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Gestational diabetes self-management and remote monitoring mobile platform
There is a high prevalence of gestational diabetes (GD) in South Africa, which is continually growing. South African women with GD are not effectively managed or educated about selfcare, do not self-monitor frequently enough and, therefore, often succumb to various GD induced complications. The inef...
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| Format: | Thesis |
| Language: | English |
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Department of Human Biology
2020
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| _version_ | 1867613142253568000 |
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| access_status_str | Open Access |
| author | Collier, Jason |
| author2 | Abrahams, Jill Fortuin |
| author_browse | Abrahams, Jill Fortuin Collier, Jason |
| author_facet | Abrahams, Jill Fortuin Collier, Jason |
| author_sort | Collier, Jason |
| collection | Thesis |
| description | There is a high prevalence of gestational diabetes (GD) in South Africa, which is continually growing. South African women with GD are not effectively managed or educated about selfcare, do not self-monitor frequently enough and, therefore, often succumb to various GD induced complications. The ineffective management of GD is largely due to financial and time constraints caused by the regularly required outpatient services. On the other hand, healthcare professionals do not monitor their patients frequently enough because of accessibility issues, which means they cannot intervene timeously to prevent diabetes complications. The aim of this project was to develop a mobile health (mHealth) platform for GD self-management and for remote monitoring to improve the GD cycle of care in South Africa. The objectives were to assess the current GD management practices in South Africa, to assess the existing mHealth solutions for GD and to design, develop and test a GD mHealth platform. The existing GD management practices and current GD mHealth solutions were investigated. The results of the investigation informed the design of low-fidelity and high-fidelity mock-ups of the platform. The high-fidelity mock-up underwent usability testing and the insights gained were used to develop a working prototype of the new mHealth platform, which was then ready for in-lab testing. It was found that GD had a prevalence of up to 25% in parts of South Africa. Over 70% of patients in both private and public healthcare sectors did not meet their diabetic goals, which directly correlated with diabetes induced complications. However, previous research found that using mHealth as an intervention caused a statistically significant decrease of 0.38 mmol/L (95% confidence interval (CI) 0.52 mmol/L to 0.23 mmol/L) in overall blood glucose levels during pregnancy when compared to a control group. There was a higher probability of vaginal deliveries in the intervention group than in the control group (risk ratio = 1.18). It was less likely for new-borns from the intervention group to be diagnosed with hypoglycaemia than new-borns from the control group (risk ratio = 0.67). Based on the research and usability studies conducted, an alpha version of the GD mHealth platform was developed, including a mobile app used to track the patient’s blood glucose levels via a Bluetooth-enabled glucose meter. The food intake, exercise and weight gain during pregnancy were manually captured by the patient. The app reminded the patient to take medication, measure glucose levels and attend appointments. A GD educational component was available for the patient throughout the pregnancy. The platform included a web app which allowed healthcare professionals to remotely monitor and communicate with their patients so that they could analyse trends in the data and intervene when necessary. The testing done on the prototype resulted in positive feedback with 60% of participants saying that they would use the GooDMoM mobile app to manage their GD and 70% of participants saying that they would use the GooDMoM web app to manage their patients with GD. This put the platform in a good position for beta development. The solution has the potential to benefit patients both financially and timewise, by reducing the frequency of hospital visits required. It also has the potential to positively impact the healthcare professionals by reducing the tediousness of their workload and allowing for remote monitoring of patients. The platform can, thus, optimise the GD management process in South Africa and worldwide. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31722 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:26.417Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Human Biology |
| publisherStr | Department of Human Biology |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31722 Gestational diabetes self-management and remote monitoring mobile platform Collier, Jason Abrahams, Jill Fortuin Adams, Siraaj Gestational Diabetes There is a high prevalence of gestational diabetes (GD) in South Africa, which is continually growing. South African women with GD are not effectively managed or educated about selfcare, do not self-monitor frequently enough and, therefore, often succumb to various GD induced complications. The ineffective management of GD is largely due to financial and time constraints caused by the regularly required outpatient services. On the other hand, healthcare professionals do not monitor their patients frequently enough because of accessibility issues, which means they cannot intervene timeously to prevent diabetes complications. The aim of this project was to develop a mobile health (mHealth) platform for GD self-management and for remote monitoring to improve the GD cycle of care in South Africa. The objectives were to assess the current GD management practices in South Africa, to assess the existing mHealth solutions for GD and to design, develop and test a GD mHealth platform. The existing GD management practices and current GD mHealth solutions were investigated. The results of the investigation informed the design of low-fidelity and high-fidelity mock-ups of the platform. The high-fidelity mock-up underwent usability testing and the insights gained were used to develop a working prototype of the new mHealth platform, which was then ready for in-lab testing. It was found that GD had a prevalence of up to 25% in parts of South Africa. Over 70% of patients in both private and public healthcare sectors did not meet their diabetic goals, which directly correlated with diabetes induced complications. However, previous research found that using mHealth as an intervention caused a statistically significant decrease of 0.38 mmol/L (95% confidence interval (CI) 0.52 mmol/L to 0.23 mmol/L) in overall blood glucose levels during pregnancy when compared to a control group. There was a higher probability of vaginal deliveries in the intervention group than in the control group (risk ratio = 1.18). It was less likely for new-borns from the intervention group to be diagnosed with hypoglycaemia than new-borns from the control group (risk ratio = 0.67). Based on the research and usability studies conducted, an alpha version of the GD mHealth platform was developed, including a mobile app used to track the patient’s blood glucose levels via a Bluetooth-enabled glucose meter. The food intake, exercise and weight gain during pregnancy were manually captured by the patient. The app reminded the patient to take medication, measure glucose levels and attend appointments. A GD educational component was available for the patient throughout the pregnancy. The platform included a web app which allowed healthcare professionals to remotely monitor and communicate with their patients so that they could analyse trends in the data and intervene when necessary. The testing done on the prototype resulted in positive feedback with 60% of participants saying that they would use the GooDMoM mobile app to manage their GD and 70% of participants saying that they would use the GooDMoM web app to manage their patients with GD. This put the platform in a good position for beta development. The solution has the potential to benefit patients both financially and timewise, by reducing the frequency of hospital visits required. It also has the potential to positively impact the healthcare professionals by reducing the tediousness of their workload and allowing for remote monitoring of patients. The platform can, thus, optimise the GD management process in South Africa and worldwide. 2020-04-30T07:52:41Z 2020-04-30T07:52:41Z 2019 2020-04-30T07:05:26Z Master Thesis Masters MSc https://hdl.handle.net/11427/31722 eng application/pdf Department of Human Biology Faculty of Health Sciences |
| spellingShingle | Gestational Diabetes Collier, Jason Gestational diabetes self-management and remote monitoring mobile platform |
| thesis_degree_str | Master's |
| title | Gestational diabetes self-management and remote monitoring mobile platform |
| title_full | Gestational diabetes self-management and remote monitoring mobile platform |
| title_fullStr | Gestational diabetes self-management and remote monitoring mobile platform |
| title_full_unstemmed | Gestational diabetes self-management and remote monitoring mobile platform |
| title_short | Gestational diabetes self-management and remote monitoring mobile platform |
| title_sort | gestational diabetes self management and remote monitoring mobile platform |
| topic | Gestational Diabetes |
| url | https://hdl.handle.net/11427/31722 |
| work_keys_str_mv | AT collierjason gestationaldiabetesselfmanagementandremotemonitoringmobileplatform |