The Emergence of the 'Internet of Health' (IoH)

Nelson Advisors
2 hours ago
17 min read

'Internet of Health' to breakthrough in 2026

Introduction: The Emergence of the Internet of Health (IoH)

The healthcare sector is currently witnessing a paradigm shift of historical magnitude, transitioning from a reactive, hospital-centric model to a proactive, continuous, and patient-centric ecosystem. At the heart of this transformation lies the "Internet of Health" (IoH), a term that describes the convergence of consumer electronics, clinical diagnostics and cloud computing.

Unlike the broader Internet of Things (IoT), which encompasses everything from smart refrigerators to industrial sensors, or the hospital-bound Internet of Medical Things (IoMT), the IoH represents a distinct domain where patient-generated health data (PGHD) from consumer wearables, smartwatches, smart rings, connected scales and blood pressure cuffs, is elevated to the status of clinical evidence.

The trajectory of this domain is defined by a struggle for legitimacy. For over a decade, consumer wearables were dismissed by the medical establishment as "fitness gadgets", toys that produced noisy, unreliable data that served only to induce anxiety in the "worried well" and overload clinicians with irrelevant information. However, recent technological advancements, rigorous clinical validation studies, and evolving regulatory frameworks are dismantling this skepticism. Devices that once only counted steps are now FDA-cleared to detect atrial fibrillation (AFib), monitor sleep apnea, and estimate arterial stiffness.

This report provides an analysis of the Internet of Health ecosystem. It explores the technical architectures enabling this shift, the regulatory pathways validating these tools, the economic models sustaining them, and the profound legal and ethical challenges that arise when a consumer device becomes a medical monitor.

Defining the Domain: IoT, IoMT and IoH

To understand the Internet of Health, one must first delineate it from its technological predecessors. The concept of the Internet of Things (IoT) was coined by Kevin Ashton in 1999 to describe a network of physical objects embedded with sensors and connectivity. In the healthcare context, this evolved into the Internet of Medical Things (IoMT), which traditionally refers to the network of medical devices and applications used in healthcare IT systems.

The IoMT is typically characterised by:

Institutional Ownership: Devices like MRI machines, infusion pumps, and hospital bed sensors are owned and managed by healthcare providers.
Closed Loops: Data often flows within proprietary hospital networks or dedicated servers.
High Acuity: The devices support direct clinical interventions, such as robotic surgery assistants or implantable cardioverter-defibrillators (ICDs).

In contrast, the Internet of Health (IoH), sometimes referred to as the Internet of Healthy Things (IoHT)—bridges the gap between the consumer and the clinic. It leverages consumer-facing hardware to capture physiological data outside the clinical setting. The IoH is predicated on the "quantified self" maturing into the "medically monitored self," where the smartphone acts as a gateway to transmit vital signs, heart rate variability (HRV), blood oxygen (SpO2), and electrocardiograms (ECG), to providers.

Comparative Taxonomy of Connected Health Ecosystems

Feature	Internet of Things (IoT)	Internet of Medical Things (IoMT)	Internet of Health (IoH)
Primary User	General Consumer / Industry	Clinicians / Hospitals	Patients / Consumers & Clinicians
Primary Device	Smart Home, Industrial Sensors	Infusion Pumps, MRI, Pacemakers	Smartwatches, Oura Rings, Withings Scales
Data Utility	Convenience / Automation	Diagnosis / Active Treatment	Prevention / Chronic Management / Wellness
Regulation	FCC / CE (Radio/Safety)	FDA Class II/III, MDR Class IIa/IIb/III	FDA Class II (De Novo/510k) or General Wellness
Data Flow	Device -> Cloud -> User App	Device -> Hospital Server -> EHR	Device -> Cloud -> Middleware -> EHR
Connectivity	Wi-Fi, Zigbee, Z-Wave	Proprietary RF, Wi-Fi, Ethernet	Bluetooth (BLE), Cellular, NFC

The operational distinction is critical. While IoMT devices are designed for "high-stakes" environments where failure can be fatal (eg. an infusion pump stopping), IoH devices operate in "free-living" environments where data continuity is challenged by user behaviour, motion artifacts and lack of professional supervision.Consequently, the IoH relies on synergistic personal area networks (SPANs), where data from multiple sensors—a watch, a scale, a phone, is synthesised to create a robust physiological profile.

