Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students

Area of Science:

• Digital health
• Behavioral science
• Network security

Background:

• Digital behaviors shape daily life, with disruptions linked to student well-being.
• Current methods for tracking digital behavior rely on self-report or active participation, limiting adherence.
• Passive sensing of network traffic offers a privacy-preserving, scalable alternative.

Purpose of the Study:

• To evaluate the feasibility and acceptability of using encrypted smartphone network traffic via VPN for digital behavior capture.
• To assess the ability of traffic-derived features to reveal aspects of digital behavior relevant to health.

Main Methods:

• A 2-week prospective observational study involving university students.
• Participants installed a VPN for passive capture of encrypted smartphone network traffic.
• Feasibility assessed via retention and data coverage; acceptability via usability, workload, and interviews.

Main Results:

• 29 out of 38 students provided valid network traffic data (76.3% analytic cohort).
• High retention (71%) and data coverage (mean 74.1%) were achieved.
• High usability (SUS score 78) and low workload (NASA-TLX minimal) reported; participants found the system unobtrusive.

Conclusions:

• VPN-based monitoring of encrypted smartphone traffic is feasible and acceptable for sustained, passive data collection.
• This method offers a scalable, device-agnostic approach to digital phenotyping, preserving privacy.
• This technique has potential for studying health and well-being in real-world settings.