(219 days)
Yes
The device description explicitly mentions "processes running on the Apple Watch" to determine the "likelihood" of tremor and dyskinesia, and the training and test set descriptions detail the data used to train and validate the "tremor detection algorithm" and "dyskinesia detection algorithm." This strongly suggests the use of algorithms that learn from data, which is characteristic of ML.
No.
The device is intended to quantify kinematics of movement disorder symptoms and provide data for display to clinicians, not to deliver any form of therapy or treatment.
Yes
The device is intended to "quantify kinematics of movement disorder symptoms including tremor and dyskinesia" and provides "derived tremor and dyskinesia probability scores" to clinicians for display, which are used to "determine how likely dyskinesias or tremors are to have occurred." This collection and analysis of patient data to assess the likelihood of medical conditions directly aligns with the definition of a diagnostic device.
Yes
The device is described as "software that receives, stores, and transfers the Apple Watch MM4PD classification data". While it relies on data from the Apple Watch's sensors, the device itself is the software component that processes and displays this data, not the hardware collecting it.
Based on the provided information, the Rune Labs Kinematic System is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. This includes things like blood, urine, tissue, etc.
- The Rune Labs Kinematic System analyzes movement data. It uses sensors (accelerometers and gyroscopes) on an external device (Apple Watch) to measure wrist movements. It does not interact with or analyze any biological samples from the patient.
The device falls under the category of a medical device that uses external sensors and software to monitor physiological parameters related to movement disorders.
No
The letter does not state that the FDA has reviewed and cleared a Predetermined Change Control Plan (PCCP) for this specific device. The provided text indicates "Not Found" for the "Control Plan Authorized (PCCP) and relevant text" section.
Intended Use / Indications for Use
The Rune Labs Kinematic System is intended to quantify kinematics of movement disorder symptoms including tremor and dyskinesia, in adults (45 years of age or older) with mild to moderate Parkinson's disease.
Product codes
GYD
Device Description
The Rune Labs Kinematic System collects derived tremor and dyskinesia probability scores using processes running on the Apple Watch, and then processes and uploads this data to Rune's cloud platform where it is available for display for clinicians.
The Rune Labs Kinematic System uses software that runs on the Apple Watch to measure patient wrist movements. These movements are used to determine how likely dyskinesias or tremors are to have occurred. The times with symptoms are then sent to the Rune Labs Cloud Platform using the Apple Watch's internet connection, which is then displayed for clinician use.
The Apple Watch contains accelerometers and gyroscopes which provide measurements of wrist movement. The Motor Fluctuations Monitor for Parkinson's Disease (MM4PD) is a toolkit developed by Apple for the Apple Watch that assesses the likely presence of tremor and dyskinesia as a function of time. Specifically, every minute, the Apple Watch calculates what percentage of the time that tremor and dyskinesia were likely to occur. The movement disorder data that is output from the Apple's MM4PD toolkit have been validated in a clinical study (Powers et al., 2021).
The Rune Labs Kinematic System is software that receives, stores, and transfers the Apple Watch MM4PD classification data to the Rune Labs Cloud Platform where it is available for visualization by clinicians. The device consists of custom software that runs on the users' smart watch and web browsers.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
Not Found
Anatomical Site
wrist
Indicated Patient Age Range
adults (45 years of age or older)
Intended User / Care Setting
clinicians / Not Found
Description of the training set, sample size, data source, and annotation protocol
Tremor detection algorithm was developed using data collected from:
- Pilot study: N=69 subjects with tremor reported during a stationary task (mainly sitting tasks such as cognitive distraction or hands-in-lap but also during standing periods).
- Longitudinal patient study: subjects in the longitudinal patient study design set (first 143 subjects enrolled) with tremor reported during a stationary task.
- Longitudinal control study: All day living data from additional subjects without Parkinson's (N=236 subjects, >59,000 hours of data).
