Evidence map›Paper›PMID 39027497›Full record

ArticleHeliyon2024

NAPping PAnts (NAPPA): An open wearable solution for monitoring Infant's sleeping rhythms, respiration and posture.

Sofie de Sena, Matias Häggman, Jukka Ranta, Oleksii Roienko, Elina Ilén, Natalia Acosta, Jonna Salama, Turkka Kirjavainen, Nathan Stevenson, Manu Airaksinen and 1 more

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Review
  2. Observational
  3. Article
  4. Review
  5. Article
  6. Article
  7. Practical Application of Polysomnography in Infants.Journal of clinical medicine · 2025
    Review
  8. Review
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

11 authors.

Sofie de SenaBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Matias HäggmanSchool of Science, Department of Mathematics and Systems Analysis, Aalto University, Espoo, Finland.
Jukka RantaBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Oleksii RoienkoBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Elina IlénDepartment of Materials Science and Engineering, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain.
Natalia AcostaBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Jonna SalamaBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Turkka KirjavainenDepartment of Paediatrics, Children's Hospital, Helsinki University Hospital, Helsinki, Finland.
Nathan StevensonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Manu AiraksinenBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Sampsa VanhataloBABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Study objectives: To develop a non-invasive and practical wearable method for long-term tracking of infants' sleep. Methods: An infant wearable, NAPping PAnts (NAPPA), was constructed by combining a diaper cover and a movement sensor (triaxial accelerometer and gyroscope), allowing either real-time data streaming to mobile devices or offline feature computation stored in the sensor memory. A sleep state classifier (wake, N1/REM, N2/N3) was trained and tested for NAPPA recordings ( Results: Overnight NAPPA recordings were successfully performed in all infants. The sleep state classifier showed good overall accuracy (78 %; Range 74-83 %) when using a combination of five features related to movement and respiration. Sleep depth trends were generated from the classifier outputs to visualise sleep state fluctuations, which closely aligned with PSG-derived hypnograms in all infants. Consistently positive parental feedback affirmed the effectiveness of the NAPPA-design. Conclusions: NAPPA offers a practical and feasible method for out-of-hospital assessment of infants' sleep behaviour. It can directly support large-scale quantitative studies and development of new paradigms in scientific research and infant healthcare. Moreover, NAPPA provides accurate and informative computational measures for body positions, respiration rates, and activity levels, each with their respective clinical and behavioural value.

Indexed as

Human activity recognitionInfantNon-invasive monitoringRespirationSleepWearable

Identifiers

PMID39027497
PMCPMC11255670

What OpenQuestion holds

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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.