Endocrinology and Metabolism

Original Article

Thyroid
Association between Thyroid Function and Heart Rate Monitored by Wearable Devices in Patients with Hypothyroidism: Ki-Hun Kim, Juhui Lee, Chang Ho Ahn, Hyeong Won Yu, June Young Choi, Ho-Young Lee, Won Woo Lee, Jae Hoon Moon; Endocrinol Metab. 2021;36(5):1121-1130. Published online October 21, 2021; DOI: https://doi.org/10.3803/EnM.2021.1216

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Background
Heart rate (HR) monitored by a wearable device (WD) has demonstrated its clinical feasibility for thyrotoxicosis subjects. However, the association of HR monitored by wearables with hypothyroidism has not been examined. We assessed the association between serum thyroid hormone concentration and three WD-HR parameters in hypothyroid subjects.
Methods
Forty-four subjects scheduled for radioactive iodine therapy (RAI Tx) after thyroid cancer surgery were included. Thirty subjects were prepared for RAI Tx by thyroid hormone withdrawal (hypothyroidism group) and 14 subjects by recombinant human thyrotropin (control group). Three WD-HR parameters were calculated from the HR data collected during rest, during sleep, and from 2:00 AM to 6:00 AM, respectively. We analyzed the changes in conventionally measured resting HR (On-site rHR) and WDHR parameters relative to thyroid hormone levels.
Results
Serum free thyroxine (T4) levels, On-site rHR, and WD-HR parameters were lower in the hypothyroid group than in the control group at the time of RAI Tx. WD-HR parameters also reflected minute changes in free T4 levels. A decrease in On-site rHR and WD-HR parameters by one standard deviation (On-site rHR, approximately 12 bpm; WD-HR parameters, approximately 8 bpm) was associated with a 0.2 ng/dL decrease in free T4 levels (P<0.01) and a 2-fold increase of the odds ratio of hypothyroidism (P<0.01). WD-HR parameters displayed a better goodness-of-fit measure (lower quasi-information criterion value) than On-site rHR in predicting the hypothyroidism.
Conclusion
This study identified WD-HR parameters as informative and easy-to-measure biomarkers to predict hypothyroidism.

Citations

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Review Articles

Diabetes
Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare: Hun-Sung Kim, Kun-Ho Yoon; Endocrinol Metab. 2020;35(3):541-548. Published online September 22, 2020; DOI: https://doi.org/10.3803/EnM.2020.675

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We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients’ health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.

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Thyroid
Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease: Jae Hoon Moon, Steven R. Steinhubl; Endocrinol Metab. 2019;34(2):124-131. Published online June 24, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.2.124

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135 Download
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Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.

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