Abstract

Body movements drop with sleep, and this behavioural signature is widely exploited to infer sleep duration. However, a reduction in body movements may also occur in periods of intense cognitive activity, and the ubiquitous use of smartphones may capture these wakeful periods otherwise hidden in the standard measures of sleep. Here, we continuously captured the gross body movements using standard wrist-worn accelerometers to quantify sleep (actigraphy) and logged the timing of the day-to-day touchscreen events (‘tappigraphy’). Using these measures, we addressed how the gross body movements overlap with the cognitively engaging digital behaviour (from n = 79 individuals, accumulating ~1400 nights). We find that smartphone use was distributed across a broad spectrum of physical activity levels, but consistently peaked at rest. We estimated the putative sleep onset and wake-up times from the actigraphy data to find that these times were well correlated to the estimates from tappigraphy (R2 = 0.9 for sleep-onset time and wake-up time). However, actigraphy overestimated sleep as virtually all of the users used their phones during the putative sleep period. Interestingly, the probability of touches remained greater than zero for ~2 h after the putative sleep onset, and ~2 h before the putative wake-up time. Our findings suggest that touchscreen interactions are widely integrated into modern sleeping habits—surrounding both sleep onset and waking-up periods—yielding a new approach to measuring sleep. Smartphone interactions can be leveraged to update the behavioural signatures of sleep with these peculiarities of modern digital behaviour.

Highlights

  • There is a well-recognised need for tracking sleep in patients, as well as in the general population

  • We estimated the variations in smartphone interactions at the different levels of physical activity (Fig. 1)

  • Pooling all of the measurements together, we found strong correlations between the putative sleep times determined by actigraphy versus tappigraphy (Fig. 2)

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Summary

Introduction

There is a well-recognised need for tracking sleep in patients, as well as in the general population. This rush to quantify sleep is partly driven by the increased awareness that sleep is crucial for cognitive performance and well-being. The common fine finger movements on the touchscreen are cognitively engaging, but they may result in no or negligible signal deflections at the wrist. This lack of a simple relationship and the widespread use of smartphones in modern behaviour warrant an up-to-date perspective on tracking sleep based on motor activity

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