Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest–activity patterns

Abstract

Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self-reports within healthy participants. First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week.The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R 2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders.

Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies

“…These devices allow for data collection from a person's so-called digital footprint [37], which includes when they start/stop using their phones over the day, peak times of phone use, and typing speed. Others have harnessed these data (most using apps downloaded to the phone, or GPS mobility patterns) to track sleep/wake timing, sleep duration, and circadian-related patterns, with good success [38][39][40][41][42]. Ceolini and Ghosh recently used 2 years of data from 401 participants to analyze over 300 million smartphone touchscreen interactions in an effort to determine the presence of multi-day rhythms across the cohort [43], which underscores the potential for passively collected digital data in providing new insights into sleep and circadian-related patterns in human health.…”

Recent Progress in Long-Term Sleep Monitoring Technology

“…In the absence of integrated dedicated health sensors, some smartphones determine the length of time a person sleeps based on the amount of time they are stationary [ 125 ]. This is one of the simplest ways to analyze sleep based on behavioral science, which is an important sleep monitoring item [ 126 ].…”

Sleep, bruxism, sleep‐related breathing disorders, insomnia, sleep, neurology and much more

“…Druijff‐van de Woestijne and colleagues (2021) postulate the use of behavioural biometrics – they suggest to use smartphone keyboard activity as a proxy for rest–activity patterns. They investigated 51 first‐year students who used a custom smartphone keyboard to objectively measure smartphone use behaviours, and they also completed the Consensus Sleep Diary for 1 week.…”