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J Crit Care 79 (2024) 154435
Available online 25 September 2023
0883-9441/© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Smartphone applications for measuring noise in the intensive care unit: A
feasibility study
Pyoung Jik Lee
a
,
*
, Thomas Hampton
b
,
c
a
Acoustics Research Unit, School of Architecture, University of Liverpool, Liverpool, UK
b
ENT Department, Alder Hey Children’s Hospital, Liverpool, UK
c
Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
ARTICLE INFO
Keywords:
Noise
Intensive care unit
Practical noise monitoring
Smartphone
Mobile applications
Low-cost microphone
ABSTRACT
Purpose: This study aims to explore the suitability of using smartphone applications with low-cost external mi-
crophones in measuring noise levels in intensive care units.
Methods: Four apps and two external microphones were tested in a laboratory by generating test signals at ve
noise levels. The average noise levels were measured using the apps and a professional device (i.e. a sound level
meter). A eld test was performed in an intensive care unit with two apps and one microphone. Noise levels were
measured in terms of average and maximum noise levels according to the World Health Organisation’s guidance.
All the measurements in both tests were conducted after acoustic calibration using a sound calibrator.
Results: Overall, apps with low-cost external microphones produced reliable results of averaged noise levels in
both the laboratory and eld settings. The differences between the apps and the sound level meter were within
±2 dB. In the eld test, the best combination of app and microphone showed negligible difference (<2 dB)
compared to the sound level meter in terms of the average noise level. However, the maximum noise level
measured by the apps exhibited signicant differences from those measured by the sound level meter, ranging
from −0.9 dB to −4.7 dB.
Conclusion: Smartphone apps and low-cost external microphones can be used reliably to measure the average
noise level in the intensive care unit after acoustic calibration. However, professional equipment is still necessary
for accurate measurement of the maximum noise level.
1. Introduction
Noise has been a nuisance in the built environment, causing diverse
adverse effects on people and communities. One such example is hos-
pitals, where noise levels often exceed the World Health Organisation’s
(WHO) guidance levels and affect patients’ well-being and the produc-
tivity of medical staff [1-4]. For instance, de Lima Andrade, et al. [5]
recently carried out a systematic review of the literature about noise
levels in hospitals and reported that daytime noise levels varied from 37
to 88.6 dBA. The intensive care unit (ICU) is one of the nosiest de-
partments in the hospital due to alarms from medical equipment and
noise generated by medical activities [6,7]. Many previous studies have
reported that noise levels in an ICU exceeded WHO recommendations
for both daytime and night-time [6-13]. For example, average noise
levels measured for 24 h from three ICUs in the UK varied from 54.9 to
58.6 dBA. Excessive noise levels in ICUs were still observed in low-
income or lower-resourced settings including China [7,14] and the
Democratic Republic of Congo [15], and it was a signicant burden
during the COVID-19 pandemic [7]. Among noise sources, conversation
and nursing activities seem to produce higher noise levels than alarms
and medical devices [7,16]. This excessive noise in the ICU is reported to
affect the wellbeing of patients and healthcare workers, with impacts on
patient’s sleep [17] and voice disorders among nurses [18]. Thus,
¨
Ozcan, et al. [19] recently proposed a conceptual framework to help
address such noise issues in critical care through multidisciplinary sci-
entic collaboration and medical innovation.
Noise measurement requires professional equipment such as a sound
level meter to guarantee precise and accurate results. But sound level
meters (Class 1 and Class 2) conforming to standard [20] are expensive
and require acoustic knowledge to operate them. Nowadays, the de-
velopments of applications for mobile devices have provided non-
experts with an accessible and low-cost alternative to measure noise
* Corresponding author at: Acoustics Research Unit, School of Architecture, University of Liverpool, Liverpool L69 7ZN, UK.
E-mail address: P.J.Lee@liverpool.ac.uk (P.J. Lee).
Contents lists available at ScienceDirect
Journal of Critical Care
journal homepage: www.journals.elsevier.com/journal-of-critical-care
https://doi.org/10.1016/j.jcrc.2023.154435
Journal of Critical Care 79 (2024) 154435
2
levels. In laboratory testing, noise measurement apps for Apple smart-
phones and tablets with built-in microphones were found to be better
than Android devices [21]. While three iOS apps were found to be un-
reliable compared to the sound level meter [22], certain apps without
calibration proved to be reliable in the laboratory conditions [21,23,24].
