Evidence map›Paper›PMID 42303697›Full record

ArticleNPJ digital medicine2026

Zero burden multi night monitoring with AI enabled technology reduces obstructive sleep apnea misdiagnosis.

Bastien Lechat, Simon Proctor, Jack Manners, Kelly A Loffler, Alison Leviton, Barbara Toson, Daniel Vena, Andrew Vakulin, Josh Fitton, Robert J Adams and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. 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

12 authors.

Bastien LechatFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia. bastien.lechat@flinders.edu.au.
Simon ProctorFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Jack MannersFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Kelly A LofflerFlinders Clinical Trials, Flinders University, Adelaide, SA, Australia.
Alison LevitonFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Barbara TosonFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Daniel VenaDivision of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Andrew VakulinFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Josh FittonFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Robert J AdamsFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Sutapa MukherjeeFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.
Danny J EckertFlinders Health and Medical Research Institute (FHMRI) Sleep Health, Flinders University, Adelaide, SA, Australia.

Funding

National Health and Medical Research Council 1196261National Health and Medical Research Council 2025886
6 · The paper itself

Abstract

Multi-night measurement of obstructive sleep apnea (OSA) using AI-enabled technologies could reduce misdiagnosis rates from night-to-night variation. However, prospective feasibility and effectiveness of multi-night assessment in clinical populations remains uninvestigated. In this study, 100 people with suspected OSA were recruited to receive an under-the-mattress Withings Sleep Analyzer (WSA) to quantify in-home sleep for 3 months alongside single-night polysomnography. The primary outcome was feasibility of novel technology to estimate the apnea-hypopnea-index (AHI), defined as ≥14 nights of AHI recordings within the first month. Secondary outcomes included diagnostic classification of moderate-to-severe OSA (AHI ≥ 15 events/h). 92 participants (53±15yo; BMI = 31 ± 7 kg/m

Identifiers

PMID42303697
PMCPMC13635168

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.