Loss of Pulse Detection is a software-only mobile medical application that is intended to be used with compatible consumer wrist-worn products to analyze pulse data to identify loss of pulse events and provide audio, visual, and haptic alerts to the user.

If the user remains unresponsive to these alerts, Loss of Pulse Detection will attempt to prompt a call to emergency services through the user's connected compatible hardware, such as a smartphone or smartwatch.

Loss of Pulse Detection is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every loss of pulse event and the absence of an alert is not intended to indicate that no such event has occurred; rather the Loss of Pulse Detection is intended to opportunistically surface an alert of possible loss of pulsatility when sufficient data are available for analysis.

These data are only captured when the user is still. Loss of Pulse Detection is not intended to replace traditional methods of diagnosis, treatment, or monitoring.

Loss of Pulse Detection has not been tested for and is not intended for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with a high risk for sudden cardiac death such as those with coronary artery disease, cardiomyopathy and/or unexplained syncope/fainting.

Device Description

The Loss of Pulse detection SaMD is intended to be used for the detection of loss of pulse using photoplethysmography (PPG) and accelerometer sensors present in a wrist worn consumer wearable device. Upon detection of loss of pulse, when the smartwatch is worn, the feature will prompt haptic and audio alerts and notifications to the user and prompt the user's compatible hardware to call emergency services if the user is unresponsive to the notifications and alerts. This is an opt-in feature and will be off by default.

The Loss of Pulse detection SaMD comprises a software component that resides on the compatible consumer wearable device (from here on referenced as a "smartwatch") and a user facing mobile application that resides on general purpose compatible consumer mobile devices such as a smartphone (from here on referenced as "Smartphone"). The software component on the smartwatch analyzes pulse data collected by photoplethysmography (PPG) and accelerometry sensors from qualified smartwatch, using an algorithm employing digital signal processing (DSP) and features-based machine learning based on a convolutional neural network (CNN) to detect possible loss of pulse events.

AI/ML Overview

The document provides the following information regarding the acceptance criteria and the study that proves the device meets the acceptance criteria for the Loss of Pulse Detection (LPD) software:

1. Table of Acceptance Criteria and Reported Device Performance

While explicit "acceptance criteria" are not presented in a formal table with specific thresholds, the document details the performance metrics achieved in the clinical validation study. The performance is primarily evaluated based on sensitivity and specificity.

Performance Metric	Acceptance Criteria (Implicit)	Reported Device Performance
Sensitivity (Loss of Pulse Events)	Sufficiently high to detect true loss of pulse events.	69.3% (95% CI: 64.3% - 74.1%) across 135 users
Sensitivity (Adjusted for Simulated Collapse)	Maintained sensitivity after simulated collapse.	64.5% (95% CI: 55.7% - 74.2%)
Specificity (Absence of Loss of Pulse Events)	Sufficiently high to minimize false positives.	99.965% (95% CI 99.804, 99.999) (day-level)
Alert De-escalation (Real-World)	Users able to respond and de-escalate.	>98% of notifications cleared from pre-phone call gates.
Emergency Calls (Real-World)	Few false emergency calls.	1 phone call placed per 75 person-years of use.

2. Sample Sizes and Data Provenance

Test Set (Clinical Validation Studies):
- Primary Study: 135 participants for sensitivity evaluation (pulseless and pulsatile data).
- Secondary Study: 21 participants for sensitivity evaluation (pulseless data).
- Specificity Evaluation: 131 evaluable participants from the first study.
- Data Provenance: The document states participants were from a "racially and ethnically diverse population, including adults of diverse sex and age." It does not specify the country of origin, nor explicitly state if it was retrospective or prospective, though clinical validation studies are generally prospective. The phrase "simulated loss of pulse events induced by an arterial occlusion model" suggests a controlled, prospective clinical study.
Training and Validation Sets (Algorithm Development):
- Training/Validation/Development Datasets: Over one hundred thousand hours of free-living data (from hundreds of participants who experienced no loss of pulse events) and data from 99 participants who experienced simulated loss of pulse events induced by an arterial occlusion model.
- Data Provenance: The document states "The training, validation, and test splits used for development included data from diverse participants with varied age, sex, BMI, and skin tone." Again, country of origin is not specified. The data comprised both "free-living data" (suggesting retrospective real-world data) and "simulated loss of pulse events" (suggesting prospective controlled data).

