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The Promise and Pitfalls of At-Home Sleep Tracking: A Deep Dive into the Withings Sleep Analyzer

4 min read

Introduction

Sleep is an essential pillar of health and well-being. The clinical gold standard for sleep assessment, polysomnography or PSG, provides a detailed analysis of sleep architecture but is impractical for routine or long-term monitoring. Its reliance on complex equipment, high cost, and typically in-lab application make it an intrusive process. The proliferation of consumer wearable and nearable devices offers more accessible alternatives, yet their accuracy often lacks rigorous scientific validation, particularly in home environments.

 

A recent study sought to address this gap by evaluating the accuracy and reliability of the Withings Sleep Analyzer (WSA). This contactless sleep mat, placed under the mattress, was compared directly against simultaneous PSG recordings in a large and diverse group of individuals in their own homes. This research investigates the sensor’s performance in real-world conditions, offering critical insights into the current state of consumer sleep-tracking technology.

 

Methods

The study involved 117 healthy participants, with 69 women, and a mean age of approximately 40 years. Each participant slept in their own bed for one night with both the PSG equipment and the under-mattress device active. This setup allowed for a direct, epoch-by-epoch comparison of the data recorded by the consumer device against the clinical reference standard. The analysis focused on two primary objectives: the accuracy of distinguishing sleep from wakefulness and the precision of classifying distinct sleep stages, including light, deep, and REM sleep. Performance was assessed using standard classification metrics to ensure a robust evaluation.

 

Results

The investigation found that the contactless device performs effectively in identifying sleep and wake states. It achieved an overall accuracy of 87% in this core task, demonstrating a high sensitivity of 93% for detecting sleep and a moderate sensitivity of 73% for detecting wakefulness. A key strength observed was the sleep mat’s consistent performance across various subgroups. The accuracy of sleep-wake detection remained stable regardless of participant age, BMI, sex, mattress type, mattress thickness, sleep quality or the presence of a bed partner.

 

Challenges emerged in the classification of specific sleep stages. The sensor’s mean accuracy for staging sleep was 63%, with a Cohen’s Kappa of 0.49. The primary difficulty was in distinguishing between light and deep sleep. This led to systematic biases in sleep duration estimates; the device tended to slightly overestimate total sleep time by an average of 20 minutes but substantially overestimated light sleep by 1 hour and 21 minutes. Conversely, it moderately underestimated REM sleep by 15 minutes and deep sleep by a more significant 46 minutes.

 

Notably, a notable proportion of misclassifications made by the sensor mirrored disagreements found between the expert human reviewers who scored the PSG data, especially concerning the boundary between light and deep sleep. Furthermore, participants reported that their perceived sleep quality was significantly altered for the worse on the night they used the PSG equipment, highlighting the intrusive nature of the gold standard itself.

In a comparative context, the Withings Sleep Analyzer exhibits highly competitive performance in sleep-wake discrimination relative to other devices on the market. For the more nuanced task of sleep stage classification, its accuracy is comparable to that of similar products. This level of performance is particularly noteworthy given the systemic challenges in sleep staging.

 

Conclusion

For individuals seeking to understand their sleep over weeks and months, the primary benefit of a device like the Withings Sleep Analyzer lies in its practicality. Its contactless, ‘set-and-forget’ nature eliminates the nightly burden of wearing a device and avoids the discomfort that can disrupt sleep, a notable issue even with the clinical gold standard. While the sensor’s accuracy in distinguishing specific sleep stages requires further refinement, its strong performance in tracking overall sleep and wake times provides reliable insights into sleep duration and consistency. This capability for accessible, unobtrusive, and longitudinal monitoring is where at-home sensors currently provide the most value, empowering users with meaningful data on their long-term sleep trends.

 

Poster Session: Time and Location

“Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection against Home Polysomnography in a Healthy Population”

 

Session Title: Poster abstract group 2

 

Session Date: Monday, September 8, 2025

 

Presentation Time: 6:00pm to 7:00pm (Presenting authors will be present near their assigned poster board throughout the scheduled one-hour presentation window.)

