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How To Build Well-Rounded Team For Your RPM Program

6 min read

Remote patient monitoring (RPM) programs are beneficial for both patients and medical practices. But an RPM initiative’s success depends on the people behind the program. In this article, we look at why RPM programs have become so popular and the roles and responsibilities of an RPM team. To close, we share five easy-to-implement strategies for creating a strong RPM program.

Why RPM is Growing

The adoption rate of remote patient monitoring has grown dramatically in recent years. According to Insider Intelligence, 30 million patients in the U.S. will be using at least one RPM device by 2024. Here are six significant catalysts driving the rapid adoption rates of RPM technology.

COVID-19 pandemic

With much of the country brought to a standstill, the COVID-19 pandemic rapidly accelerated already emerging trends in virtual care. More insurers began covering telehealth services, dramatically increasing adoption. Remote patient monitoring devices allow medical professionals to monitor their patients’ key health metrics without having them come into the office, making RPM a significant value-add to a practice’s telehealth services.

Increase in chronic conditions such as diabetes

As the rates of long-term conditions such as heart disease, diabetes and obesity continue to rise in the U.S., remote patient monitoring devices empower patients and providers. RPM devices actively monitor key health metrics like blood pressure and weight, and the devices can be set to send data to the patient’s physician. These capabilities give patients and providers the longitudinal information they need to make changes that improve health outcomes.

Convenience

Work, family, and other obligations can quickly crowd out a schedule. RPM devices make it easy for patients to quickly take vital measures like blood pressure and weight. These measures can be automatically transmitted to the healthcare provider via cellular, WiFi, or Bluetooth connection and are instantly accessible to the patient via an easy-to-use mobile app.

Higher levels of patient care

Remote patient monitoring devices enable more frequent health measurements. With access to near real-time health data and the ability to observe historical trends in this data, healthcare providers can make better-informed care recommendations and treatment decisions.

Expand care to more diverse patient groups

For many patients, work schedules, child care or eldercare obligations, distance, or lack of reliable transportation are formidable barriers to receiving care. Low income, elderly, and patients living in underserved or rural areas can receive outsized benefits from participating in a remote patient monitoring program since it can reduce the number of office visits needed.

New revenue streams for healthcare practices

Because many RPM services are billable, RPM programs are a source of additional revenue, allowing practices to tap into new revenue streams that can improve overall profitability.

Key RPM Program Team Members

Building a strong, sustainable remote patient monitoring program requires teamwork and a shared vision. The following team members form the core of a successful remote patient monitoring program.

Top-level management

Beginning any new initiative requires the strong support and buy-in of top-level management. Healthcare executives play a key role in securing funding and staff resources, as well as providing the long-range vision needed to set a new remote patient monitoring program on a secure footing.

Physicians

Physicians can reap substantial benefits from using remote patient monitoring technology with their patients. With a wealth of patient health data, providers can make better-informed treatment decisions. Educating providers on benefits, obtaining their buy-in, and offering focused training on how to access remote patient monitoring data are essential for success.

IT support

The support of the IT department is crucial for ensuring that the technical aspects of creating and running a remote patient monitoring program are executed. Some remote patient monitoring providers like Withings Health Solutions handle most of the IT-related setup, freeing your tech support staff to focus their efforts elsewhere.

Patient navigator

Patient navigators help patients access and use their devices, troubleshooting issues that prevent them from making the most out of participating in a remote patient monitoring program. These staff members are one of the most critical components, providing front-line support and encouragement to patients as they become familiar with their new devices. Some connected devices, like those Withings Health Solutions offers, are intuitive and easy to use, making the jobs of patient navigators much simpler.

Digital health staff trainer

Staff trainers are responsible for training physicians and other practice staff involved in the program’s administration. Key topics for training include how to access and interpret patient data via the physician data dashboard and the correct use of RPM billing codes for payer reimbursement.

Medical billing representative

Medicare and many private insurers will pay for certain patient services provided via remote patient monitoring programs. Remote patient monitoring programs have their own unique set of billing codes, and the medical billing representative will ensure that the correct codes are being used on all claims filed.

Program coordinator

The remote patient monitoring program coordinator is responsible for overseeing the overall health of the program, managing the interactions between team members, troubleshooting patient and provider issues, and assessing program success based on predefined goals.

5 Tips for creating a stronger, more sustainable RPM program

Remote patient monitoring programs require careful planning and diligent follow-through during implementation in order for patients, providers, and practices to realize the full range of potential benefits this technology has to offer. These five tips can help you and the patients you serve get the most out of remote patient monitoring.

