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The Otsuka Digital 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 co-incidence with, or co ingestion with, the ingestible sensor accessory. When the ingestible sensor is ingested, the Otsuka Digital 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 Otsuka Digital 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 Otsuka Digital Feedback Device consists of 3 components: a wearable sensor, an ingestible sensor accessory, and software that aggregates, processes, and enables display of data collected by the sensors.

The wearable sensor in the Otsuka Digital Feedback Device is a body-worn sensor that consists of a single-use patch known as the D-Tect wearable sensor or D-Tect Patch. The D-Tect Patch collects physiological and behavioral metrics such as heart rate, activity, body angle, and time stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. Note: While the device includes automated heart rate (HR) measurement, it does not provide an ECG waveform recording for display or analysis. The device is not intended to diagnose heart-related conditions and does not include alarms. HR measurement is not intended to be used in alarm system. HR data may not be accurate for patients with pacemakers.

The ingestible sensor is embedded inside an inactive tablet (the pill or sensor-enabled 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 Otsuka Digital Feedback Device is intended to log, track, and trend medicine intake times to measure medication adherence.

The software on the general computing device receives the data from the wearable sensor for further processing and analysis of metrics. The processed data is then sent to the user interface for display, as well as being saved in a local record database for storage. The software component includes firmware running on the wearable sensor that collects and records the data from the sensors and a software that receives data from the wearable sensor for further analysis, processing, storage, and display to the user.

For purposes of this 510(k), the changes from the predicate device (Otsuka Digital Feedback Device-RW, K223463) to the Otsuka Digital Feedback Device with D-Tect Patch (the device subject of this 510[k]) are the wearable sensor and the software for the user interface. The ingestible sensor remains the same as the predicate device.

Here's a breakdown of the acceptance criteria and the study that proves the Otsuka Digital Feedback Device meets them, based on the provided FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

The document implicitly defines acceptance criteria through its performance testing sections. For the purpose of this analysis, I will focus on the most explicitly quantified performance metrics from the provided text.

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

• Sample Size (Clinical Performance Data): A total of 55 subjects were tested for the clinical performance evaluation.
• Data Provenance: The study was conducted at 1 site in the United States. It was a prospective noninterventional validation study in healthy subjects.

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

The document does not specify the number or qualifications of experts used to establish a "ground truth" for the test set in the conventional sense (e.g., radiologists reviewing images). The clinical performance study focused on objective measurements like wear duration, comfort ratings self-reported by subjects, and skin irritation scores, which are typically assessed by trained clinical staff rather than expert consensus on a diagnostic outcome. Skin irritation scores are based on established clinical grading scales.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method (like 2+1 or 3+1 consensus) for the test set. For the wear duration, comfort, and skin irritation metrics, the data collection methods (instrumental recording for wear duration, self-reporting for comfort, and clinical scoring for irritation) are inherently less reliant on such adjudication compared to subjective diagnostic tasks.

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 MRMC comparative effectiveness study was mentioned in this document. The device, an "Ingestible Event Marker with wearable patch," is primarily focused on automated data collection for physiological and behavioral metrics and medication adherence, not on aiding human readers in interpreting complex diagnostic images or data where "AI assistance" would directly improve a reader's performance in a diagnostic task.

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

Yes, the testing described appears to largely cover standalone algorithm and device performance, particularly for:

• Pill Detection Capability: "Enhanced low-noise analog front end improves sensitivity of the IEM detection by >50%, increasing the likelihood of detection." This is an intrinsic algorithmic/hardware capability.
• Step Counting: "Improved step metric based on passive fully continuous step counting." This is an algorithmic function.
• Heart Rate Measurements: "Verified against real ECG signals and 100% measurement accuracy for simulated ECG signal." This tests the algorithm's performance.
• Wear Duration: This is objectively measured by the device's logging capability.

The clinical performance study focuses on the overall system's ability to log data correctly and be comfortable and safe, which inherently relies on the standalone performance of its components.

