Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review
Bibliographic record
Abstract
Background.As diabetes incidence continues to rise, active medical intervention and self-management have now become crucial.Evidence showed that diabetes patients are at high risk of developing complications.Self-care management is of utmost importance when it comes to diabetes care.Recent situations such as the COVID-19 pandemic, however, have restricted vital communications between diabetes patients and their caregivers.This factor significantly deteriorates the glycaemic control of diabetes patients.At this point, mobile health applications can tackle the mentioned problems.Objectives.This article aims to investigate the recent developments in mobile applications for diabetes care.We also discuss the effectiveness of mobile health in controlling blood glucose levels through self-care for diabetes patients, especially during the pandemic.Material and methods.We present a review derived from articles published in the last 8 years.We extensively searched major databases for articles related to mobile health applications for diabetes care.Results.We found that mobile health applications are effective in diabetes self-management.Appropriate health applications enable communications between patients and caregivers/medical professionals at a distance, minimising the need for physical interactions during difficult circumstances.Via applications, patients can now maintain their healthy lifestyle through routine exercise reminders, food intake supervision and sleep quality monitoring.Certain health applications even allow users to interact with others having similar health situations so that they can be a motivation for each other. Conclusions.Various studies showed that health applications help user to control blood sugar levels, which may come in handy to mitigate glycaemic self-control problems.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".