Clarify RPM program goals

First, defining what expect your remote patient monitoring program to accomplish. Clear goals not only sharpen the team’s focus but provide an objective set of criteria with which to measure program success.

Quantify expected cost/revenue metrics

Remote patient monitoring programs must be financially sustainable. Quantifying anticipated program costs and revenue enables teams to determine expected ROI. These metrics can justify the time and capital investments required for a successful launch. RPM can reduce overall costs, as it enables physicians to take preventative measurements before a condition becomes severe.

Engage the support of internal stakeholders

Top-level management, providers, and office support staff all have an important role to play in the success of a remote patient monitoring program. Gaining their support upfront is critical. If stakeholders aren’t engaged, program performance will suffer.

Invest the resources needed for program success

Engaging key personnel is an essential ingredient for ensuring long-term success, so additional staff members may need to be hired. Dedicating a program manager to oversee the effort ensures program continuity and provides a single point of contact for other team members when issues arise. Patient navigators are front-line workers who ensure patients are familiar with their devices and responsible for removing barriers to consistent use. Fortunately, the cost of hiring may be offset by billing and reimbursement for RPM services and/or lowered costs if the practice is part of a value-based care system.

Select a high-quality RPM provider to partner with

Some remote patient monitoring providers are better than others. Invest the time to research each potential partner, ensuring they offer higher quality, patient-friendly devices that are easy to use, an intuitive patient-facing health data app, and a streamlined practice-level program dashboard. The ideal RPM provider will have substantial industry experience and a track record of providing superior customer service.

Withings Health Solutions and Withings RPM: an All-in-One RPM Solution

Withings Health Solutions is committed to transforming the lives of healthcare consumers and professionals through beautifully simple monitoring solutions. Here’s how we make implementing a successful remote patient monitoring program seamlessly simple.

Effortless onboarding — Onboard your patients directly from the platform in 3 minutes or less.

Time-efficient monitoring — You’ll appreciate the one-click patient triaging through standard alerts and measurement plans.

Automatic time logging — Time spent taking care of your patients (when reviewing their charts or calling them) is automatically documented to streamline claim creation while maximizing revenue. Time spent outside the platform can easily be manually logged as well.

Optimized billing — In real-time, assess how much a CPT code can be billed and identify which patients to focus on. At the end of the month, generate comprehensive reports in one click to create claims including the CMS1500 information required for the billing.

Program dashboard — Manage your practice at a glance. A comprehensive dashboard allows care teams to view patients’ status, take action based on patient vitals and treatment plans, and easily bill for care rendered.

Patient application — The patient application allows the patient to visualize their measurements history, progress, and measurements objective. Login is simple and doesn’t require any signup or app download.

EHR integration — Withings RPM integrates with virtually every EHR to improve each step of the clinical workflow from device ordering to billing.

Learn more about Withings Health Solutions for remote patient monitoring.

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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.

Interested in partnering with us?

Contact Us [post_title] => The Promise and Pitfalls of At-Home Sleep Tracking: A Deep Dive into the Withings Sleep Analyzer [post_excerpt] => Chronic Kidney Disease stage 5 on dialysis (CKD5D) presents one of the most complex and high-risk scenarios in modern medicine.But what if technology could help bridge the gap between dialysis sessions, offering clinicians a window into the patient's health in real-time? [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => the-promise-and-pitfalls-of-at-home-sleep-tracking-a-deep-dive-into-the-withings-sleep-analyzer [to_ping] => [pinged] => [post_modified] => 2025-09-02 16:49:48 [post_modified_gmt] => 2025-09-02 16:49:48 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=2034 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 2015 [post_author] => 11 [post_date] => 2025-06-12 15:39:12 [post_date_gmt] => 2025-06-12 15:39:12 [post_content] =>

Chronic Kidney Disease stage 5 on dialysis (CKD5D) presents one of the most complex and high-risk scenarios in modern medicine. Among the many challenges faced by these patients, cardiovascular disease (CVD) stands out as the leading cause of mortality—a stark reminder of the systemic stress that accompanies kidney failure and dialysis.

 

But what if technology could help bridge the gap between dialysis sessions, offering clinicians a window into the patient's health in real-time? An article in Frontiers in Nephrology explores exactly that—highlighting the transformative potential of digital health technologies to monitor and manage CKD5D patients beyond the clinic.

 

The Hidden Risks Between Dialysis Sessions

For CKD5D patients, the risks of CVD are amplified by both traditional and disease-specific factors:

 

  • Traditional risks like hypertension, diabetes, and obesity.

  • CKD-specific risks such as inflammation, fluid overload, protein-energy wasting and vascular calcification.

