Developing an Evidence-Based Evaluation Framework for mHealth Applications.

Area of Science:

• Digital Health
• Health Informatics
• Health Services Research

Background:

• The proliferation of mobile health (mHealth) applications necessitates rigorous evaluation methods.
• Existing platforms often lack dynamic attribute weighting and multi-criteria decision analysis for comprehensive quality assessment.
• Ensuring stakeholder alignment in mHealth app development and deployment is a significant challenge.

Purpose of the Study:

• To introduce a quantitative, evidence-based framework for evaluating mHealth applications.
• To address the need for robust platforms ensuring quality, efficacy, and stakeholder alignment.
• To provide actionable insights for diverse mHealth applications and stakeholder requirements.

Main Methods:

• Development of a quantitative framework incorporating dynamic attribute weighting.
• Application of multi-criteria decision analysis (MCDA) techniques, including CRITIC-TOPSIS.
• Integration of Artificial Intelligence (AI)-driven attribute prioritization.
• Validation through systematic reviews and expert analyses.

Main Results:

• Preliminary evaluations indicate the framework's potential to generate tailored, actionable insights.
• The proposed platform demonstrates effectiveness in addressing gaps in mHealth app evaluation.
• Dynamic attribute weighting and MCDA enhance the assessment of mHealth application quality and efficacy.

Conclusions:

• The developed framework offers a robust solution for evaluating the quality and efficacy of mHealth applications.
• AI-driven prioritization and MCDA provide a dynamic approach to stakeholder alignment.
• Further development and real-world deployment are expected to refine the framework's applicability and impact in digital health.