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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; 13:21096. [PMID: 38036639 PMCID: PMC10689821 DOI: 10.1038/s41598-023-48199-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023] Open
Abstract
Previous studies have shown a correlation between resting heart rate (HR) measured by wearable devices and serum free thyroxine concentration in patients with thyroid dysfunction. We have developed a machine learning (ML)-assisted system that uses HR data collected from wearable devices to predict the occurrence of thyrotoxicosis in patients. HR monitoring data were collected using a wearable device for a period of 4 months in 175 patients with thyroid dysfunction. During this period, 3 or 4 thyroid function tests (TFTs) were performed on each patient at intervals of at least one month. The HR data collected during the 10 days prior to each TFT were paired with the corresponding TFT results, resulting in a total of 662 pairs of data. Our ML-assisted system predicted thyrotoxicosis of a patient at a given time point based on HR data and their HR-TFT data pair at another time point. Our ML-assisted system divided the 662 cases into either thyrotoxicosis and non-thyrotoxicosis and the performance was calculated based on the TFT results. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of our system for predicting thyrotoxicosis were 86.14%, 85.92%, 52.41%, and 97.18%, respectively. When subclinical thyrotoxicosis was excluded from the analysis, the sensitivity, specificity, PPV, and NPV of our system for predicting thyrotoxicosis were 86.14%, 98.28%, 94.57%, and 95.32%, respectively. Our ML-assisted system used the change in mean, relative standard deviation, skewness, and kurtosis of HR while sleeping, and the Jensen-Shannon divergence of sleep HR and TFT distribution as major parameters for predicting thyrotoxicosis. Our ML-assisted system has demonstrated reasonably accurate predictions of thyrotoxicosis in patients with thyroid dysfunction, and the accuracy could be further improved by gathering more data. This predictive system has the potential to monitor the thyroid function status of patients with thyroid dysfunction by collecting heart rate data, and to determine the optimal timing for blood tests and treatment intervention.
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Affiliation(s)
- Kyubo Shin
- THYROSCOPE INC., Ulsan, Republic of Korea
| | | | | | - Tae Jung Oh
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Chang Ho Ahn
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Joon Ho Moon
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Min Joo Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jae Hoon Moon
- THYROSCOPE INC., Ulsan, Republic of Korea.
- Department of Internal Medicine, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea.
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
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Hirai K, Fujimoto Y, Bamba Y, Kageyama Y, Ima H, Ichise A, Sasaki H, Nakagawa R. Continuous Monitoring of Changes in Heart Rate during the Periprocedural Course of Carotid Artery Stenting Using a Wearable Device: A Prospective Observational Study. Neurol Med Chir (Tokyo) 2023; 63:526-534. [PMID: 37648537 PMCID: PMC10725827 DOI: 10.2176/jns-nmc.2023-0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/03/2023] [Indexed: 09/01/2023] Open
Abstract
This prospective observational study will evaluate the change in heart rate (HR) during the periprocedural course of carotid artery stenting (CAS) via continuous monitoring using a wearable device. The participants were recruited from our outpatient clinic between April 2020 and March 2023. They were instructed to continuously wear the device from the last outpatient visit before admission to the first outpatient visit after discharge. The changes in HR of interest throughout the periprocedural course of CAS were assessed. In addition, the Bland-Altman analysis was adopted to compare the HR measurement made by the wearable device during CAS with that made by the electrocardiogram (ECG). A total of 12 patients who underwent CAS were included in the final analysis. The time-series analysis revealed that a percentage change in HR decrease occurred on day 1 following CAS and that the most significant HR decrease rate was 12.1% on day 4 following CAS. In comparing the measurements made by the wearable device and ECG, the Bland-Altman analysis revealed the accuracy of the wearable device with a bias of -1.12 beats per minute (bpm) and a precision of 3.16 bpm. Continuous HR monitoring using the wearable device indicated that the decrease in HR following CAS could persist much longer than previously reported, providing us with unique insights into the physiology of carotid sinus baroreceptors.
