Upkar Varshney1, Neetu Singh2, Anu G Bourgeois3, Shanta R Dube4. 1. Department of Computer Information Systems, Georgia State University, Atlanta, Georgia, USA. 2. Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA. 3. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA. 4. Department of Public Health, Levine College of Health Sciences, Wingate University, Wingate, North Carolina, USA.
Abstract
OBJECTIVE: The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps. MATERIALS AND METHODS: The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy. RESULTS: Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps. DISCUSSION: Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions. CONCLUSION: With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.
OBJECTIVE: The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps. MATERIALS AND METHODS: The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy. RESULTS: Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps. DISCUSSION: Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions. CONCLUSION: With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.
Authors: Rebecca Arden Harris; David S Mandell; Kyle M Kampman; Yuhua Bao; Kristen Campbell; Zuleyha Cidav; Donna M Coviello; Rachel French; Cecilia Livesey; Margaret Lowenstein; Kevin G Lynch; James R McKay; David W Oslin; Courtney Benjamin Wolk; Hillary R Bogner Journal: Contemp Clin Trials Date: 2021-02-22 Impact factor: 2.226
Authors: Alessandro Liberati; Douglas G Altman; Jennifer Tetzlaff; Cynthia Mulrow; Peter C Gøtzsche; John P A Ioannidis; Mike Clarke; P J Devereaux; Jos Kleijnen; David Moher Journal: PLoS Med Date: 2009-07-21 Impact factor: 11.069
Authors: Pegah Afra; Carol S Bruggers; Matthew Sweney; Lilly Fagatele; Fareeha Alavi; Michael Greenwald; Merodean Huntsman; Khanhly Nguyen; Jeremiah K Jones; David Shantz; Grzegorz Bulaj Journal: Front Hum Neurosci Date: 2018-05-01 Impact factor: 3.169