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Exploring the Correlations Between Measures of Listening Effort in Adults and Children: A Systematic Review with Narrative Synthesis.

Callum Andrew ShieldsMark SladenIain Alexander BruceKarolina KlukJaya Nichani
Published in: Trends in hearing (2023)
Listening effort (LE) describes the cognitive resources needed to process an auditory message. Our understanding of this notion remains in its infancy, hindering our ability to appreciate how it impacts individuals with hearing impairment effectively. Despite the myriad of proposed measurement tools, a validated method remains elusive. This is complicated by the seeming lack of association between tools demonstrated via correlational analyses. This review aims to systematically review the literature relating to the correlational analyses between different measures of LE. Five databases were used- PubMed, Cochrane, EMBASE, PsychINFO, and CINAHL. The quality of the evidence was assessed using the GRADE criteria and risk of bias with ROBINS-I/GRADE tools. Each statistically significant analysis was classified using an approved system for medical correlations. The final analyses included 48 papers, equating to 274 correlational analyses, of which 99 reached statistical significance (36.1%). Within these results, the most prevalent classifications were poor or fair. Moreover, when moderate or very strong correlations were observed, they tended to be dependent on experimental conditions. The quality of evidence was graded as very low. These results show that measures of LE are poorly correlated and supports the multi-dimensional concept of LE. The lack of association may be explained by considering where each measure operates along the effort perception pathway. Moreover, the fragility of significant correlations to specific conditions further diminishes the hope of finding an all-encompassing tool. Therefore, it may be prudent to focus on capturing the consequences of LE rather than the notion itself.
Keyphrases
  • systematic review
  • healthcare
  • young adults
  • hearing loss
  • quality improvement
  • machine learning
  • working memory
  • weight gain
  • big data
  • data analysis
  • drug administration