“Know Addiction” was designed to record smartphone usage behaviors, mainly through the timestamps of notifications, and of screen-on and screen-off events. The App in turn generates parameters that reflect smartphone use frequency and duration. These app-generated parameters provide new insights into identifying smartphone addictions and facilitate large-scale cross-sectional and longitudinal studies.
“Know Addiction” is, as the name suggests, designed to help smartphone users “know” their level of phone usage addiction and “prevent” excessive addiction (No Addiction). This is the world’s first algorithmic time-tracking app, and we have found that “the app records 50% more screen time than self-awareness.” For example, a college student who thinks they use their phone for about 20 hours a week, when tested with our app, actually uses their phone for 30 hours, 50% more than their self-awareness. Moreover, the longer the phone usage time, the greater the underestimation of actual usage.
We have published multiple papers confirming that “Know Addiction” can predict the judgments of psychiatrists on smartphone addiction, and can also assist professionals in improving diagnostic accuracy. The smartphone usage behavior recorded by “Know Addiction” can provide researchers with different algorithmic methods to interpret various human behaviors.
The unique feature of “Know Addiction” is that it operates automatically in the background with extremely low power consumption. Once installed and configured, there is no need to open the app again as it will automatically analyze and chart the data. This can be used for self-management, and can also be shared with designated family members, friends or doctors. The big data on smartphone usage behavior can also be used to interpret various behaviors.
1. Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis.
Chen IM, Chen YY, Liao SC, Lin YH.
Journal of Personalized Medicine. 2022;12(6), 936.
2. Interpretation of Daily Human Behavior via Smartphone Digital Footprints: Examples from Smartphone Use, Sleeping Patterns, and Working Hours
Chiang TW, Chen SY, Lin YH.
Data Science：Survey Research-Method and Application. 2020 Oct ;(No.45)043-071.
3. Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle
Pan YC, Lin HH, Chiu YC, Lin SH, Lin YH.
JMIR Mhealth Uhealth 2019;7(3):e12171
4. Development of a mobile application (App) to delineate “digital chronotype” and the effects of delayed chronotype by bedtime smartphone use.
Lin YH, Wong BY, Lin SH, Chiu YC, Pan YC, Lee YH.
Journal of Psychiatric Research. 2019 Mar;110:9-15.
Lin YH, Lin PH, Chiang CL, Lee YH, Yang CCH, Kuo TBJ, Lin SH.
The Journal of clinical psychiatry. 2017 Jul;78(7):866-872.
Lin YH, Lin YC, Lin SH, Lee YH, Lin PH, Chiang CL, Chang LR, Yang CC, Kuo TB.
Translational psychiatry. 2017 Feb 14;7(2):e1030.
7. Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App).
Lin YH, Lin YC, Lee YH, Lin PH, Lin SH, Chang LR, Tseng HW, Yen LY, Yang CC, Kuo TB.
Journal of psychiatric research. 2015 Jun;65:139-45.