Alberta Rating Index for Apps (ARIA): An Index to Rate the Quality of Mobile Health Applications
Bibliographic record
Abstract
Introduction: The number of mobile health applications (m-health apps) available to the public through online application (app) stores is rapidly increasing. In addition, the general public’s interest to use m-health apps as an adjunct to conventional health care services is increasing. However, because of the inadequate quality control mechanisms on app stores and lack of stringent regulations for m-health apps, there is a risk of apps to have inferior quality or even be harmful. In the absence of formal guidelines, users may choose apps based on unreliable information such as reviews and ratings on the app download page, or the number of downloads for an app. Rating scales to evaluate apps exist for m-health app users, health care providers, and researchers. Most are long and complicated scales that are not appropriate for the general public. None have been developed using a theoretical framework. The purpose of this thesis was to develop the Alberta Rating Index for Apps (ARIA) based on the theories of technology acceptance, frameworks of app evaluation, and lived experience of users of mobile health applications. Methods: A multi strategy study was conducted in three phases. In phase one, the investigator conducted six focus groups with users of mobile health applications including older adults, adults with a mental health condition, health care providers, and app developers to identify quality criteria that were important to users and developers of m-health apps. Next, an item pool was generated based on a review of app rating scales. In phase two, the content of the item pool was validated using an online survey and a calculation of the content validation index for each item. Also in phase two, a sample of participants from the online survey participated in a focus group to shortlist the item pool and develop the first draft of ARIA. In phase three, ARIA was piloted by nine potential users of m-health apps, including older adults and adults with a mental health condition. Also, in phase three, the inter-rater reliability and criterion-related validity of ARIA were examined using 16 participants consisting of 4 older adults, 4 adults with a mental health condition, and 4 health care providers. The scores of ARIA were correlated with the scores of users’ version of Mobile Apps Rating Scale (U-MARS) to examine the criterion-related validity. Results: Nine quality criteria measure the quality of m-health apps: the purpose of the app, trustworthiness, privacy, security, affordability, ease of use, functionality, appropriateness to target users, and usefulness and satisfaction. Generalizability coefficients (G-coefficients) were calculated using ARIA total scores as the measure of reliability. High G-coefficients for health care providers (G = 0.98), older adults (G = 0.83) and adults with mental health conditions (G = 0.88) indicated that users could reliably rate the quality of m-health apps based on total scores of ARIA. The positive but low correlation of ARIA’s total scores with U-MARS indicated that both assessment tools measure the quality of m-health apps. However, the quality criteria were different between the ARIA and U-MARS. Participants in phase three reported that ARIA was easier and more convenient compared to U-MARS. Conclusion: ARIA is the first mobile application rating index developed based on theories of technology acceptance and frameworks of app evaluation. ARIA was designed to be used by health care providers and the general public, including older adults, adults with mental health conditions, and family caregivers. The content of ARIA was validated through a rigorous process. Moreover, three types of app users tested the inter-rater reliability of ARIA. Users perceived ARIA to be easier and more convenient compared to U-MARS. Clinical implications of ARIA are to help patients, family caregivers, and healthcare providers rate the quality of mobile health applications and identify the ones that are acceptable. Health informatics researchers may use ARIA to develop health apps that are useful and acceptable to end users, especially older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".