Framework for estimating sleep timing from digital footprints. Issue 4 (30th August 2018)

• Record Type: Journal Article
• Authors: Chen, Bo-chiuan; Seo, Dong-Chul; Lin, Hsien-Chang; Crandall, David
• Is Part Of: BMJ innovations. Volume 4:Issue 4(2018)
• Page Start: 172
• Page End: 177
• Publication Date: 2018-08-30
• Subjects: sleep -- sleep timing -- Twitter -- digital footprint -- social media -- social network services; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjinnov-2018-000274
• Languages: English
• ISSNs: 2055-8074

Abstract

Abstract : 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.