Abstract

In the last ten years, findings in medical science reveal that light plays important roles in maintaining optimum regulation of biological rhythms and hormones on a daily basis. Despite the decades of research, it was only in 2002 that David Berson discovered the connection between light and a third type of photoreceptor in the retina and this was the missing link in the description of the mechanism of biological effects controlled by the light and dark cycle. This discovery revolutionized the research on the spectrum, the intensity, the duration and the type of light that affects biological responses. This work addresses this issue of non-visual impacts of human exposure to light, in an attempt to relate the quality of lighting design to health, comfort, and well-being of female retail store employees. The sample for the cross-sectional study was randomly established with 30 female volunteers in street retail stores (possibility of outside visual contact) and shopping mall retail stores (no outside visual contact). Assessment of lighting considered the occurrence of glare, color appearance of light, flexibility, and possibility of lighting control by employees. The tools to assess well-being and health were psychometric scales internationally validated by the psychiatric field to measure depression, anxiety, and stress symptoms. Assessment of sleep conditions and analysis of the activity/rest rhythm was carried by a wrist monitor with attached luximeter and the analysis of the body temperature rhythm was made by a temperature sensor, to which each participant was submitted for five consecutive days. The lighting pattern’s influence on the circadian system was verified by measuring saliva melatonin and cortisol levels. The degree of satisfaction of employees and their preferences regarding work environment lighting were surveyed by applying questionnaires. Data were analyzed using Pearson correlations, ANOVA, and stepwise regression.

Keywords:

Lighting Design, Health, Circadian Rhythm, Cortisol, Melatonin, Retail Stores

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Jul 7th, 12:00 AM

Lighting Design: Non-visual Impacts and its Influence on Employees’ Health and Well-being Betina Tschiedel Martau, Paulo Sergio Scarazzato,

In the last ten years, findings in medical science reveal that light plays important roles in maintaining optimum regulation of biological rhythms and hormones on a daily basis. Despite the decades of research, it was only in 2002 that David Berson discovered the connection between light and a third type of photoreceptor in the retina and this was the missing link in the description of the mechanism of biological effects controlled by the light and dark cycle. This discovery revolutionized the research on the spectrum, the intensity, the duration and the type of light that affects biological responses. This work addresses this issue of non-visual impacts of human exposure to light, in an attempt to relate the quality of lighting design to health, comfort, and well-being of female retail store employees. The sample for the cross-sectional study was randomly established with 30 female volunteers in street retail stores (possibility of outside visual contact) and shopping mall retail stores (no outside visual contact). Assessment of lighting considered the occurrence of glare, color appearance of light, flexibility, and possibility of lighting control by employees. The tools to assess well-being and health were psychometric scales internationally validated by the psychiatric field to measure depression, anxiety, and stress symptoms. Assessment of sleep conditions and analysis of the activity/rest rhythm was carried by a wrist monitor with attached luximeter and the analysis of the body temperature rhythm was made by a temperature sensor, to which each participant was submitted for five consecutive days. The lighting pattern’s influence on the circadian system was verified by measuring saliva melatonin and cortisol levels. The degree of satisfaction of employees and their preferences regarding work environment lighting were surveyed by applying questionnaires. Data were analyzed using Pearson correlations, ANOVA, and stepwise regression.

 

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