Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
15 participants
OBSERVATIONAL
2025-07-01
2026-12-31
Brief Summary
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Detailed Description
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Each participant will wear a smartwatch (Apple or Samsung, depending on their smartphone OS) to measure heart rate during sleep over a 12-week period. Blood samples for TFTs will be collected at four separate visits (baseline and at 4, 8, and 12 weeks). The primary endpoint is the F1 score between the investigational device's output and the diagnosis of thyrotoxicosis based on free T4 values. Secondary endpoints include sensitivity, specificity, and area under the curve (AUC) of the device's performance.
Data from the wearable device and TFTs will be used to create multiple evaluation-reference data pairs per patient, enabling within-subject validation across different time points. The study does not involve any investigational treatment or alteration to standard care and is classified as non-significant risk (NSR). The output of the software device will not influence clinical decisions during the trial.
The study also aims to assess the generalizability of the software's performance by comparing results from this U.S.-based cohort with prior studies conducted in Korea.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Glandy HYPER Validation Cohort
articipants diagnosed with Graves' disease who are either newly diagnosed or currently under treatment. All participants will wear a commercially available smartwatch (Apple or Samsung) to measure sleep heart rate, and undergo thyroid function testing (free T4 and TSH) at baseline and follow-up visits over 12 weeks. No therapeutic intervention will be applied, and data from wearable devices will be used solely for observational performance evaluation of the investigational software.
Heart rate-based AI software for detecting thyrotoxicosis
A software-based investigational medical device that uses artificial intelligence to detect the thyrotoxic state in patients with hyperthyroidism. The device analyzes resting heart rate data collected from wearable devices along with thyroid function test results (free T4 and TSH). The device is not FDA-approved and will be used solely for observational performance evaluation without influencing clinical care.
Interventions
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Heart rate-based AI software for detecting thyrotoxicosis
A software-based investigational medical device that uses artificial intelligence to detect the thyrotoxic state in patients with hyperthyroidism. The device analyzes resting heart rate data collected from wearable devices along with thyroid function test results (free T4 and TSH). The device is not FDA-approved and will be used solely for observational performance evaluation without influencing clinical care.
Eligibility Criteria
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Inclusion Criteria
* Individuals who are newly diagnosed with Graves' disease or currently undergoing treatment for it.
* Individuals who have received sufficient explanation about the investigational software and are able to use it appropriately.
* Individuals who voluntarily agree to participate in the study and have signed informed consent, either directly or via a legally authorized representative.
Exclusion Criteria
* Individuals taking medications that significantly affect heart rate, including antiarrhythmics or antihistamines. (Intermittent short-acting beta-blockers are allowed.)
* Pregnant or breastfeeding individuals, or those planning pregnancy during the study period or not using appropriate contraception.
* Individuals with significant comorbidities that interfere with follow-up or study compliance.
* Individuals with severe psychiatric disorders, substance use disorder, or alcohol dependence.
* Individuals deemed ineligible at the discretion of the investigator for safety or ethical concerns.
22 Years
ALL
No
Sponsors
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THYROSCOPE INC.
INDUSTRY
Responsible Party
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Principal Investigators
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Umeshi Masharani, MD
Role: PRINCIPAL_INVESTIGATOR
University of California, San Francisco
Central Contacts
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References
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Shin K, Kim J, Park J, Oh TJ, Kong SH, Ahn CH, Moon JH, Kim MJ, Moon JH. A machine learning-assisted system to predict thyrotoxicosis using patients' heart rate monitoring data: a retrospective cohort study. Sci Rep. 2023 Nov 30;13(1):21096. doi: 10.1038/s41598-023-48199-x.
Kim KH, Lee J, Ahn CH, Yu HW, Choi JY, Lee HY, Lee WW, Moon JH. Association between Thyroid Function and Heart Rate Monitored by Wearable Devices in Patients with Hypothyroidism. Endocrinol Metab (Seoul). 2021 Oct;36(5):1121-1130. doi: 10.3803/EnM.2021.1216. Epub 2021 Oct 21.
Steinberger E, Pilz S, Trummer C, Theiler-Schwetz V, Reichhartinger M, Benninger T, Pandis M, Malle O, Keppel MH, Verheyen N, Grubler MR, Voelkl J, Meinitzer A, Marz W. Associations of Thyroid Hormones and Resting Heart Rate in Patients Referred to Coronary Angiography. Horm Metab Res. 2020 Dec;52(12):850-855. doi: 10.1055/a-1232-7292. Epub 2020 Sep 4.
Griffith ML, Bischoff LA, Baum HBA. Approach to the Patient With Thyrotoxicosis Using Telemedicine. J Clin Endocrinol Metab. 2020 Aug 1;105(8):dgaa373. doi: 10.1210/clinem/dgaa373.
Lee JE, Lee DH, Oh TJ, Kim KM, Choi SH, Lim S, Park YJ, Park DJ, Jang HC, Moon JH. Clinical Feasibility of Monitoring Resting Heart Rate Using a Wearable Activity Tracker in Patients With Thyrotoxicosis: Prospective Longitudinal Observational Study. JMIR Mhealth Uhealth. 2018 Jul 13;6(7):e159. doi: 10.2196/mhealth.9884.
Other Identifiers
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01-01-UCSF-001
Identifier Type: -
Identifier Source: org_study_id
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