Dyskinesia detection algorithm was designed using data collected from:
- Pilot study: N=10 subjects, divided evenly between subjects observed to have choreiform dyskinesia regularly affecting the wrist on which the watch was worn and subjects with no history of any dyskinetic symptoms (one week of all-day data for each subject).
- Longitudinal patient study: N=97 subjects from the longitudinal patient study design set (first 143 subjects enrolled), consisting of 22 subjects with choreiform dyskinesia and 75 with no history of choreiform dyskinesia (>25,000 hours of all-day data).
- Longitudinal control study: N=171 subjects without Parkinson's from the Longitudinal Control study (>59,000 hours of all-day data).
The choreiform movement score (CMS) was calculated from sensor data in the pilot study and compared to dyskinesia ratings from three MDS-certified experts during multiple MDS-UPDRS assessments.
Description of the test set, sample size, data source, and annotation protocol
Tremor Validation:
A hold-out set of 43 subjects was used to ensure that the cutoffs determined from the design set were well correlated in additional subjects. The mean daily tremor detection rate for all subjects from the longitudinal patient study was compared to the clinician's overall tremor rating. The daily tremor percentage was calculated as the total detected tremor time divided by the total time period the watch was worn. Watch wear time excluded periods where the subject was likely asleep or where the watch was not being worn as indicated by a lack of device movement. This percentage was then averaged across all the days the subject was in the study. Six subjects were excluded because they had insufficient data for analysis. The Spearman's rank correlation coefficient between the daily tremor percentage and the clinicians' tremor constancy score was calculated.
Dyskinesia Validation:
The dyskinesia algorithm was designed and validated across 343 participants with PD (61 with dyskinesia) and 171 elderly, non-PD controls. In a hold-out dataset from the longitudinal patient study, the percentage of time dyskinesias were detected for the chorea group (5.9 ± 5.3%) significantly differed from subjects with no reported dyskinesias (2.0 ± 2.2%) (P = 0.027, Wilcoxon rank sum test; Figure 4, right).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Non-Clinical and/or Clinical Tests Summary
Sample Size:
Pilot Study:
* Tremor: N=69 subjects
* Dyskinesia: N=10 subjects
Longitudinal Patient Study:
* Tremor (design set): 143 subjects
* Tremor (hold-out set): 43 subjects
* Dyskinesia (design set): N=97 subjects (22 with choreiform dyskinesia, 75 without)
* Dyskinesia (hold-out set): N=57 subjects (10 chorea, 47 no reported dyskinesia)
Longitudinal Control Study:
* Tremor: N=236 subjects (>59,000 hours of data)
* Dyskinesia: N=171 subjects (>59,000 hours of all-day data)
Dyskinesia algorithm overall validation: 343 participants with PD (61 with dyskinesia) and 171 elderly, non-PD controls.
Key Results:
Measured Watch displacements compared to motion measurements:
* Pearson correlation coefficient between displacement measured by the motion capture system (Vicon) and the watch estimate was 0.98 in a control subject.
* Mean signed error: -0.04 ± 0.17 cm.
Tremor Validation:
* Spearman's rank correlation coefficient between daily tremor percentage and MDS-UPDRS tremor constancy score: 0.72 in the design set (n = 95) and 0.72 in the hold-out set (n = 43).
* False positives: 0.25% in 171 elderly, non-PD longitudinal control subjects (over 43,300 hours of all-day data).
* False positives during targeted activities in young, healthy controls: 8% for manual teeth brushing, 2% for playing a musical instrument.
Dyskinesia Validation:
* CMS showed significant differences (P
§ 882.1950 Tremor transducer.
(a)
Identification. A tremor transducer is a device used to measure the degree of tremor caused by certain diseases.(b)
Classification. Class II (performance standards).
0
June 10, 2022
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is a seal with an abstract design. To the right of the seal, there is the FDA logo in blue, with the words "U.S. FOOD & DRUG" stacked on top of "ADMINISTRATION". The logo is simple and professional, and it is easily recognizable.
