ArticlePloS one2021
ACCU3RATE: A mobile health application rating scale based on user reviews.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.
- Patients' Perspectives on the Data Confidentiality, Privacy, and Security of mHealth Apps: Systematic Review.Journal of medical Internet research · 2024Pooled it
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- HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals.Brain informatics · 2024Article
- Problems and Barriers Related to the Use of mHealth Apps From the Perspective of Patients: Focus Group and Interview Study.Journal of medical Internet research · 2024Article
- Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection.Brain informatics · 2024Review
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- Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function.Brain informatics · 2023Article
- Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.Journal of business research · 2023Article
- The Definitions of Health Apps and Medical Apps From the Perspective of Public Health and Law: Qualitative Analysis of an Interdisciplinary Literature Overview.JMIR mHealth and uHealth · 2022Review
- The Current State of Mobile Apps Owned by Large Pediatric Hospitals in the United States: Systematic Search and Analysis on Google Play and Apple App Stores.JMIR pediatrics and parenting · 2022Article
- Detecting COVID-19 infection status from chest X-ray and CT scanFrontiers in genetics · 2022Article
- How to design digital health interventions with artificial intelligence: A scoping review.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being.
objectiveThis paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings.
methodBased on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users' sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer's statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. RESULTS AND
conclusionsACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.
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What OpenQuestion holds
Registered trials
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.