 

Poster Board Number: 531

 

Location: Posters will be displayed in the exhibit hall on Level 4 and accessible during regular congress hours.

About Marie-Ange Stefanos

Marie-Ange Stefanos is a  Machine Learning Research Scientist and a PhD candidate pursuing a joint doctorate in Computer Science and Neuroscience from Université Paris Cité (France) and Reykjavik University (Iceland). Building on her background with an Engineering degree in Signal Processing from Grenoble INP – Phelma and an M.Sc. in Machine Learning from KTH Royal Institute of Technology, her path into health research was driven by a central question: how can my technical background be best applied to solve meaningful challenges in human health?

 

Her doctoral research focuses on insomnia, where she develops algorithms using data from wearables and self-reports to identify predictive biomarkers and differentiate subtypes of the disorder. This work depends entirely on data integrity, which is why she believes the rigorous validation of consumer devices, as discussed in this article, is the essential first step in translating complex signals into reliable, actionable insights for users.

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The recent study, "Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea" by Bastien Lechat and colleagues, sheds light on the limitations of single-night assessments in diagnosing obstructive sleep apnea (OSA).

 

OSA is a common sleep disorder in which breathing repeatedly stops and starts throughout the night. It's typically diagnosed using a sleep study that measures the apnea-hypopnea index (AHI)—the number of times per hour that a person experiences reduced or blocked airflow. Current clinical practice to diagnose OSA requires a single, overnight, in-laboratory or in-home polysomnography or polygraphy study.

 

The researchers looked at data from over 67,278 individuals aged 18 to 90 who had used the Withings sleep mat, a non-invasive, under-mattress sensor. In addition to the vast number of participants, the researchers were able to monitor an average of approximately 170 nights of data per participant between July 2020 and March 2021. This extensive data collection resulted in more than 11.6 million nights of sleep data–the largest standardized, objective assessment of OSA collected to date.

 

The study found that the global prevalence of moderate to severe OSA, defined as a nightly mean apnea-hypopnea index (AHI) of more than 15 events per hour, was 22.6% (95% confidence interval: 20.9–24.3%). Notably, the research highlighted significant night-to-night variability in OSA severity. When relying on a single night's data, the likelihood of misdiagnosis ranged from approximately 20% and up to 50% for those with mild to moderate OSA. Increasing the number of monitoring nights improved diagnostic accuracy, with misdiagnosis error rates decreasing and stabilizing after 14 nights of monitoring.

 

The substantial variability of OSA suggests that single-night assessments may not provide a comprehensive picture of an individual's sleep patterns, leading to possible misclassifications. However, fourteen nights of polysomnography in a sleep lab or clinic is not feasible. Tools like the Withings sleep mat offer a non-invasive, cost-effective means of multinight in-home monitoring leading to more accurate and reliable diagnosis of OSA. By offering an accessible way to capture more nights of sleep data, clinicians can reduce misdiagnosis and create a shift in how sleep disorders are screened for and tracked.

References
  1. Lechat, Bastien et al. “Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.” American journal of respiratory and critical care medicine vol. 205,5 (2022): 563-569. doi:10.1164/rccm.202107-1761OC

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Contact Us [post_title] => A Single-Night Sleep Study Can Misdiagnose Sleep Apnea Up to Half the Time [post_excerpt] => New large-scale, longitudinal studies show that monitoring sleep for at least 14 nights at home with a connected sleep tracking mat, can reduce the 30% high error rate of sleep apnea diagnosis from the usual single-night, in hospital polysomnography (PSG) technique. This technology can identify which patients most need expensive, intrusive, and difficult to access PSG. Longitudinal data also detects variability of Obstructive Sleep Apnea from night to night which is associated with hypertension [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => a-single-night-sleep-study-can-misdiagnose-sleep-apnea-up-to-half-the-time [to_ping] => [pinged] => [post_modified] => 2025-03-13 13:57:56 [post_modified_gmt] => 2025-03-13 13:57:56 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=1852 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 1845 [post_author] => 11 [post_date] => 2025-03-13 13:25:51 [post_date_gmt] => 2025-03-13 13:25:51 [post_content] =>

Professor Danny Eckert of the Adelaide Sleep Institute at Flinders University didn’t set out to be one of the world’s foremost sleep experts. He started as an elite cyclist, representing Australia at the World Championships. Wanting to use his head as well as his body, he studied exercise physiology and sports science in the afternoons, trained on his mountain bike in the mornings, raced competitively, and presumably left some time for sleep. After receiving his degree, he was offered a prestigious appointment with the South Australia Sports Institute. 

