Views of Patients on Using mHealth to Monitor and Prevent Diabetic Foot Ulcers: 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: People with diabetes are at risk for diabetic foot ulcers (DFUs), which can lead to limb loss and a significant decrease in quality of life. Evidence suggests that mHealth can be an effective tool in diabetes self-management. mHealth presents an opportunity for the prevention and monitoring of DFUs. However, there is a paucity of research that explores its effectiveness in the DFU patient population, as well as the views and attitudes of these patients toward technology and mHealth. OBJECTIVE: This study aimed to explore the views, attitudes, and experiences of a diabetic patient population with or at risk of DFUs regarding technology, mHealth, and the diabetic foot. METHODS: We used a qualitative research approach using in-depth interviews with 8 patients with DFUs. Questions were structured around experience with technology, current health practices related to diabetic foot care, and thoughts on using an mHealth device that prevents and monitors DFUs. We transcribed and thematically analyzed all interviews. RESULTS: All patients had positive responses for an mHealth intervention aimed at preventing and monitoring DFUs. We found 4 themes in the data: diversity in use of technology, feet-checking habits, 2-way communication with health care professionals (HCPs), and functionality. There were varying levels of familiarity with and dependence on technology within this patient population. These relationships correlated with distinct generations found in North America, including baby boomers and Generation X. Furthermore, we found that most patients performed daily feet checks to monitor any changes in health. However, some did not perform feet checks prior to the development of a DFU. Patients expressed interest in 2-way communication with HCPs that would allow for easier appointment scheduling, sharing of medical data, decreased number of visits, and use of alerts for when medical attention is required. Patients also identified conditions of functionality for the mHealth intervention. These included consideration of debilitating complications because of diabetes, such as retinopathy and decreased mobility; ease of use of the intervention; and implementation of virtual communities to support continued use of the intervention. CONCLUSIONS: Our patient population expressed an interest in mHealth for preventing and monitoring DFUs, although some participants were not frequent users of technology. mHealth continues to show potential in improving patient outcomes, and this study provides a foundation for designing interventions specific to a DFU population. Further research is needed to confirm these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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