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

According to the Lancet report on the global burden of disease published inOctober 2020, air pollution is among the five highest risk factors for globalhealth, reducing life expectancy on average by 20 months. This paper describesa data-driven method for establishing causal relationships within and betweentwo multivariate time series data streams derived from wearable sensors:personal exposure to airborne particulate matter of aerodynamic sizes less than2.5um (PM2.5) gathered from the Airspeck monitor worn on the person andcontinuous respiratory rate (breaths per minute) measured by the Respeckmonitor worn as a plaster on the chest. Results are presented for a cohort of113 asthmatic adolescents using the PCMCI+ algorithm to learn the short-termcausal relationships between lags of \pm exposure and respiratory rate. Weconsider causal effects up to a maximum delay of 8 hours, using data at both a1 minute and 15 minute resolution in different experiments. For the first timea personalised exposure-response relationship between PM2.5 exposure andrespiratory rate has been demonstrated to exist for short-term effects inasthmatic adolescents during their everyday lives. Our results lead torecommendations for work on specific open problems in causal discovery, toincrease the feasibility of this approach for similar epidemiology studies inthe future.

Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics