Evidence map›Paper›PMID 33811634›Full record

ArticleSleep & breathing = Schlaf & Atmung2022

Validation of snoring detection using a smartphone app.

Jui-Kun Chiang, Yen-Chang Lin, Chih-Wen Lin, Ching-Shiung Ting, Yi-Ying Chiang, Yee-Hsin Kao

Abstract readValidation Study
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  7. High-Speed Network DDoS Attack Detection: A Survey.Sensors (Basel, Switzerland) · 2023
    Review
  8. Article
  9. Article
  10. Accuracy of a Smartphone Application Measuring Snoring in Adults-How Smart Is It Actually?International journal of environmental research and public health · 2021
    Article
  11. 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

6 authors.

Jui-Kun ChiangDepartment of Family Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, 2, Minsheng Road, Dalin, 62247, Chiayi, Taiwan.
Yen-Chang LinNature Dental Clinic, 341, Sec. 3, Zhongshan Rd, Puli Township, Nantou, 545, Taiwan.
Chih-Wen LinDepartments of School of Medicine, Tzu Chi University, Hualien, Taiwan.
Ching-Shiung TingDepartment of Otolaryngology, Tainan Municipal, 670 Chung-Te Road, Tainan, 70173, Taiwan.
Yi-Ying ChiangNature Dental Clinic, 341, Sec. 3, Zhongshan Rd, Puli Township, Nantou, 545, Taiwan.
Yee-Hsin KaoDepartment of Family Medicine, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), 670 Chung-Te Road, Tainan, 70173, Taiwan. m2200767@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSnoring is closely related to obstructive sleep apnea in adults. The increasing abundance and availability of smartphone technology has facilitated the examination and monitoring of snoring at home through snoring apps. However, the accuracy of snoring detection by snoring apps is unclear. This study explored the snoring detection accuracy of Snore Clock-a paid snoring detection app for smartphones.

methodsSnoring rates were detected by smartphones that had been installed with the paid app Snore Clock. The app provides information on the following variables: sleep duration, snoring duration, snoring loudness (in dB), maximum snoring loudness (in dB), and snoring duration rate (%). In brief, we first reviewed the snoring rates detected by Snore Clock; thereafter, an ear, nose, and throat specialist reviewed the actual snoring rates by using the playback of the app recordings.

resultsIn total, the 201 snoring records of 11 patients were analyzed. Snoring rates measured by Snore Clock and those measured manually were closely correlated (r = 0.907). The mean snoring detection accuracy rate of Snore Clock was 95%, with a positive predictive value, negative predictive value, sensitivity, and specificity of 65% ± 35%, 97% ± 4%, 78% ± 25%, and 97% ± 4%, respectively. However, the higher the snoring rates, the higher were the false-negative rates for the app.

conclusionSnore Clock is compatible with various brands of smartphones and has a high predictive value for snoring. Based on the strong correlation between Snore Clock and manual approaches for snoring detection, these findings have validated that Snore Clock has the capacity for at-home snoring detection.

Indexed as

AlgorithmsAdultHumansMaleMiddle AgedMobile ApplicationsReproducibility of ResultsSleep Apnea, ObstructiveSmartphoneSnoringAppSmartphonesSnore ClockSnoring

Identifiers

PMID33811634
PMCPMC8857100

What OpenQuestion holds

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LicenceCC BY
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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.