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The HeroTracker Sense Device (HTS) is an add-on device which is attached by patients on a metered-dose inhaler (MDI).

HTS is intended for single-patient multiple use in the home environment as an electronic data capture accessory for monitoring and recording actuation, inhaler shake, inhaler orientation, inhalation coordination, and inspiratory strength and duration for prescribed inhaler usage.

HTS may be used in the following applications: in clinical practice or clinical trials, where specialists, general practitioners, nurses and educators need to know if a patient has used their prescribed medication or assess inhaler technique.

HTS is designed to work only with the MDI and medication indicated on the HTS label.

HTS is not intended to indicate the remaining quantity of medication in an inhaler and does not include a dose counting function.

HTS is not intended to provide spirometry measurements.

HTS is intended to be used by Metered Dose Inhaler (MDI) users aged 12 years and over.

The HeroTracker Sense (HTS) is a nebulizer accessory. It is an add-on device which is attached by patients on a metered dose inhaler (MDI) and is used to record and analyze data related to medication actuation and technique of use for prescribed inhaler usage. Data is transferred to a mobile application with appropriate settings and is displayed to end users.

The provided FDA 510(k) Clearance Letter for HeroTracker Sense (HTS) does not contain the detailed information necessary to answer all parts of your request regarding acceptance criteria and a study proving the device meets those criteria. The document primarily focuses on demonstrating substantial equivalence to a predicate device based on non-clinical performance data.

Here's a breakdown of what can be gleaned from the document and what information is missing:

Missing Information: The document explicitly states "No clinical data was necessary to determine the substantial equivalence of this device." This means there was likely no "performance study" in the traditional sense, with a clinical test set, ground truth experts, adjudication, or MRMC studies. The acceptance criteria for performance would have been established and demonstrated through non-clinical testing.

Therefore, many of the sections you requested (e.g., sample size for test set, number of experts, adjudication, MRMC, specific effect sizes, ground truth type for test set the training set) cannot be answered from the provided text.

Inference from Document's Purpose: The 510(k) process for this device relies heavily on demonstrating that the HeroTracker Sense (HTS) functions similarly to its predicate device (Hailie® Sensor NF0110) and that any differences do not raise new questions of safety or effectiveness. The performance validation would have been against defined specifications for each function, likely derived from the predicate device's capabilities and internal design requirements.

Acceptance Criteria and Device Performance (Based on available non-clinical data)

Since clinical data was not required, the "acceptance criteria" for the device would be met by passing the non-clinical performance and safety tests. The "reported device performance" is essentially that it "passed" these tests and met its internal specifications.

Table of Acceptance Criteria and Reported Device Performance (Inferred from Non-Clinical Testing):

Study Details (Based on available information and limitations)

Given the explicit statement that "No clinical data was necessary to determine the substantial equivalence of this device," the following sections reflect this limitation. The "study" refers to the non-clinical verification and validation activities.

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

   • (Provided above, based on non-clinical testing outcomes).

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

   • Test Set Sample Size: Not applicable in the context of a clinical test set. For non-clinical performance testing (e.g., electrical safety, software validation, sensor accuracy), the sample sizes would refer to the number of devices or test iterations used. This specific detail is not provided in the document.
   • Data Provenance: Not applicable in the sense of patient data. The tests were performed in a lab or testing facility environment.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. No clinical ground truth experts were used as no clinical data was required. Ground truth for non-clinical performance (e.g., whether a sensor accurately detects an actuation) would be established by validated test methods and reference standards, not human expert consensus.

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

   • Not applicable. There was no human expert adjudication as no clinical test set with subjective interpretations was used.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

   • No. The document explicitly states "No clinical data was necessary." Therefore, no MRMC study comparing human readers with and without AI assistance was performed.

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

   • Yes, implicitly. The "Performance Testing" section confirms that the device was "verified to meet specifications" and its "correct functionality and compatibility" was verified. This implies standalone performance testing of the device's ability to record and analyze data from the MDI. The "algorithm" itself (which processes sensor data to identify events like actuation, shake, etc.) would have been validated against controlled physical actions.

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

   • Instrumental/Physical Ground Truth: For the non-clinical performance tests, the "ground truth" would be established by controlled physical inputs and reference measurements. For example:
     • For "actuation," the ground truth would be a definitively measured actuation event.
     • For "inhaler shake," the ground truth would be a quantitatively measured shaking motion.
     • For software functionality, the ground truth would be predefined correct outputs for given inputs.

8. The sample size for the training set:

   • Not applicable / Not specified. This device is an electronic data capture accessory. While it uses sensors and potentially algorithms to interpret movements, the document doesn't indicate that it employs machine learning or AI that would require a "training set" in the typical sense (e.g., for image classification or complex pattern recognition). Its function seems to be based on pre-defined thresholds and sensor data processing rather than adaptive learning from a large training dataset. If any "calibration" or initial parameter setting was done, the size of that dataset is not provided.

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

   • Not applicable. As no training set is indicated.

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