Evidence map›Paper›PMID 40017337›Full record

ArticleJournal of sleep research2025

Performance evaluation of an under-mattress sleep sensor versus polysomnography in > 400 nights with healthy and unhealthy sleep.

Jack Manners, Eva Kemps, Bastien Lechat, Peter Catcheside, Danny J Eckert, Hannah Scott

Abstract read
In one paragraph

Article in Journal of sleep research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  8. The effect of standard time to daylight-saving clock transition on sleep and migraine headaches.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Observational
  9. Article
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  14. "Social Apnea": Obstructive Sleep Apnea Is Exacerbated on Weekends.American journal of respiratory and critical care medicine · 2025
    Article
  15. Article
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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.

Jack MannersFlinders Health and Medical Research Institute: Sleep Health, Flinders University, Bedford Park, South Australia, Australia.ORCID 0000-0003-4925-4725
Eva KempsCollege of Education, Psychology, and Social Work, Flinders University, Bedford Park, South Australia, Australia.
Bastien LechatFlinders Health and Medical Research Institute: Sleep Health, Flinders University, Bedford Park, South Australia, Australia.ORCID 0000-0003-0760-0714
Peter CatchesideFlinders Health and Medical Research Institute: Sleep Health, Flinders University, Bedford Park, South Australia, Australia.
Danny J EckertFlinders Health and Medical Research Institute: Sleep Health, Flinders University, Bedford Park, South Australia, Australia.ORCID 0000-0003-3503-2363
Hannah ScottFlinders Health and Medical Research Institute: Sleep Health, Flinders University, Bedford Park, South Australia, Australia.ORCID 0000-0002-0707-8068

Funding

Defence Science and Technology Group
6 · The paper itself

Abstract

Consumer sleep trackers provide useful insight into sleep. However, large-scale performance evaluation studies are needed to properly understand sleep tracker accuracy. This study evaluated performance of an under-mattress sensor to estimate sleep and wake versus polysomnography in a large sample, including individuals with and without sleep disorders and during day versus night sleep opportunities, across multiple in-laboratory studies. One-hundred and eighty-three participants (51%/49% male/female, mean [SD] age = 45 [18] years) attended the sleep laboratory for a research study including simultaneous polysomnography and under-mattress sensor (Withings Sleep Analyser) recordings. Epoch-by-epoch analyses determined accuracy, sensitivity and specificity of the Withings Sleep Analyser versus polysomnography. Bland-Altman plots examined bias in sleep duration, efficiency, onset-latency, and wake after sleep onset. Overall Withings Sleep Analyser sleep-wake classification accuracy was 83%, sensitivity 95% and specificity 37%. The Withings Sleep Analyser significantly overestimated total sleep time (48 [81] min), sleep efficiency (9 [15]%) and sleep-onset latency (6 [26] min), and underestimated wake after sleep onset (54 [78] min). Accuracy and specificity were higher for night versus daytime sleep opportunities in healthy individuals (89% and 47% versus 82% and 26%, respectively, p < 0.05). Accuracy and sensitivity were also higher for healthy individuals (89% and 97%) versus those with sleep disorders (81% and 91%, p < 0.05). Withings Sleep Analyser performance is comparable to other consumer sleep trackers, with high sensitivity but poor specificity compared with polysomnography. Withings Sleep Analyser performance was reasonably stable, but more variable in daytime sleep opportunities and in people with a sleep disorder. Contactless, under-mattress sleep sensors show promise for accurate sleep monitoring, noting the tendency to over-estimate sleep particularly where wake time is high.

Indexed as

PolysomnographySleepSleep Wake DisordersAdultFemaleHumansMaleMiddle AgedReproducibility of ResultsSensitivity and Specificityperformance evaluationpolysomnographysleepsleep measuressleep trackersvalidation studywearables

Identifiers

PMID40017337
PMCPMC12592840

What OpenQuestion holds

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LicenceCC BY-NC
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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.