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). For the clinical validation study where simulated loss of pulse was induced, it implies that the ground truth for "loss of pulse" was established directly through the experimental protocol (arterial occlusion model) rather than by expert review of physiological signals post-hoc. For "no loss of pulse" data, the ground truth is inherently the absence of such an event in free-living data of healthy individuals.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the test set. Given that the ground truth for simulated loss of pulse was established by an induced event, and for pulsatile data by its natural occurrence, a separate adjudication method using human experts on the collected data is not described as part of the primary ground truth establishment.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC comparative effectiveness study was not explicitly mentioned or detailed. The study described focuses on the standalone performance of the algorithm and its ability to trigger an emergency call workflow, rather than comparing human reader performance with and without AI assistance. The "Real World Evidence Summary" indicates that human users interact with the device's alerts, but it does not quantify human improvement due to AI assistance in a comparative MRMC framework.

6. Standalone (Algorithm-Only) Performance

Yes, standalone performance was done. The reported sensitivity and specificity values (69.3% and 99.965%) represent the performance of the Loss of Pulse Detection algorithm. The clinical validation study was designed to evaluate the software's ability to detect loss of pulse events.

7. Type of Ground Truth Used

For Loss of Pulse Events: Ground truth was established through simulated loss of pulse events induced by an arterial occlusion model. This is a physiological, objective method.
For Absence of Loss of Pulse: Ground truth for pulsatile data was established by the absence of induced events in control participants and "free-living data across hundreds of participants who experienced no loss of pulse events." This relies on the natural state of healthy individuals.

8. Sample Size for the Training Set

The training set was part of a larger dataset used for algorithm development, which consisted of:

"Over one hundred thousand hours of free-living data across hundreds of participants who experienced no loss of pulse events."
Data from "99 participants who experienced simulated loss of pulse events induced by an arterial occlusion model."
The document states this dataset was split at the participant level into training, validation, and held-out test sets. The exact number of participants or hours specifically in the training set is not separately quantified, but it is a substantial portion of the entire development dataset.

9. How Ground Truth for the Training Set was Established

The ground truth for the training set was established in the same manner as for the test set:

For loss of pulse events, it was based on simulated events induced by an arterial occlusion model.
For the absence of loss of pulse, it was based on free-living data from participants who did not experience such events, implying a ground truth established by the absence of a known medical condition/event.

Summary

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February 25, 2025

Fitbit Dinesh Puppala Regulatory Affairs Lead 215 Fremont Street San Francisco, California 94105

Re: K242967

Trade/Device Name: Loss of Pulse Detection Regulation Number: 21 CFR 870.2790 Regulation Name: Photoplethysmograph Analysis Software For Over-The-Counter Use Regulatory Class: Class II Product Code: SDY Dated: September 25, 2024 Received: September 26, 2024

Dear Dinesh Puppala:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory

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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Jennifer W. Shih -S

Jennifer Kozen Assistant Director Division of Cardiac Electrophysiology, Diagnostics, and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.

Submission Number (if known)

K242967

Device Name

Loss of Pulse Detection

Indications for Use (Describe)

These data are only captured when the user is still. Loss of Pulse Detection is not intended to replace traditional methods of diagnosis, treatment, or monitoring.

