A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia (SAED)

Alotaibi, Mohammed; Istepanian, R.S.H.; Philip, Nada

doi:10.21037/mhealth.2016.08.01

Cited by 39 publications

(44 citation statements)

References 7 publications

(7 reference statements)

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“…Miller et al used a machine learning approach to extract information from drug prescriptions from electronic health record (EHR) data and identify factors associated with patient care flow deviations [ 23 ]. Another DSS with care flow tools was presented in the work of Alotaibi et al [ 137 ]. This system focuses on the management of T2D patients using advanced features, such as computerized alerts and reminders.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

364

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

364

207

View full text Add to dashboard Cite

show abstract

“…Additionally, we cannot measure the usage of MPA by each patient. Most previous studies highlighted that self‐care activities, which contain diet and exercise patterns, and diabetes knowledge, as well as the clinical outcomes, were improved by mobile phone intervention. On the contrary, one study of type 1 diabetes patients found that there was no improvement in self‐efficacy and self‐care behavior, despite a significant decrease in HbA1c.…”

Section: Discussionmentioning

confidence: 99%

Effects of mobile phone application combined with or without self‐monitoring of blood glucose on glycemic control in patients with diabetes: A randomized controlled trial

Yuan

Yan

et al. 2019

J of Diabetes Invest

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Aims/Introduction There is potential for mobile applications to deliver new self‐management interventions for chronic disease, especially in diabetes. The aim of the present study was to evaluate the effects of a mobile phone application (MPA) combined with or without self‐monitoring of blood glucose (SMBG) on glycemic control in patients with diabetes. Materials and Methods The study was a 24‐week period, four‐arm parallel group, non‐blinded, randomized trial. A total of 185 patients with mean age of 52 years were randomized to group A (no MPA and no SMBG), group B (SMBG only), group C (MPA only) and group D (both MPA and SMBG were used). Changes in glycated hemoglobin (HbA1c), fasting plasma glucose and 1,5‐anhydroglucitol from baseline to week 24 were analyzed. Results At 24 weeks, the HbA1c levels in patients of all groups decreased significantly from baseline. There were significant differences in the proportions of patients that achieved HbA1c <7% between groups, especially in group C and group D, compared with group A at week 24 (60.4%, 62.2% vs 25.5%, all P < 0.05). 1,5‐Anhydroglucitol changes were obvious in group A and group C at week 24 from baseline (all P < 0.05 within groups). Factorial analysis of anova showed that MPA intervention was the main effective factor for HbA1c change (F = 4.59, P = 0.034), and there was no effect on HbA1c change for SMBG intervention (P = 0.975). Conclusions Implementation of the MPA, Diabetes‐Carer, is effective in improving the proportion of HbA1c <7% in patients with type 2 diabetes.

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“…A mobile‐based diabetes management and education system SAED was developed in Saudi Arabia for managing patients with type 2 diabetes. SAED intervention was able to significantly decrease the HbA1c in the patients when compared to control group …”

mentioning

confidence: 87%

“…SAED intervention was able to significantly decrease the HbA1c in the patients when compared to control group. 12 The effective implementation of mHealth systems would require the availability of suitable devices and mobile networks for patients.…”

mentioning

confidence: 99%

Health apps usage and preferences among Saudi patients with diabetes: A survey

Rafiullah

David

2019

Int J Clin Pract

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Background Information regarding patterns of smartphone usage, user preferences, and difficulties faced by patients is necessary to initiate any mHealth related programs. Objective To investigate patterns of usage pertaining to various different smartphone health applications amongst diabetic patients in Saudi Arabia as well as patient preferences and the challenges of using health apps from a user perspective. Methods This study was a cross sectional survey based on a self‐administered, close‐ended, pre‐designed, structured questionnaire. The main emphasis of the questions were as follows: (a) to investigate current patterns of smartphone use, (b) to examine the use of mobile for health applications and (c) to understand patient preferences when it comes to the specific health related use of mobile phone apps (d) to accrue patient feedback in using health apps. Results 36.62% of the study population reported using health applications. Patients preferred to follow up blood sugar measurements, body weight, exercise accomplishments, and caloric intake through a health application. More than a third of the participants who were surveyed found the health apps to be very easy to understand with an equal number of participants responding that some training was either necessary. Approximately 32.54% of the respondents indicated that they did not want to know about their health. However, almost an equal number of participants responded that they were unsure of how to start on health apps. Conclusion The results of this survey indicate that there is huge potential for utilizing these health applications via smartphones in the implementation of various health programs.

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A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia (SAED)

Cited by 39 publications

References 7 publications

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Effects of mobile phone application combined with or without self‐monitoring of blood glucose on glycemic control in patients with diabetes: A randomized controlled trial

Health apps usage and preferences among Saudi patients with diabetes: A survey

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