Evaluation of the Diagnostic Capacity of a Smart Mattress Versus Conventional Polysomnography

NCT07432919 | Not Yet Recruiting DMC

• 1 other identifier: CEImLar PS-27
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 2

Brief Summary

This project aims to develop and evaluate an innovative, non-invasive diagnostic system based on a smart mattress for detecting obstructive sleep apnea (OSA), as well as assessing overall sleep quality and identifying periodic limb movements. The main goal is to improve the accuracy of sleep apnea diagnosis while providing a less invasive solution suitable for home use, ultimately enhancing patients' quality of life. A descriptive, observational, prospective study will be conducted to analyze data obtained from diagnostic polysomnographies performed at the Sleep Unit of San Pedro Hospital between November 17, 2026, and March 1, 2028. Patients will use the smart mattress, and its measurements will be compared with polysomnography results. This comparison will allow for the optimization of the mattress's artificial intelligence, training it to accurately recognize respiratory patterns and sleep-related events, including positional apneas and periodic limb movements. Key technical objectives include: Determining the sensitivity, specificity, and predictive values of the mattress in detecting apneas, hypopneas, and limb movements compared to polysomnography. Evaluating the agreement between the mattress and polysomnography for sleep variables such as total sleep time, sleep efficiency, sleep stages, micro-arousals, and patient position. Assessing whether measurement accuracy varies by sleeping position or age group (adults vs. children). Measuring subjective sleep quality using the Groningen Sleep Quality Scale (GSQS-8). Performing a descriptive analysis of patient demographics. Hypotheses: The smart mattress will detect obstructive sleep apnea, sleep quality, and periodic limb movements with accuracy comparable to polysomnography. The system will provide a reliable, non-invasive, home-friendly diagnostic method. Measurements of the apnea-hypopnea index (AHI) and limb movements will show high sensitivity, specificity, and predictive values, both overall and according to OSA severity. There will be good agreement between mattress measurements and polysomnography for most sleep variables. Accuracy may vary depending on the patient's sleeping position. Measurements will correlate well across adults and pediatric patients. Subjective sleep quality scores (GSQS-8) will be consistent with objective mattress data. This project seeks to develop a more accurate, accessible, and non-invasive diagnostic system for OSA, combining advanced technology with ease of home use. By training the mattress's AI to recognize sleep patterns and events, it aims to optimize the detection of positional apneas, providing patients with better monitoring, early intervention, and improved quality of life.

Conditions

Obstructive Sleep Apnea (OSA)

Keywords

Smart mattress; Sleep diagnosis; Artificial intelligence in healthcare; Sleep treatment innovation; Obstructive Sleep Apnea (OSA)

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of the smart mattress for detecting sleep apnea.

  Measured by: Sensitivity Specificity Predictive values (PPV/NPV) Compared with polysomnography (PSG).

  Night of simultaneous PSG and mattress recording (one night per participant).

Secondary Outcomes (8)

• Total sleep time

  Night of simultaneous PSG and mattress recording (one night per participant).

• Diagnostic accuracy according to sleep position.

  Night of simultaneous PSG and mattress recording (one night per participant).

• Diagnostic accuracy by age group.

  Night of simultaneous PSG and mattress recording (one night per participant).

• Subjective sleep quality (GSQS-8).

  Night of simultaneous PSG and mattress recording (one night per participant).

• Descriptive demographic data.

  Night of simultaneous PSG and mattress recording (one night per participant).

Study Arms (1)

People with suspected obstructive sleep apnea (OSA)

Patients will undergo the PSG on a smart mattress, which will allow simultaneous recording of: Standard PSG data, considered the gold standard in sleep studies. Data generated by the smart mattress, including signals and metrics related to movement, breathing, and other physiological parameters detectable by the device. The data obtained from the mattress will be compared with the PSG results in order to: Validate the mattress's ability to detect respiratory patterns and events during sleep. Optimize and train the mattress's artificial intelligence system, improving its diagnostic accuracy in identifying respiratory events and other sleep disturbances.

Device: Integrated polysomnographic assessment with smart mattress.

Interventions

Integrated polysomnographic assessment with smart mattress. DEVICE

During a single night of recording, the participant will sleep on a smart mattress equipped with sensors for the continuous monitoring of sleep parameters. The data obtained will subsequently be compared and validated against polysomnography (PSG) recordings, considered the gold-standard reference for the objective evaluation of sleep architecture and quality, as well as respiratory events.

People with suspected obstructive sleep apnea (OSA)

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Diagnostic polysomnographies performed at the Sleep Unit of San Pedro Hospital from November 17, 2026 to March 1, 2028.

You may qualify if:

• Polysomnographies performed at San Pedro Hospital between November 17, 2026, and March 1, 2028.
• Polysomnographies of patients under 16 years of age and polysomnographies performed at San Pedro Hospital on patients over 16 years of age (as separate study groups).

You may not qualify if:

• Poor technical quality of the polysomnography.
• Patients with \>50% central apneas or presence of Cheyne-Stokes respiration (CSResp).
• Lack of polysomnography analysis and/or mattress data.

Sponsors & Collaborators

• Hospital San Pedro de Logroño: lead
• Fundacion Rioja Salud: collaborator

Study Sites (2)

Center for Biomedical Research of La Rioja

Logroño, La Rioja, 26006, Spain

Location

San Pedro University Hospital

Logroño, La Rioja, 26006, Spain

Location

Related Publications (11)

• 11. Feihong Ding, Andrew Cotton-Clay et al. Polysomnographic validation of an under-mattress monitorin device in estimating sleep architecture and obstructive sleep apnea in adults. Sleep Med. Abril 2022. DOI: 10.1016/j.sleep.2022.04.010

  BACKGROUND

• 10. Jong-Ho Byun 1, Keun Tae Kim 1, Hye-Jin Moon 2, Gholam K Motamedi 3, Yong Won Cho 4. The first night effect during polysomnography, and patients' estimates of sleep quality

  BACKGROUND

• 9. Welltech Electronics.

  BACKGROUND

• 8. Kobayashi M, Namba K, Tsuili S, et al. Validdity of sheet-type portable monitoring device for screening obstrucitive sleep apnea síndrome. Sleep breath 2013; 17: 589-95.

  BACKGROUND

• 7. Tenhunen M, Elomaa E, sistonen H et al, Emfit movement sensor in evaluating nocturnal breathing. Respir Physiol neurobiolo 2013; 187: 183-9.

  BACKGROUND

• 6. Anttalainen, U. Polo, O. Vahlberg, T et AL. Reimbursed drugs in Patients with sleep-disordered breathing: a static charge-sensitive bed study. Sleep medicine 2010 11, 49-55.

  BACKGROUND

• 5. Polo O, Brissaud L, sales B et al. The validity of the static charge sensitive bed in detecting obstructive sleep apneas. Eur repir J 1988; 1:330.

  BACKGROUND

• 4. Mediano O, González Mangado N, Montserrat JM, Alonso-Álvarez ML, Almendros I, Alonso-Fernández A, et al. International Consensus Document on Obstructive Sleep Apnea. Arch Bronconeumol. 2022 Jan;58(1):52-68. doi: 10.1016/j.arbres.2021.03.017.

  BACKGROUND

• 3. Benjafield, A. V., Ayas, N. T., Eastwood, P. R., Heinzer, R., Ip, M. S., Morrell, M. J., & Malhotra, A. (2019). Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. The Lancet Respiratory Medicine, 7(8), 687-698.

  BACKGROUND

• 2. Senaratna, C. V., Perret, J. L., Lodge, C. J., Lowe, A., Campbell, B. E., Matheson, M. C., & Dharmage, S. C. (2017). Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Medicine Reviews, 34, 70-81.

  BACKGROUND

• 1. Duran, J., Esnaola, S., Rubio, R., & Iztueta, A. (2001). Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. American Journal of Respiratory and Critical Care Medicine, 163(3), 685-689.

  BACKGROUND

MeSH Terms

Conditions

Sleep Apnea, Obstructive

Condition Hierarchy (Ancestors)

Sleep Apnea Syndromes; Apnea; Respiration Disorders; Respiratory Tract Diseases; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Nervous System Diseases

Study Officials

• Alejandra Roncero Lázaro, MD

  Hospital Universitario San Pedro de Logroño

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alejandra Roncero Lázaro, MD

CONTACT

+ 34 941 29 80 00; aroncerol@riojasalud.es

Jorge Lázaro Galán, MSc

CONTACT

+ 34 941 29 80 00; jlazaro@riojasalud.es

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 10, 2026
• First Posted: February 25, 2026
• Study Start (Estimated): November 17, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): March 1, 2028
• Last Updated: March 3, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (2)