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The Effectiveness of Hearing Protection Devices: A Systematic Review and Meta-Analysis.

Chanbeom KwakWoojae Han
Published in: International journal of environmental research and public health (2021)
To prevent intensive noise exposure in advance and be safely controlled during such exposure, hearing protection devices (HPDs) have been widely used by workers. The present study evaluates the effectiveness of these HPDs, partitioned into three different outcomes, such as sound attenuation, sound localization, and speech perception. Seven electronic journal databases were used to search for published articles from 2000 to 2021. Based on inclusion criteria, 20 articles were chosen and then analyzed. For a systematic review and meta-analysis, standardized mean differences (SMDs) and effect size were calculated using a random-effect model. The funnel plot and Egger's regression analysis were conducted to assess the risk of bias. From the overall results of the included 20 articles, we found that the HPD function performed significantly well for their users (SMDs: 0.457, 95% confidence interval (CI): 0.034-0.881, p < 0.05). Specifically, a subgroup analysis showed a meaningful difference in sound attenuation (SMDs: 1.080, 95% CI: 0.167-1.993, p < 0.05) when to wear and not to wear HPDs, but indicated no significance between the groups for sound localization (SMDs: 0.177, 95% CI: 0.540-0.894, p = 0.628) and speech perception (SMDs: 0.366, 95% CI: -0.100-1.086, p = 0.103). The HPDs work well for their originally designated purposes without interfering to find the location of the sound sources and for talking between the workers. Taking into account various factors, such as the characteristics of the users, selection of appropriate types, and fitting methods for wearing in different circumstances, seems to be necessary for a reliable systematic analysis in terms of offering the most useful information to the workers.
Keyphrases
  • randomized controlled trial
  • systematic review
  • healthcare
  • air pollution
  • drinking water
  • clinical trial
  • metabolic syndrome
  • insulin resistance
  • deep learning
  • social media
  • open label