Rune Labs, Inc. % Courtney Lane CEO/Principal Consultant Anacapa Clinical Research Inc. 2421 Sunset Dr. Ventura, CA 93001
Re: K213519
Trade/Device Name: Rune Labs Kinematics System Regulation Number: 21 CFR 882.1950 Regulation Name: Tremor Transducer Regulatory Class: Class II Product Code: GYD Dated: May 11, 2022 Received: May 13, 2022
Dear Courtney Lane:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
for Jay Gupta Assistant Director DHT5A: Division of Neurosurgical, Neurointerventional and Neurodiagnostic Devices OHT5: Office of Neurological and Physical Medicine Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K213519
Device Name Rune Labs Kinematic System
Indications for Use (Describe)
The Rune Labs Kinematic System is intended to quantify kinematics of movement disorder symptoms including tremor and dyskinesia, in adults (45 years of age or older) with mild to moderate Parkinson's disease.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
3
Image /page/3/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube made of lines, followed by the text "rune labs" in a simple, sans-serif font. The logo is in black and white.
510(k) Summary
Contact Details
Applicant Name: Rune Labs Inc. Applicant Address: 649 Irving Street, San Francisco, CA 94122, United States Applicant Contact Telephone: 360-606-2929 Applicant Contact: Mr. Brian Pepin Applicant Contact Email: brian@runelabs.io
Correspondent Name: Anacapa Clinical Research Inc. Correspondent Address: 2421 Sunset Dr.. Ventura. CA. 93001. United States Correspondent Contact Telephone: 805-856-8141 Correspondent Contact: Dr. Courtney Lane Correspondent Contact Email: courtney@runelabs.io
Device Name
Device Trade Name: Rune Labs Kinematics System Common Name: Tremor transducer Classification Name: Transducer, Tremor Regulation Number: 882.1950 Product Code: GYD
Legally Marketed Predicate Devices
Predicate # K140086 Predicate Trade Name: Personal Kinetigraph (PKG) System Product Code: GYD
Device Description Summary
The Rune Labs Kinematic System collects derived tremor and dyskinesia probability scores using processes running on the Apple Watch, and then processes and uploads this data to Rune's cloud platform where it is available for display for clinicians.
The Rune Labs Kinematic System uses software that runs on the Apple Watch to measure patient wrist movements. These movements are used to determine how likely dyskinesias or tremors are to have occurred. The times with symptoms are then sent to the Rune Labs Cloud Platform using the Apple Watch's internet connection, which is then displayed for clinician use.
The Apple Watch contains accelerometers and gyroscopes which provide measurements of wrist movement. The Motor Fluctuations Monitor for Parkinson's Disease (MM4PD) is a toolkit developed by Apple for the Apple Watch that assesses the likely presence of tremor and
4
Image /page/4/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube or a stylized representation of interconnected nodes, placed to the left of the text "rune labs". The text is in lowercase and appears to be in a sans-serif font.
dyskinesia as a function of time. Specifically, every minute, the Apple Watch calculates what percentage of the time that tremor and dyskinesia were likely to occur. The movement disorder data that is output from the Apple's MM4PD toolkit have been validated in a clinical study (Powers et al., 20211).
The Rune Labs Kinematic System is software that receives, stores, and transfers the Apple Watch MM4PD classification data to the Rune Labs Cloud Platform where it is available for visualization by clinicians. The device consists of custom software that runs on the users' smart watch and web browsers.
Intended Use/Indications for Use
The Rune Labs Kinematic System is intended to quantify kinematics of movement disorder symptoms including tremor and dyskinesia, in adults (45 years of age or older) with mild to moderate Parkinson's disease.
Indications for Use Comparison
The predicate indication for use statement is as follows:
"The Personal Kinetigraph (PKG) System is intended to quantify kinematics of movement disorder symptoms in conditions such as Parkinson's disease, including tremor, bradykinesia and dyskinesia. It includes a medication reminder, an event marker and is intended to monitor activity associated with movement during sleep. The device is indicated for use in individuals 46 to 83 years of age."
