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Cluster-Based Predictive Modeling of User Ratings for Physical Activity Apps Using Mobile App Rating Scale (MARS)
Ayush Bhattacharya1, Jose Fernando Florez-Arango1
1Department of Population Health Sciences, Weill Cornell Medicine, 575 Lexington Ave, Room FP 1025, New York, NY, 10022, United States, 1 6469622435.
JMIR Mhealth and Uhealth
|November 10, 2025
Summary
This study shows that combining k-means clustering with machine learning models can accurately predict user satisfaction for mobile health apps, improving app quality assessment before deployment.
Area of Science:
- Mobile Health
- Machine Learning
- Data Science
Background:
- Mobile health apps require tools to evaluate quality before deployment.
- The Mobile App Rating Scale (MARS) assesses apps but has limited predictive power for user satisfaction.
- Predictive modeling for app quality is an emerging need.
Purpose of the Study:
- To predict user ratings for physical activity apps using MARS dimensions and machine learning.
- To forecast app ratings before production using k-means clustering and ML models.
- To identify key drivers of user satisfaction in mobile health apps.
Main Methods:
- Analyzed 155 MARS-rated physical activity apps, splitting data into training and testing sets.
- Applied k-means clustering to identify app clusters, followed by training 5 ML models.
- Evaluated model performance using accuracy, mean absolute error, and R², with validation on external datasets.
Main Results:
- Clustering revealed distinct app types: feature-rich (cluster 1) and simpler (cluster 2).
- A combined model (support vector regression + k-nearest neighbors) achieved 88.64% accuracy, outperforming unclustered models.
- Clustering improved prediction accuracy and generalization to external datasets.
Conclusions:
- The combined clustering and modeling approach enhances prediction accuracy for app user ratings.
- This method transforms MARS into a predictive tool, aiding app development and quality assessment.
- The approach offers a scalable and transparent method for forecasting user ratings, especially in early development stages.
