Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance

Chunjong Park, Morelle Arian, Xin Liu, Leon Sasson, Jeffrey Kahn, Alex Mariakakis, Shwetak Patel, Tim Althoff


Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night’s sleep is important for job performance. However, both real-world sleep behavior and job performance are difficult to measure at scale. In this work, we demonstrate that people’s everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep be- havior and job performance of salespeople (𝑁 = 15) and athletes (𝑁 = 19) for 18 months, leveraging a mattress sensor and online mobile app to conduct the largest study of this kind to date. We first demonstrate that cumulative sleep measures are significantly correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% average re- duction in contracts established for salespeople and a 9.5% average reduction in game grade for the athletes. We then investigate the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the job performance of the athletes, but not the salespeople. To support that our app-based performance indicator truly captures meaningful variation in psychomotor function as it relates to sleep and is robust against potential confounds, we con- ducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited average app interaction times that were 5.0% slower. We also find that app interaction time ex- hibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. The findings from this work reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.