Rune Labs does not currently detect bradykinesia so this symptom measurement is removed. However, bradykinesia can still be assessed clinicians and/or reported by the patient so this change does not constitute a change in the type or level of risk compared to the predicate device.
Medication reminders, event markers, sleep movement, and activity measurements are not included with the Rune Labs Kinematic System. However, this functionality is readily provided by commercially available off-the-shelf software. Therefore, this change does not constitute a significant change in type or level of risk compared to the predicate device.
The algorithm used in the Rune Labs Kinematic System was validated in a clinical study¹ on adults with Parkinson's disease with an age range of 71.4 vrs [±8.9 standard deviation]. The lower cutoff therefore represents three standard deviations from the mean for patients in the validation study, and the upper cutoff is likely limited by the life expectancy of the user. Parkinson's disease typically affects only adults aged 60 or older, and their life expectancy is
1 Powers R, Etezadi-Amoli M, Arnold EM, Kianian S, Mance I, Gibiansky M, Trietsch D, Alvarado AS, Kretlow JD, Herrington TM, Brillman S, Huang N, Lin PT, Pham HA, Ullal AV. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease. Sci Transl Med. 2021 Feb 3;13(579):eabd7865. doi: 10.1126/scitranslmed.abd7865. PMID: 33536284.
This document is the sole property of Rune Labs and cannot be reproduced without written consent.
5
Image /page/5/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube or a stylized representation of interconnected nodes, followed by the text "rune labs" in a simple, sans-serif font. The geometric shape is composed of lines that create a sense of depth and connectivity, while the text is clean and modern.
estimated to be 83.3 years?. Therefore, this change does not constitute a significant change in type or level of risk compared to the predicate device.
The environment of use for the PKG System and Rune Labs System are similar, but the Rune Labs device can be used continuously whereas the PKG Watch must be mailed back to the company for data analysis after several days' use. Continuous monitoring is likely to improve the ability for physician's to monitor their patients over time so this change does not constitute a significant change in type or level of risk compared to the predicate device.
Technological Comparison
The key operating principle of the system and the predicate is the recording and analysis of the patient's wrist movement to provide a report to the clinician regarding the presence or absence of movement disorders systems.
Comparison of Outputs and Features
The Rune Lab device outputs are the percentage of the time that tremor and dyskinesia were likely to occur while the PKG device outputs are an estimate of when tremor is present, a percent time that tremor is present (PTT), and an estimate of dyskinesia scores every two minutes over 10 days. The PKG device also provides information about bradykinesia (see above).
While the technological details of the tremor and dyskinesia detection algorithms are not the same as the predicates, this difference does not raise new types of safety or effectiveness questions because the algorithms used were both correlated with accepted scientific methods, such as the UPDRS III.
Comparison of Data Transmission
There is a difference between the Rune Kinematic System and the predicate device with respect to the mechanism of data transmission. Rune Labs uploads data from the Apple Watch to the Rune Labs Cloud Platform using either a cellular or wireless network. The predicate device requires the device to be mailed back to the manufacturer for processing, and then a report is emailed to the clinician.
We have noted that a newer device by the same manufacturer has been cleared by the FDA and is deemed substantially equivalent to the predicate device (K161717³), which uses wireless communication to upload the patient data via the internet. This device can be considered a reference device for the Rune Kinematics and serves to demonstrate that the type of communication protocols used do not impact the safety and effectiveness of the device, provided that controls are in place that the data is preserved across the the various communication methods, which we have shown in our verification testing.
2 https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/syc-20376055
3 https://www.accessdata.fda.gov/cdrh_docs/pdf16/[K161717](https://510k.innolitics.com/search/K161717).pdf
This document is the sole property of Rune Labs and cannot be reproduced without written consent.
6
Image /page/6/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube made of lines, followed by the text "rune labs" in a sans-serif font. The cube is positioned to the left of the text, and both elements are in a dark color, likely black or a dark gray, against a white background.
