Evidence map›Paper›PMID 40032579›Full record

ReviewRespirology (Carlton, Vic.)2025

Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.

Lucía Pinilla, Ching Li Chai-Coetzer, Danny J Eckert

Abstract readReview
In one paragraph

Review in Respirology (Carlton, Vic.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Observational
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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

3 authors.

Lucía PinillaAdelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, Australia.
Ching Li Chai-CoetzerAdelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0000-0001-9132-0197
Danny J EckertAdelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0000-0003-3503-2363

Funding

National Health and Medical Research Council (NHMRC) of Australia Leadership Fellowship 1116942
6 · The paper itself

Abstract

Underpinned by rigorous clinical trial data, the use of existing home sleep apnoea testing is now commonly employed for sleep disordered breathing diagnostics in most clinical sleep centres globally. This has been a welcome addition for the field given the considerable burden of disease, cost, and access limitations with in-laboratory polysomnography testing. However, most existing home sleep apnoea testing approaches predominantly aim to replicate elements of conventional polysomnography in different forms with a focus on the estimation of the apnoea-hypopnoea index. New, simplified technology for sleep disordered breathing screening, detection/diagnosis, or monitoring has expanded exponentially in recent years. Emerging innovations in sleep monitoring technology now go beyond simple single-night replication of varying numbers of polysomnography signals in the home setting. These novel approaches have the potential to provide important new insights to overcome many of the existing limitations of sleep disordered breathing diagnostics and transform disease diagnosis and management to improve outcomes for patients. Accordingly, the current review summarises the existing evidence for sleep study testing in people with suspected sleep-related breathing disorders, discusses novel and emerging technologies and approaches according to three key categories: (1) wearables (e.g., body-worn sensors including wrist and finger sensors), (2) nearables (e.g., bed-embedded and bedside sensors), and (3) airables (e.g., audio and video recordings), and outlines their potential disruptive role to transform sleep disordered breathing diagnostics and care.

Indexed as

PolysomnographySleep Apnea SyndromesHumansdiagnosislungpolysomnographysleep apnoeawearables

Identifiers

PMID40032579
PMCPMC11965016

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.