Optimizing health outcomes: a detailed comparison of features and user sentiment in popular fitness tracking applications

Objective: This study aimed to compare the user sentiment and functionality of popular fitness tracking applications, specifically Strava™, Google Fit™, and Fitbit™, to determine how their features contributed to health optimization and user engagement. Methodology: The research utilized sentiment analysis and functionality classification based on user reviews gathered from the Google Play Store. The analysis employed Naive Bayes and Logistic Regression methods to assess user sentiment and application performance. Results: The analysis revealed that Strava™ demonstrated superior emotional and functional engagement, although it faced concerns regarding privacy. Google Fit™ was recognized for its usability, but it showed limitations in tracking accurate data. Fitbit™ exhibited a balanced performance but lacked significant innovation compared to the other two platforms. Discussion: The findings of this research were consistent with existing studies on user engagement, highlighting the importance of emotional connection in fitness applications. However, unlike previous studies, the current research also emphasized the role of data accuracy, which was a limitation in Google Fit™. Furthermore, the comparison among the three applications provided new insights into how emotional and functional features impact user satisfaction. Conclusions: Emotional engagement and data accuracy were found to be critical factors in user satisfaction and the success of fitness applications. Developers are encouraged to strike a balance between technical features and social elements to enhance user experience and support healthier lifestyles.