Comparison of System Design
The Rune Labs device is a software-only device that interfaces with a toolkit provided by a consumer electronics device manufacturer (Apple) whereas the predicate device is a hardware and software system. However, the Apple Watch is used as the hardware component for other medical devices, such as the Apple electrocardiograph device (DEN180044) and photoplethysmograph device (DEN180042), which can be considered reference devices. Rune Labs will monitor and evaluate toolkit and Apple Watch releases to ensure that software or hardware changes released by the manufacturer do not affect the device performance. Therefore this difference will not impact the safety or effectiveness of the device.
Summary of Technical Comparison
Overall, the differences in the usability and design of the Rune Labs Kinematics System, which allows for longer use and direct upload of data, do not affect the safety and effectiveness of the device as compared to the predicate device.
Non-Clinical and/or Clinical Tests Summary
Software testing established that the system meets the software requirements and user needs for the intended uses.
Apple's MM4PD has been clinically validated as described in Powers et al. (2021)1, and the validation is summarized below. Table 1 shows baseline demographics for patients used in the validation studies.
7
Image /page/7/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube made of lines, followed by the text "rune labs" in a simple, sans-serif font. The logo is in black and white.
| | Pilot study
PD patients in-clinic + 1
week live-on | Longitudinal patient study
PD patients long-term live-on | Longitudinal control
study
Elderly controls |
|-------------------------------------------------|----------------------------------------------------------|-------------------------------------------------------------|---------------------------------------------------|
| Age [± Standard
Dev] | 68.1 yrs [±9.0] | 71.4 yrs [±8.9] | 74.7 yrs [±5.4] |
| Years with PD [±
Standard Dev] | 6.5 yrs [±5.6] | 10.3 yrs [±6.5] | n/a |
| Gender | 36 Female, 82 Male | 69 Female, 156 Male | 85 Female, 85 Male, 1
unknown |
| Most Affected Side | 62 Right / 39 Left / 17
unspecified | 105 Right / 120 Left | n/a |
| History of Tremor | - | 166/225 Participants | n/a |
| History of
Dyskinesia
(History of Chorea) | - | 94/225 Participants
(66/94 with dyskinesia) | n/a |
| History of Freezing
Gait | - | 85/225 Participants | n/a |
| History of Slow
Gait | - | 172/225 Participants | n/a |
| | | *self-reported history | |
Table 1: Subject demographics for the Powers et al. (2021) study1
1.1. Measured Watch displacements compared to motion measurements
The measured watch movement was correlated with the measurements taken from a commercially available motion tracking system (Vicon; see Figure 1). A healthy control subject simulated tremor movements with varying amplitudes while wearing the Apple Watch in seated and standing positions. The Pearson correlation coefficient between displacement measured by the motion capture system and the watch estimate was 0.98 in a control subject with a mean signed error of -0.04 ± 0.17 cm.
8
Image /page/8/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube or a stylized representation of interconnected nodes, followed by the text "rune labs" in a simple, sans-serif font. The logo and text are both in black and white.
Image /page/8/Figure/1 description: The figure is a scatter plot comparing smartwatch estimates to motion capture reference values, both measured in centimeters. Data points are categorized by number and activity (sit or stand), with different colors representing each category. The plot shows a generally positive correlation between the two measurement methods, as indicated by the dashed line, but there is some scatter around the line, especially at higher values.
Fiqure 1: Apple Watch estimate of motion as a function of measurements from a commercially available motion capture system (Vicon). From Powers et al., 2021, Figure 3A.
