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510(k) Data Aggregation
(252 days)
Fitbit Inc
The Fitbit Irregular Rhythm Notifications is a software-only mobile medical application that is intended to be used with compatible consumer wrist-worn products to analyze pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provide a notification to the user.
The Fitbit Irregular Rhythm Notifications is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the Fitbit Irregular Rhythm Notifications is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis.
These data are only captured when the user's still. Along with the user's risk factors, the Fitbit irreqular Rhythm Notifications can be used to supplement the decision for AFib screening. The Fitbit Irregular Rhythm Notfications is not intended to replace traditional methods of diagnosis or treatment.
The Fitbit Irregular Rhythm Notifications has not been tested for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.
The Fitbit Irreqular Rhythm Notifications consists of an algorithm that classifies pulse rate data, and a mobile application run within the Fitbit app that serves as the user interface (UI) and device display.
The Fitbit Irregular Rhythm Notifications leverages pulse rate data collected from compatible commercially available, general purpose wrist-worn products (e.g., smartwatch or fithess tracker). Photoplethysmograph (PPG) sensors consist of light-emitting diodes (LED) and photodiodes that detect changes in blood flow of a user's vasculature at any given moment. When the heart beats, it sends a pressure wave through the vasculature causing a blood flow increase. By monitoring the fluctuations the consumer wrist-worn products can measure pulse rate data. When the user is still the sensor detects when individual pulses reach the periphery (i.e., wrist) and measures beat-to-beat intervals.
If the analyzed data are consistent with signs of atrial fibrillation, a notification indicating that a heart rhythm showing signs suggestive of AFib will be displayed to the user. The Fitbit Irregular Rhythm Notifications will only surface a notification of a heart rhythm showing signs of AFib once in a 24-hour period.
The Fitbit Irregular Rhythm Notifications mobile app functions within the Fitbit consumer application and is run on a compatible, user-provided general purpose mobile computing product (e.g., smartphone or tablet). The Fitbit Irregular Rhythm Notifications mobile app serves as the display/user interface for the Fitbit Irregular Rhythm Notifications.
Acceptance Criteria and Device Performance for Fitbit Irregular Rhythm Notifications
1. Table of Acceptance Criteria and Reported Device Performance
The provided document focuses on the substantial equivalence to a predicate device rather than explicitly stating acceptance criteria as a numerical target. However, it details the performance metrics demonstrated by the clinical study to support this equivalence. The key performance metric reported is the Positive Predictive Value (PPV) for AFib detection.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Clinical performance supportive of substantial equivalence to predicate device. | Positive Predictive Value (PPV) of 98.2% (97.5% LCB: 96.4%) in subjects with a positive algorithm detection. |
2. Sample Size and Data Provenance
- Test Set Sample Size: 225 subjects received a positive algorithm detection and wore an ECG patch for comparison.
- Data Provenance: The study recruited subjects from Fitbit's U.S. user population. The data is prospective, as users consented and were instructed to perform specific actions (schedule a telehealth visit, wear an ECG patch) following algorithm detection.
3. Number of Experts and Qualifications for Ground Truth
The document states that "Data gathered from the ECG patch was analyzed by medical professionals to determine whether signs of AFib were present." It does not specify the exact number of experts or their detailed qualifications (e.g., "radiologist with 10 years of experience").
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method like 2+1 or 3+1. It states that "data gathered from the ECG patch was analyzed by medical professionals to determine whether signs of AFib were present," implying a single assessment or a consensus process not detailed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned. The study focused on the standalone algorithm's performance against ECG ground truth, not on how human readers improve with or without AI assistance.
6. Standalone (Algorithm Only) Performance
Yes, a standalone performance study was conducted. The clinical study evaluated the Fitbit Irregular Rhythm Notifications algorithm's ability to identify irregular heart rhythms suggestive of AFib. The reported PPV of 98.2% is a measure of this standalone algorithmic performance.
7. Type of Ground Truth Used
The ground truth used for the test set was ECG patch data analyzed by medical professionals. This is a direct measure of cardiac electrical activity, considered a gold standard for AFib diagnosis.
8. Sample Size for the Training Set
The document does not specify the sample size for the training set. It mentions that the clinical study "recruited subjects from Fitbit's U.S. user population, inviting them to participate in a study. Upon consent, users had their PPG data analyzed for signs consistent with AFib by the algorithm." This describes part of the clinical validation, but not the training process or the data used for it.
9. How Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth establishment for the clinical validation test set (ECG patch data analyzed by medical professionals).
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(156 days)
Fitbit, Inc.
The Fitbit ECG App is a software-only mobile medical application intended for use with Fitbit wrist wearable devices to create, record, store, transfer, and display a single channel electrocardiogram (ECG) qualitatively similar to a Lead I ECG. The Fitbit ECG App determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The AFib detection feature is not recommended for users with other known arrhythmias.
The Fitbit ECG App is intended for over-the-counter (OTC) use. The ECG data displayed by the Fitbit ECG App is intended for informational use only. The user is not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment. The Fitbit ECG App is not intended for use by people under 22 years old.
