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Developing an Evidence-Based Evaluation Framework for mHealth Applications.

Sabrina Yu1, Paul Aiello1,2, Aby Mathews Maluvelil1,2

  • 1Lamina Solutions, Toronto, Canada.

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|February 19, 2025
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Summary

This study presents a new quantitative framework for evaluating mobile health (mHealth) apps, using dynamic attribute weighting and AI to ensure quality and stakeholder alignment. The platform offers actionable insights for diverse mHealth applications.

Keywords:
application evaluationattribute prioritizationevidence-basedmHealthmulti-criteria decision analysismulti-stakeholderquantitative analysis

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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.