Evidence map›Paper›PMID 42623291›Full record

ArticleTrends in hearing

Test-Retest Reliability and Preliminary Validation of Smartphone-Based Threshold and Categorical loudness Measures in Home Settings.

Chen Xu, Lena Schell-Majoor, Birger Kollmeier

Abstract readValidation Study
In one paragraph

Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chen XuMedizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.ORCID 0000-0003-3233-3179
Lena Schell-MajoorMedizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.
Birger KollmeierMedizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable hearing assessment at home can improve accessibility and reduce dependence on in-clinic testing. To be viable, home-based procedures must provide accurate results within short measurement times and remain robust to factors such as ambient noise and variable user attention. In this proof-of-concept study with a limited sample size, we evaluated two such procedures-a Graded Response Bracketing method (GRaBr) for pure-tone threshold estimation and a reinforced adaptive categorical loudness scaling method (rACALOS) for loudness-growth assessment - using remote, smartphone-based testing, and compared their outcomes with those obtained inside the laboratory using established reference procedures. Fifteen young adults with normal hearing completed the tasks at home and in the laboratory. Test-retest reliability was assessed by repeating the home measurements within one week of the initial session. Ambient noise levels in home environments were also recorded. The intraclass correlation coefficients for GRaBr measured at home exceeded 0.75, indicating good test-retest reliability. Similarly, home-based rACALOS showed generally high reliability, with across-run biases below 5 dB at all frequencies; however, mean interquartile ranges reached up to 18.4 dB at some loudness categories. Remote GRaBr measurements showed small, non-significant deviations from laboratory audiometry (0.4 ± 7.1 dB); however, frequency-specific biases of approximately ±5 dB were observed at 250 Hz and 4 kHz, with root-mean-square errors ranging from 4.6 to 8.0 dB across frequencies. Compared with the in-lab ACALOS, remote rACALOS showed an overall bias of 3.38 ± 11.8 dB across loudness categories, with no significant overall effect of test environment. These findings suggest that smartphone-based pure-tone audiometry and loudness-scaling assessments provide accurate and reliable results when using these procedures at home, at least in normal-hearing young adults, who are experienced loudness raters, under suitable acoustic conditions with low ambient noise.

Indexed as

Audiometry, Pure-ToneAuditory ThresholdLoudness PerceptionSmartphoneAcoustic StimulationAdultAuditory AcuityFemaleHearingHumansMaleNoisePredictive Value of TestsReproducibility of ResultsYoung Adultambient noisecategorical loudness scalingremote audiologyvalidity and reliability

Identifiers

PMID42623291
PMCPMC13494286

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.