ArticleApplied clinical informatics2021
Heuristic Evaluation of a Top-Rated Diabetes Self-Management App.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Enhancing Resilience in Family Caregivers Using an mHealth App.Applied clinical informatics · 2022Trial
- Development and Preliminary Evaluation of iCanPlan: A Mobile Health Application for Intimate Partner Violence Prevention in Thailand.International journal of environmental research and public health · 2026Article
- Developing and evaluating a gamified self-management application for inflammatory bowel disease using the ADDIE model and Sukr framework.BMC medical informatics and decision making · 2025Article
- An mHealth Design to Promote Medication Safety in Children with Medical Complexity.Applied clinical informatics · 2024Article
- The ACTIVATE Digital Health Pilot Program for Diabetes and Hypertension in an Underserved and Rural Community.Applied clinical informatics · 2023Article
- Monitoring Upper Extremity Function of Individuals With Breast Cancer: Development and Usability of theInquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this study was to evaluate the usability of a top-rated diabetes app. Such apps are intended to markedly support the achievement of optimal health and financial outcomes by providing patients with substantive and continual support for self-management of their disease between periodic clinician visits. Poor usability can deter use which is especially concerning in patients with diabetes due to prevalence of the disease and impact of self-management on long-term prognosis.
methodsA diabetes app was selected due to the prevalence and seriousness of the disease. A heuristic evaluation was then performed to collect and analyze data on the usability of the app based on Nielsen's heuristics. Pareto analysis was used to illustrate the contribution of each type of heuristic violation, augmented by a stacked bar chart illuminating associated severity.
resultsThere were 51 heuristic violations on the opening screen, violating 6 of Nielsen's 10 heuristics. Pareto analysis revealed 29 (57%) of the heuristic violations involved a match between system and real world and 8 (16%) aesthetic and minimalist design. Severity ratings ranged from 1.0 to 4.0 (mean: 3.01) with 80% comprising a major usability problem and 6% a usability catastrophe.
conclusionStudies show that people with diabetes are more likely to receive greater benefit from a diabetes app if they are easy to use. The number and severity of heuristic violations in this study suggest that the commercialization of mobile health apps may play a factor in bypassing experts in clinical informatics during the design phase of development. Usability and associated benefits received from mobile health apps can be enhanced by debugging the user interface of identified heuristic violations during design. Waiting to correct ongoing usability issues while apps are in production can result in patients disengaging from use of digital health tools engendering poorer outcomes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.