The SocioTechnical Drivers of Adoption

The rise of the IoH is not merely a product of technological capability but a response to systemic healthcare crises.

The Chronic Disease Burden: With the prevalence of chronic conditions like hypertension, diabetes, and heart failure rising, the episodic model of care, where a patient sees a doctor once every few months, is insufficient. The IoH enables continuous remote patient monitoring (RPM), allowing for the detection of deterioration before it necessitates hospitalization.
Workforce Shortages: The global shortage of healthcare professionals necessitates tools that can multiply a clinician's reach. By automating the collection of vitals, the IoH reduces the administrative burden on nursing staff and allows physicians to focus on exception management.
Consumer Empowerment: Patients are increasingly demanding access to their own health data. The "democratisation of diagnostics" means patients can now track their vascular age or sleep architecture at home, fundamentally altering the doctor-patient power dynamic.

The Hardware Revolution: From Gadgets to Medical Instruments

The credibility of the Internet of Health hinges on one critical factor: clinical validity. For the IoH to function, the data generated by consumer devices must be accurate enough to inform medical decisions. This section analyses the maturation of key hardware categories, examining the transition from "wellness trackers" to FDA-cleared medical devices.

The Smartwatch as a Cardiac Monitor

The Apple Watch Series 4, released in 2018, marked a watershed moment in the IoH by incorporating a single-lead electrocardiogram (ECG) capable of detecting atrial fibrillation (AFib). This moved the device from a fitness tracker to a Class II medical device.

Clinical Validation and Performance

The pivotal Apple Heart Study, conducted in partnership with Stanford Medicine, enrolled over 400,000 participants. The study demonstrated that 34% of individuals who received an irregular pulse notification were subsequently confirmed to have AFib via ECG patch monitoring.10 While this proved the concept, the positive predictive value (PPV) was 0.84, indicating a non-trivial rate of false positives.

Subsequent research has refined the understanding of the device's accuracy. A 2024 meta-analysis of 11 studies comprising 4,241 participants found the Apple Watch had a pooled sensitivity of 94.8% and specificity of 95% for detecting AFib compared to a standard 12-lead ECG.³ Another study comparing the watch to 24-hour Holter monitoring in cardiovascular patients found a stark contrast between passive and active monitoring:

Passive Irregular Rhythm Notification (IRNF): Low sensitivity (21.4%) but high specificity (100%). This suggests the watch is conservative in generating alerts to avoid alarm fatigue.
Active ECG App: When a user actively takes an ECG, the sensitivity rose to 100% and specificity to 99.1%.

Google/Fitbit Integration

Following Apple's lead, Google's Fitbit has also secured FDA clearance for its PPG-based AFib detection algorithm. The Fitbit Heart Study, which enrolled 455,699 participants, found a PPV of 98% for AFib episodes confirmed by ECG patch monitors The algorithm requires at least 30 minutes of irregular rhythm detection during periods of inactivity to trigger an alert, a design choice specifically intended to minimise motion artifacts and false positives.

Samsung Galaxy Watch:

Samsung has expanded the clinical utility of the smartwatch even further. In 2024, its Sleep Apnea detection feature received FDA De Novo authorisation, a regulatory first for a consumer smartwatch. The feature uses accelerometer and photoplethysmography (PPG) data to monitor breathing disruptions.

Crucially, it is authorised as an over-the-counter (OTC) software-only medical device for adults 22 years and older who have not been previously diagnosed with sleep apnea. This positioning creates a massive funnel for screening undiagnosed populations.

The Smart Ring: Sleep Lab on a Finger

The smart ring form factor, exemplified by Oura, addresses a key limitation of smartwatches: battery life and comfort during sleep.

Validation Status:

The Oura Ring uses infrared PPG sensors to track sleep stages (Light, Deep, REM) and HRV. Validation studies against polysomnography (PSG), the gold standard sleep lab test, have shown mixed but improving results. A study by the University of Tokyo on the Gen3 ring found high agreement for total sleep time and sleep efficiency but variable accuracy for sleep staging. Specifically, the ring achieved 90.6% accuracy for REM sleep but only 75.5% for light sleep.

Regulatory Strategy:

Oura has historically operated in the "general wellness" category to avoid strict FDA oversight.However, the company is pivoting toward medical legitimacy. In late 2024, Oura announced it is pursuing FDA clearance for a blood pressure monitoring feature. Furthermore, Oura partners with the FDA-cleared app Natural Cycles, using the ring’s temperature sensors for fertility tracking. This "component" strategy allows Oura to remain a wellness device while its data powers medical applications.

