Exploring adult hearing aid user experiences: meaning extraction methods, content patterns, and associations with demographic and outcome variables.

Objective: Employing automated language analysis, specifically Meaning Extraction Method (MEM) and Principal Component Analysis (PCA), to identify key factors in open-text responses about hearing aid experiences.

Design: Exploratory, cross-sectional design, using an online questionnaire. Responses to a single open-ended question were analysed using MEM, PCA, regression, and correlation analyses.

Study Sample: Participants (n = 538) included adult hearing aid users sampled from the Hearing Tracker website community and Lexie Hearing user databases in the United States.

Results: The MEM-derived items revealed six factors related to hearing aid experiences: (1) life change, (2) social situation, (3) quality of life, (4) impact and speech understanding, (5) communication and interaction, and (6) music and environmental sounds. IOI-HA item 3 had the most statistically significant correlations with PCA factors. Quantile regression revealed that factors one and two significantly predicted the IOI-HA total score. Positive correlations were observed between self-reported hearing difficulty and factors one, four, and five, as well as between factor one and general health and factor two and physical activity.

Conclusion: Natural language analysis of open-ended textual responses can offer valuable insights into hearing aid users' experiences. Future studies should aim to refine this methodology to enhance clinical relevance and generalisability.