Consequently, several attempts have been made to measure noise levels
using apps outdoors [25], in slaughterhouses [26], and in hospitals
[22,27-30]. More recently, apps with external microphones were tested
to enhance the accuracy of the noise measurements [31]. Kardous and
Shaw [31] highlighted that using external microphones signicantly
improved the precision of smartphone noise measurements in the lab-
oratory setting. Serpanos, et al. [32] tested apps with and without
external calibration of microphones in clinical rooms, but they simu-
lated different noise levels using music. Therefore, the smartphone apps
with external microphones were not tested in real environments such as
ICUs, and the maximum noise levels were not validated.
This study aimed to explore the suitability of using smartphone apps
and external microphones for non-professionals, including healthcare
workers, in measuring noise levels in ICUs. First, the apps and external
microphones were tested in a laboratory against a Class 1 sound level
meter. Second, noise levels were measured in a single-bedded room of
the ICU using the apps and external microphones. Both averaged and
maximum noise levels were measured in the ICU according to the
WHO’s guidance.
2. Material and methods
Two tablets (iPad 6th generation with iOS 15.3.1 (hereafter called
“iPad 1”) and iPad 8th generation with iOS 15.6.1 (hereafter called
“iPad 2”)) were used. As listed in Table 1, four free iOS apps (Decibel
Meter, Decibel X, NIOSH SLM and NoiseLab) were downloaded from the
App Store. They were selected from among the available apps designed
to measure both the average (L
Aeq
) and maximum noise levels (L
AFmax
),
based on the highest number of reviews from past users. In addition, two
low-cost external microphones (i437L (MicW) and iMM-6 (Dayton
Audio)) were selected which were based on performance in the afore-
mentioned study [31].
Before the test, all the apps and microphones were calibrated using a
Class 1 sound calibrator (B&K, Type 4230). After the calibration pro-
cedures, correction factors, which indicate the difference between the
measured level and the reference level, were applied to the apps. The
correction factors varied from 0.4 dB to 13.4 dB for the i437L and from
−18.7 dB to 6.0 dB for iMM-6. This study consists of two parts: 1) the
laboratory test and 2) the eld test. The laboratory test was aimed to
discover the most appropriate technically viable solution in terms of the
accuracy of measured noise levels for the selected apps and micro-
phones. The eld test, on the other hand, was designed to see how these
solutions behave in the real-life context of the ICUs. The laboratory test
was conducted in a reverberation chamber with walls, oor and ceiling
that reect sound. Pink noise with an audible frequency range (20 Hz –
20 kHz) was generated from an omnidirectional loudspeaker (B&K,
Type 4292) and subwoofer (Yamaha, SW1181V) at ve levels (65, 75,
85 and 95 dB). Unweighted averaged noise levels (L
eq
) were then
measured for 30 s using different combinations. A Class 1 sound level
meter (hereafter called “SLM”; Svantek, SV971A) was also used as a
reference to determine the accuracies of the tablets. The measurements
were repeated ve times at each level. A eld test was performed in the
single-bedded room of the ICU at the Royal Liverpool Hospital,
following the recommendations for conducting measurements in hos-
pitals [33]. The dimensions of the room were 5.12 m ×4.98 m ×3.00 m
(W ×D ×H). It had vinyl ooring on the oor, gypsum board or glass
windows for the walls, and an acoustic ceiling on the ceiling. The
measured reverberation time (T20, averaged between 500 Hz and 1
kHz) for the room was 0.5 s. For the eld test, only one external
microphone (i437L) and two apps (NIOSH SLM and NoiseLab) were used
in our eld testing as they produced results closer to the SLM in the
laboratory experiment. They also showed signicantly smaller correc-
tion factors. All the microphones and an SLM were installed in the
single-bedded room and they were positioned 0.5 m above the patient’s
head. They were also placed as far away as possible from hard surfaces
such as walls and doors (at least 1 m). Measurements were repeated 10
times, with each measurement lasting for 10 min. All the microphones
were calibrated using a sound calibrator and correction factors of 1.9 dB
(iPad 1) and 1.1 dB (iPad 2). The sliding door of the room was kept open
during the measurements.
3. Results
The test results were illustrated using Gardner-Altman plots to pre-
sent individual readings and effect sizes. The top section reports all in-
dividual measurements as a swarmplot to display the underlying
distribution. The effect size is reported in the bottom section, with the
mean difference between the groups depicted as a black dot and 95%
bootstrap condence intervals calculated from nonparametric sampling
of the collected data, shown by the shaded curve and whiskers. Fig. 1
shows the laboratory test result of the iPad 2 with an i437L microphone
for four apps at four SPLs. Differences between the SLM and apps were
within ±2 dB, varying from −1.1 dB to 1.6 dB. Among the four apps,
NIOSH over-measured noise levels, whereas the other three apps under-
measured levels. Similar results were obtained from the other tablet
with the iMM-6 microphone.
