Review, Assess, Classify, and Evaluate (RACE): a framework for studying m-health apps and its application for opioid...

Area of Science:

• Digital Health
• Mobile Health (mHealth) App Analysis
• Health Informatics

Background:

• The rapid growth of mHealth interventions necessitates standardized methods for app analysis.
• Existing methodologies for app evaluation are fragmented, requiring integration for comprehensive assessment.

Purpose of the Study:

• To introduce and apply the RACE (Review, Assess, Classify, and Evaluate) framework for analyzing mHealth apps.
• To systematically analyze opioid-related mHealth apps using the developed RACE framework.

Main Methods:

• The RACE framework integrates Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), inter-rater reliability analysis, and the Nickerson-Varshney-Muntermann taxonomy.
• A 3-step process was employed to identify, assess, and classify 153 opioid mHealth apps.

Main Results:

• Analysis of 153 opioid apps revealed key dimensions: Target Audience, Key Function, Operation, Security & Privacy, and Impact.
• Predominant functions included treatment & recovery (37%), education (24%), and prescription (16%). Most apps were passive (56%) targeting patients (49%) and healthcare professionals (39%).
• Security & Privacy features were present in 84% of the analyzed apps.

Conclusions:

• The RACE framework provides a robust and replicable method for systematizing knowledge on mHealth apps for specific conditions.
• Lessons learned from analyzing opioid apps can inform the development and evaluation of mHealth interventions for other health areas.
• Future research should leverage the RACE framework to understand mHealth app characteristics and inform best practices for connected care.