  • The dialysis process itself, which induces rapid fluid shifts, blood pressure fluctuations, and metabolic imbalances.

Current clinical care models often focus on in-center dialysis data, leaving a crucial blind spot during the interdialytic period—a time when many adverse events begin to develop unnoticed.

 

A New Monitoring Paradigm: The Withings Toolkit

The article introduces a compelling case for home-based, connected health technologies—specifically, the Withings toolkit. This suite of medical-grade, consumer-friendly devices allows CKD patients to monitor key health indicators in the comfort of their homes:

 

  • Weight, body composition and ECG monitoring with the BodyScan smart scale.

  • Blood pressure, heart rate and survey responses for added context via BPM Pro 2.

  • Sleep quality and breathing event metrics using the Sleep Rx.

All data is seamlessly uploaded to the Withings Remote Patient Monitoring platform, providing healthcare providers and researchers with real-time, longitudinal insights into a patient’s well-being.

 

Why This Matters: Real-World Clinical Benefits

1. Early Detection of Complications

Weight gain could signal fluid retention, but muscle loss could indicate protein-energy wasting. A sudden spike in blood pressure or irregular heartbeat might indicate arrhythmias or volume overload. Poor sleep patterns could reflect apnea or restless leg syndrome—conditions with known ties to CKD.

2. Personalized, Data-Driven Care

These devices enable a dynamic view of health trends, allowing clinicians to tailor treatments proactively rather than reactively. Medication adjustments, fluid restrictions, or further diagnostics can be made with greater confidence.

3. Patient Empowerment

When patients can see and understand their own data, they become more engaged in their care. This promotes better self-management, increased treatment adherence, and a stronger sense of control over their condition.

4. Systemic Healthcare Advantages

Remote monitoring can reduce emergency visits and hospitalizations, easing the burden on overtaxed healthcare systems and offering a cost-effective alternative to frequent in-person evaluations.

 

The Future: Digital Tools as Standard of Care?

While still in its early stages, this integration of digital health into CKD care reflects a broader movement toward remote, preventative, and personalized medicine. The Withings case study serves as a promising example of how everyday technology can be adapted to serve complex clinical needs.

 

However, as the authors note, more clinical trials are needed to validate these tools in nephrology settings, establish protocols for data use, and ensure equitable access across diverse patient populations.

 

Final Thoughts

As we face growing rates of kidney disease and limited nephrology resources, connected health technologies offer a lifeline—not just to patients, but to an entire care infrastructure in need of modernization.

 

The Withings toolkit is more than a gadget suite; it's a glimpse into the future of chronic disease management, where data flows continuously, care is adaptive, and patients are active participants in their own health journey.

 

References
 Article: Frontiers in Nephrology, 2023 - DOI: 10.3389/fneph.2023.1148565

Interested in partnering with us?

Contact Us [post_title] => Revolutionizing Chronic Kidney Disease Management with Digital Health Tools: The Withings Case Study [post_excerpt] => Chronic Kidney Disease stage 5 on dialysis (CKD5D) presents one of the most complex and high-risk scenarios in modern medicine.But what if technology could help bridge the gap between dialysis sessions, offering clinicians a window into the patient's health in real-time? [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => revolutionizing-chronic-kidney-disease-management-with-digital-health-tools-the-withings-case-study [to_ping] => [pinged] => [post_modified] => 2025-06-12 15:41:31 [post_modified_gmt] => 2025-06-12 15:41:31 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=2015 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 2012 [post_author] => 11 [post_date] => 2025-06-03 19:17:23 [post_date_gmt] => 2025-06-03 19:17:23 [post_content] =>

Introduction

 

Diabetic foot ulcers (DFUs) are a major and often debilitating complication of diabetes, contributing significantly to patient morbidity, mortality, and healthcare costs. Despite advancements in diabetes care, the incidence of DFUs remains high, with a substantial impact on quality of life and healthcare resources. A recent study published in the journal Frontiers in Endocrinology compared the use of electrochemical skin conductance (ESC) to the current standards in DFU detection. The current method for assessing DFU risk primarily involves clinical examination, including the monofilament test, which is subjective and dependent on the examiner’s skills. Therefore, there is a need for objective, reproducible, and reliable methods for early detection of at-risk patients.

 

One of the many complications of diabetes is peripheral neuropathy, which, if left untreated, can lead to DFUs. Electrochemical Skin Conductance (ESC) is a promising non-invasive diagnostic tool that can be used to assess autonomic nerve activity. ESC is measured in-clinic using Sudoscan, which assesses small fiber peripheral neuropathies, specifically the innervation around the sweat glands, by stimulating the glands and measuring the conductance (in µS) of chloride ions contained in the sweat. Lower ESC values indicate more severe neuropathy. This study investigates the association between ESC and DFU risk stratification, offering a potential new approach to managing and preventing diabetic foot complications.

