Adherence to Usability and Accessibility Principles in Digital Health Applications for Patients With Diabetes: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Health apps have the potential to enable people with diabetes to access care more easily, monitor their condition, and reduce the number of times they need to attend health care appointments. However, the development pipeline for apps may differ widely before the apps are released for use, due to limited funding, difficulty in obtaining iterative feedback from patients/users, and varying levels of developer expertise. In response to concerns about the quality and consistency of apps being released, two guidelines were created: the Digital Technology Assessment Criteria (DTAC) and the National Institute for Health and Care Excellence (NICE) Evidence Standards Framework. These two frameworks aim to standardize the development and evaluation of digital health technologies (DHTs). They outline core requirements, such as accessibility, clinical safety, data protection, interoperability, usability, and safeguarding, which help ensure that digital health apps are accessible, safe, effective, and suitable for real-world use. OBJECTIVE: This systematic review evaluated the performance of diabetes digital health apps, as presented in published studies, in terms of adherence to DTAC 2021 and NICE 2022 guidelines during development. METHODS: We systematically searched Embase and MEDLINE and identified 43 studies that met the inclusion criteria. Each study was assessed against 13 binary scoring criteria derived from the two frameworks. RESULTS: Our findings highlighted that 93% (n=40) of the studies met fewer than 40% of the recommended criteria. Specifically, 88.4% (n=38) studies did not report accurate and reliable measurements, 86% (n=37) omitted app accuracy validation, and 83.7% (n=36) failed to address inequalities considerations. Only 3 (7%) studies achieved scores between 7 and 9 out of a possible 13, and none fully adhered to the guideline criteria. CONCLUSIONS: These results suggest a significant gap between digital health guidelines and real-world app development practices. We recommend the adoption of DTAC and NICE guidelines more widely and consistently during design and development. Additionally, we suggest that journals request that authors submit an adherence checklist alongside their manuscript to improve standardization and transparency across digital health publications. TRIAL REGISTRATION: PROSPERO CRD42022322040; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022322040.
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 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 | 0.047 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".