Personalized Digital Instructor Based on Arduino for Buerger Exercises in Older Adults with Diabetes: Feasibility Study
Bibliographic record
Abstract
Diabetes in older adults can lead to complications such as peripheral artery disease, neuropathy, and foot ulcers. Accessing treatments can be challenging due to limited resources. Implementing low-cost preventive therapies like Buerger's exercises is essential. However, these exercises must be standardized. The purpose was to develop an Arduino-based electronic system as a gerontechnological tool for homologating Buergers exercises and facilitating their execution by older adults with diabetes mellitus. The Intervention Theory by Sidani guided the development of this study. A feasibility and pilot study with one group, pretest/posttest design, was conducted in twenty older adults with HbA1c 8% and their caregivers in Saltillo, Coahuila, Mexico, from November 2020 to June 2021. Feasibility was measured with an acceptability and satisfaction instrument; the ankle-arm index was measured with 8 Hertz Doppler and neuropathy symptoms with a modified Toronto Clinical Neuropathy Score. The mean age was 67.50 5.61 years old in older adults and 48.32 16.26 in caregivers. The digital instructor was accepted by 73.3% (11 older adults) without any issues; 47.4% (9 older adults) and 26.3% (6 caregivers) expressed high levels of satisfaction. Participants noted significant benefits such as improved peripheral circulation, reduced pain, numbness, and tingling. These promising results underscore the potential of the electronic system to make a noticeable improvement in the lives of older adults with diabetes mellitus and their caregivers. The device was meticulously designed to be user-friendly and accessible, making it ideal gerontechnological tool to manage health at home.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".