 

But on the Friday before he was due to start his internship, he was also offered a 3-year position with a respiratory and sleep unit in a hospital. When he reported for his new job at the Sports Institute on Monday, he immediately asked his new boss if he could take a walk to consider his options, came back, and left for a field he knew nothing about. “It turned out that everything I’d learned about the body being at its most vulnerable and physiologically interesting when you push it as hard as you can during exercise is also true when you are sleeping and the body is at its quietest.”

 

Professor Eckert was on fertile ground for his newly chosen field. Australia has long been a leader and innovator in sleep science, especially in respiratory medicine. When he started studying sleep apnea, the research centered on anatomy: the collapse of the upper airway, especially due to obesity. As such, treatments such as Continuous Positive Airway Pressure, (CPAP, invented in Australia), mouthguards, and surgery were designed to open the airway.

 

Professor Eckert’s groundbreaking work changed nearly everything we thought we knew about sleep apnea. Importantly, he identified three nonanatomical endotypes that can cause sleep apnea. Between them, they account for 70% of sleep apnea cases. 

 

1) Low Arousal Threshold: Light sleepers who wake up when their muscles relax. 

2) Instability in Breathing Control: Sleepers who are too sensitive to minor changes in carbon dioxide. Their overactive respiratory system turns off their drive to breathe.

3) Poor Muscle Responsiveness to the Narrowing Airway

 

Simplified, scalable diagnostic tools and machine learning can identify the endotype(s) that causes a particular patient’s sleep apnea. Endotypes can inform targeted therapies and lead to an 80% success rate for treating sleep apnea. This individualized, precision medicine approach includes emerging pharmaco-therapies, such as drugs that can activate the muscles, or strategies, such as cognitive behavioral therapy, that can help light sleepers. 

 

What’s next? With 70 researchers working on 40-50 studies at any one time at the Adelaide Sleep Institute, there are many exciting developments to come. One area of interest is sleep irregularity. Researchers have found varying bedtime by even 30 minutes throughout the week is associated with a 30% higher chance of hypertension; vary by 90 minutes and increase the odds by 90%, regardless of the total amount of sleep. 

 

New technologies that allow longitudinal data are especially useful for studying sleep variability. Combining sleep data with measures like body fat, step counts, and blood pressure will allow for quicker treatment changes, better monitoring of treatment effectiveness, and reach many more people who suffer from sleep disorders.

 

The three pillars of health are exercise, diet, and sleep, but sleep directly affects the other two. Not getting sufficient or quality sleep makes us crave bad food and makes it hard to exercise. That’s why Dr. Eckert believes sleep is the foundation for optimal health. Through research and scientific and technological breakthroughs, Professor Eckert thinks we are at the precipice of transforming the entire sleep field. That’s a comforting thought for the billions of people around the globe desperate for a good night’s sleep.

Interested in partnering with us?

Contact Us [post_title] => From Bicycles to Sleep Cycles: How Professor Danny Eckert’s Pursuit of Peak Performance Led to Major Advances in our Understanding of Sleep [post_excerpt] => Professor Danny Eckert of the Adelaide Sleep Institute at Flinders University didn’t set out to be one of the world’s foremost sleep experts, but he has changed nearly everything we know about sleep apnea. [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => from-bicycles-to-sleep-cycles-how-professor-danny-eckerts-pursuit-of-peak-performance-led-to-major-advances-in-our-understanding-of-sleep [to_ping] => [pinged] => [post_modified] => 2025-03-13 13:25:53 [post_modified_gmt] => 2025-03-13 13:25:53 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=1845 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 1729 [post_author] => 11 [post_date] => 2024-12-09 16:36:44 [post_date_gmt] => 2024-12-09 16:36:44 [post_content] =>

Several new, large-scale studies1, 2, 3, 4 from the Flinders Medical Research Institute (FHMRI) in Australia found studying sleep in real-world settings over multiple nights can greatly reduce the high 30% error rate of sleep apnea diagnosis from polysomnography (PSG), the traditional, single night, in hospital gold standard technique. Using Withings Sleep Analyzer, researchers were able to easily track multiple biomarkers for participants over time revealing large variability in sleep indicators from night to night.