7. The Type of Ground Truth Used

• Wear Duration: Objective logging by the device itself, validated against study protocols of subjects wearing patches for specified periods.
• Comfort: Subject self-reporting on a defined scale.
• Skin Irritation: Clinical assessment based on established grading scales (Grade 0, 1, 2).
• Pill Detection, Step Counting, Heart Rate: Verified against reference methods/signals (e.g., real ECG signals, simulated ECG signals).
• Mechanical, Electrical, Software, Cybersecurity, Biocompatibility, Shelf Life: Ground truth is defined by compliance with applicable standards, specifications, and successful completion of pre-defined test protocols.

8. The Sample Size for the Training Set

The document does not provide details on the sample size used for the training set of any algorithms within the device. It focuses on the validation of the finalized device.

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

Since the document does not mention the training set size, it also does not elaborate on how the ground truth for any potential training data was established. The improvements noted (e.g., improved pill detection, step counting, HR measurements) suggest iterative development and potentially machine learning, but the specifics of their training data and ground truth are not detailed in this regulatory summary.

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Rejoyn is a prescription digital therapeutic for the treatment of Major Depressive Disorder (MDD) symptoms as an adjunct to clinician-managed outpatient care for adult patients with MDD aged 22 years and older who are on antidepressant medication. It is intended to reduce MDD symptoms.

Rejoyn (also known as CT-152) is a digital therapeutic smartphone application (app) for the treatment of Major Depressive Disorder (MDD) symptoms. Rejoyn is a prescription smartphone app-based digital therapeutic administered to a user via the user's smartphone device (running Apple iPhone operating system [iOS®] or Android™ operating system [OS]), which delivers a proprietary interactive cognitiveemotional and behavioral therapeutic intervention. The core components of Rejoyn are the Emotional Faces Memory Task (EFMT) exercises, brief cognitive behavioral therapy (CBT)-based lessons to learn and apply key therapeutic skills, and short message service (SMS) text messaging to reinforce CBT-based lesson content and to encourage engagement with the app. It is intended for the treatment of MDD symptoms as an adjunct to clinician-managed outpatient care for adult patients with MDD aged 22 years and older. It is intended to reduce MDD symptoms.

Rejoyn is designed for use as an adjunct to clinician-managed outpatient care over a period of 6 weeks for the treatment of MDD symptoms, followed by a 4-week extension period where CBT-based lesson content will be accessible but no new therapeutic content or EFMT exercises will be available. Rejoyn is not intended to be used as a stand-alone therapy or as a substitution for the patient's clinician prescribed medications.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Device Name: Rejoyn™
Regulation Number: 21 CFR 882.5801
Regulation Name: Computerized Behavioral Therapy Device For Psychiatric Disorders
Regulatory Class: Class II

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for Rejoyn are defined by the "Special Controls" for computerized behavioral therapy devices for psychiatric disorders. These special controls mandate clinical data and detailed software documentation. The device's performance is demonstrated through the Mirai trial.

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

• Sample Size (Test Set):

  • Intent-To-Treat (ITT) population: 386 participants (194 Rejoyn, 192 Sham)
  • Modified Intent-To-Treat (mITT) population: 354 participants (177 Rejoyn, 177 Sham) (This was the primary population for the primary efficacy endpoint analysis).
  • Safety Sample: 373 participants (187 Rejoyn, 186 Sham)

• Data Provenance: The Mirai trial (NCT04770285) was a pivotal, multicenter, remote, double-blinded (patients also blinded to hypothesis), randomized, controlled trial. The study was conducted virtually, with participants across multiple centers, implying a prospective and multi-site data collection. No specific country of origin is mentioned, but "multicenter" typically implies multiple sites within a region (e.g., US).