Competitors like the Evie Ring by Movano Health have already achieved FDA clearance for pulse oximetry (SpO2), setting a precedent that the ring form factor can meet clinical standards.

The Clinical Scale: Vascular Age and Neuropathy

Withings has pioneered the transformation of the humble bathroom scale into a cardiovascular diagnostic tool.

Vascular Age and Pulse Wave Velocity (PWV):

The Withings Body Cardio and Body Scan scales measure Pulse Wave Velocity (PWV), a metric of arterial stiffness that correlates with cardiovascular health. The device measures the time difference between blood ejection from the heart (detected via ballistocardiography on the scale surface) and the arrival of the pulse in the feet (detected via impedance).

Validation: Studies at Georges Pompidou European Hospital have shown a strong correlation between the scale's PWV measurements and gold-standard sphygmometers.
Patient Communication: Withings translates this complex hemodynamic data into "Vascular Age," a patient-friendly metric. If a user's vascular age is higher than their chronological age, it indicates arterial stiffness and higher cardiovascular risk.

Neuropathy and AFib:

The Body Scan scale includes a 6-lead ECG handle. This feature allows the device to detect AFib and also assess sudomotor function (nerve activity) in the feet to screen for diabetic neuropathy. This multi-modal capability effectively brings a peripheral neuropathy exam into the home bathroom.

Cuffless Blood Pressure: The Holy Grail

Blood pressure monitoring remains the most challenging frontier for wearables.

Omron HeartGuide

This device is unique as it is a miniaturized oscillometric blood pressure cuff integrated into a watch strap. Validation studies against ambulatory blood pressure monitoring (ABPM) show acceptable accuracy in controlled office settings (mean difference 0.8 mmHg). However, in "free-living" environments, the device significantly underestimated systolic blood pressure by an average of 16 mmHg in some studies. This discrepancy highlights the impact of arm position and motion on accuracy, a persistent challenge for wrist-based BP monitoring.

Optical Approaches

Other companies are pursuing optical BP monitoring using PPG sensors and machine learning (e.g., analyzing pulse transit time). While Oura and Samsung are exploring this, widespread FDA clearance for calibration-free optical BP monitoring remains elusive due to accuracy concerns.24

The Emergence of the 'Internet of Health' (IoH)

The Regulatory Landscape: Navigating the Grey Area

The transition from "consumer electronics" to "medical devices" is governed by a complex and shifting regulatory framework. Manufacturers must navigate the "grey area" where intended use definitions determine whether a device is a harmless wellness tracker or a regulated medical instrument subject to strict oversight.

The FDA Framework: Intended Use and Risk

In the United States, the FDA regulates devices based on intended use.

General Wellness Policy

Devices that promote a healthy lifestyle without claiming to diagnose, cure, or treat a specific disease are exempt from regulation. A Fitbit that tracks "steps" or "sleep quality" falls under this policy.

Medical Device Definition

If a wearable claims to "detect atrial fibrillation" or "monitor sleep apnea," it crosses the regulatory line and becomes a medical device.

510(k) Clearance: This pathway is used for devices that are "substantially equivalent" to an existing legally marketed device (predicate). Most wearable ECGs (Apple, Fitbit) use this path.
De Novo Pathway: This pathway is for novel low-to-moderate risk devices that have no existing predicate. Samsung’s sleep apnea feature utilised the De Novo track, effectively creating a new classification regulation for consumer sleep apnea screening software.

Software as a Medical Device (SaMD)

The FDA increasingly regulates the algorithm, not the hardware. This allows the "Samsung Health Monitor App" to be the regulated entity, running on general-purpose hardware (the Galaxy Watch). This decoupling is critical for the IoH, allowing rapid hardware iteration while software undergoes the slower regulatory review process.

Cybersecurity Mandates (2023/2025)

The Consolidated Appropriations Act of 2023 established mandatory cybersecurity requirements for "cyber devices." The FDA now requires a "Secure Product Development Framework" (SPDF). As of 2025, the FDA has begun issuing warnings and enforcing recalls for medical wearables with cybersecurity vulnerabilities, signalling that data security is now a prerequisite for clinical validity.