Fig. 2 shows the results of the iPad 2 with an iMM-6 microphone.
Differences between the SLM and apps were also within ±2 dB. Most
levels from apps were slightly lower than those measured from SLM and
the differences between them were statistically signicant except for
two cases (Decibel Meter at 75 and 85 dB; NIOSH at 65 dB). Similar
results were observed from the other iPad across different settings
(microphones and apps) and they can be found in Supplementary
Figs. S1 and S2.
The results of average and maximum noise levels from the eld test
are plotted in Fig. 3. The average noise level varied from 53.7 dB to 62.4
dB, while the maximum noise level ranged between 71.7 dB and 93.1
dB. For the average noise level, the differences between the SLM and
apps were smaller than 2 dB. In particular, the NoiseLab showed very
good agreements with the values from the SLM (<0.5 dB). In contrast,
the maximum noise level results from the apps were slightly bigger than
the values from the SLM. The differences between the NoiseLab and SLM
varied between −0.9 dB and −3.6 dB, while, those between the NIOSH
and SLM ranged from −0.2 dB to −4.7 dB.
4. Discussion
Overall, apps with external microphones performed well in terms of
average noise levels. The differences in average noise levels between the
SLM and apps were within ±2 dB both in the laboratory and eld tests.
In particular, NoiseLab with i437L microphone showed very little dif-
ferences against the Class 1 SLM in the ICU test (≤ ±0.5 dB). When
comparing the noise levels in the ICU over a 10-min period, the differ-
ences between SLM and NoiseLab (i.e. SLM-NoiseLab) varied from −3.8
to −0.7 dB (please refer to Supplementary Fig. S3) and these differences
Table 1
List of iOS noise measurement apps and external microphones used in this study.
Name Developer
Apps Decibel Meter Vlad Polyanskiy
Decibel X SkyPaw Co. Ltd
NIOSH SLM The National Institute for Occupational Safety
and Health (NIOSH)
NoiseLab (Light) MicW
Model Manufacturer Price
Microphones i437L MicW Around £140
iMM-6 Dayton Audio Around £40
P.J. Lee and T. Hampton
Journal of Critical Care 79 (2024) 154435
3
were larger than the differences observed in the overall noise levels. This
nding indicates that further improvements are necessary for these apps
or other future software to deliver more reliable results. Contrary to the
average noise levels, the results of maximum noise level from apps
exhibited much larger differences in comparison to those of SLM in the
eld test (±5 dB). This is possibly due to the signal processing errors in
Fast time-weighting detectors. Robinson and Hopkins [34] reported that
even Class 1 SLMs had signicant variations (up to 3 dB) when
considering Fast time-weighted maximum levels. However, the mea-
surement of maximum noise level using apps represents a signicant
methodological issue because we have demonstrated that the differences
between apps and SLM are >3 dB which is a just noticeable difference in
loudness. Thus, any readings of noise level from the apps should be used
only for illustrative purposes for comparison against the guidelines.
The ndings of this study revealed that reliable noise levels can be
measured using smartphone apps that are equipped with external mi-
crophones. The use of apps might be useful for non-acousticians, such as
healthcare workers, who are interested in noise monitoring in ICUs and
other hospital settings. However, it should be noted that all the mea-
surement settings in the current study were calibrated using a profes-
sional acoustic device (i.e. sound calibrator) before the measurements.
Previous acoustic studies [23,31,35], which reported reliable results
using smartphone apps, also calibrated microphones and apps before
their measurements. In this study, the correction factors of the apps after
the calibration were very large, varying from −18.7 dB to 13.4 dB which
might cause signicant errors. However, the i437L microphone with two
apps (NIOSH and NoiseLab) showed relatively low correction factors
(<±2 dB) in both laboratory and eld tests, so it can be argued that the
noise could be measured without acoustic calibration using this micro-
phone. Nonetheless, the correction factors of this microphone were >3
dB when alternative smartphones were employed. This indicates that in
general, these noise measurements cannot guarantee reliable results
without acoustic calibration [35,36] even with i437L microphone.
Therefore, several noise readings in hospitals measured by using mobile
devices such as Apple Watch [3-5] are questionable due to the absence
of a calibration procedure. In the future, it would be necessary to
Fig. 1. Laboratory test results: Average noise levels (L
eq
) of the iPad 2 with i437L microphone across the apps at different noise levels.
P.J. Lee and T. Hampton
Journal of Critical Care 79 (2024) 154435
4
enhance the awareness of healthcare workers about the importance of
calibration. In addition, maximum noise levels from the apps in the ICU
were not precise with ±5 dB differences against the sound level meter.