Type of Use (Select one or both, as applicable)

Prescription Use (Part 21 CFR 801 Subpart D)

X Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

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510(K) Summary

1. Submitter Information: Fitbit LLC
  215 Fremont Street. San Francisco, CA 94105 Contact Person: Dinesh Puppala Regulatory Affairs Lead Phone: (650)-267-1263 Email: puppalad(@google.com Date Prepared: Feb 19, 2025
1. Subject Device Information Name of Device: Loss of Pulse Detection Common or Usual Name: Loss of Pulse Detection Classification Name: Photoplethysmograph Analysis Software For Over-The-Counter Use Regulatory Class: Class II Product Code: SDY - 21 CFR 870.2790
1. Predicate Device Fitbit Irregular Rhythm Notifications (K212372)

4. Indications for Use

If the user remains unresponsive to these alerts. Loss of Pulse Detection will attempt to prompt a call to emergency services through the user's connected compatible hardware, such as a smartphone or smartwatch.

These data are only captured when the user is still. Loss of Pulse Detection is not intended to replace traditional methods of diagnosis, treatment, or monitoring.

Confidential

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Device Description న.

Intended Use

Photoplethysmograph analysis software for over-the-counter use. A photoplethysmograph analysis software device for over-the-counter use analyzes photoplethysmograph data and provides information for identifying loss of pulse. This device is not intended to provide a diagnosis.