 

Withings under-the-mattress sleep trackers have enabled researchers to study large groups over time. The FHMRI studies tracked 67,278 and 12,287 participants respectively over a total of 11 million nights, a feat not feasible with traditional polysomnography.

 

A key finding from the study of 12,000 users is the variability in the severity of Obstructive Sleep Apnea (OSA) from night to night. The variability of OSA, independent of severity, is associated with uncontrolled hypertension which is the leading cardiovascular risk factor. Sleep Analyzer also reveals other risks associated with hypertension, such as snoring, irregular waking and sleep hours, and duration of sleep.

 

Using the same hardware technology as Withings Sleep Analyzer, Sleep Rx is a noninvasive, at home device that users place under their mattresses to gather biomarkers such as heart rate, respiratory rate, snoring, sleep cycles, and the Withings Sleep Index, a measure of breathing events per hour, which can aid in the diagnosis of sleep apnea. Using this simple device for at least 14 nights gives a much clearer picture of sleep quality.

 

The multi-night Sleep Rx data can be used to predict the right patients at the right time for in hospital PSG. Better identification of patients who most need PSG will reduce overall spending on the costly tests and ease scheduling difficulties.

 

Sleep Rx offers an inexpensive, easy to use method to better target high risk cardiovascular patients, reduce the high error rate of sleep apnea diagnosis, and efficiently gather longitudinal, large-scale sleep data for a variety of chronic diseases. For more information about the Withings Sleep Rx, click here.

References

1 Lechat, Bastien et al. “Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.” American journal of respiratory and critical care medicine vol. 205,5 (2022): 563-569. doi:10.1164/rccm.202107-1761OC


2 Lechat, Bastien et al. “High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension.” NPJ digital medicine vol. 6,1 57. 30 Mar. 2023, doi:10.1038/s41746-023-00801-2

3 Lechat, Bastien et al. “Regular snoring is associated with uncontrolled hypertension.” NPJ digital medicine vol. 7,1 38. 17 Feb. 2024, doi:10.1038/s41746-024-01026-7

4 Scott, Hannah et al. “Sleep Irregularity Is Associated With Hypertension: Findings From Over 2 Million Nights With a Large Global Population Sample.” Hypertension (Dallas, Tex. : 1979) vol. 80,5 (2023): 1117-1126. doi:10.1161/HYPERTENSIONAHA.122.20513

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Contact Us [post_title] => Sleep Apnea Data from Multiple Nights is Key to Predicting Hypertension and Cardiovascular Risk [post_excerpt] => New large-scale, longitudinal studies show that monitoring sleep for at least 14 nights at home with a connected sleep tracking mat, can reduce the 30% high error rate of sleep apnea diagnosis from the usual single-night, in hospital polysomnography (PSG) technique. This technology can identify which patients most need expensive, intrusive, and difficult to access PSG. Longitudinal data also detects variability of Obstructive Sleep Apnea from night to night which is associated with hypertension [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => sleep-apnea-data-from-multiple-nights-is-key-to-predicting-hypertension-and-cardiovascular-risk [to_ping] => [pinged] => [post_modified] => 2024-12-10 18:14:34 [post_modified_gmt] => 2024-12-10 18:14:34 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=1729 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 3 [current_post] => -1 [before_loop] => 1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 1852 [post_author] => 11 [post_date] => 2025-03-11 20:01:38 [post_date_gmt] => 2025-03-11 20:01:38 [post_content] =>

The recent study, "Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea" by Bastien Lechat and colleagues, sheds light on the limitations of single-night assessments in diagnosing obstructive sleep apnea (OSA).