Thus, the readings of maximum noise level from the apps should be used
only for illustrative purposes against the guidelines. While measure-
ments taken with low-cost microphones and apps may not be as accurate
as the gold standard, they can still serve as useful tools in the daily
routines of healthcare workers to track changes in noise levels and their
impact on patients. However, for reliable results, it is recommended to
either calibrate the devices or seek the expertise of a professional, such
as an acoustician.
4.1. Limitations
There are several limitations to consider. First, the eld test was
conducted only in a single-bedded room for a relatively short duration.
Furthermore, there were no instances of severe noise events, such as
medication administration, during the test. Therefore, it is necessary to
conduct noise measurements over longer periods (e.g., 24 h) in more
challenging conditions that include noisy events. Second, the validations
of the microphone and apps were performed based on sound pressure
level. While comparing time histories is an effective method to validate
the accuracy of the measured noise level, most of the apps used in this
study do not provide time histories. Among the apps used, only NoiseLab
offered this feature.
5. Conclusion
In summary, it was found that app-based measurements performed
well in measuring average noise levels, showing ±2 dB in comparison to
the measurements with the gold standard sound level meter. The best
combination of the app and external microphone showed very high
accuracies in the ICU test. In contrast to the average sound pressure
level, maximum noise levels from the apps in the ICU were not precise
with ±5 dB differences against the sound level meter. These levels of
precision from the apps with external microphones were obtained after
SLM
N = 5
Decibel Meter
N = 5
Decibel X
N = 5
NIOSH SLM
N = 5
NoiseLab
N = 5
92.0
92.5
93.0
93.5
94.0
94.5
95.0
Sound pressure level [dB]
Decibel Meter
minus
SLM
Decibel X
minus
SLM
NIOSH SLM
minus
SLM
NoiseLab
minus
SLM
1.5
1.0
0.5
0.0
0.5
1.0
1.5
Mean difference
SLM
N = 5
Decibel Meter
N = 5
Decibel X
N = 5
NIOSH SLM
N = 5
NoiseLab
N = 5
82.5
83.0
83.5
84.0
84.5
85.0
85.5
Sound pressure level [dB]
Decibel Meter
minus
SLM
Decibel X
minus
SLM
NIOSH SLM
minus
SLM
NoiseLab
minus
SLM
1.5
1.0
0.5
0.0
0.5
1.0
1.5
Mean difference
SLM
N = 5
Decibel Meter
N = 5
Decibel X
N = 5
NIOSH SLM
N = 5
NoiseLab
N = 5
72.5
73.0
73.5
74.0
74.5
75.0
75.5
Sound pressure level [dB]
Decibel Meter
minus
SLM
Decibel X
minus
SLM
NIOSH SLM
minus
SLM
NoiseLab
minus
SLM
2.0
1.5
1.0
0.5
0.0
0.5
1.0
Mean difference
SLM
N = 5
Decibel Meter
N = 5
Decibel X
N = 5
NIOSH SLM
N = 5
NoiseLab
N = 5
62.0
62.5
63.0
63.5
64.0
64.5
65.0
Sound pressure level [dB]
Decibel Meter
minus
SLM
Decibel X
minus
SLM
NIOSH SLM
minus
SLM
NoiseLab
minus
SLM
2.5
2.0
1.5
1.0
0.5
0.0
0.5
Mean difference
Fig. 2. Laboratory test results: Average noise levels (L
eq
) of the iPad 2 with iMM-6 microphone across the apps at different noise levels.
P.J. Lee and T. Hampton
Journal of Critical Care 79 (2024) 154435
5
acoustic calibration; thus, it is recommended to calibrate any apps and
external microphones before conducting such measurements.
Ethics approval and consent to participate:
Not applicable.
Funding
Not applicable.
Credit authorship contributions statement
PJL and TH designed the study. PJL collected and analysed the data.
PJL drafted the rst version of the manuscript and TH critically revised
the manuscript. All authors read and approved the nal manuscript. All
authors agree to be accountable for all aspects of the work. All authors
read and approved the nal manuscript.
Declaration of Competing Interest
TH is in receipt of a Wellcome Trust grant (203919/Z/16/Z). How-
ever the authors declare they have no competing interests.
There are no nancial conicts of interest to disclose.
Data availability
All data generated or analysed during this study are included in this
published
article (and its supplementary information les).
Acknowledgements
The authors would like to thank Dr Alicia Waite and the Royal Liv-
erpool University ICU nurses and research nurse team.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jcrc.2023.154435.
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SLM
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