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A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study
BACKGROUND: Selecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available. OBJECTIVE: This study aimed to evaluate the App Rating Inventory, which was developed by the Defense...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055478/ https://www.ncbi.nlm.nih.gov/pubmed/35436227 http://dx.doi.org/10.2196/32643 |
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author | Mackey, Rachel Gleason, Ann Ciulla, Robert |
author_facet | Mackey, Rachel Gleason, Ann Ciulla, Robert |
author_sort | Mackey, Rachel |
collection | PubMed |
description | BACKGROUND: Selecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available. OBJECTIVE: This study aimed to evaluate the App Rating Inventory, which was developed by the Defense Health Agency’s Connected Health branch, to support clinical decisions regarding app selection and evaluate medical and behavioral apps. METHODS: To enhance the tool’s performance, eliminate item redundancy, reduce scoring system subjectivity, and ensure a broad application of App Rating Inventory–derived results, inventory development included 3 rounds of validation testing and 2 trial periods conducted over a 6-month interval. The development focused on content validity testing, dimensionality (ie, whether the tool’s criteria performed as operationalized), factor and commonality analysis, and interrater reliability (reliability scores improved from 0.62 to 0.95 over the course of development). RESULTS: The development phase culminated in a review of 248 apps for a total of 6944 data points and a final 28-item, 3-category app rating system. The App Rating Inventory produces scores for the following three categories: evidence (6 items), content (11 items), and customizability (11 items). The final (fourth) metric is the total score, which constitutes the sum of the 3 categories. All 28 items are weighted equally; no item is considered more (or less) important than any other item. As the scoring system is binary (either the app contains the feature or it does not), the ratings’ results are not dependent on a rater’s nuanced assessments. CONCLUSIONS: Using predetermined search criteria, app ratings begin with an environmental scan of the App Store and Google Play. This first step in market research funnels hundreds of apps in a given disease category down to a manageable top 10 apps that are, thereafter, rated using the App Rating Inventory. The category and final scores derived from the rating system inform the clinician about whether an app is evidence informed and easy to use. Although a rating allows a clinician to make focused decisions about app selection in a context where thousands of apps are available, clinicians must weigh the following factors before integrating apps into a treatment plan: clinical presentation, patient engagement and preferences, available resources, and technology expertise. |
format | Online Article Text |
id | pubmed-9055478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-90554782022-05-01 A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study Mackey, Rachel Gleason, Ann Ciulla, Robert JMIR Mhealth Uhealth Original Paper BACKGROUND: Selecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available. OBJECTIVE: This study aimed to evaluate the App Rating Inventory, which was developed by the Defense Health Agency’s Connected Health branch, to support clinical decisions regarding app selection and evaluate medical and behavioral apps. METHODS: To enhance the tool’s performance, eliminate item redundancy, reduce scoring system subjectivity, and ensure a broad application of App Rating Inventory–derived results, inventory development included 3 rounds of validation testing and 2 trial periods conducted over a 6-month interval. The development focused on content validity testing, dimensionality (ie, whether the tool’s criteria performed as operationalized), factor and commonality analysis, and interrater reliability (reliability scores improved from 0.62 to 0.95 over the course of development). RESULTS: The development phase culminated in a review of 248 apps for a total of 6944 data points and a final 28-item, 3-category app rating system. The App Rating Inventory produces scores for the following three categories: evidence (6 items), content (11 items), and customizability (11 items). The final (fourth) metric is the total score, which constitutes the sum of the 3 categories. All 28 items are weighted equally; no item is considered more (or less) important than any other item. As the scoring system is binary (either the app contains the feature or it does not), the ratings’ results are not dependent on a rater’s nuanced assessments. CONCLUSIONS: Using predetermined search criteria, app ratings begin with an environmental scan of the App Store and Google Play. This first step in market research funnels hundreds of apps in a given disease category down to a manageable top 10 apps that are, thereafter, rated using the App Rating Inventory. The category and final scores derived from the rating system inform the clinician about whether an app is evidence informed and easy to use. Although a rating allows a clinician to make focused decisions about app selection in a context where thousands of apps are available, clinicians must weigh the following factors before integrating apps into a treatment plan: clinical presentation, patient engagement and preferences, available resources, and technology expertise. JMIR Publications 2022-04-15 /pmc/articles/PMC9055478/ /pubmed/35436227 http://dx.doi.org/10.2196/32643 Text en ©Rachel Mackey, Ann Gleason, Robert Ciulla. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 15.04.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Mackey, Rachel Gleason, Ann Ciulla, Robert A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title | A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title_full | A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title_fullStr | A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title_full_unstemmed | A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title_short | A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study |
title_sort | novel method for evaluating mobile apps (app rating inventory): development study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055478/ https://www.ncbi.nlm.nih.gov/pubmed/35436227 http://dx.doi.org/10.2196/32643 |
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