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510(k) Data Aggregation
(324 days)
The Happy Health Home Sleep Test is a Software as a Medical Device that uses data from wearable devices to record, analyze, display, export, and store biophysical parameters to aid in evaluating sleep‐related breathing disorders of adult patients suspected of sleep apnea. The device is intended for use on individuals who are 22 years of age or older in clinical and home settings under the direction of a trained healthcare provider.
The Happy Health Home Sleep Test is a Software as a Medical Device that uses data from wearable devices to record, analyze, display, export, and store biophysical parameters to aid in evaluating sleep-related breathing disorders of adult patients suspected of sleep apnea.
The device is intended for use on individuals who are 22 years of age or older in clinical and home settings under the direction of a trained healthcare provider. The device is intended to process input data streams received from an external hardware device (i.e., a smart ring, K240236) and uses these signals to determine various sleep parameters that may be used and interpreted by a clinician in diagnosing sleep disorders such as sleep apnea.
The input physiologic signals from the external device are:
- Acceleration / Movement
- Photoplethysmography (PPG)
The external hardware device (K240236) includes a PPG sensor and accelerometer embedded within a housing to capture the above physiological signals. The K240236 device is worn on the finger and is indicated for continuous data collection of the above signals. Data from the external hardware device is transmitted over a secure API to the subject device for analysis.
The device then uses a set of algorithms to compute the following outputs:
- Happy Health Apnea Hypopnea Index (hAHI)
- Total Sleep Time
The outputs are available for a clinician to review as a report, accessible through a web-based viewer application.
The provided FDA 510(k) clearance letter and summary for the Happy Health Home Sleep Test give a good overview of the device's performance testing. Here's a structured breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly list "acceptance criteria" as a separate section with specific thresholds that were agreed upon before the study. Instead, it presents performance metrics of the Happy Health Home Sleep Test and compares them to the predicate and reference devices, implying these metrics are the basis for demonstrating substantial equivalence. For clarity, I'm inferring the acceptance criteria from the "Equivalent" column in the comparison tables and the detailed performance results.
| Metric (Inferred Acceptance Criteria) | Happy Health Home Sleep Test Reported Performance | Justification for Acceptance (from document) |
|---|---|---|
| hAHI Regression Slope (Regression: PSG_AHI = Slope * hAHI + Intercept, Slope between 0.9 and 1.1) | 0.98 [0.91, 1.06] | "Equivalent - both subject and predicate devices demonstrate strong correlation with manually scored AHI, each with a regression slope between 0.9 and 1.1 and intercept between -5 and 5." |
| hAHI Regression Intercept (Intercept between -5 and 5) | 0.81 [-0.35, 1.91] | "Equivalent - both subject and predicate devices demonstrate strong correlation with manually scored AHI, each with a regression slope between 0.9 and 1.1 and intercept between -5 and 5." |
| hAHI Bland-Altman Mean Bias (Not explicitly quantified as a criterion, but a low bias is desired) | 0.5 [-0.1, 1.1] events/hr | Demonstrates low systematic difference from PSG AHI. |
| hAHI Bland-Altman Limits of Agreement (LOA) (Comparable to predicate/reference, generally aiming for tighter LOA) | Lower LOA: -9.8 [-10.6, -9] events/hrUpper LOA: 10.7 [-9.9, 11.5] events/hr | "Equivalent - both subject and predicate devices demonstrate strong correlation with manually scored AHI..." (Implied that these LOA are acceptable/comparable to predicate when predicate's full data is considered). |
| Total Sleep Time (TST) Mean Absolute Difference (Comparable to predicate/reference, around 30 minutes or less) | 24.9 minutes (SD 32.6 minutes) | "Equivalent - both subject and reference devices demonstrate strong correlation with manually scored AHI, each with a mean absolute difference of around 30 minutes or less." |
2. Sample Size and Data Provenance
- Test Set Sample Size: 90 subjects.
- Data Provenance:
- Country of Origin: Not explicitly stated, but the study was conducted at "two sleep labs", implying a clinical setting within the country of submission (likely USA, given FDA submission).
- Retrospective or Prospective: The wording "Data from a total of 90 subjects referred to the sleep clinic by a physician was manually scored" suggests the data was collected prospectively for the purpose of the study. The phrasing "A clinical study was performed to evaluate the performance..." also indicates a planned, prospective study.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not explicitly stated how many experts were involved in the manual scoring. The text only mentions "manually scored in accordance with the American Academy of Sleep Medicine (AASM) guidelines."
- Qualifications of Experts: Not explicitly stated, but implied to be qualified sleep technicians/physicians capable of AASM-compliant scoring.
4. Adjudication Method for the Test Set
The adjudication method for reconciling discrepant manual scores (if multiple scorers were used) is not specified in the provided text. It simply states "manually scored." If only one scorer per patient, no adjudication would be needed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The study focuses solely on the device's standalone performance compared to manual PSG scoring.
6. Standalone (Algorithm Only) Performance
Yes, a standalone performance study was done. The entire clinical testing section details the performance of the "Happy Health Home Sleep Test" algorithm (hAHI and TST) compared to manually scored Polysomnography (PSG) data, without human-in-the-loop assistance.
7. Type of Ground Truth Used
The primary ground truth used was expert consensus / manual scoring of Polysomnography (PSG) data in accordance with American Academy of Sleep Medicine (AASM) guidelines. This is the gold standard for sleep studies.
8. Sample Size for the Training Set
The sample size for the training set is not provided in this document. The clinical study details describe the test set used for validation.
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established. It only discusses the ground truth for the clinical validation (test) set.
