English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An increasing number of people use wearables and other smart devices toquantify various health conditions, ranging from sleep patterns, to bodyweight, to heart rates. Of these Quantified Selfs many choose to openly sharetheir data via online social networks such as Twitter and Facebook. In thisstudy, we use data for users who have chosen to connect their smart scales toTwitter, providing both a reliable time series of their body weight, as well asinsights into their social surroundings and general online behavior.Concretely, we look at which social media features are predictive of physicalstatus, such as body weight at the individual level, and activity patterns atthe population level. We show that it is possible to predict an individual'sweight using their online social behaviors, such as their self-description andtweets. Weekly and monthly patterns of quantified-self behaviors are alsodiscovered. These findings could contribute to building models to monitorpublic health and to have more customized personal training interventions.  While there are many studies using either quantified self or social mediadata in isolation, this is one of the few that combines the two data sourcesand, to the best of our knowledge, the only one that uses public data.

Quantified Self Meets Social Media: Sharing of Weight Updates on Twitter