting the uptake of healthy behaviour changes and the use of digital technology among women and their healthcare providers (HCPs) to support healthy behaviour changes in women with GDM.Methods and analysisThe study will be modelled according to the Capability, Opportunity, Motivation and Behaviour and Behaviour Change Wheel techniques, and use the DoTTI framework to identify needs, solutions and testing of a preliminary mobile app, respectively. In phase 1 (design and development), a focus group discussion (FGDs) of 5–8 individuals will be conducted with an estimated 60 women with GDM and 40 HCPs (doctors, dietitians and nurses). Synthesised data from the FGDs will then be combined with content from an expert committee to inform the development of the mobile app. In phase 2 (testing of early iterations), a preview of the mobile app will undergo alpha testing among the team members and the app developers, and beta testing among 30 women with GDM or with a history of GDM, and 15 HCPs using semi-structured interviews. The outcome will enable us to optimise an intervention using the mobile app as a diabetes prevention intervention which will then be evaluated in a randomised controlled trial.Ethics and disseminationThe project has been approved by the Malaysia Research Ethics Committee. Informed consent will be obtained from all participants. Outcomes will be presented at both local and international conferences and submitted for publications in peer-reviewed journals.

scholarly journals Diabetes prevention interventions for women after gestational diabetes mellitus: an overview of reviews

2021 ◽  
Author(s):  
Anne‐Mette Hedeager Momsen ◽  
Diana Høtoft ◽  
Lisbeth Ørtenblad ◽  
Finn Friis Lauszus ◽  
Rubab Hassan Agha Krogh ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Diabetes Prevention ◽  
Overview Of Reviews ◽  
Prevention Interventions

scholarly journals Gestational Diabetes Mellitus Can Be Prevented by Lifestyle Intervention: The Finnish Gestational Diabetes Prevention Study (RADIEL)

Diabetes Care ◽  
2015 ◽  
Vol 39 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Saila B. Koivusalo ◽  
Kristiina Rönö ◽  
Miira M. Klemetti ◽  
Risto P. Roine ◽  
Jaana Lindström ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Lifestyle Intervention ◽  
Diabetes Prevention ◽  
Prevention Study ◽  
Diabetes Prevention Study

scholarly journals Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus (Preprint)

2017 ◽  
Author(s):  
Evgenii Pustozerov ◽  
Polina Popova ◽  
Aleksandra Tkachuk ◽  
Yana Bolotko ◽  
Zafar Yuldashev ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Blood Glucose ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Recommender System ◽  
Prediction Models ◽  
Mobile App ◽  
Prediction System ◽  
Processing Software ◽  
Diabetes Patients

BACKGROUND Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. OBJECTIVE This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients’ electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. METHODS A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients’ characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. RESULTS The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. CONCLUSIONS The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.

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