METHOD
We sought to reproduce background noise in the clinical context by
adjusting the signal to noise ratio (SNR) during testing. We chose
adaptive SNR, using the Bamford-Kowal-Bench (BKB) Sentence Lists read by
a clinician14 whilst a PARROT
machine15 produced the background babble noise
(simulated speech such as you might hear in a crowded pub or emergency
department) at predetermined levels of noise. The PARROT is a portable
digital speech screening system used to assess speech discrimination
using a range of recognised speech discrimination tests.
The BKB sentences used in this test were published in 1979 as a protocol
for testing hearing impaired children and developed as a SiN test by
Niquette et al, 200316. There are 10 sentences in each
list with 18 lists. Each sentence has three or four words which must be
repeated by the subject.
A percentage score can be given for how many words are correctly
repeated.
To determine background noise levels in our hospital we conducted 2x 30
second sound meter recordings (ATP Digital sound level meter/ 8928,
[calibrated by NHS audiometric calibration service, Audiology
department, Withington community Hopsital, Manchester]) in 4 discrete
environments; The office, the emergency department (ED), the intensive
care unit (ITU) and the operating theatre during normal, daylight
working hours.
Background noise level minimum and maximums (during daylight hours with
regular levels of staff) were recorded as follows:
40-55dB Office
48-66dB ED
50-78dB ITU
53-84dB Theatre
Five candidates representing our hospital ENT department were chosen, 2
women and 3 men. Age range was 29-49 years. Median age of candidates was
39 years old.
Initial 250Hz – 8kHz Pure tone audiograms were conducted to confirm no
significant hearing loss in our 5 candidates, who had no previous
otology history or significant co-morbidity.
All testing was conducted in a soundproofed audiometry booth. Baseline
Standard BKB sentences were conducted in silence for all candidates
without PPE. Scores were 100% for all candidates.
We then conducted BKB sentence testing whilst each subject wore the
facial PPE suitable for aerosol generating procedures (AGPs) (fit-tested
FFP3 mask and head visor). All subjects had previously undergone fit
testing to ensure that the PPE worn was fitting appropriately for each
individual.
The researcher spoke using the BKB word lists whilst wearing AGP PPE.
The subject wore the same PPE at a distance of 2m. The PARROT machine
was placed behind and above the head of the researcher (Parrotplus 2,
produced by Soundbyte). Background noise (adult) babble settings were
chosen as characteristic values representing different environments as
follows:
45 dB Office
55 dB ED
65 dB ITU
70 dB Theatre
Each candidate underwent BKB testing at the 4 background noise levels,
in three test conditions:
a) Candidate and researcher in normal conditions without PPE, normal
researcher voice
b) Candidate and researcher both in AGP PPE, normal researcher voice
c) Candidate and researcher both in AGP PPE with researcher attempting
to raise voice.
Raised voice was deemed researcher voice raised to the point at which
the researcher felt their voice was comprehensible against background
noise.
The percentage of key words in the BKB sentences repeated by the
candidate was recorded. Each sentence was read once by the researcher
and was not repeated.
During day-to-day working and conversation, people do not speak at the
same intensity during the whole conversation and background noise also
fluctuates rather than remaining at constant levels, hence our decision
to use live, fluctuating voice rather than pre-recorded voices amplified
to a fixed and constant volume17. When speakers adjust
their voice to overcome background noise, this is known as the Lombard
effect. Although attempting to raise one’s voice or shout usually causes
only a small increase in volume between 5-10dB18, we
chose to measure the volume of voice produced by the researcher as a
secondary outcome measure. This was not our primary concern as attempts
to raise one’s voice in day to day clinical practice will have both
inter and intra-person variability, so we felt the integrity of the
simulation was preserved regardless of actual volume levels produced by
the researcher.