JMIR Publications

ABSTRACT

Background:

A mobile application (app) generates passive data without any direct involvement from the subject, such as Global Positioning System (GPS) data traces. These passive data transformed the manner of traditional assessment, which requires active participation from the subject. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device.

Objective:

The primary aim of this study was to quantify user retention for “Staff Hours” app by using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as associated factors of their survival rates of user retention.

Methods:

We developed an app “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention.

Results:

A total of 342 volunteers (224 men; age: 33.8 ± 7.0 years) were included in this study. Passive data had higher user retention than active data (p = 0.011). In addition, user retention for Android in passive data was higher than that for iOS (p = 0.015). Trainee physicians had higher user retention than trainees from other occupations in active data, whereas no significant differences between these two groups were observed in passive data (p = 0.700).

Conclusions:

Our findings demonstrated that passive data in Android had the best user retention in this app recording GPS-based work hours.