Escuela de Politécnica Superior

Light profoundly impacts many aspects of human physiology and behaviour, including the synchronization of the circadian clock, the production of melatonin, and cognition. These effects of light, termed the non-visual effects of light, have been primarily investigated in laboratory settings, where light intensity, spectrum and timing can be carefully controlled to draw associations with physiological outcomes of interest. Recently, the increasing availability of wearable light loggers has opened the possibility of studying personal light exposure in free-living conditions where people engage in activities of daily living, yielding findings associating aspects of light exposure and health outcomes, supporting the importance of adequate light exposure at appropriate times for human health. However, comprehensive protocols capturing environmental (e.g., geographical location, season, climate, photoperiod) and individual factors (e.g., culture, personal habits, behaviour, commute type, profession) contributing to the measured light exposure are currently lacking. Here, we present a protocol that combines smartphone-based experience sampling (experience sampling implementing Karolinska Sleepiness Scale, KSS ratings) and high-quality light exposure data collection at three body sites (near-corneal plane between the two eyes mounted on spectacle, neck-worn pendant/badge, and wrist-worn watch-like design) to capture daily factors related to individuals’ light exposure. We will implement the protocol in an international multi-centre study to investigate the environmental and socio-cultural factors influencing light exposure patterns in Germany, Ghana, Netherlands, Spain, Sweden, and Turkey (minimum n = 15, target n = 30 per site, minimum n = 90, target n = 180 across all sites). With the resulting dataset, lifestyle and context-specific factors that contribute to healthy light exposure will be identified. This information is essential in designing effective public health interventions.

People may be exposed to energy sources that they cannot perceive with their senses, but which may be harmful to their organism, and therefore, individuals cannot avoid them. One of these energy sources is the sound, particularly sound out of the hearing range (20–20000 Hz). Although the sounds are imperceptible for frequencies below 200 Hz unless they have high intensities. Sound with frequencies below 200 Hz is called “low frequency sound”. This study focuses on low frequency sound generated by artificial sources, and specially in sound located in urban areas. Specifically in the measurement and detection of low frequency sources from the perspective of individuals who are manifesting the symptoms associated with their exposure. To this end, a household of Madrid with individuals who have symptoms is taken as sample. This home did not have large potential sources of low-frequency sounds near its location, such as streets with high intensity of traffic or the subway in order to better contrast other possible sources that are not so obvious. The results show high levels of sound emission at the lowest frequency range (20–200 Hz). These results also show that filters should not be applied to remove non-audible frequency spectrums, such as A type, because it omits sounds in urban areas that could affect people. Data treatment incorporates analysis methods based on machine learning which allow differentiate between sources without measuring on them. Finally, further developments must incorporate measurements bellow 20 Hz and will increase the numbers of households sampled.