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The ActiGraph LEAP™ is a small worn activity monitor designed for documenting physical movement associated with applications in physiological monitoring. The device is intended to monitor the activity associated with movement during sleep. The ActiGraph LEAP™ can be used to analyze circadian rhythms and assess activity in any instance where quantifiable analysis of physical motion is desirable.

The ActiGraph LEAP™ is a wrist-worn wearable device intended to continuously record high resolution digital acceleration data associated with a patient's physical movement. In practice, a healthcare professional or researcher can prescribe the device to collect physiological data from patients in applications where quantifiable analysis of physical motion is desirable. Having physical characteristics like those of an electronic wristwatch, the device is set to collect data by the healthcare professional then placed on the subject's wrist. The device is designed to be worn during normal activities and/or during sleep over a period of days to weeks. The patient does not need to interact with the device to control the operation or data collection. The data stored on the device can be downloaded via USB or Bluetooth Low Energy and made accessible to healthcare professionals or researchers for further analysis.

The ActiGraph LEAP™ device will be supported by accessories for recharging the battery and transferring data from the device. A USB Charging Dock with a three-foot USB A cable for both charging and data transfer to a PC using the supplied communication software. The USB Charging Dock connects to the recessed electrical contacts on the back of the device. An off-the-shelf international Wall Mount AC Adapter is also supplied for optional wall charging. The USB Charging Dock can be plugged into the Wall Mount AC Adapter's USB A port for charging the device.

The device uses a high-resolution digital accelerometer to accurately measure linear accelerations in 3axes associated with the patient's physical movement. The accelerometer technology is a microelectromechanical system (MEMS) implemented as an integrated circuit. The accelerometer data is converted to a digital representation on the MEMS accelerometer and then recorded, with timestamp, to the device's on-board memory. The memory is an 8 Gb serial NAND flash capable of storing 30 days of accelerometer data under the default operating mode. The sample rate of the accelerometer is configurable at the following rates: 32Hz, 64Hz, 128 Hz and 256Hz.

The LCD display indicates the battery level, current functional state of the device, and date and time. The device has a 30-day battery life under the default operating mode and can be charged using the USB Charging Dock accessory. The display does not provide feedback to the wearer/patient regarding data measures. There is a simple button on the side used to turn on the display so the wearer can read the date/time and button presses are recorded in the log.

The device firmware executes on internal processors to control the device operations, display, and external communication protocols. The accelerometer sensor data can be downloaded from the device either via USB (using the dock) or via Bluetooth Low Energy.

The provided text is a 510(k) summary for the ActiGraph LEAP activity monitor. It details device characteristics, intended use, and comparison to a predicate device. However, it does not contain any information about acceptance criteria or a study that proves the device meets specific performance criteria related to its functionality (e.g., accuracy of movement tracking, sleep monitoring, or circadian rhythm analysis).

The document focuses on demonstrating substantial equivalence to a predicate device based on:

• Same Indications for Use: Both the predicate and subject devices are intended to monitor activity associated with movement during sleep, analyze circadian rhythms, and assess activity where quantifiable analysis of physical motion is desirable.
• Similar Technological Characteristics: Both use MEMS accelerometers, are wrist-worn, have similar displays, power sources, and data transfer methods.
• Biocompatibility Testing: This addresses changes in patient-contacting materials, ensuring they are still safe.

The document explicitly states: "Clinical testing is not applicable to this submission." This means that no clinical study was conducted to establish performance metrics like accuracy or effectiveness against ground truth on human subjects for this 510(k) clearance.

Therefore, I cannot provide the requested information regarding acceptance criteria and a study proving the device meets them, as that information is not present in the provided text. The submission focuses on showing that the new device is substantially equivalent to an already cleared device, rather than proving de novo performance against specific acceptance criteria.

To answer your request, if this were a dataset that did contain a study with acceptance criteria, the information would typically be presented as follows:

Example of how the information would be presented if available in a different document:

1. Table of Acceptance Criteria and Reported Device Performance (Hypothetical):

2. Sample Size and Data Provenance (Hypothetical):

• Test Set Sample Size: 150 participants (e.g., 50 healthy adults, 50 insomnia patients, 50 shift workers).
• Data Provenance: Prospective, multi-center study conducted in the USA, UK, and Germany.

3. Number and Qualifications of Experts (Hypothetical):

• Experts: 3 Board-Certified Sleep Physicians (average 12 years of experience in sleep medicine, specializing in polysomnography interpretation).

4. Adjudication Method (Hypothetical):

• Adjudication: 2+1. Initial assessment by two experts; in cases of disagreement, a third senior expert provided a binding decision.

5. MRMC Comparative Effectiveness Study (Hypothetical):

• MRMC Study: Yes, an MRMC study was conducted comparing sleep staging performance of human experts with and without AI assistance from the ActiGraph LEAP data.
• Effect Size: Human readers improved sleep stage classification accuracy by an average of 7% (from 82% to 89%) when assisted by the AI algorithm compared to performing the task unassisted.

6. Standalone Performance (Hypothetical):

• Standalone Performance: Yes, the algorithm achieved 91% accuracy in detecting sleep onset/offset events and 87% accuracy in differentiating wake, NREM, and REM sleep stages when compared to polysomnography.

7. Type of Ground Truth (Hypothetical):

• Ground Truth: Polysomnography (PSG) for sleep parameters, motion capture system for activity counts, and validated actigraphy devices for circadian rhythm analysis.

8. Training Set Sample Size (Hypothetical):

• Training Set Sample Size: 5,000 subjects.

9. How Ground Truth for Training Set was Established (Hypothetical):

• Training Ground Truth: Ground truth for the training set was established through a combination of expert-annotated polysomnography data from a diverse patient population, alongside simultaneously recorded high-resolution motion data from the device and other reference sensors. Annotations were initially made by trained technicians and then reviewed and confirmed by a panel of 5 board-certified sleep specialists using an iterative consensus approach.

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