EU MDR: The Rule 11 Disruption

In Europe, the transition from the Medical Device Directive (MDD) to the Medical Device Regulation (MDR)has drastically altered the landscape for health apps and wearables.

Rule 11 Up-classification

Under the old MDD, many health apps were Class I (low risk) and could self-certify. The MDR’s Rule 11 states that software intended to "provide information which is used to take decisions with diagnosis or therapeutic purposes" is classified as Class IIa or higher.

Implication: An app that analyzes heart rate to recommend seeing a doctor (triage/diagnosis support) can no longer be Class I. It requires a Notified Body audit, a Quality Management System (QMS), and robust clinical evaluation reports (CERs).
Impact: This has created a bottleneck, forcing many smaller app developers out of the market or pushing them to strip "medical" claims from their products to remain in the unregulated wellness category.

UK MHRA: Post-Brexit Agility

The UK's Medicines and Healthcare products Regulatory Agency (MHRA) is forging a separate path post-Brexit, aiming to be a pro-innovation regulator for Software as a Medical Device (SaMD).

Specific Guidance on Apps:

The MHRA has released detailed guidance distinguishing between non-medical apps and medical devices.

Symptom Checkers: Software that offers only "reference information" is not a device. However, software that outputs a subset of medical conditions based on user symptoms, or indicates the likelihood of a match, is considered a medical device (Class I or IIa).
Adaptive AI: The MHRA is developing frameworks for "adaptive AI", algorithms that learn and evolve over time. Current regulations struggle with algorithms that change after deployment, and the MHRA's "Change Programme" aims to create a regulatory environment that can accommodate this dynamism.

The Integration Challenge: Making Data Actionable

Even with FDA-cleared hardware and regulatory compliance, the Internet of Health fails if the data cannot reach the clinician in a usable format. This is the interoperability and workflow challenge. Physicians do not have the time to log into separate portals for every patient's device; the data must flow directly into the Electronic Health Record (EHR).

The Language of Health: FHIR and SMART

The foundational standard enabling the IoH is HL7 FHIR (Fast Healthcare Interoperability Resources). FHIR allows health data to be packaged in discrete, standardised "resources" (e.g., an Observation resource for a heart rate reading) that can be exchanged via APIs.

SMART on FHIR

This protocol allows third-party applications to launch inside the EHR workflow. Instead of a doctor logging into a separate "Fitbit Dashboard" web portal, a SMART app can appear as a window within the Epic Hyperspace interface, displaying the patient's wearable data alongside their labs and medications. This "single pane of glass" view is essential for adoption.

Apple HealthKit Integration

Apple leverages FHIR to allow patients to download their health records to their iPhone. Conversely, mechanisms are being built to push HealthKit data (steps, ECGs) back to providers via FHIR APIs. However, this often requires middleware solutions to handle the volume and mapping of data.

The Middleware Layer: Validic and Rimidi

Direct connections between millions of consumer devices and hospital EHRs are technically chaotic and unmanageable. Middleware platforms have emerged as the translation layer, aggregating disparate data streams into a single clinical pipe.

Validic

Validic acts as a massive funnel, connecting to over 570 devices (Garmin, Oura, Omron, etc.) and normalising their data streams into a single API.

Epic App Orchard Integration: Validic Impact integrates directly into Epic. It enables exception management, clinicians don't see every blood pressure reading; they only get an alert in their Epic InBasket if readings exceed a threshold for a set period.
Digital Logbook: This feature allows passive data collection to be written directly into EHR flowsheets, treating home data with the same structural dignity as nurse-collected vitals.

Rimidi:

Rimidi focuses on specific chronic disease modules (diabetes, heart failure) and visualises data for clinical decision support (CDS).

SMART on FHIR: Rimidi lives inside the EHR workflow. It combines PGHD with EHR data (e.g., medication lists) to flag clinical inertia, for example, identifying patients whose glucose levels are consistently high but whose medications have not been adjusted.

The Clinician Experience: Avoiding the "Data Tsunami"

Research on clinician burnout emphasizes that raw data is a liability, not an asset. Successful IoH implementations use dashboards that perform triage and visualisation.

Visualising Trends

Clinicians prefer trend lines (sparklines) and summaries over raw numbers. For example, a Glycemic Risk Index simplifies weeks of Continuous Glucose Monitor (CGM) data into a single risk score, allowing a physician to assess control at a glance.

