Background and aims: The use of mobile devices is increasingly recognized as a valuable tool for interventions targeting mental health issues. This brief report aims to share insights into the unexpected positive changes observed during the data collection process for predicting addictive behaviors using mobile technology. Methods: We analyze self-reported baseline and 4-week follow-up data collected to develop an algorithm for predicting addictive digital media use. Two hundred and six adults in their 20 s completed a smartphone self-report survey on digital media use behaviors and emotion, while their step and heart rate data was passively collected via Fitbit. Results: Despite the absence of any interventional feedback beyond the data collection process, positive changes were observed, including time spent on digital media use (p = 0.031), a reduction in high-risk digital media use score (p < 0.001), and depressive symptom score (p = 0.004). Conclusions: When collecting data on digital media use and related emotion via mobile devices, it is important to recognize that positive changes may occur in participants even without planed intervention. Failing to consider this phenomenon could lead to an overestimation of the effects of interventions provided through smart devices.  Kulcsszavak: addictive behavior, mobile applications, healthcare, wearable electronic devices, monitoring, internet