1.2. Tremor Validation
The tremor detection algorithm was developed using data collected from the following data sets:
-
Pilot study: N=69 subjects in the pilot study, with tremor reported during a stationary task (mainly sitting tasks such as cognitive distraction or hands-in-lap but also during standing periods)
-
Longitudinal patient study: subiects in the longitudinal patient study design set (first 143 subjects enrolled) with tremor reported during a stationary task
-
Longitudinal control study: All day living data from additional subjects without Parkinson's (N=236 subjects, >59,000 hours of data)
The mean daily tremor detection rate for all subjects from the longitudinal patient study was compared to the clinician's overall tremor rating, which takes both constancy of tremor and severity into account. Design set patients were used to determine the tremor detection algorithm, and a hold-out set was used to ensure that these cutoffs were well correlated in additional subjects. The daily tremor percentage was calculated as the total detected tremor time divided by the total time period the watch was worn. Watch wear time excluded periods where the subject was likely asleep or where the watch was not being worn as indicated by a lack of device movement. This percentage was then averaged across all the days the subject was in the study. Six subjects were excluded because they had insufficient data for analysis. The Spearman's rank correlation coefficient between the daily tremor percentage and the clinicians' tremor constancy score was calculated.
All-day tremor estimates from the longitudinal patient study, as quantified by an individual's mean percentage of time with tremor detected per day, correlated with their MDS-UPDRS
9
Image /page/9/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube made of lines, followed by the text "rune labs" in a simple, sans-serif font. The logo and text are both in black and white.
tremor constancy score assessed during a brief, in-clinic visit at the start of the study, with a Spearman's rank correlation coefficient of 0.72 in the design set (n = 95) and in the hold-out set (n = 43) (Figure 2).
Image /page/9/Figure/2 description: The image shows two box plots comparing the mean daily tremor (%) to the MDS-UPDRS tremor constancy. Both plots are labeled as a longitudinal patient study with ( \rho = 0.72 ). The left plot shows the number of patients with MDS-UPDRS tremor constancy of 0, 1, 2, 3, and 4 are 36, 18, 17, 11, and 13, respectively. The right plot shows the number of patients with MDS-UPDRS tremor constancy of 0, 1, 2, 3, and 4 are 19, 6, 4, 7, and 7, respectively.
Figure 2: Mean daily tremor percentage compared to MDS-UPDRS tremor constancy score from the longitudinal study for the design set (left; n = 95) and hold-out set (right; n = 43). Rank correlation coefficient for the design set is 0.72; for the hold-out set rank correlation coefficient is also 0.72. From Powers et al., 2021, Figure 3D and E.
False positives occurred 0.25% of the time when evaluated in 171 elderly, non-PD longitudinal control subjects using over 43,300 hours of all-day data. False positives were also rare during targeted activities in young, healthy controls, such as manual teeth brushing (8%) and playing a musical instrument (2%; see Table S2 in Powers et al., 2021).
- 1.3. Dyskinesia Validation
The dyskinesia detection algorithm was designed using data collected from the following data sets:
-
Pilot study: N=10 subjects from the pilot study, divided evenly between subjects observed to have choreiform dyskinesia reqularly affecting the wrist on which the watch was worn and subjects with no history of any dyskinetic symptoms (one week of all-day data for each subject)
-
Longitudinal patient study: N=97 subjects from the longitudinal patient study design set (first 143 subjects enrolled), consisting of 22 subjects with choreiform dyskinesia and 75 with no history of choreiform dyskinesia (>25,000 hours of all-day data)
This document is the sole property of Rune Labs and cannot be reproduced without written consent.
10
Image /page/10/Picture/0 description: The image shows the logo for Rune Labs. The logo consists of a geometric shape resembling a cube or a series of interconnected lines, followed by the text "rune labs" in a simple, sans-serif font. The logo and text are both in black and white.
- Longitudinal control study: N=171 subjects without Parkinson's from the Longitudinal Control study (>59,000 hours of all-day data)
The dyskinesia algorithm was designed and validated across 343 participants with PD (61 with dyskinesia) and 171 elderly, non-PD controls. The choreiform movement score (CMS) was calculated from sensor data in the pilot study and compared to dyskinesia ratings from three MDS-certified experts during multiple MDS-UPDRS assessments. The CMS was used to classify data into 1 minute segments where dyskinesia was likely or not.
CMS showed significant differences (P