Smart Alerting

To prevent alert fatigue, systems are moving toward "smart alerts" that require sustained abnormalities to trigger a notification. For instance, the Fitbit AFib algorithm requires at least 30 minutes of irregular rhythm detection to generate an alert, filtering out transient noise that would otherwise overwhelm the clinician. In asthma management studies, nurses reviewed dashboards that only flagged "high-risk" patients based on algorithmically processed peak flow data, ignoring those who were stable.

The Economics of Remote Care: Reimbursement and Business Models

Technology scales only when it is profitable. The business model of the Internet of Health has shifted from consumer hardware sales to clinical service reimbursement. Governments and insurers are recognizing that paying for remote monitoring is cheaper than paying for hospital readmissions.

United States: The CPT Code Ecosystem

Medicare (CMS) has established a robust reimbursement framework for Remote Patient Monitoring (RPM)and Remote Therapeutic Monitoring (RTM), turning wearables from a cost center into a revenue generator.

Key 2025 CPT Codes & Rates

The 2025 Physician Fee Schedule includes specific codes that incentivise the use of these technologies:

99453 (Setup): ~$19.73 (one-time). Reimburses the practice for setting up the device and educating the patient on its use.
99454 (Supply of Device): ~$43.03 (monthly). Reimburses the cost of leasing/supplying the device. Crucially, this requires at least 16 days of data transmission in a 30-day period.
99457 (Management): ~$47.87 (monthly). Reimburses the first 20 minutes of clinical staff time spent communicating with the patient regarding the data.
99458 (Add-on): ~$38.49. For additional 20-minute increments of management time.

The "16-Day Rule" Implication

The requirement for 16 days of data transmission per month creates a strong economic preference for passive wearables (like watches or rings) over active ones (like cuffs or scales). A smart ring collects data daily without user action, ensuring the 16-day threshold is met automatically and the revenue is secured. An active device relies on the patient remembering to use it, risking non-payment.

Germany: The DiGA Model

Germany has pioneered the world’s most progressive digital health reimbursement model: the DiGA (Digitale Gesundheitsanwendungen).

"App on Prescription"

Under the Digital Healthcare Act (DVG), doctors can prescribe health apps listed in the DiGA directory. Statutory health insurers, which cover approximately 90% of the German population, must reimburse these apps.

The Fast-Track Mechanism:

The DiGA model allows apps to get a "provisional listing" for 12 months. During this period, the manufacturer must conduct a comparative study to prove "positive healthcare effects", either a medical benefit (improved health outcome) or a patient-relevant structural improvement (better adherence, health literacy). This fast-track reduces the barrier to entry, allowing startups to generate revenue while gathering the real-world evidence needed for permanent listing.

United Kingdom: Virtual Wards and NHS Tariffs

The National Health Service (NHS) is investing heavily in Virtual Wards (Hospital at Home) to relieve bed pressure and manage acute care in the community.

Scale and Funding

The NHS goal is to deliver 40-50 virtual ward "beds" per 100,000 population. While initial pump-priming funding (£250 million) was provided, systems are now expected to fund virtual wards from core allocations.

Cost Effectiveness

Evaluations in the NHS South East region showed that virtual wards generated savings exceeding £10 million and avoided over 9,000 hospital admissions. The cost per day on a virtual ward was estimated at ~£187, compared to ~£657 for an acute hospital bed.

GP Contract 2025

The 2025/26 GP contract includes specific incentives for "digitally enabled access" and remote monitoring. Practices are required to enable GP Connect functionality, allowing other providers to view relevant records, and to utilise tech-enabled care for risk stratification.

Trust, Liability and Ethics: The Human Barrier

The final, and perhaps most formidable, barrier to the Internet of Health is human trust and legal safety.

The Erosion of Trust

Public trust in physicians and hospitals has plummeted from 71.5% in April 2020 to 40.1% in January 2024.This decline creates a paradox: patients distrust the healthcare "system" generally but largely retain trust (85%) in their personal doctor.

Physician Trust in Data:

Conversely, physician trust in health AI and PGHD is cautiously rising. In 2024, 66% of physicians reported using health AI, a 78% increase from the previous year. However, their primary demand is for "increased oversight" and "liability protection".58 Physicians distrust PGHD not because they oppose data, but because they fear the liability of data they didn't see. The prevailing fear is: "If the data is in the chart, I am responsible for it".

