Cross-platform discrepancies in smartphone sound level meter applications: a multicenter clinical and urban acoustic analysis in Indian context
DOI:
https://doi.org/10.18203/issn.2454-5929.ijohns20262360Keywords:
Sound level meter, Tele-audiology, Cross-platform calibration, Environmental noise, Mobile application rating scale, Occupational healthAbstract
Background: The widespread adoption of tele-audiology necessitates accurate environmental noise monitoring via patient smartphones. This study evaluates the acoustic accuracy and clinical usability of 10 sound level meter (SLM) applications across iOS and Android platforms, comparing performance in controlled audiometric environments against unstructured, real-world Indian settings.
Methods: This cross-sectional study incorporates a three-phase design. Phase 1 assessed technical accuracy in a sound-treated booth using pure tones and narrowband noise (250-8000 Hz). Phase 2 evaluated real-world performance, contrasting steady-state noise against impulsive urban traffic noise, highly reverberant outpatient departments (OPD), and a neonatal intensive care uznit (NICU). Measurements utilized a reference class 1 SLM alongside an iPhone 15 Pro, a flagship Android (Samsung S24), and a budget-tier Android. Phase 3 assessed app usability using the mobile application rating scale (MARS).
Results: Cross-platform applications demonstrated significant platform-dependent variability. Apps that were accurate on iOS frequently overestimated noise on Android hardware (+3.80 to +4.60 dBA), while native Android apps underestimated noise on the same device (-5.40 dBA), highlighting a software-hardware calibration disconnect rather than uniform hardware failure. Budget-tier Androids exhibited the highest error margins (up to±9.5 dBA), particularly during impulsive urban noise and high-frequency tones (>4000 Hz).
Conclusions: Smartphone SLM applications exhibit profound cross-platform discrepancies. Solely validating these tools in controlled laboratories is insufficient for clinical application. To ensure reliable occupational and tele-audiology screening, particularly in resource-limited rural networks, clinicians must utilize standardized biological calibration protocols to derive device-specific correction factors.
References
World Health Organization. Hearing loss due to recreational exposure to loud sounds: A review. Geneva: WHO; 2015. Available at: https://iris.who.int/handle/10665/154589. Accessed on 3 June 2026.
Neitzel RL, Swinburn TK, Hammer MS, Eisenberg D. Economic Impact of Hearing Loss and Reduction of Noise-Induced Hearing Loss in the United States. J Speech Lang Hear Res. 2017;60(1):182-9.
International Electrotechnical Commission. IEC 61672-1:2013 Electroacoustics-Sound level meters. Geneva: IEC; 2013.
Ventola CL. Mobile devices and apps for health care professionals: uses and benefits. P T. 2014;39(5):356-64.
Williams W. The accuracy of sound level meter 'apps'. Acoust Aust. 2013;41(1):108-10.
Nast DR, Speer WS, Le Prell CG. Sound level measurements using smartphone "apps": useful or inaccurate? Noise Health. 2014;16(72):251-6.
Fausti SA, Wilmington DJ, Helt PV, Helt WJ, Konrad-Martin D. Hearing health and care: the need for improved hearing loss prevention and hearing conservation practices. J Rehabil Res Dev. 2005;42(4-2):45-62.
Robinson DP, Tingay J. Comparative study of the performance of smartphone-based sound level meter apps, with and without the application of a " IEC-61094-4working standard microphone, to IEC-61672 standard metering equipment in the detection of various problematic workplace noise environments. Inter Noise. 2014;1-10.
Serpanos YC, Renne B, Schoepflin JR, Davis D. The Accuracy of Smartphone Sound Level Meter Applications with and Without Calibration. Am J Speech Lang Pathol. 2018;27(4):1319-28.
Kardous CA, Shaw PB. Evaluation of smartphone sound measurement applications. J Acoust Soc Am. 2014;135(4):EL186-92.
Roberts B, Kardous C, Neitzel R. Improving the accuracy of smart devices to measure noise exposure. J Occup Environ Hyg. 2016;13(11):840-6.
Serpanos YC, Schoepflin JR, Cox SR, Davis D. The Accuracy of Smartphone Sound Level Meter Applications (SLMAs) in Measuring Sound Levels in Clinical Rooms. J Am Acad Audiol. 2021;32(1):27-34.
American National Standards Institute. ANSI S3.1-1999 (R2018) Maximum Permissible Ambient Noise Levels for Audiometric Test Rooms. New York: ANSI. 2018.
Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3(1):e27.
Celestina M, Hrovat J, Kardous CA. Smartphone-based sound level measurement apps: Evaluation of compliance with international sound level meter standards. Appl Acoust. 2018;139:119-28.
Sun K, Kardous CA, Shaw PB, Kim B, Mechling J, Azman AS. The potential use of a NIOSH sound level meter smart device application in mining operations. Noise Control Eng J. 2019;67(1):23-30.
Bocher E, Judicaël P, Gwenaël G, Gwendall P, Nicolas F, Arnaud C, et al. Crowdsourcing of Noise Map Pollution using Smartphones. European NEtwork for Redistributing Geospatial Information; 2015.
Shim H, Lee S, Hwang S. Accuracy of smartphone noise measurement applications for occupational noise assessment. Saf Health Work. 2 020;11(1):119-23.
Kraft R, Reichert M, Pryss R. Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. Sensors (Basel). 2021;22(1):170.
Khan H, Findlay C, Stevenson RA, Singh T. Evaluating the Accuracy of Android Applications in Monitoring Environmental Noise Levels. Cureus. 2025;17(3):e81471.
van Tonder J, Swanepoel W, Mahomed-Asmail F, Myburgh H, Eikelboom RH. Automated Smartphone Threshold Audiometry: Validity and Time Efficiency. J Am Acad Audiol. 2017;28(3):200-8.
Brambilla G, Pedrielli F. Smartphone-Based Participatory Soundscape Mapping for a More Sustainable Acoustic Environment. Sustainability. 2020;12(19):7899.
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