Cluster-Based Predictive Modeling of User Ratings for Physical Activity Apps Using Mobile App Rating Scale (MARS) Dimensions: Model Development and Validation.

ABSTRACT:

Background

The expansion of mobile health app or apps has created a growing need for structured and predictive tools to evaluate app quality before deployment. The Mobile App Rating Scale (MARS) offers a standardized, expert-driven assessment across 4 key dimensions-engagement, functionality, aesthetics, and information-but its use in forecasting user satisfaction through predictive modeling remains limited.

Objective

This study aimed to investigate how k-means clustering, combined with machine learning models, can predict user ratings for physical activity apps based on MARS dimensions, with the goal of forecasting ratings before production and uncovering insights into user satisfaction drivers.

Methods

We analyzed a dataset of 155 MARS-rated physical activity apps w

SUBMITTER: Bhattacharya A 

PROVIDER: S-EPMC12599983 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature