Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL).
Objective: Determine the extent to which pre-fitting acceptable noise level (ANL), with or without other predictors such as hearing-aid experience, can predict real-world hearing-aid outcomes at three and 12 months post-fitting.Design: ANLs were measured before hearing-aid fitting. Post-fitting outc...
| Publicado en: | International Journal of Audiology Vol. 55; no. 5; pp. 285 - 295 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=114328429&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114328429 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14992027 JW2 jtl: International Journal of Audiology issn: 14992027 maglogo: Y pubinfo: dt: May2016 vid: 55 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 114328429 114328429 114328429 10.3109/14992027.2015.1120892 114328429 ppf: 285 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL). aug: au: Wu, Yu-Hsiang Ho, Hsu-Chueh Hsiao, Shih-Hsuan Brummet, Ryan B. Chipara, Octav affil: Department of Communication Sciences and Disorders, The University of Iowa, Iowa City, USA sug: subj: Hearing Aids Noise Human Machinery Learning Time Factors Questionnaires Logistic Regression Cross Sectional Studies Funding Source ab: Objective: Determine the extent to which pre-fitting acceptable noise level (ANL), with or without other predictors such as hearing-aid experience, can predict real-world hearing-aid outcomes at three and 12 months post-fitting.Design: ANLs were measured before hearing-aid fitting. Post-fitting outcome was assessed using the international outcome inventory for hearing aids (IOI-HA) and a hearing-aid use questionnaire. Models that predicted outcomes (successful vs. unsuccessful) were built using logistic regression and several machine learning algorithms, and were evaluated using the cross-validation technique.Study sample: A total of 132 adults with hearing impairment.Results: The prediction accuracy of the models ranged from 61% to 68% (IOI-HA) and from 55% to 61% (hearing-aid use questionnaire). The models performed more poorly in predicting 12-month than three-month outcomes. The ANL cutoff between successful and unsuccessful users was higher for experienced (∼18 dB) than first-time hearing-aid users (∼10 dB), indicating that most experienced users will be predicted as successful users regardless of their ANLs.Conclusions: Pre-fitting ANL is more useful in predicting short-term (three months) hearing-aid outcomes for first-time users, as measured by the IOI-HA. The prediction accuracy was lower than the accuracy reported by some previous research that used a cross-sectional design. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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