# The Digital Footprints on the Run: A Forensic Examination of Android Running Workout Applications
## 2 Related Work
In prior research [9], we analyzed the Garmin Connect application paired with the Garmin Vivosmart 4 smartband. Our developed open-source tools can extract several artifacts, such as (_i_) daily summary data; (_ii_) GPS data; (_iii_) response cache data; (_iv_) network logs; (_v_) Facebook API tokens; (_vi_) device synchronization cache; and (_vii_) SpO\({}_{2}\) reading charts. These results motivated us to delve deeper into Android applications, focusing on applications to monitor running activities. Several papers study wearable and smartphone accuracy in physical activity measurements such as step count and walking distances [18; 19; 20]. van Zandwijk and Boztas [21] reported that the iPhone has a low 2% error in step count reporting but can diverge up to 40% for walking distance measurement. Goh et al. [22] documented that smartphones overestimated step counts when assessed on a 3-day free-living condition, with precision affected by factors such as walking style and phone-wearing location. Precision assessment is beyond the scope of this work. To summarize this section, Table 1 shows a comparison between our work and the ones presented above, focusing on the different goals of the analysis, applications targets and major findings. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Applications/Devices Analyzed** & **Focus of Analysis** & **Key Forensic Artifacts Found** \\ \hline Scott et al. [11] & 20 health apps (general, not app-specific) & Security and privacy analysis & Security flaws, privacy concerns \\ Hassenfeldt et al. [4] & Android fitness apps (Runkeeper, Strava, Runtastic/Adidas Running) & Forensic analysis of fitness apps & Account data, personal info, GPS data \\ Sinha et al. [15] & Fitness apps (Nike Training Club, Nike Run Club, MapMyFitness, MapMyWalk) & Forensic analysis of fitness apps & User profile data, health/exercise data, device data, GPS data \\ Hutchinson et al. [16] & Companion apps (Amazon Halo, Garmin Connect, Mobvoi) & Forensic analysis of companion apps & Health data, profile info, notifications, exercise data, GPS data, steps data \\ Donaire-Calleja et al. [17] & Wearables (smartwatches) & Forensic challenges with wearables & N/A—Focus on challenges, not specific artifacts \\ Nunes et al. [9] & Garmin Connect with Garmin Vivosmart 4 & Forensic analysis of fitness companion apps & Daily summary, GPS data, response cache, network logs, Facebook API tokens, sync cache, SpO\({}_{2}\) charts \\ van Zandwijk and Boztas [18] & Wearables, smartphones & Accuracy in physical activity measurements & Step count error rates, distance measurement discrepancies \\ Goh et al. [22] & Smartphones (general) & Validation of smartphone activity measurement & Overestimated steps, affected by walking style, phone location \\ Our Work, 2024 & 6 running apps (Strava, Adidas Running, Nike Run Club, etc.) & Forensic analysis of Android running apps & Post mortem artifacts, 12 ALEAPP modules, FIT file handling, timeline plugin \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of forensic research on fitness applications.