Evidence map›Paper›PMID 37990552›Full record

ArticleJMIR mHealth and uHealth2023

Assessing the Usefulness of Mobile Apps for Noise Management in Occupational Health and Safety: Quantitative Measurement and Expert Elicitation Study.

Jingchen Huyan, Chandnee Ramkissoon, Mah Laka, Sharyn Gaskin

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
  2. Assessment of Noise Exposure in United States Urban Public Parks: A Scoping Review.International journal of environmental research and public health · 2025
    Article
  3. Article
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

4 authors.

Jingchen Huyan *Adelaide Exposure Science and Health, School of Public Health, The University of Adelaide, Adelaide, Australia.ORCID 0009-0006-8300-5211
Chandnee Ramkissoon *Adelaide Exposure Science and Health, School of Public Health, The University of Adelaide, Adelaide, Australia.ORCID 0000-0002-3395-8835
Mah LakaAdelaide Health Technology Assessment, School of Public Health, The University of Adelaide, Adelaide, Australia.ORCID 0000-0003-3228-5031
Sharyn Gaskin *Adelaide Exposure Science and Health, School of Public Health, The University of Adelaide, Adelaide, Australia.ORCID 0000-0002-1507-6425

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Overexposure to occupational noise can lead to hearing loss. Occupational noise mapping is conventionally performed with a calibrated sound level meter (SLM). With the rise of mobile apps, there is a growing number of SLM apps available on mobile phones. However, few studies have evaluated such apps for accuracy and usefulness to guide those with occupational noise detection needs in selecting a quality app. Objective: The purpose of this study was to evaluate the accuracy and usefulness of SLM mobile apps to guide workplace health and safety professionals in determining these apps' suitability for assessing occupational noise exposure. Methods: The following three iOS apps were assessed: the NIOSH (National Institute for Occupational Safety and Health) Sound Level Meter, Decibel X, and SoundMeter X apps. The selected apps were evaluated for their accuracy in measuring sound levels in low-, moderate-, and high-noise settings within both simulated environments and real-world environments by comparing them to a conventional SLM. The usefulness of the apps was then assessed by occupational health specialists using the Mobile App Rating Scale (MARS). Results: The NIOSH Sound Level Meter app accurately measured noise across a range of sound levels in both simulated settings and real-world settings. However, considerable variation was observed between readings. In comparison, the Decibel X and SoundMeter X apps showed more consistent readings but consistently underestimated noise levels, suggesting that they may pose a risk for workers. Nevertheless, none of the differences in sound measurements between the three apps and the conventional SLM were statistically significant (NIOSH Sound Level Meter: P=.78; Decibel X: P=.38; SoundMeter X: P=.40). The MARS scores for the three apps were all above 3.0, indicating the usefulness of these apps. Conclusions: Under the conditions of this study, the NIOSH Sound Level Meter app had equivalent accuracy to the calibrated SLM and a degree of usefulness according to the MARS. This suggests that the NIOSH Sound Level Meter app may be suitable for mapping noise levels as part of a monitoring strategy in workplaces. However, it is important to understand its limitations. Mobile apps should complement but not replace conventional SLMs when trying to assess occupational noise exposure risk. Our outcomes also suggest that the MARS tool may have limited applicability to measurement-based apps and may be more suited to information-based apps that collect, record, and store information.

Indexed as

Cell PhoneMobile ApplicationsOccupational HealthHumansNoiseUnited StatesWorkplacehearinghearing lossmanagementMARSmHealthmobile applicationsMobile App Rating Scalemobile appsmobile phonenoise detectionnoise managementoccupational healthtoolusefulness

Identifiers

PMID37990552
PMCPMC10686533

What OpenQuestion holds

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Registered trials

None linked

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 OpenQuestion graph.