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.
- sleep timing
- digital footprint
- social media
- social network services
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Contributors Chen conceptualised the framework with substantial input from Seo, Lin and Crandall. Chen drafted the manuscript and Seo provided critical revisions. All authors participated in manuscript preparation, and read and approved the final manuscript. All authors agree to be accountable for all aspects of the work.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
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