Technological Characteristics

	Subject DeviceLoss of Pulse DetectionK242967	Predicate DeviceFitbit Irregular RhythmNotificationK212372	EquivalenceDiscussion
Intended Use	Photoplethysmographanalysis software forover-the-counter use. Aphotoplethysmograph analysissoftware device forover-the-counter use analyzesphotoplethysmograph dataand provides information foridentifying loss of pulse. Thisdevice is not intended toprovide a diagnosis.	Photoplethysmograph analysissoftware for over-the-counteruse. A photoplethysmographanalysis software device forover-the-counter use analyzesphotoplethysmograph dataand provides information foridentifying irregular heartrhythms. This device is notintended to provide adiagnosis.	Same
Indications forUse	Loss of Pulse Detection is asoftware-only mobile medicalapplication that is intended to beused with compatible consumerwrist-worn products to analyzepulse data to identify loss ofpulse events and provide anotification to the user.If the user remains unresponsiveto these alerts, Loss of PulseDetection will attempt to prompta call to emergency servicesthrough the user's connectedcompatible hardware, such as asmartphone or smartwatch.Loss of Pulse Detection isintended for over-the-counter(OTC) use. It is not intended toprovide a notification on everyloss of pulse event and theabsence of a notification is notintended to indicate no diseaseprocess is present; rather theLoss of Pulse Detection isintended to opportunisticallysurface a notification of possible	The Fitbit Irregular RhythmNotifications is a software-onlymobile medical application thatis intended to be used withcompatible consumer wrist-wornproducts to analyze pulse ratedata to identify episodes ofirregular heart rhythmssuggestive of atrial fibrillation(AFib) and provide a notificationto the user.The Fitbit Irregular RhythmNotifications is intended forover-the-counter (OTC) use. It isnot intended to provide anotification on every episode ofirregular rhythm suggestive ofAFib and the absence of anotification is not intended toindicate no disease process ispresent; rather the Fitbit IrregularRhythm Notifications is intendedto opportunistically surface anotification of possible AFibwhen sufficient data are availablefor analysis.These data are only captured	SimilarBoth devices are software-onlymobile medical applicationsintended for use withcompatible consumerwrist-worn products. They bothutilize PPG analysis software toidentify potential irregular heartrhythms, analyze pulse data,and provide notifications to theuser if an irregular heart rhythmis detected. Neither device isintended to provide a diagnosisor monitor the outcome of theiralerts. Additionally, bothdevices are intended for usersover 22 years old and are notintended for individuals withpre-existing cardiac conditions.
	pulselessness when sufficientdata are available for analysis.These data are only capturedwhen the user is still. Loss ofPulse Detection is not intendedto replace traditional methods ofdiagnosis or treatment.Loss of Pulse Detection has notbeen tested for and is notintended for use in people under22 years of age. It is also notintended for use in individuals	when the user is still. Along withthe user's risk factors, the FitbitIrregular Rhythm Notificationscan be used to supplement thedecision for AFib screening. TheFitbit Irregular RhythmNotifications is not intended toreplace traditional methods ofdiagnosis or treatment.The Fitbit Irregular RhythmNotifications has not been testedfor and is not intended for use inpeople under 22 years of age. Itis also not intended for use inindividuals previously diagnosedwith AFib.
	previously diagnosed with a highrisk for sudden cardiac deathsuch as those with coronaryartery disease, cardiomyopathyand/or unexplainedsyncope/fainting.
Use Environment	Home/General Use	Home/General Use	SameBoth are intended for homeuse.
Anatomical Site	Wrist-worn consumer wearablewith PPG sensors.	Wrist-worn consumer wearablewith PPG sensors.	SameBoth use consumer gradewrist-worn devices to gathersignals from PPG sensors.
User interface	Mobile application run withinthe Pixel watch consumer app.	Mobile application run withinthe Fitbit consumer app	SameBoth devices include a mobileapplication component that isrun within a consumerapplication on the user'smobile device (smartphone).This mobile application servesthe purpose of the UserInterface for both devices.
Use Method	Collects and analyzes pulse dataduring periods that meet input	Collects and analyzestachograms (based on pulse data)during periods of stillness orsleep using input from PPGsensors.	SimilarBoth devices analyze sensordata from the PPG sensor andonly analyze data that meetinput signal qualityrequirements. Stillness is partof both devices' input signalquality requirements. Bothdevices limit the analysiswindow to time periodsrelevant to the irregular heartrhythm being detected. Theuse of the term "tachogram"by the predicate device refersto subsets of the PPG sensordata cropped to approximateindividual R-R intervals, anddoes not meaningfully alterthe use method, as both the
			subject and predicate devicesuse signal processingalgorithms to analyze PPGsensor data.Both devices are passivebackground screening toolsand users cannot initiatereadings.Both devices opportunisticallysurface a notification to theuser.
Limiting Factors	Motion, hand, and fingermovements during pulselessperiod; tattoos on the wrist;inadequate blood flow.Does not continuously monitorfor signs of loss of pulseA lack of notification does notindicate an absence of anunderlying loss of pulse	Motion, hand and fingermovements, dark tattoos on thewrist, inadequate blood flow.Does not continuously monitorfor signs of AFib.A lack of notification does notindicate an absence of AFib.	SameBoth devices have similarlimiting factors.Both devices are not intendedfor continuous monitoring.A lack of notification does notindicate an absence of anirregular rhythm for bothdevices.
Compatibledevices	Consumer wrist-worn productswith PPG sensors (e.g.,smartwatch) that have beenqualified for use with the Loss ofPulse Detection feature	Consumer wrist-worn productswith PPGsensors (e.g., smartwatch orfitness tracker) that have beenqualified for usewith Fitbit Irregular HeartRhythm Notifications	SameBoth devices are compatiblewith qualified wrist wornproducts with PPG sensorsthat have been qualified foruse with the respective feature.The subject device iscompatible with a subset ofconsumer wrist-worn productson which the predicate isqualified (i.e., smartwatches).
Principles ofOperation	Pulse rate data is gathered from aconsumer wrist-worn device.The algorithm analyzes the dataand classifies it as having signsof an irregular heart rhythm, lossof pulse, or no signs thereof.When signs of loss of pulse aredetected, a notification issurfaced to the user.	Pulse rate data gathered from aconsumer wrist-worn device isuploaded to a server. Thealgorithm analyzes the data andclassifies it as having signs ofAFib or no signs of AFib. Whensigns of AFib are detected anotification is surfaced to theuser through the mobile	SimilarBoth devices have the samebasic principle of operation,with nonmaterial differencesstemming from the particularirregular pulse rhythm that isintended to be detected. Bothdevices analyze pulse data
		application.	from consumer wrist-worndevices. Apparent differencessuch as algorithmic analysison the watch in the subjectdevice and the server on thepredicate have no materialimpact on safety oreffectiveness. Both devicesopportunistically surfacenotifications to the user if anirregular pulse rhythm isdetected.