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(239 days)
The Happy Ring Health Monitoring System is a wearable device system to remotely monitor physiologic parameters of patients in professional healthcare facilities, such as hospitals or skilled nursing facilities, or their own home. The device is intended for use on individuals who are 22 years of age or older.
The device supports continuous data collection for monitoring of the following physiological parameters:
- Acceleration / Movement
- Electrodermal Activity (EDA)
- Blood Oxygen Saturation
- Pulse Rate
- Peripheral Skin Temperature
The Happy Ring Health Monitoring System is intended for peripheral skin temperature monitoring, where monitoring temperature at the finger is clinically indicated.
The Happy Ring Health Monitoring System is not intended for SpO2, pulse rate, respiration rate monitoring in conditions of motion or low perfusion.
The Happy Ring Health Monitoring System is a wearable device and software platform comprising:
- A wearable medical device smart ring,
- A mobile app-based bluetooth-to-internet gateway,
- A cloud-based API,
- A set of data processing algorithms, and
- A Physician data viewer.The Ring is worn on the user's finger and continuously collects raw data via specific sensors. These raw data are transmitted via Bluetooth Low Energy to a paired mobile device. The data received are transmitted by the mobile app gateway, via the cloud-based AP, to the data processing algorithms where various physiological parameters are computed. The raw and processed data are stored, further analyzed, and accessible by healthcare providers or researchers via the Physician data viewer.
The Happy Ring Health Monitoring System is intended for retrospective remote monitoring of physiological parameters in ambulatory adults in home-healthcare environments. It is designed to continuously collect data to support intermittent monitoring of the following physiological parameters by trained healthcare professions or researchers: Acceleration / movement, electrodermal activity (EDA), blood oxygen saturation, pulse rate, and peripheral skin temperature.
The provided text describes information about the Happy Ring Health Monitoring System, including its intended use, technological comparison to a predicate device, and a summary of non-clinical and clinical tests performed. However, it does not contain specific acceptance criteria for the device performance or detailed results of a study proving the device meets those criteria, beyond a general statement about SpO2 accuracy.
Therefore, I cannot fully complete the requested table and answer all questions with the provided text. I will extract all available information and explicitly state what is not present.
Here's a breakdown of the available information and what's missing:
1. Table of Acceptance Criteria and Reported Device Performance
| Parameter | Acceptance Criteria (Not explicitly stated as such, but inferred from testing standards) | Reported Device Performance |
|---|---|---|
| Blood Oxygen Saturation (SpO2) | Per ISO 80601-2-61 (Accuracy for pulse oximeters, typically ARMS within a certain percentage) | Was within 3.5% ARMS for the range of oxygen saturation measured by the device. |
| Pulse Rate | Per ISO 80601-2-61 (Accuracy for pulse oximeters) | Tested in accordance with ISO 80601-2-61, but specific quantitative performance not reported. |
| Peripheral Skin Temperature | Per ISO 80601-2-56 (Accuracy for clinical thermometers) | Tested in accordance with relevant sections of ISO 80601-2-56, but specific quantitative performance not reported. |
| Electrodermal Activity (EDA) | Bench testing to verify performance | Bench testing to verify performance, but specific quantitative performance not reported. |
| Acceleration / Movement | Bench testing to verify performance | Bench testing to verify performance, but specific quantitative performance not reported. |
| Electrical, Mechanical & Thermal Safety | IEC 60601-1 and IEC 60601-1-11 compliance | Testing in accordance with standards. |
| Electromagnetic Compatibility | IEC 60601-1-2 compliance | Testing in accordance with standards. |
| Wireless Coexistence | FDA's guidance: Radio Frequency Wireless Technology in Medical Devices compliance | Testing in accordance with guidance. |
| Usability | IEC 62366 and FDA's guidance: Applying Human Factors and Usability Engineering to Medical Devices compliance | Testing in accordance with standards/guidance. |
| Software V&V & Cybersecurity | FDA's guidance for Software Contained in Medical Devices and Cybersecurity in Medical Devices compliance | Documentation provided as recommended by guidance. |
2. Sample size used for the test set and the data provenance:
- Sample Size (SpO2 Clinical Study): 12 subjects
- Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective. The study was described as "clinical tests" and involved "subjects," which implies a prospective clinical trial.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not specified. The text mentions "oxyhemoglobin saturation using a radial arterial line" as the comparison method for SpO2, which is an objective measurement and doesn't typically involve expert consensus for ground truth establishment in the same way image interpretation might. For other physiological parameters, the ground truth establishment method is not detailed beyond "bench testing to verify performance."
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable/Not specified. Given the nature of objective physiological measurements (like radial arterial line for SpO2), a multi-reader adjudication method as seen in image interpretation studies is not typically used.
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 study was not described. The study focused on the device's accuracy in measuring physiological parameters, not on how it assists human readers or clinicians in interpreting data or making decisions. The "Physician data viewer" is mentioned, suggesting human review, but no comparative effectiveness study with human readers is detailed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, implicitly. The SpO2 and other bench tests (EDA, accelerometer, temperature) assess the device's ability to measure physiological parameters independently of human interpretation. The "data processing algorithms" compute the parameters, and their accuracy is evaluated.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Reference Devices/Objective Measurements:
- For SpO2: Oxyhemoglobin saturation using a radial arterial line (a gold standard, objective measurement).
- For other parameters: Implied reference instruments for "bench testing to verify the performance." This typically means comparing the device's output against a highly accurate, calibrated reference measurement.
8. The sample size for the training set:
- Not specified. The document discusses the test set but provides no information about the training set size for the device's algorithms.
9. How the ground truth for the training set was established:
- Not specified. Since information about the training set size or its existence is not provided, the method for establishing its ground truth is also not elaborated upon in this document.
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