- Title
- Using information visualization to support the self-management of type 2 diabetes mellitus
- Creator
- Nauder, Meggan Kate
- Subject
- Information visualization
- Subject
- Diabetics --Treatment --South Africa
- Date Issued
- 2022-04
- Date
- 2022-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/55711
- Identifier
- vital:53409
- Description
- The globally increasing number of individuals suffering from Type 2 Diabetes Mellitus (T2DM), a completely preventable incurable disease of the pancreas, highlights the need for an effective tool for users to understand the relationship between their behaviours and the effect that those behaviours can have on their blood glucose levels (BGLs). There are few Information Visualisation (IV) tools available that can be used to reduce the cognition required to understand correlations between behaviour and BGLs. Existing tools require time-consuming, lengthy inputs and provide simple visualisations that do not show correlations. This leads to ineffective self-management of T2DM. Information Visualisation (IV) techniques can be used to support effective self-management of T2DM and reduce the cognition required to interpret DM data. Suitable IV techniques were identified and used to visualize T2DM data to aid in the self-management of the disease. Temporal charts, i.e. The Bar, Pie and Line Chart as well as heat maps, were selected as the most appropriate IV techniques to visualize T2DM data as they support time-series data well. A prototype, MedicMetric was created as an IV tool for visualizing T2DM data. MedicMetric incorporated three designed charts, namely the Change Rate Line View, the Radial Progress View, and the Annotated Line View. The Change Rate Line View and Annotated Line View both used line IV techniques, while the Radial Progress View made use of the bar IV technique. The Change Rate Line View performed the worst overall. A usability evaluation was conducted to compare these techniques and to determine which technique is most suitable for visualizing T2DM data. The results leaned significantly in favour of the Annotated Line View. This view is most similar to the line charts typically used in other IV tools. For this reason, the MedicMetric app was briefly compared to the MySygr and Diabetes:M application. In effectiveness and efficiency, MedicMetric and MySugr obtained almost identical results. However, participants indicated that MedicMetric supported their tasks using the Visual Information Seeking Mantra (VISM) the best overall, with 100% of participants stating that they would prefer to use the MedicMetric application. Several usability problems were identified with the IV techniques and they were addressed shortly after the study was complete. Overall participants were most satisfied with the Annotated Line View.
- Description
- Thesis (MSc) -- Faculty of Science, Computing Sciences, 2022
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (230 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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NMU School of Computer Science, Mathematics, Physics and Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Naude, M.pdf | 4 MB | Adobe Acrobat PDF | View Details Download |