Methods

 

This study was a retrospective analysis involving 2,149 diabetic patients from four clinics in Greater Paris University Hospitals, the largest hospital system in Europe and one of the largest in the world. The primary aim was to evaluate the relationship between ESC measurements and DFU risk, as classified using the 2016 International Working Group on Diabetic Foot (IWGDF) grading system. This grading system assigns DFU risk based on clinical evaluation, including the presence of neuropathy, ulceration, and other factors.

To assess the predictive performance of ESC in DFU risk stratification, the study incorporated a range of factors: age, sex, type of diabetes, and results from the monofilament test, which is a standard assessment of peripheral neuropathy. The study employed regression and Receiver Operating Characteristic (ROC) analyses to explore the predictive value of ESC measurements for different DFU risk categories.

 

Results

 

The study revealed a significant correlation between ESC values and DFU risk grades (p<0.001). Specifically, lower FESC values were associated with higher grades of DFU risk, suggesting that reduced sweat gland function, indicative of small fiber neuropathy, plays a role in the progression of foot ulcers in diabetic patients.

 

One of the most noteworthy findings of this study was that ESC measurements were able to identify patients at risk for DFUs who would not have been classified as high risk using the standard IWGDF grading system. Specifically, ESC detected autonomic dysfunction and small fiber nerve involvement in 43% patients classified as grade 0 (13% with severe cases of neuropathy), who otherwise showed no obvious signs of risk through traditional assessments, showing better granularity in the lower grades for better risk stratification.

 

The findings of this study suggest that Electrochemical Skin Conductance (ESC) provides a valuable, reproducible, and operator-independent tool for assessing DFU risk. ESC measurements offer an objective method for identifying early signs of small fiber neuropathy, a critical factor in the development of DFUs. Unlike traditional risk stratification, which relies heavily on clinical judgment and may overlook early-stage neuropathy, ESC can detect subtle changes in nerve function that precede visible foot ulcers.

 

The ability of ESC to detect at-risk patients in the grade 0 category, who would otherwise be overlooked by conventional classification methods, highlights its potential role in preventing DFUs. By identifying patients with early-stage nerve dysfunction, ESC could facilitate earlier intervention, potentially reducing the incidence of foot ulcers, amputations, and associated healthcare costs.

 

The ability to detect DFU risk early using ESC shows promise for the prevention of amputation.Therefore, we conclude that feet skin conductance is a relevant parameter for detecting diabetic foot syndrome, specifically at an early stage when there is still no presence of feet ulceration or wounds. A recent meta-analysis on ESC supports this conclusion, indicating that ESC, when combined with temperature measurements, serves as a valuable tool for the early detection of diabetic foot syndrome. ESC can be measured in-clinic, using Sudoscan, and at home using Withings Body Pro 2. Measuring ESC through home use of the Body Pro 2 scale allows for additional data collection and better assessment of trends and progression between appointments. Through this enhanced monitoring of DFU risk, care teams can better risk-stratify and provide targeted care that could prevent amputations and complications.

Interested in partnering with us?

Contact Us [post_title] => Electrochemical Skin Conductance as a Novel Tool for Diabetic Foot Ulcer Risk Stratification and Prevention [post_excerpt] => Diabetic foot ulcers (DFUs) are a major and often debilitating complication of diabetes, contributing significantly to patient morbidity, mortality, and healthcare costs.Electrochemical Skin Conductance (ESC) is a promising non-invasive diagnostic tool that can be used to assess autonomic nerve activity. [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => electrochemical-skin-conductance-as-a-novel-tool-for-diabetic-foot-ulcer-risk-stratification-and-prevention [to_ping] => [pinged] => [post_modified] => 2025-06-16 13:31:21 [post_modified_gmt] => 2025-06-16 13:31:21 [post_content_filtered] => [post_parent] => 0 [guid] => https://withingshealthsolutions.com/?p=2012 [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] => 2034 [post_author] => 11 [post_date] => 2025-09-02 16:47:55 [post_date_gmt] => 2025-09-02 16:47:55 [post_content] =>

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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Withings On-The-Go

Our patient-centric care solution utilizes portable Withings cellular devices that are not tied to a single patient. Instead, care teams can use one device to collect and transmit data for an unlimited number of individuals. The integrated cellular connectivity automatically directs the data into the correct patient’s medical record, simplifying data collection and improving care delivery regardless of the setting.
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