 

OSA is a common sleep disorder in which breathing repeatedly stops and starts throughout the night. It's typically diagnosed using a sleep study that measures the apnea-hypopnea index (AHI)—the number of times per hour that a person experiences reduced or blocked airflow. Current clinical practice to diagnose OSA requires a single, overnight, in-laboratory or in-home polysomnography or polygraphy study.

 

The researchers looked at data from over 67,278 individuals aged 18 to 90 who had used the Withings sleep mat, a non-invasive, under-mattress sensor. In addition to the vast number of participants, the researchers were able to monitor an average of approximately 170 nights of data per participant between July 2020 and March 2021. This extensive data collection resulted in more than 11.6 million nights of sleep data–the largest standardized, objective assessment of OSA collected to date.

 

The study found that the global prevalence of moderate to severe OSA, defined as a nightly mean apnea-hypopnea index (AHI) of more than 15 events per hour, was 22.6% (95% confidence interval: 20.9–24.3%). Notably, the research highlighted significant night-to-night variability in OSA severity. When relying on a single night's data, the likelihood of misdiagnosis ranged from approximately 20% and up to 50% for those with mild to moderate OSA. Increasing the number of monitoring nights improved diagnostic accuracy, with misdiagnosis error rates decreasing and stabilizing after 14 nights of monitoring.

 

The substantial variability of OSA suggests that single-night assessments may not provide a comprehensive picture of an individual's sleep patterns, leading to possible misclassifications. However, fourteen nights of polysomnography in a sleep lab or clinic is not feasible. Tools like the Withings sleep mat offer a non-invasive, cost-effective means of multinight in-home monitoring leading to more accurate and reliable diagnosis of OSA. By offering an accessible way to capture more nights of sleep data, clinicians can reduce misdiagnosis and create a shift in how sleep disorders are screened for and tracked.

References
  1. Lechat, Bastien et al. “Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.” American journal of respiratory and critical care medicine vol. 205,5 (2022): 563-569. doi:10.1164/rccm.202107-1761OC

Interested in partnering with us?

Contact Us [post_title] => A Single-Night Sleep Study Can Misdiagnose Sleep Apnea Up to Half the Time [post_excerpt] => New large-scale, longitudinal studies show that monitoring sleep for at least 14 nights at home with a connected sleep tracking mat, can reduce the 30% high error rate of sleep apnea diagnosis from the usual single-night, in hospital polysomnography (PSG) technique. This technology can identify which patients most need expensive, intrusive, and difficult to access PSG. Longitudinal data also detects variability of Obstructive Sleep Apnea from night to night which is associated with hypertension [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => a-single-night-sleep-study-can-misdiagnose-sleep-apnea-up-to-half-the-time [to_ping] => [pinged] => [post_modified] => 2025-03-13 13:57:56 [post_modified_gmt] => 2025-03-13 13:57:56 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=1852 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [comment_count] => 0 [current_comment] => -1 [found_posts] => 3 [max_num_pages] => 1 [max_num_comment_pages] => 0 [is_single] => [is_preview] => [is_page] => [is_archive] => [is_date] => [is_year] => [is_month] => [is_day] => [is_time] => [is_author] => [is_category] => [is_tag] => [is_tax] => [is_search] => [is_feed] => [is_comment_feed] => [is_trackback] => [is_home] => 1 [is_privacy_policy] => [is_404] => [is_embed] => [is_paged] => [is_admin] => [is_attachment] => [is_singular] => [is_robots] => [is_favicon] => [is_posts_page] => [is_post_type_archive] => [query_vars_hash:WP_Query:private] => d44b95c08ff7881ec18235673b80e4b9 [query_vars_changed:WP_Query:private] => [thumbnails_cached] => [allow_query_attachment_by_filename:protected] => [stopwords:WP_Query:private] => [compat_fields:WP_Query:private] => Array ( [0] => query_vars_hash [1] => query_vars_changed ) [compat_methods:WP_Query:private] => Array ( [0] => init_query_flags [1] => parse_tax_query ) [query_cache_key:WP_Query:private] => wp_query:63528008720b193a4aedf2a232bb2896:0.76130800 1764852881 )
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