RT Journal Article
SR Electronic
T1 Framework for estimating sleep timing from digital footprints
JF BMJ Innovations
JO BMJ Innov
FD All India Institute of Medical Sciences
SP 172
OP 177
DO 10.1136/bmjinnov-2018-000274
VO 4
IS 4
A1 Bo-chiuan Chen
A1 Dong-Chul Seo
A1 Hsien-Chang Lin
A1 David Crandall
YR 2018
UL http://innovations.bmj.com/content/4/4/172.abstract
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.