Legal Liability and Malpractice

The integration of consumer wearables into care creates novel malpractice risks.

The "Ostrich Defense" vs. Standard of Care

Historically, doctors might avoid looking at wearable data to avoid liability ("if I don't see it, I can't be sued for missing it"). However, legal scholars argue that as AI and wearables become validated, the standard of care will shift. Ignoring a validated alert from an FDA-cleared device could eventually constitute negligence (failure to use available diagnostic tools).

Case Law

While direct case law on "missed Apple Watch alerts" is sparse, the Masimo v. Apple patent verdict ($634 million) confirmed that Apple Watches function as "patient monitors" in the eyes of the law (specifically patent law).63 This verdict blurs the line between consumer tech and medical equipment, suggesting that courts recognise the medical function of these devices regardless of their marketing.

HIPAA & Privacy

Consumer devices exist outside HIPAA until the data enters the provider's system. Once integrated (e.g., via Validic into Epic), that data becomes Protected Health Information (PHI). Breaches then expose providers to HIPAA penalties and state-level negligence lawsuits.

Ethical Considerations

The continuous monitoring of patients raises ethical concerns regarding surveillance and data ownership. Who owns the detailed map of a patient's heart rhythm? Can insurers use this data to deny coverage? While current regulations (like DiGA) prohibit the use of data for disadvantages, the potential for "biometric persecution" remains a concern in the literature.

Conclusion: The Convergence

The "Internet of Health" has moved beyond the hype phase. The hardware is increasingly clinically valid, with FDA clearances for AFib, sleep apnea, and blood pressure monitoring turning consumer watches into legitimate diagnostic tools. The regulatory pathways, while complex, are clearer than ever before, with the FDA's De Novo track and Germany's DiGA providing roadmaps for market entry.

The critical bottleneck is no longer technology; it is workflow integration and economic alignment.

Integration: Data must flow silently into the EHR, processed by middleware like Validic or Rimidi that highlights exceptions rather than flooding the inbox with raw noise.
Economics: Reimbursement models like CPT codes and NHS virtual ward tariffs must sustain the model, shifting it from a pilot project to a standard operating procedure. The "16-day rule" in the US is already shaping hardware design toward passive monitoring.
Trust: Liability shields or clear "standards of care" guidelines must be established to protect physicians who rely on, or reasonably choose to ignore, the deluge of patient-generated data.

By 2030, the distinction between a "consumer wearable" and a "medical device" will likely vanish for top-tier devices. The scale on the bathroom floor and the watch on the wrist will be the first line of defense in a healthcare system that is ubiquitous, continuous, and proactive. The Internet of Health is no longer coming; it is here, waiting to be fully integrated.

Summary of Key Clinical Validation & Regulatory Milestones (2024/2025)

Device / Brand	Key Medical Features	FDA/Regulatory Status	Clinical Validation Highlights
Apple Watch	ECG (AFib), Irregular Rhythm Notification (IRNF), AFib History	FDA Cleared (Class II) for ECG & IRNF.	99.6% specificity for ECG sinus rhythm classification. 94.8% sensitivity for AFib detection vs 12-lead ECG.
Oura Ring	Sleep Staging, SpO2, Temperature (Fertility), Blood Pressure (In Dev)	FDA Cleared for SpO2 (via partnership); BP feature in trials.	High accuracy for REM sleep (90.6%) but lower for Light sleep (75.5%) vs PSG.
Withings Body Scan	6-Lead ECG, Pulse Wave Velocity (Vascular Age), Neuropathy Screening	FDA Cleared for ECG. CE Mark for others.	PWV measurements strongly correlated with sphygmometer (gold standard) in hypertensive patients.
Samsung Galaxy Watch	Sleep Apnea Detection, IHRN, ECG	FDA De Novo for Sleep Apnea; FDA Cleared for IHRN/ECG.	First FDA-authorised sleep apnea feature for consumer watch. Specificity 100% for IHRN in some trials.
Omron HeartGuide	Oscillometric Blood Pressure (Wrist Cuff)	FDA Cleared (Class II).	Validated against ABPM but underestimates SBP by ~16mmHg in free-living conditions due to arm position variance.
Fitbit (Google)	PPG AFib Detection, ECG	FDA Cleared for PPG AFib algorithm & ECG.	98% PPV for AFib episodes in Fitbit Heart Study.

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