The Fitbit ECG App is a software-only medical device used to create, record, display, store and analyze a single channel ECG. The Fitbit ECG App consists of a Device application ("Device app") on a consumer Fitbit wrist-worn product and a mobile application tile ("mobile app") on Fitbit's consumer mobile application. The Device app uses data from electrical sensors on a consumer Fitbit wrist-worn product to create and record an ECG. The algorithm on the Device app analyzes a 30 second recording of the ECG and provides results to the user. Users are able to view their past results as well as a pdf report of the waveform similar to a Lead I ECG on the mobile app.
Below is the information regarding the Fitbit ECG App's acceptance criteria and the study that proves it, based on the provided document:
1. Table of acceptance criteria and the reported device performance
| Category | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| AFib Detection (Sensitivity) | Not explicitly stated in the provided text as a numerical criterion, but implicitly expected to be high for AFib detection. The predicate device's performance often forms the basis for substantial equivalence. | 98.7% for AFib detection |
| AFib Detection (Specificity) | Not explicitly stated in the provided text as a numerical criterion, but implicitly expected to be high for ruling out AFib. The predicate device's performance often forms the basis for substantial equivalence. | 100% for AFib detection |
| ECG Waveform Morphological Equivalence to Lead I | ECG waveform "qualitatively similar to a Lead I ECG" and expected to meet specific morphological equivalence criteria. | 95.0% of AF and SR tracings deemed morphologically equivalent to Lead I of a 12-Lead ECG waveform. |
2. Sample size used for the test set and the data provenance
- Sample Size: 475 subjects.
- Data Provenance: Subjects were recruited across 9 US sites. This indicates prospective data collection from the United States.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: For subjects with a known history of AFib, a "single qualified physician" performed the screening and assigned them to the AFib cohort. The document doesn't specify how many experts reviewed the 12-lead ECGs for the ground truth of AFib or Sinus Rhythm (NSR) for all 475 subjects, beyond the single physician for the AFib cohort screening. For the overall study, it implies a 12-lead ECG was the reference, which would typically be interpreted by qualified cardiologists or electrophysiologists.
- Qualifications of Experts: For AFib screening, the expert was referred to as a "single qualified physician." Specific qualifications like "radiologist with 10 years of experience" are not provided.
4. Adjudication method for the test set
The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It mentions that subjects with a known history of AFib were screened by a "single qualified physician." For the simultaneous 12-lead ECG, it implies a clinical standard interpretation which often involves adjudicated reads, but this is not detailed in the provided text.
5. If a Multi-Reader, Multi-Case (MRMC) comparative effectiveness study was done
No, a Multi-Reader, Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not reported in this document. The study focuses on evaluating the standalone performance of the Fitbit ECG App against a clinical standard (12-lead ECG).
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance study was done. The document states: "The Fitbit ECG App software algorithm was able to detect AF with the sensitivity and specificity of 98.7% and 100%, respectively." This indicates a direct evaluation of the algorithm's performance.
7. The type of ground truth used
The ground truth was established using a simultaneous 30-second 12-lead ECG. This is a clinical gold standard for rhythm analysis.
8. The sample size for the training set
The document does not provide the sample size for the training set. It only details the clinical testing conducted for validation/evaluation of the device.
9. How the ground truth for the training set was established
The document does not provide information on how the ground truth for the training set was established, as it focuses on the validation study.
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(164 days)
FITBIT, INC.
The ARIA WiFi Smart Scale is a body analyzer that measures body weight and uses bioelectrical impedance analysis (BIA) technology to estimate body fat percentage in generally healthy individuals 10 years of age or older. It is intended for home use only.
ARIA is a body weight scale and a body fat analyzer that operates by using a low, safe, battery-generated electrical current through the body (using a bioelectrical impedance analysis technique) to provide body fat and body weight information. After the user registers their scale, the scale automatically recognizes the subject based on body weight and body fat readings. ARIA contains a WiFi module (802.11 module) that allows it to connect to the Internet in the user's home. The module provides a complementary interface to the Fitbit website. Body weight and body fat measurements are independent of internet communication after initial product registration.
The ARIA scale automatically measures body weight and body fat composition. The scale recognizes the user based on previous weight readings, and can accept up to eight (8) different users. The 16 most recent readings are kept in memory on the scale and readings are also transmitted to the user's optional fitbit.com personal account for trending. If users have similar weight, the proper identity can be selected by tapping the scale.
Here's a breakdown of the acceptance criteria and study information for the Fitbit ARIA WiFi Smart Scale, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the ARIA WiFi Smart Scale are established not through explicit numerical thresholds but by demonstrating substantial equivalence to a predicate device (Withings Smart Body Scale K121971) and showing that its body fat measurements are not statistically different from the predicate, with variation within an acceptable range.
| Feature/Metric | Acceptance Criteria (Implied from Predicate/Study) | Reported Device Performance |
|---|---|---|
| Substantial Equivalence | Demonstrates equivalence in technology, intended use, classification, product code, indication for use, device description, analysis method, operating parameters, number of electrodes, power source, IP connectivity, and measured parameters to the predicate device (Withings WBS01 Smart Body Scale K121971). | The ARIA WiFi Smart Scale is listed as substantially equivalent to the Withings Smart Body Scale (K121971) across all listed features. Differences noted (e.g., age range, specific power source type, minor IP connectivity details) are presented as not impacting substantial equivalence. |
| Body Fat Measurement | Body fat composition (%) measurements should not be statistically different (p>0.05) from the predicate device, and body fat measurements should vary by 0.05) and body fat measurements varied by |
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