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

The ground truth for the clinical effectiveness was established through commonly used and validated psychiatric assessment scales:

• Montgomery-Asberg Depression Rating Scale (MADRS): This is a clinician-rated scale. The study states the benefit was "consistently rated by independent assessors via the MADRS," indicating multiple clinicians likely contributed to these assessments. Specific number and qualifications are not detailed, but it is implied they are qualified clinicians for psychiatric assessment.
• Clinical Global Impression-Severity Scale (CGI-S): This is also a clinician-rated scale, where benefit was "rated by study investigators via the CGI-S." Again, specific numbers and qualifications of these "study investigators" are not explicitly stated, but they would be medical professionals involved in the clinical trial.

4. Adjudication Method for the Test Set

The text indicates that the trial was "double-blinded (patients also blinded to hypothesis)" and assessments were made by "independent assessors" (for MADRS) and "study investigators" (for CGI-S). There is no explicit mention of an adjudication method like 2+1 or 3+1 for resolving discrepancies in assessments. However, the use of "independent assessors" for the primary outcome measure (MADRS) suggests a measure to reduce bias.

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

No, an MRMC comparative effectiveness study was not done in the context of radiologists or similar image interpretation professions. This device is a digital therapeutic for psychiatric disorders, not an imaging diagnostic tool requiring multiple readers to interpret cases. The effectiveness study compared the device (Rejoyn) to a Sham control group, not human readers with and without AI assistance.

6. Standalone Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) study was effectively done. Rejoyn is a "prescription digital therapeutic" that provides "proprietary interactive cognitive-emotional and behavioral therapeutic intervention" directly to the user via a smartphone app. The trial design assessed the effectiveness of this app-based intervention (Rejoyn) against a Sham app, with both groups continuing "clinician-managed outpatient care" and "antidepressant medication." The primary efficacy endpoint measured the change in MADRS total score directly attributable to the Rejoyn app's use as an adjunct, demonstrating its standalone contribution to reducing MDD symptoms beyond standard care.

7. Type of Ground Truth Used

The ground truth was based on expert clinical assessments and patient-reported outcomes using validated scales:

• Clinician-rated scales: Montgomery-Asberg Depression Rating Scale (MADRS) and Clinical Global Impression-Severity Scale (CGI-S).
• Patient-reported outcomes (PROs): Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7).

These are standard, widely accepted measures for assessing depressive and anxiety symptoms in clinical trials.

8. Sample Size for the Training Set

The provided document describes a pivotal clinical trial (Mirai Trial) used for validation. It does not provide details about a training set for the development of the Rejoyn algorithm itself. Digital therapeutics often undergo iterative development and testing, but the specifics of a "training set" in the machine learning sense are not included in this regulatory summary, which focuses on the clinical validation of the final product.

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

As mentioned above, the document does not include information about a "training set" for the algorithm itself. The focus is on the clinical validation of the device's effectiveness using the Mirai trial. If Rejoyn's "proprietary interactive cognitive-emotional and behavioral therapeutic intervention" involves machine learning components that were "trained," the methods and ground truth for that training are not detailed in this 510(k) summary. The summary highlights that the software documentation demonstrates Rejoyn "effectively implements the behavioral therapy model," suggesting the model itself is based on established therapeutic principles (EFMT and CBT).

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The Otsuka Digital Feedback Device-RW 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 timestamped patient-logged events, including events signaled by the co-incidence with, or co-ingestible sensor accessory. When the ingestible sensor is ingested, the Otsuka Digital Feedback Device-RW is intended to log, track and trend intake times. When co-ingested with medication, the tracking of intake times may be used as an aid to measure medication adherence. The Otsuka Digital Feedback Device-RW may instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable, and enables unattended data collection for clinical and research applications.

