Evidence map›Paper›PMID 41461802›Full record

ArticleScientific reports2025

NIOSH sound level meter application as an adjunct in noise measurement in manufacturing industry.

Zhi Qing Ooi, Sheng Qian Yew, Azizah Zafira, Nur Alya Asyqin Irwan Syarzizi, Chan Hui Ying, Mohammad Fuad Mohammad Affader

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Zhi Qing OoiDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia.
Sheng Qian YewDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia. shengqian@ukm.edu.my.
Azizah ZafiraDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia.
Nur Alya Asyqin Irwan SyarziziDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia.
Chan Hui YingDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia.
Mohammad Fuad Mohammad AffaderDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur, 56000, Malaysia.

Funding

Faculty of Medicine UKM FF-2024-080
6 · The paper itself

Abstract

Mobile sound level applications have been proposed as potential adjuncts for conventional sound level meters (SLM) in noise measurement. However, their performance remains uncertain in the manufacturing industry setting. This study was aimed to determine the validity, reliability, and agreement of the NIOSH SLM app in measuring noise in the manufacturing industry with reference to conventional SLM. A total of 93 samples were collected from five machineries. An iPhone 12 Pro and an iPhone 12 mini, each installed with the latest version of NIOSH SLM app, were tested against the conventional SLM (as gold standard). Three noise parameters, namely the average noise level (i.e., LA

Indexed as

Environmental MonitoringManufacturing IndustryMobile ApplicationsNoiseNoise, OccupationalHumansNational Institute for Occupational Safety and Health, U.S.Reproducibility of Results

Identifiers

PMID41461802
PMCPMC12749573

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.