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The Proteus® Digital Health Feedback Device consists of a miniaturized, wearable sensor for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), and time-stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. When the ingestible sensor is ingested, the Proteus Digital Health Feedback Device is intended to log, track and trend intake times. When co-ingested with medication, the tracking and trending of intake times may be used as an aid to measure medication adherence. The Proteus Digital Health Feedback Device may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable, and enables unattended data collection for clinical and research applications.

The Proteus Digital Health Feedback Device consists of a wearable sensor, an ingestible sensor, and a software application.

The Proteus wearable sensor is a body-worn sensor that collects physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), skin temperature, and time-stamped user-logged events signaled by the co-incidence with, or coingestion with, the Proteus ingestible sensor. The display application of the Proteus Digital Health Feedback Device may be used to analyze circadian rhythms and patterns.

The ingestible sensor is embedded inside an inactive tablet. (the Proteus Pill or sensorenabled pill) for ease of handling and swallowing. After the ingestible sensor reaches the stomach, it activates and communicates its presence with a unique identifier to the wearable sensor. When the ingestible sensor is co-ingested with medication, the Proteus device is intended to log, track, and trend medicine intake times as an aid to measure medication adherence.

The software application receives the data from the wearable sensor for further processing and analysis of the physiological and behavioral metrics. The processed data is then sent to the user interface (UI) for display as well as to Proteus databases for storage and sharing.

Here's an analysis of the provided text, focusing on the acceptance criteria and study information for the Proteus Digital Health Feedback Device:

Based on the provided text, a comprehensive study proving the device meets acceptance criteria, including specific quantifiable acceptance criteria and detailed study results, is not present. The document is a 510(k) summary for FDA clearance, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study report with acceptance criteria.

However, we can extract information regarding non-clinical performance data and the general approach to validating different aspects of the device.

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, explicit acceptance criteria with specific thresholds are not provided in this document. The text describes how different parameters were validated or quantified, rather than defining performance targets.

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

• Test Set Sample Size: Not specified. The document mentions "tested using selected guidelines" or "validated against," but does not provide details on the number of subjects or samples used in these non-clinical evaluations.
• Data Provenance: The document does not specify the country of origin. It indicates that the data are from "Non-Clinical Performance Data" and implies laboratory or controlled test environments. The data is retrospective in the sense that it was collected prior to this submission for FDA clearance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Not Applicable / Not Specified. The evaluations described are primarily technical performance tests of sensors and algorithms, not evaluations against human expert ground truth for diagnostic purposes. For example, the accelerometer was "validated against a known acceleration," which implies a controlled physical standard rather than expert consensus. Similarly, heart rate was tested against "selected guidelines set forth in the ANSI/AAMI EC 13 standard," which refers to industry standards, not human interpreters.

4. Adjudication Method for the Test Set

• Not Applicable / Not Specified. Since there is no mention of human experts establishing ground truth or making judgments, there is no adjudication method 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

• No, an MRMC comparative effectiveness study was not done or described. This device focuses on objective physiological and behavioral data collection, not on aiding human interpretation of complex medical images or data that typically require MRMC studies.

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

• Yes, standalone non-clinical performance data was done for individual device components. The section "Summary of Non-Clinical Performance Data" describes evaluating the accelerometer, biopotential low-frequency amplifier (for heart rate), thermistor, and ingestible sensor activation/lifetime independently. For example, "The three-axis accelerometer provided motion and angle relative to gravity (body position) data and was validated against a known acceleration applied against each of its three axes." This indicates direct evaluation of the device's technical output.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• The ground truth used was primarily controlled physical inputs, laboratory standards, and industry guidelines.
  • For the accelerometer: "known acceleration."
  • For heart rate: "selected guidelines set forth in the ANSI/AAMI EC 13 standard."
  • For skin temperature: "compared to a reference resistor" (implying a controlled reference temperature/voltage).
  • For ingestible sensor: Testing "activation time and lifetime" implies comparison against internal specifications or direct observation.

8. The Sample Size for the Training Set

• Not specified. The document does not provide details on a separate training set for any algorithms. It describes a "proprietary algorithm" for heart rate, but does not detail its development or training data. Given the context of a 510(k) for a sensor device, emphasis would be on device performance rather than complex machine learning algorithm training.

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

• Not specified, as no training set details are provided.

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