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Affiliation(s)
| | | | - Yohei Bamba
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Yu Kageyama
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Hiroyuki Ima
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Ayaka Ichise
- Department of Neurosurgery, Osaka Rosai Hospital
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Steffens B, Koch G, Gächter P, Claude F, Gotta V, Bachmann F, Schropp J, Janner M, l'Allemand D, Konrad D, Welzel T, Szinnai G, Pfister M. Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves' disease. Front Med (Lausanne) 2023; 10:1099470. [PMID: 37206476 PMCID: PMC10188966 DOI: 10.3389/fmed.2023.1099470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/14/2023] [Indexed: 05/21/2023] Open
Abstract
Objectives Graves' disease (GD) with onset in childhood or adolescence is a rare disease (ORPHA:525731). Current pharmacotherapeutic approaches use antithyroid drugs, such as carbimazole, as monotherapy or in combination with thyroxine hormone substitutes, such as levothyroxine, as block-and-replace therapy to normalize thyroid function and improve patients' quality of life. However, in the context of fluctuating disease activity, especially during puberty, a considerable proportion of pediatric patients with GD is suffering from thyroid hormone concentrations outside the therapeutic reference ranges. Our main goal was to develop a clinically practical pharmacometrics computer model that characterizes and predicts individual disease activity in children with various severity of GD under pharmacotherapy. Methods Retrospectively collected clinical data from children and adolescents with GD under up to two years of treatment at four different pediatric hospitals in Switzerland were analyzed. Development of the pharmacometrics computer model is based on the non-linear mixed effects approach accounting for inter-individual variability and incorporating individual patient characteristics. Disease severity groups were defined based on free thyroxine (FT4) measurements at diagnosis. Results Data from 44 children with GD (75% female, median age 11 years, 62% receiving monotherapy) were analyzed. FT4 measurements were collected in 13, 15, and 16 pediatric patients with mild, moderate, or severe GD, with a median FT4 at diagnosis of 59.9 pmol/l (IQR 48.4, 76.8), and a total of 494 FT4 measurements during a median follow-up of 1.89 years (IQR 1.69, 1.97). We observed no notable difference between severity groups in terms of patient characteristics, daily carbimazole starting doses, and patient years. The final pharmacometrics computer model was developed based on FT4 measurements and on carbimazole or on carbimazole and levothyroxine doses involving two clinically relevant covariate effects: age at diagnosis and disease severity. Discussion We present a tailored pharmacometrics computer model that is able to describe individual FT4 dynamics under both, carbimazole monotherapy and carbimazole/levothyroxine block-and-replace therapy accounting for inter-individual disease progression and treatment response in children and adolescents with GD. Such clinically practical and predictive computer model has the potential to facilitate and enhance personalized pharmacotherapy in pediatric GD, reducing over- and underdosing and avoiding negative short- and long-term consequences. Prospective randomized validation trials are warranted to further validate and fine-tune computer-supported personalized dosing in pediatric GD and other rare pediatric diseases.
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Affiliation(s)
- Britta Steffens
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
- *Correspondence: Britta Steffens
| | - Gilbert Koch
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Pascal Gächter
- Pediatric Endocrinology and Diabetology, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Fabien Claude
- Pediatric Endocrinology and Diabetology, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Verena Gotta
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Freya Bachmann
- Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany
| | - Johannes Schropp
- Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany
| | - Marco Janner
- Division of Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Dagmar l'Allemand
- Department of Pediatric Endocrinology and Diabetology, Children's Hospital of Eastern Switzerland, St. Gallen, Switzerland
| | - Daniel Konrad
- Division of Pediatric Endocrinology and Diabetology and Children's Research Centre, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Tatjana Welzel
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Marc Pfister
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland
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Nissen M, Slim S, Jäger K, Flaucher M, Huebner H, Danzberger N, Fasching PA, Beckmann MW, Gradl S, Eskofier BM. Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study. JMIR Form Res 2022; 6:e33635. [PMID: 35230250 PMCID: PMC8924780 DOI: 10.2196/33635] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/14/2021] [Accepted: 01/13/2022] [Indexed: 02/06/2023] Open
Abstract
Background
Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.
Objective
This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.
Methods
Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.
Results
A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).
Conclusions
Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.