The Otsuka Digital Feedback Device-RW consists of three components: a wearable sensor, an ingestible sensor accessory, and software that aggregates, processes and enables display of data collected by the sensors. The wearable sensor is a body-worn sensor (also called the patch) that collects physiological and behavioral metrics such as heart rate, activity, body angle, and time-stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. The wearable sensor in the Otsuka Digital Feedback Device-RW is a 2-component patch known as the RW2 wearable sensor or RW2 patch. The ingestible sensor is embedded inside an inactive tablet (the pill or sensor-enabled pill) for ease of handling and swallowing. After the ingestible sensor reaches the stomach, it activates and communicates its presence with a unique identifier (ID) to the wearable sensor. When the ingestible sensor is co-ingested with medication, the Otsuka Digital Feedback Device-RW is intended to log, track and trend medicine intake times to measure medication adherence. The software on a general computing device (eg, mobile device) receives the data from the body-worn sensor or patch for further processing and analysis of the behavioral and physiological metrics. The processed data is then sent to the user interface (UI) for display as well as being saved in a local record database for storage.

The provided text describes the Otsuka Digital Feedback Device-RW, which is an ingestible event marker with a wearable patch. The primary function highlighted is logging and trending medication intake times to aid in measuring medication adherence, as well as tracking physiological and behavioral metrics.

However, the document is an FDA 510(k) clearance letter and its associated summary. While it mentions "acceptance criteria" for "firmware verification, mechanical verification, electrical verification, biocompatibility evaluation, and system verification testing," it does not provide specific numerical acceptance criteria for the device's performance in its intended use (i.e., accurately tracking ingestion or medication adherence). It also clarifies that no new clinical studies were performed to support the changes in this submission, indicating that the performance data for the core functionality would likely be derived from previous predicate device studies or non-clinical testing.

Therefore, many of the requested details about a study proving the device meets acceptance criteria related to its clinical performance (e.g., accuracy of ingestion detection, improved human reader performance with AI) cannot be extracted directly from this document. The document focuses on showing substantial equivalence to a predicate device, based on similar technology, non-clinical testing, and existing clinical data from the predicate for some aspects.

Based on the provided text, here's what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

The document mentions acceptance criteria were met for:

• Firmware verification
• Mechanical verification
• Electrical verification
• Biocompatibility evaluation
• System verification testing

No specific numerical acceptance criteria or performance metrics related to the core function of "logging, tracking, and trending intake times" or "measuring medication adherence" are provided in this document. The document states "The results demonstrate that the acceptance criteria for firmware verification, mechanical verification, electrical verification, biocompatibility evaluation, and system verification testing were met," but it does not quantify these.

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 explicitly states: "However, new clinical studies were not performed to support the changes proposed in this submission." It implies that any performance data for the core functions relies on the predicate devices. Therefore, details about sample size, provenance, and retrospective/prospective nature of a new clinical test set are not available in this document.

The document mentions: "Clinical studies were conducted to support the expanded wear location of the patch to include the entire front abdomen." This suggests some clinical data was generated for this specific change, but details on sample size, design, or performance metrics for that specific study are not provided.

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)

Not applicable, as no new clinical study on the primary function is detailed, and the document does not describe efforts to establish ground truth with experts for the device's performance (e.g., in medication adherence).

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

Not applicable, as no new clinical study on the primary function is detailed.

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 ingestible event marker and wearable sensor, not an AI-assisted diagnostic imaging device that would typically involve human "readers" interpreting images. Its purpose is data collection (ingestion times, physiological metrics), not interpretation by human experts.

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

The device itself is essentially a "standalone" data collection system. It's intended to continuously log data without constant human intervention.

• Performance: The document indicates non-clinical verification and validation testing was done (firmware, mechanical, electrical, system testing), and those "acceptance criteria were met." However, no quantifiable performance metrics for the device's accuracy in detecting ingestion events are provided.

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

The document does not specify the method for establishing ground truth for ingestion events or medication adherence from any past studies. Given the nature of the device (detecting ingestion), ground truth likely involves direct observation or very high-fidelity data from a controlled setting.

8. The sample size for the training set

Not applicable to this document. The document describes a medical device cleared via 510(k), not an AI/ML model for which a "training set" in the computational sense would be explicitly outlined. While the device contains firmware and software, the development process and data used for its internal algorithms are not detailed here as a separate "training set."

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

Not applicable, as explained in point 8.

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