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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".