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The Sleep Apnea Notification Feature (SANF) is a software-only mobile medical application that analyzes Apple Watch sensor data to identify patterns of breathing disturbances suggestive of moderate-to-severe sleep apnea and provides a notification to the user. This feature is intended for over-the-counter (OTC) use by adults age 18 and over who have not previously received a sleep apnea diagnosis and is not intended to diagnose, treat, or aid in the management of sleep apnea. The absence of a notification is not intended to indicate the absence of sleep apnea.

The Sleep Apnea Notification Feature (SANF) is an over-the-counter mobile medical application (MMA) intended to identify patterns of breathing disturbances suggestive of moderate-to-severe sleep apnea and provide a notification to the user. SANF is intended to run on compatible iOS (e.g. iPhone, iPad) and Apple Watch platforms. Users set up SANF and view their health data on the iOS platform. Prior to use, users must undergo educational onboarding. SANF uses accelerometer sensor data collected by the Apple Watch to calculate breathing disturbance values while a user is asleep. Breathing disturbances describe transient changes in breathing patterns, such as temporary breathing interruptions.

Breathing disturbance data is analyzed in discrete, consecutive 30-day evaluation windows, If patterns consistent with moderate-to-severe sleep apnea are identified within the 30-day evaluation window, the user is notified. SANF provides visualizations depicting the user's breathing disturbance data over various time scales. SANF is not intended to provide instantaneous measurements. Instead, once activated, SANF runs opportunistically in the background receiving signals from Apple Watch sensors for processing.

Here's a summary of the acceptance criteria and study details for the Sleep Apnea Notification Feature (SANF), based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document emphasizes that performance was "optimized for high specificity" given the opportunistic detection nature of the device. This implies that while a specific numerical sensitivity might not have been a hard "acceptance criterion" per se, the reported sensitivity alongside high specificity demonstrated sufficient effectiveness for clearance.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set (Clinical Study):

  • Notification Performance Analysis: 1,278 subjects
  • Breathing Disturbance Performance Analysis: 1,305 subjects
  • Total Subjects Enrolled: 1,499 subjects (some had insufficient data for analysis)

• Data Provenance:

  • Country of Origin: United States (from "several sites across the United States").
  • Retrospective or Prospective: Prospective. The study "enrolling 1,499 subjects" suggests a prospective collection of data specifically for this validation study.

3. Number of Experts Used to Establish the Ground Truth and Qualifications

The document refers to the "Nox T3s home sleep apnea testing (HSAT) device (K192469) as a reference device" for ground truth. The HSAT device itself provides the AHI (Apnea-Hypopnea Index) which is the clinical standard for sleep apnea diagnosis.

Thus, the ground truth was established by the HSAT device, not by human experts directly adjudicating each case. The output of the HSAT device is the ground truth measure (AHI).

4. Adjudication Method for the Test Set

The ground truth was established by the Nox T3s HSAT device, which is an objective measurement device. Therefore, a human expert adjudication method (like 2+1 or 3+1) was not explicitly mentioned or performed for the primary clinical endpoint, as the HSAT device is considered the reference standard. The AHI values derived from the HSAT device served as the diagnostic ground truth.

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

There is no mention of an MRMC comparative effectiveness study involving human readers with or without AI assistance. The study focuses on the standalone performance of the SANF device against a reference standard (HSAT).

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance)

Yes, a standalone performance study was done. The reported sensitivity and specificity values are for the algorithm's performance in identifying patterns suggestive of moderate-to-severe sleep apnea and providing a notification, without human intervention in the interpretation or decision-making process based on the device's output. The device itself "provides a notification to the user," implying direct algorithm output.

7. Type of Ground Truth Used

The ground truth used was objective diagnostic data derived from a medical device: The Nox T3s home sleep apnea testing (HSAT) device, which provides the Apnea-Hypopnea Index (AHI). This is considered a gold standard for diagnosing and classifying the severity of sleep apnea in a home setting.

8. Sample Size for the Training Set

The algorithm development dataset included over 11,000 nights of concurrent reference and watch sensor data.

9. How the Ground Truth for the Training Set Was Established

The ground truth for the training set was established using concurrent in-lab polysomnography (PSG) and Home Sleep Apnea Test (HSAT) reference recordings. These are the gold standard diagnostic tests for sleep apnea, providing objective measures like the Apnea-Hypopnea Index (AHI). The document also mentions that the distribution of sleep apnea classifications (normal, mild, moderate, severe) was broad in this dataset.

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