Context Duration in Naturalistic Smartphone Use: Evidence for Millisecond-Scale Processing

Smartphone use is typically reported as hours of daily screen time, and content is still designed for second-to-minute attention. Recommendation and content systems have become good at predicting what a person might want, yet people often do not act on what they are shown. We call this the recommendation–action gap, and we show that it has a timescale. Using continuous on-device telemetry from 35 participants over 15 days (3.2 million screen touches; keyboard input excluded; logging only while the device was unlocked), we treat inter-touch intervals as a behavioral proxy for context duration, the time a user remains engaged with a given on-screen context. Relative to a 2022 measurement, mean daily use rose 36% (to 4 h 53 min) and daily touches rose 19% (to 5,384), yet aggregate context duration contracted 23% to 3.8 to 7.8 s, orders of magnitude below television-scale engagement (5 to 21 min). The inter-touch distribution is strongly right-skewed. Half of all touches arrive within 266 ms of the previous touch, and 75% within 1.06 s. Half of all interaction therefore sits inside the $\sim$300 ms window in which people decide to approach or avoid, well before the plays, add-to-carts, and orders that systems are trained to predict. Time on the device is accumulated through revolving-door sessions rather than sustained attention (Instagram: median 67 s sessions summing to 75 min/day; WhatsApp: median 23 s sessions summing to 38 min/day), and only the extreme tail of context duration approaches long-form media. We repeat this analysis across social media, streaming, music, food and quick commerce, and AI assistants. Decision windows differ by category: 7 to 14 s for what to view on Instagram, 4 to 7 s for what to reply to on WhatsApp, about 10 to 30 s for weekday streaming choice, 4 to 9 s for food orders, and 4 to 9 s on average across AI assistants. In every category the choice is made within seconds. Closing the recommendation–action gap needs content that can be processed in milliseconds and acted on within the measured window of each category.