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

The ActiGraph CentrePoint Insight Watch is a compact, battery-operated wearable activity recording device with physical characteristics similar to those of a small wristwatch. The watch is intended to acquire and store data from an onboard accelerometer while being worn during normal activities and/or during sleep. The data record is timestamped and stored in non-volatile memory for later retrieval. Downloaded data can be post-processed based on the timestamp and magnitude of acceleration along each axis.

The housing is constructed of a combination of opaque and clear copolyesters formulated specifically for medical devices (i.e., tested and determined biocompatible), and the core data collection sensor is a 3-axis microelectromechanical system (MEMS) accelerometer. A charging dock connected to a USB power source is used to charge the device battery and communicate with a PC or peripheral.

The ActiGraph CentrePoint Insight Watch is a wrist-worn activity monitor designed for physiological monitoring, particularly for tracking movement during sleep to analyze circadian rhythms and assess physical motion.

Here's a breakdown of the acceptance criteria and supporting studies:

1. Table of acceptance criteria and the reported device performance:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
   The document states that "This premarket submission did not rely on the assessment of clinical performance data to demonstrate substantial equivalence." Therefore, there is no clinical test set described in this submission. The "test set" in this context refers to devices used for non-clinical bench testing. The sample size for these non-clinical tests is not explicitly mentioned, nor is the provenance of data for these tests beyond being "bench testing."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as no clinical test set for ground truth establishment is described. The acceptance is based on substantial equivalence to a predicate device through non-clinical bench testing.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as no clinical test set requiring adjudication is described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This device is an activity monitor, not an AI-assisted diagnostic tool that would involve human readers or MRMC studies.

6. If a standalone (i.e., algorithm only without human-in-the loop performance) was done: The document describes the device as a standalone activity monitor that records and stores data from an accelerometer. Non-clinical bench testing was performed to demonstrate its performance and reliability in this standalone function. The "study" mentioned is the series of non-clinical bench tests performed to support substantial equivalence.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the non-clinical tests, the "ground truth" would be established engineering specifications and recognized voluntary consensus standards. For example, accelerometer dynamic range, sampling rate, memory capacity, water resistance, weight, size, and recording time were compared against the predicate device's capabilities and against the device's own internal specifications validated through bench testing. The non-clinical bench tests included:

   • Performance and reliability testing
   • Comparative data analysis
   • Basic safety and essential performance in accordance with AAMI ES60601
   • Electromagnetic compatibility (EMC) in accordance with IEC 60601
   • Biocompatibility and material standards confirms there is no harm to the patient wearing the device.
   • System compatibility with ActiGraph software for data download and collection

8. The sample size for the training set: Not applicable. This is not an AI/ML device that requires a training set for model development.

9. How the ground truth for the training set was established: Not applicable, as there is no training set for an AI/ML model for this device.

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The ActiTrainer 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 ActiTrainer can be used to analyze circadian rhythms and assess activity in any instance where quantifiable analysis of physical motion is desirable.

The ActiTrainer is housed in a polycarbonate plastic housing. It is 8.5 cm long by 3.4 cm wide by 1.6 cm thick and it weights 51 grams. It also has an optional Polar heart strap. Data is downloaded into a PC via a USB plug and the data is displayed with ActiGraph LLC's ActiLife software.

This document describes the ActiTrainer, an activity monitor, and its substantial equivalence to a predicate device, the Actigraph. However, it does not contain a study that quantitatively proves the device meets specific acceptance criteria with numerical performance values for parameters like sensitivity, specificity, accuracy, or other commonly used metrics in medical device studies.

Instead, the document focuses on demonstrating substantial equivalence based on technological characteristics and intended use, which is a common pathway for 510(k) clearance.

Therefore, many of the requested sections regarding acceptance criteria and performance study details cannot be fully answered from the provided text.

Here is an attempt to answer based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria. Instead, the "acceptance criterion" for 510(k) clearance is demonstrating substantial equivalence to a predicate device in terms of intended use and technological characteristics. The performance is assessed by comparing the ActiTrainer's specifications to those of the Actigraph (K040554).

Study Details (as much as can be inferred from the document):

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not describe a "test set" in the context of a performance study with human subjects or a dataset for algorithm validation. The comparison is based on the specifications of the new device (ActiTrainer) against the specifications of the predicate device (Actigraph). This is a technical comparison, not a clinical trial or algorithm validation study. Therefore, there is no sample size for a test set or data provenance in the traditional sense.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This is not applicable as there was no "test set" requiring ground truth establishment by experts for performance evaluation. The ground truth for proving substantial equivalence lies in the technical specifications and intended uses being comparable.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no test set was described that would require adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. The ActiTrainer is an activity monitor, not an AI-assisted diagnostic tool that would involve human readers or MRMC studies.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

This refers to the device's inherent function. The ActiTrainer operates as an algorithm-only device (it records and processes physical movement data without continuous human intervention during data collection). The "study" presented is a comparison of its technical specifications to a predicate, not an evaluation of its standalone performance in a clinical setting with numerical outcomes.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

For the purpose of 510(k) clearance in this context, the "ground truth" is primarily the established technical specifications and intended use of the legally marketed predicate device (Actigraph K040554). The ActiTrainer's specifications and intended use are compared against these predicate "truths" to demonstrate substantial equivalence.

8. The sample size for the training set

Not applicable. This document describes a 510(k) submission for a physical activity monitor, not an AI/machine learning algorithm that requires a "training set."

9. How the ground truth for the training set was established

Not applicable, as there is no training set for an AI/machine learning algorithm.

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