PT  - JOURNAL ARTICLE
AU  - Bo-chiuan Chen
AU  - Dong-Chul Seo
AU  - Hsien-Chang Lin
AU  - David Crandall
TI  - Framework for estimating sleep timing from digital footprints
AID  - 10.1136/bmjinnov-2018-000274
DP  - 2018 Oct 01
TA  - BMJ Innovations
PG  - 172--177
VI  - 4
IP  - 4
4099  - http://innovations.bmj.com/content/4/4/172.short
4100  - http://innovations.bmj.com/content/4/4/172.full
SO  - BMJ Innov2018 Oct 01; 4
AB  - Objective We propose a method that estimates sleep timing from publicly observable activity on online social network sites. The method has the potential to minimise participant-related biases, does not require specialised equipment and can be applied to a large population.Materials and methods We propose a framework that estimates midpoints of habitual sleep time from activity records on a social media—Twitter. We identified sets of before-bedtime and after-wake-up tweets that marked the periods of reduced Twitter activity, which we use as a proxy of sleep. We then estimated the timing of sleep by deriving the median among midpoints of paired before-bedtime and after-wake-up tweets. Visualisations and examples of our estimates comparing sleep timing of users from different countries are provided.Discussion Initial results suggest that the proposed framework could detect differences in sleep timing among user groups of different countries. The proposed framework may be a cost-efficient complement for future research regarding sleep-related health concerns. Researchers and practitioners may benefit from accessing habitual sleep data. While validation is still required prior to actual applications, the proposed framework may be a first step towards a convenient and cost-efficient complement to currently available methods.