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The MobileCare Monitor remote monitoring system is intended as a support in the care of patients and the elderly in institutional, home, and community care settings. Intended users include the monitored individuals and their caregivers.

MobileCare Monitor includes a MyPHD personal help device that is intended to monitor patients and the elderly as they go about their daily activities. MyPHD can be affixed to the wrist, clipped to the waist or used in a bandage for attachment at other locations on the monitored individual, as determined by the patient or caregiver. MobileCare monitor provides the MyPHD location information, battery life and indication that it is being worn. MyPHD also includes a help button and a tri-axial accelerometer used to convey the degree of motion created by a wearer's movements to support an assessment of impacts.

MobileCare Monitor may also relay physiological data from legally marketed Class I and Class II wireless medical devices (e.g. blood pressure cuff, weight scale, blood glucometer, etc.) to AFrame Digital secure remote servers by means of industry standard networks and public carriers. Physiological data transmitted from these devices are stored and may be retrieved with a secure web browser after the user presents satisfactory security credentials. Users may also request the data to be presented in graphical format.

Alerts can be sent when a patient presses a help button on the myPHD or when any data value from MyPHD or relayed device exceeds an upper or lower limit threshold established by a user. These data include impact sensitivity settings and/or physiological measurements from relayed Class I and Class II devices. Rules may be created for reminders to be scheduled for periodic physiological measurements. Alerts are sent to the browser or to a computer or mobile device address that the user designates.

MobileCare Monitor is not interpretive or predictive, nor is it intended to provide an automated treatment decision or act as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under the direct supervision and oversight of an appropriate healthcare professional.

AFrame Digital's MobileCare Monitor is a communications platform consisting of a local wireless gateway, a remote computer server and software.

Local Wireless Gateway. A Powered Automated Network Data Aggregator (PANDA) is the wireless gateway to the remote server, similar to wireless routers used in homes and offices to provide wireless internet access. Three or more PANDAs are placed in a residential or patient environment to support internet- or cellular-based communications between the AFrame Digital MyPHD (personal health device) and a remote computer server. In some configurations the server will be in the same facility. All wireless messages are securely encrypted with 256-bit AES. The PANDA access points form a reliable, low-power wireless network 'mesh' in the facility for complete wireless coverage of all residents with MyPHD watches.

Remote Computer and Software. The remote computer server communicates with the PANDA devices. It analyzes the messages received against alert thresholds established by designated staff or caregivers. Staff may make inquiries of the status of wearable monitors. The location of a wearable monitor may be determined from its position relative to nearby fixed-position PANDAs. The server will store messages and status information concerning the residents. Staff will be able to annotate records with support information related to individual residents and patients. To enhance caregiver productivity and mobility, alerts, resident location and other important person-specific information is available using a secure web browser application from a portable electronic device (PED), laptop or fixedstation PC.

The remote computer server also communicates with COTS local wireless gateways that incorporate standardized wireless communications protocols such as Bluetooth to receive health-related data from commercially available, wireless measurement devices (e.q. blood pressure cuffs, pulse-oximeters, weight scales, blood alucose meters, etc.). AFrame Digital's configuration protocols and web-based software tools specify whether a measurement device may be used by a single user or multiple users. They also integrate data from these devices with other personspecific information without the transmission of personal health information. The server's software displays the data remotely to access-enabled users or caregivers over a secure browser, and provides tools for them to select patient-specific alert thresholds.

MyPHD™ Wearable Monitor. This is a small plastic housing and wrist strap with an external "panic" button. Some variations will have additional buttons that may be assigned a messaging or privacy function by the product software (softsettable). Internally, there is an impact sensor that may indicate a fall and a skin temperature sensor to indicate if the monitor has been removed from the wrist. A microprocessor with an industry standard very low power wireless transceiver sends messages to a nearby PANDA.

The AFrame Digital MobileCare Monitor device is a communications platform consisting of a local wireless gateway (PANDA), a remote computer server and software, and a MyPHD wearable monitor. It is intended to support the care of patients and the elderly by monitoring their daily activities, providing location information, battery life, wear indication, and a help button. It also relays physiological data from legally marketed wireless medical devices.

Here's an analysis of the provided information regarding acceptance criteria and the study:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly define typical acceptance criteria (e.g., specific accuracy percentages, sensitivity, specificity) for the MobileCare Monitor as a whole or its individual components. Instead, the "Safety and Efficacy" and "Performance Testing" sections describe the methods used to ensure the device's reliability and usability.

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

• Test Set Sample Size: The clinical study (Phase I grant by HHS National Institutes of Health) "made up of elderly participants." The exact number of participants is not explicitly stated.
• Data Provenance: The study was conducted by "independent clinical researchers." The funding source is the HHS National Institutes of Health (Grant number 1R43AG029196-01A1), implying it was likely conducted in the United States. The study appears to be prospective, as it involved participants completing daily surveys and researchers evaluating usability and reliability based on data collected during the study.

