Experiences of Using Web-Based and Mobile Technologies to Support Self-Management of Type 2 Diabetes: Qualitative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The prevalence of type 2 diabetes is rising, placing increasing strain on health care services. Web-based and mobile technologies can be an important source of information and support for people with type 2 diabetes and may prove beneficial with respect to reducing complications due to mismanagement. To date, little research has been performed to gain an insight into people's perspectives of using such technologies in their daily management. OBJECTIVE: The purpose of this study was to understand the impact of using Web-based and mobile technologies to support the management of type 2 diabetes. METHODS: In-depth interviews were conducted with 15 people with type 2 diabetes to explore experiences of using Web-based and mobile technologies to manage their diabetes. Transcripts were analyzed using the framework method. RESULTS: Technology supported the users to maintain individualized and tailored goals when managing their health. A total of 7 themes were identified as important to participants when using technology to support self-management: (1) information, (2) understanding individual health and personal data, (3) reaching and sustaining goals, (4) minimizing disruption to daily life, (5) reassurance, (6) communicating with health care professionals, and (7) coordinated care. CONCLUSIONS: Patients need to be supported to manage their condition to improve well-being and prevent diabetes-related complications from arising. Technologies enabled the users to get an in-depth sense of how their body reacted to both lifestyle and medication factors-something that was much more difficult with the use of traditional standardized information alone. It is intended that the results of this study will inform a new questionnaire designed to assess self-management in people using Web-based and mobile technology to manage their health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it