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EndeavorOTC is a digital therapeutic indicated to improve attention as measured by computer-based testing in patients 18 and older with primarily inattentive or combined type ADHD, who have a demonstrated attention issue. Patients who engage with EndeavorOTC demonstrate improvements in a digitally assessed measure, Test of Variables of Attention (TOVA®) of sustained and selective attention and may not display benefits in typical behavioral symptoms such as hyperactivity. EndeavorOTC is not intended to be a replacement for any form of treatment and should be used as part of a therapeutic program that may include clinician-directed therapy, medicational programs, which further address symptoms of the disorder.

EndeavorOTC is software-as-a-medical device (SaMD) that resides on the user's mobile device and can be executed at home. It is an over-the-counter (OTC) digital therapeutic indicated to improve attention function as measured by computer-based testing in patients 18 and older with primarily inattentive or combined type ADHD, who have a demonstrated attention issue. Patients who engage with EndeavorOTC demonstrate improvements in a digitally assessed measure, Test of Variables of Attention (TOVA®) of sustained and selective attention and may not display benefits in typical behavioral symptoms such as hyperactivity. EndeavorOTC is not intended to be a replacement for any form of treatment and should be used as part of a therapeutic program that may include clinician-directed therapy, medication, and/or educational programs, which further address symptoms of the disorder. The device is built on Akili's proprietary, patented, technology platform. EndeavorOTC uses adaptive algorithms (also known as Selective Stimulus Management Engine, SSME™) to deliver stimuli that are designed to engage the patient in a manner that improves their attention function. In a closed-loop system, the adaptive SSME™ algorithms automatically adjust the difficulty level for a personalized treatment experience that is tailored to the needs of each individual patient. EndeavorOTC is delivered through a video game experience which leverages art, music, storytelling, and reward cycles to keep patients engaged. The adaptive algorithm constantly pushes patients precisely at predefined performance bounds relative to each individual, such that they are continuously encouraged to exceed their historic performance. The science behind EndeavorOTC was developed at the University of California, San Francisco by Adam Gazzaley, M.D., Ph.D., Founding Director of the University of California San Francisco's Neuroscape and Akili's Chief Science Advisor. The basic program inputs are steering, which is accomplished by using the mobile device's internal accelerometer to measure the degree to which it is tilted, and tapping, which is accomplished using the touch screen to measure correct and incorrect targeting. The basic outputs are the visual display of the game progression along with audio, which is accomplished by using the internal high resolution display and internal speaker. The program includes features to ensure it is used per the recommended regimen (approximately 25 minutes per day, 5 days per week, for 6 weeks).

Here's a summary of the acceptance criteria and the study proving the EndeavorOTC device meets those criteria, based on the provided FDA 510(k) summary:

Device Acceptance Criteria and Performance Study: EndeavorOTC

Device Name: EndeavorOTC
Regulatory Class: Class II
Product Code: QFT
Indication for Use: Digital therapeutic indicated to improve attention as measured by computer-based testing in patients 18 and older with primarily inattentive or combined type ADHD, who have a demonstrated attention issue.

1. Table of Acceptance Criteria and Reported Device Performance

The primary acceptance criteria for EndeavorOTC are based on demonstrating an improvement in attention, as measured by the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS), and showing a favorable safety profile in the indicated adult population. The 510(k) summary refers to "clinical performance study support[ing] the performance and safety of EndeavorOTC in the adult age range" and "a statistically significant positive mean change from baseline to study day 42 in the TOVA".

Based on the provided document, the key efficacy performance criterion appears to be a statistically significant positive change in the TOVA-ACS.

2. Sample Size and Data Provenance

• Test Set (Clinical Study Population):
  • Safety Population (ITT): 221 participants
  • Efficacy Population (mITT): 153 participants (all enrolled subjects with sufficient data at baseline and exit to calculate change scores)
• Data Provenance: The study was a "multicenter open-label study enrolled 221 subjects... across the US (a mix of institutional sites and private practice centers)." It was a prospective clinical performance study.

3. Number of Experts for Ground Truth and Qualifications

The provided document does not specify the number of experts used to establish the ground truth for the test set or their specific qualifications (e.g., "Radiologist with 10 years of experience").

However, the diagnosis of ADHD was determined in study participants using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria as confirmed by the Mini-International Neuropsychiatric Interview (MINI) for ADHD adult version 7.0.2. This implies the involvement of trained clinicians (likely psychiatrists or psychologists) to administer and interpret these diagnostic tools. The TOVA® test itself is a standardized, computer-based assessment of attention.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for the interpretation of the primary outcomes. The primary outcome (TOVA-ACS) is a quantitative, digitally assessed measure, which generally does not require traditional expert adjudication of results in the same way image interpretations might. The ADHD diagnosis was clinician-confirmed using standardized interviews.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or performed. This study was a single-arm, open-label study, primarily evaluating the device's effect on attention in the target population. It was not designed to compare human readers' improvement with or without AI assistance, as the device is a direct therapeutic intervention, not an AI-assisted diagnostic tool.

6. Standalone (Algorithm Only) Performance

EndeavorOTC is a therapeutic device (software-as-a-medical device) that directly interacts with the user as a game-like experience. Its performance is the algorithm's effect on the user's attention, measured by standardized tests like TOVA. Therefore, the clinical study results represent the standalone performance of the device's algorithm in a human-in-the-loop context (the human being the patient using the device). It's not an AI model that outputs a diagnostic interpretation for a human to review.

7. Type of Ground Truth Used

The primary ground truth for efficacy was objective, digitally assessed measures (Test of Variables of Attention - TOVA-ACS). The patient's ADHD diagnosis, which defined the study population, was established by clinician-administered standardized diagnostic interviews (MINI) based on DSM-5 criteria.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set of the adaptive algorithms (Selective Stimulus Management Engine - SSME™). It notes that the science behind EndeavorOTC was developed at the University of California, San Francisco by Adam Gazzaley, M.D., Ph.D. The algorithms are "adaptive" and "in a closed-loop system, automatically adjust the difficulty level," implying continuous adaptation rather than a single, fixed training dataset in the typical machine learning sense.

9. How Ground Truth for Training Set was Established

Given that the device uses "adaptive algorithms (also known as Selective Stimulus Management Engine, SSME™) to deliver stimuli that are designed to engage the patient in a manner that improves their attention function," and adjusts difficulty "for a personalized treatment experience," the "ground truth" for the training of these adaptive algorithms is inherently tied to the patient's real-time performance within the game and their physiological responses or attentional engagement.

The document does not detail how the initial parameters or underlying "training" for these adaptive algorithms were established. It mentions the algorithms were "developed at the University of California, San Francisco by Adam Gazzaley, M.D., Ph.D." This suggests a research-based, iterative development process informed by neuroscience and cognitive psychology, rather than a single, labeled dataset typical of supervised machine learning. The "ground truth" for fine-tuning the adaptive difficulty would likely be the user's performance and implicit learning within the game itself.

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