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

The concept of "ground truth" as typically understood in AI/medical device performance (e.g., for diagnostic accuracy) does not directly apply here. The study focused on usability and reliability of the device in integrating COTS medical devices and acquiring data.

• The evaluation was performed by "independent clinical researchers." Their specific number or qualifications (e.g., type of physician, years of experience) are not specified. They acted as the evaluators of the system's performance, but not in establishing a medical "ground truth" for diagnostic purposes.

4. Adjudication Method for the Test Set

Not applicable. The study was not designed to establish a diagnostic ground truth or involve multiple expert readers adjudicating cases. It was a usability and reliability study where researchers assessed system function and participant compliance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not described. The submission focuses on the device's reliability and usability in collecting and transmitting data, not on its impact on human reader performance or diagnostic accuracy.

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

Yes, a form of standalone performance was evaluated, though not in the typical diagnostic sense. The device's "system reliability and data concurrence when integrated with third party measuring devices" was assessed through internal quality assurance testing and by independent researchers, without direct human intervention in the data processing and transmission once the system was set up. The device itself is described as "not interpretive or predictive, nor is it intended to provide an automated treatment decision."

7. The Type of Ground Truth Used

The ground truth or evaluation metric used was related to:

• System Reliability: Measured by internal QA and researcher observation.
• Data Concurrence: Assessed by comparing transmitted data to expected data from third-party devices.
• Usability: Quantified by the ratio of actual readings gathered/received to expected readings, and participant compliance with study protocols.
• Feasibility: A qualitative determination by the researchers.

This is distinct from diagnostic ground truth (e.g., pathology, confirmed outcomes).

8. The Sample Size for the Training Set

The submission does not provide any information about a specific "training set" for the MobileCare Monitor. The device is primarily a data aggregation and transmission platform, not an interpretive AI model that typically requires a large training dataset for learning. The performance tests described are more akin to system validation and usability studies rather than machine learning model validation.

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

Not applicable, as no training set or machine learning model with a distinct "ground truth" for training purposes is described for the MobileCare Monitor. The device's functionality relies on established communication protocols, data handling, and preset alert thresholds, rather than on a learned model derived from a training dataset.

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The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The provided 510(k) summary for the KOGO 138 MobileCare Monitor™ does not contain information about specific acceptance criteria related to a clinical study or device performance metrics like sensitivity, specificity, or accuracy. It focuses on demonstrating substantial equivalence to predicate devices primarily through comparison of technical characteristics and intended use, and conformance to non-clinical safety (FCC regulations) and software validation standards.

Therefore, the following information cannot be extracted from the provided text:

• A table of acceptance criteria and the reported device performance: No specific performance metrics or acceptance criteria are stated. The document indicates software validation was performed, but no results are provided.
• Sample size used for the test set and the data provenance: Not applicable as no clinical test set is described.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method for the test set: Not applicable.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device is a monitor with alerting capabilities; performance is not typically described in terms of "algorithm only" in this context. The document mentions an "impact sensor that may indicate a fall" and an "emergency (panic) button."
• The type of ground truth used: Not applicable as no clinical performance study is detailed with ground truth.
• The sample size for the training set: Not applicable as a machine learning training set is not mentioned for this device type.
• How the ground truth for the training set was established: Not applicable.

Here's a summary of the available information regarding acceptance criteria and studies:

1. Acceptance Criteria and Reported Device Performance:

The document primarily relies on demonstrating substantial equivalence to predicate devices and adherence to non-clinical standards rather than clinical performance metrics.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified, as no clinical test set for performance evaluation is described in the provided text. The evaluation focuses on non-clinical aspects and substantial equivalence.
• Data Provenance: Not applicable, as no clinical performance data are presented.

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 requiring expert-established ground truth is described.

4. Adjudication method 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:

• No, a multi-reader multi-case comparative effectiveness study was not done or reported. This type of study is more common for diagnostic imaging AI devices, whereas the MobileCare Monitor™ is a monitoring and alerting system.

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

• The document does not describe a "standalone" algorithmic performance study in the context typically used for AI/ML devices (e.g., measuring accuracy of an image analysis algorithm). The device itself functions as a standalone monitoring system that provides alerts.

7. The type of ground truth used:

• Not applicable, as no clinical performance study with defined ground truth is described. The "performance" aspect refers to software validation.

8. The sample size for the training set:

• Not applicable, as no machine learning algorithm requiring a training set is explicitly mentioned or detailed in the provided information. The device functions based on sensors and pre-set thresholds, not a learned model.

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

• Not applicable, as no machine learning algorithm or training set is described.

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