Summary of Substantial Equivalence

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6. Performance Data

Testing verifying the performance requirements of the subject device was conducted and is included in this premarket notification, the results of which support substantial equivalence. A summary of the testing is included below:

Algorithm Development

The Loss of Pulse Detection algorithm identifies potential loss of pulse events. The algorithm includes a machine learning component that consists of a convolutional network, which analyzes data from photoplethysmography (PPG) and accelerometer sensors. The algorithm was developed on a dataset consisting of over one hundred thousand hours of free-living data across hundreds of participants who experienced no loss of pulse events and 99 participants who experienced simulated loss of pulse events induced by an arterial occlusion model. The dataset used for model development was split at the participant level into training, validation, and held-out test sets The Loss of Pulse Detection algorithm was trained on the training split and evaluated on the validation set. When algorithm development was complete, the algorithm was locked and the algorithm was evaluated on the held-out test set. The training, validation, and test splits used for development included data from diverse participants with varied age, sex, BMI, and skin tone.

Bench Testing/Non-Clinical Testing

The Loss of Pulse Detection utilizes signals derived from compatible consumer grade wrist-worn products. The wrist-worn products were qualified for use with the software to ensure that the data provided could meet signal attribute and quality requirements necessary for heart rhythm analysis and detect adequate PPG signal quality. Bench testing included product signal acquisition testing, aggressor testing for known challenge conditions potentially impacting the quality of PPG signal acquisition, and accuracy assessment of the inputs to the Loss of Pulse Detection that were derived from the wrist-worn products. Qualification testing was repeated for all qualified wrist-wearable products. The algorithm was tested to ensure that it accepted and rejected data correctly and that it can correctly analyze the input data.

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The Loss of Pulse Detection presents a "Basic Documentation" Software Documentation Level based on the risks of device software function in the context of the device's intended use as defined in FDA's Guidance for the Content of Premarket Submissions for Device Software Functions (June 14, 2023) (Guidance Document). Testing of the mobile application demonstrates that the Loss of Pulse Detection feature adequately performs all the necessary functionalities including education/onboarding and generating user notifications when pulselessness is identified.

Human Factors Testing Summary

A Human Factors Validation Study was designed to evaluate the critical and noncritical tasks associated with the use of the device. The Human Factors Validation Study was performed using a simulated version of the Loss of Pulse Detection within the Pixel watch mobile app, representative of the final app. 19 users were recruited from the general population for the study, with the goal of recruiting at least 15 intended users. During the test session, each participant performed various tasks within representative, naturalistic use scenarios. These tasks included setup and onboarding to an initial check-in, responding to an escalation, triggering emergency response services, and passive feature use. The first evaluation activity (setup and onboarding) included a self-selection component where participants indicated whether or not they are an intended Loss of Pulse Detection application user, resulting in 16 intended and 3 unintended users. Participants also performed a knowledge task to evaluate critical tasks that could not be evaluated during the simulated use scenarios.

During each evaluation activity, use errors, instances of moderator assistance, close calls, and difficulties were documented. Open-ended questions were used to collect participants' subjective assessments of the root cause(s) associated with any such event and to collect participants' feedback on the product's use-safety. Data was consolidated and analyzed with a focus on events with the potential for serious harm (critical tasks). The human factors testing found that the loss of pulse detection feature was safe and effective for the intended users, uses, and use environments.

Real World Evidence Summary

Real-world evidence (RWE) indicates users are able to respond to haptic, audio, and visual feedback and de-escalate based solely on reading the device labeling. A sample of data from RWE shows that users clear >98% of notifications from the pre-phone call gates in real-world free-living conditions. There was, on average, 1 phone call placed by Loss of Pulse Detection in 75 person-years of real-world use.

