Developing an Evidence-Based Evaluation Framework for mHealth Applications

The rapid growth of mobile health (mHealth) applications underscores the pressing need for robust evaluation platforms that ensure quality, efficacy, and stakeholder alignment. This paper introduces a quantitative, evidence-based framework that addresses these gaps through dynamic attribute weighting and multi-criteria decision analysis. The platform integrates methodologies such as CRITIC-TOPSIS and AI-driven attribute prioritization, validated through systematic reviews and expert analyses. Preliminary evaluations demonstrate potential for generating actionable insights tailored to diverse mHealth applications and stakeholder needs. Future work includes detailed literature reviews, platform development and real-life use case deployment to refine its applicability and impact.

Authors

Institutions

Publication Details

Journal
Studies in health technology and informatics
Published
2025-02-18
DOI
https://doi.org/10.3233/shti250025
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing an Evidence-Based Evaluation Framework for mHealth Applications

Shu Yu, Vinod P. Shah, Paul Aiello, Aby Mathews Maluvelil et al.
Studies in health technology and informatics
Mobile Health and mHealth Applications
article

Developing an Evidence-Based Evaluation Framework for mHealth Applications

Shu Yu, Vinod P. Shah, Paul Aiello, Aby Mathews Maluvelil, Obed Ehoneah, Samuel Numor
article en

Abstract

The rapid growth of mobile health (mHealth) applications underscores the pressing need for robust evaluation platforms that ensure quality, efficacy, and stakeholder alignment. This paper introduces a quantitative, evidence-based framework that addresses these gaps through dynamic attribute weighting and multi-criteria decision analysis. The platform integrates methodologies such as CRITIC-TOPSIS and AI-driven attribute prioritization, validated through systematic reviews and expert analyses. Preliminary evaluations demonstrate potential for generating actionable insights tailored to diverse mHealth applications and stakeholder needs. Future work includes detailed literature reviews, platform development and real-life use case deployment to refine its applicability and impact.

Studies in health technology and informaticsVol. 322
University of Toronto (CA)
Openalex Percentile: Top 90%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.