Noise Indicators Relating to Non-Auditory Health Effects in Children-A Systematic Literature Review.
Michail Evangelos TerzakisMaud DohmenIrene van KampMaarten HornikxPublished in: International journal of environmental research and public health (2022)
A systematic literature review was conducted to investigate which objective noise indicators related to various noise sources (i.e., aircraft, road-traffic, and ambient noise) are the best predictors of non-auditory health-effects in children. These relationships are discussed via a conceptual framework, taking into account main parameters such as the type of noise source, the exposure locations and their environments, the type of noise indicators, the children's mediating factors, and the type of non-auditory health effects. In terms of the procedure, four literature databases were screened and data was extracted on study design, types of noise sources, assessment method, health-based outcomes and confounders, as well as their associations. The quality of the studies was also assessed. The inclusion criteria focused on both indoor and outdoor environments in educational buildings and dwellings, considering that children spend most of their time there. From the 3337 uniquely collected articles, 36 articles were included in this review based on the defined inclusion and exclusion criteria. From the included literature, it was seen that noise exposure, assessed by energetic indicators, has significant associations with non-auditory health effects: psychophysiological, cognitive development, mental health and sleep effects. Percentile and event-based indicators provided significant associations to cognitive performance tasks and well-being dimension aspects.
Keyphrases
- air pollution
- particulate matter
- mental health
- working memory
- young adults
- systematic review
- healthcare
- public health
- hearing loss
- drinking water
- type diabetes
- physical activity
- big data
- minimally invasive
- skeletal muscle
- electronic health record
- depressive symptoms
- insulin resistance
- metabolic syndrome
- machine learning
- social media
- climate change
- health risk
- human health