Clinical Testing Summary

The Loss of Pulse Detection (LPD) software, intended for use with wrist-worn devices, was clinically validated in a study. The study aimed to evaluate the software's ability to detect loss of pulse events and initiate an emergency call.

A total of 135 participants were enrolled in one clinical study and 21 participants in another clinical study. The participants included a racially and ethnically diverse population, including adults of diverse sex and age. The results from the clinical validation study showed a sensitivity of 69.3% (95% C1: 64.3%

74.1%)

Confidential

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across 135 users and a sensitivity of 64.5% (95% CI: 55.7% to 74.2%) after adjusting for performance after a simulated collapse. The specificity was analyzed using evaluable data from 131 participants from the first study. It showed a day-level specificity of 99.965% (95% CI 99.804, 99.999). A total of 5 adverse events were reported. No adverse events reported were related to the LPD software. Most adverse events were identified as possible anticipated risks in the study protocol, namely skin irritation from wearing a consumer wrist-worn device. Demographic characteristics of the study population are summarized in the table below.

	Clinical validation study(pulseless and pulsatile)	Clinical validation study(pulseless)
N	135	21
Age
<35	16.3% (22)	14.3% (3)
36-59	38.5% (52)	61.9% (13)
60+	45.2% (61)	23.8% (5)
Sex
Female	60.7% (82)	47.6% (10)
Male	39.3% (53)	52.4% (11)
Monk skin tone
1-4	40.7% (55)	28.6% (6)
5-6	35.6% (48)	66.7% (14)
7-8	20.7% (28)	4.8% (1)
9-10	3.0% (4)	0.0% (0)
Individualtypology angle scale
1	3.0% (4)	0.0% (0)
2	5.9% (8)	9.5% (2)
3	11.1% (15)	28.6% (6)
4	11.1% (15)	33.3% (7)
5	43.0% (58)	19.0% (4)
6	25.9% (35)	9.5% (2)
Body mass index
<18.5	0.7% (1)	4.8% (1)
	Clinical validation study(pulseless and pulsatile)	Clinical validation study(pulseless)
18.5–<25	18.5% (25)	47.6% (10)
25–<30	27.4% (37)	38.1% (8)
≥30	53.3% (72)	9.5% (2)

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7. Conclusion

The Loss of Pulse Detection and its predicate have the same Intended Use, similar Indications for Use, similar technological characteristics and principles of operation. The technological differences between the subject Loss of Pulse Detection and its predicate, Fitbit Irregular Rhythm Notifications, are in relation to the very specific formulation of the respective algorithms. However, any such differences do not raise new or different questions of safety or effectiveness. Moreover, testing and clinical data provided in the submission demonstrate that the subject device meets all Special Controls and operates in a manner that is as safe and effective as the predicate device. Therefore, Loss of Pulse Detection is considered substantially equivalent to the predicate device.

Regulation Number and Section

§ 870.2790 Photoplethysmograph analysis software for over-the-counter use.

(a)
Identification. A photoplethysmograph analysis software device for over-the-counter use analyzes photoplethysmograph data and provides information for identifying irregular heart rhythms. This device is not intended to provide a diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Clinical performance testing must demonstrate the performance characteristics of the detection algorithm under anticipated conditions of use.
(2) Software verification, validation, and hazard analysis must be performed. Documentation must include a characterization of the technical specifications of the software, including the detection algorithm and its inputs and outputs.
(3) Non-clinical performance testing must demonstrate the ability of the device to detect adequate photoplethysmograph signal quality.
(4) Human factors and usability testing must demonstrate the following:
(i) The user can correctly use the device based solely on reading the device labeling; and
(ii) The user can correctly interpret the device output and understand when to seek medical care.
(5) Labeling must include:
(i) Hardware platform and operating system requirements;
(ii) Situations in which the device may not operate at an expected performance level;
(iii) A summary of the clinical performance testing conducted with the device;
(iv) A description of what the device measures and outputs to the user; and
(v) Guidance on interpretation of any results.