The Adaptive Olfactory Measure of Threshold (ArOMa-T) : a rapid test of olfactory function

© The Author(s) 2022. Published by Oxford University Press..

Many widely used psychophysical olfactory tests have limitations that can create barriers to adoption. For example, tests that measure the ability to identify odors may confound sensory performance with memory recall, verbal ability, and prior experience with the odor. Conversely, classic threshold-based tests avoid these issues, but are labor intensive. Additionally, many commercially available tests are slow and may require a trained administrator, making them impractical for use in situations where time is at a premium or self-administration is required. We tested the performance of the Adaptive Olfactory Measure of Threshold (ArOMa-T)-a novel odor detection threshold test that employs an adaptive Bayesian algorithm paired with a disposable odorant delivery card-in a non-clinical sample of individuals (n = 534) at the 2021 Twins Day Festival in Twinsburg, OH. Participants successfully completed the test in under 3 min with a false alarm rate of 7.5% and a test-retest reliability of 0.61. Odor detection thresholds differed by sex (~3.2-fold lower for females) and age (~8.7-fold lower for the youngest versus the oldest age group), consistent with prior studies. In an exploratory analysis, we failed to observe evidence of detection threshold differences between participants who reported a history of COVID-19 and matched controls who did not. We also found evidence for broad-sense heritability of odor detection thresholds. Together, this study suggests the ArOMa-T can determine odor detection thresholds. Additional validation studies are needed to confirm the value of ArOMa-T in clinical or field settings where rapid and portable assessment of olfactory function is needed.

Errataetall:

UpdateOf: medRxiv. 2022 Apr 12;:. - PMID 35313597

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:47

Enthalten in:

Chemical senses - 47(2022) vom: 01. Jan.

Sprache:

Englisch

Beteiligte Personen:

Weir, Elisabeth M [VerfasserIn]
Hannum, Mackenzie E [VerfasserIn]
Reed, Danielle R [VerfasserIn]
Joseph, Paule V [VerfasserIn]
Munger, Steven D [VerfasserIn]
Hayes, John E [VerfasserIn]
Gerkin, Richard C [VerfasserIn]

Links:

Volltext

Themen:

Adaptive algorithm
Anosmia
COVID-19
Detection threshold
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Sex differences
Smell

Anmerkungen:

Date Completed 30.12.2022

Date Revised 05.02.2024

published: Print

UpdateOf: medRxiv. 2022 Apr 12;:. - PMID 35313597

Citation Status MEDLINE

doi:

10.1093/chemse/bjac036

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM349815836