Assessing the Need for Mobile Health (mHealth) in Monitoring the Diabetic Lower Extremity
Bibliographic record
Abstract
BACKGROUND: Complications of the diabetic lower extremity (such as diabetic foot ulcers, DFUs) occur when monitoring is infrequent, and often result in serious sequelae like amputation or even death. OBJECTIVE: To evaluate the potential application of mobile health (mHealth) to diabetic foot monitoring. We surveyed the self-management routines of a group of diabetic patients, as well as patient and clinician opinions on the use of mHealth in this context. METHODS: Patients with DFUs in Toronto, Ontario, Canada completed a 25-item questionnaire addressing their foot care practices, mobile phone use, and views on mHealth. Wound care clinicians across Canada were also surveyed using a 9-item questionnaire. RESULTS: Of the patients surveyed, 59/115 (51.3%) spend less than a minute checking their feet, and 17/115 (15%) of patients find it difficult to see their doctor or get to the hospital regularly. Mobile phone use was widespread in our patient cohort (93/115, 80.9%). Of mobile phone users, 68/93 (73.1%) would use a device on their mobile phone to help them check their feet. Of the clinicians who completed the questionnaire, only 7/202 (3.5%) were familiar with mHealth; however, 181/202 (92%) of clinicians expressed interest in using mHealth to monitor their patients between visits. CONCLUSIONS: Patient education or motivation and clinician training were identified as the major barriers to mHealth use in the diabetic lower extremity, which may be a viable mechanism to improve DFU monitoring practices.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".