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Affiliation(s)
- Michael Nissen
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Syrine Slim
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Katharina Jäger
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Madeleine Flaucher
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany
| | - Nina Danzberger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany
| | - Stefan Gradl
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M Eskofier
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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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; 36:1121-1130. [PMID: 34674500 PMCID: PMC8566120 DOI: 10.3803/enm.2021.1216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/27/2021] [Indexed: 11/30/2022] Open
Abstract
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.
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Affiliation(s)
- Ki-Hun Kim
- Department of Industrial Engineering, Pusan National University, Busan,
Korea
- Faculty of Industrial Design Engineering, Delft University of Technology, Delft,
Netherland
- Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan,
Korea
| | | | - Chang Ho Ahn
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
| | - Hyeong Won Yu
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
| | - June Young Choi
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
| | - Ho-Young Lee
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
| | - Won Woo Lee
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
| | - Jae Hoon Moon
- Thyroscope Inc., Ulsan,
Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
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Sukor N, Lachumanan D, Kamaruddin NA. Letter to the Editor from Sukor et al: "Evaluation and Treatment of Amiodarone-Induced Thyroid Disorders". J Clin Endocrinol Metab 2021; 106:e2457-e2458. [PMID: 33624762 DOI: 10.1210/clinem/dgab104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Norlela Sukor
- Endocrine Unit, Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia
| | - Deviga Lachumanan
- Endocrine Unit, Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia
| | - Nor Azmi Kamaruddin
- Endocrine Unit, Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia
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Kim M, Choi HJ. Digital Therapeutics for Obesity and Eating-Related Problems. Endocrinol Metab (Seoul) 2021; 36:220-228. [PMID: 33761233 PMCID: PMC8090472 DOI: 10.3803/enm.2021.107] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/17/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, digital technologies have rapidly advanced and are being applied to remedy medical problems. These technologies allow us to monitor and manage our physical and mental health in our daily lives. Since lifestyle modification is the cornerstone of the management of obesity and eating behavior problems, digital therapeutics (DTx) represent a powerful and easily accessible treatment modality. This review discusses the critical issues to consider for enhancing the efficacy of DTx in future development initiatives. To competently adapt and expand public access to DTx, it is important for various stakeholders, including health professionals, patients, and guardians, to collaborate with other industry partners and policy-makers in the ecosystem.
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Affiliation(s)
- Meelim Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul,
Korea
- BK21 Plus Biomedical Science Project Team, Seoul National University College of Medicine, Seoul,
Korea
| | - Hyung Jin Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul,
Korea
- BK21 Plus Biomedical Science Project Team, Seoul National University College of Medicine, Seoul,
Korea
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Quigg M, Bazil CW, Boly M, Louis ES, Liu J, Ptacek L, Maganti R, Kalume F, Gluckman BJ, Pathmanathan J, Pavlova MK, Buchanan GF. Proceedings of the Sleep and Epilepsy Workshop: Section 1 Decreasing Seizures-Improving Sleep and Seizures, Themes for Future Research. Epilepsy Curr 2021; 21:15357597211004566. [PMID: 33787387 PMCID: PMC8609596 DOI: 10.1177/15357597211004566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Epileptic seizures, sleep, and circadian timing share bilateral interactions, but concerted work to characterize these interactions and to leverage them to the advantage of patients with epilepsy remains in beginning stages. To further the field, a multidisciplinary group of sleep physicians, epileptologists, circadian timing experts, and others met to outline the state of the art, gaps of knowledge, and suggest ways forward in clinical, translational, and basic research. A multidisciplinary panel of experts discussed these interactions, centered on whether improvements in sleep or circadian rhythms improve decrease seizure frequency. In addition, education about sleep was lacking in among patients, their families, and physicians, and that focus on education was an extremely important "low hanging fruit" to harvest. Improvements in monitoring technology, experimental designs sensitive to the rigor required to dissect sleep versus circadian influences, and clinical trials in seizure reduction with sleep improvements were appropriate.
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Affiliation(s)
- Mark Quigg
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Neurology and Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | | | - Melanie Boly
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | | | - Judy Liu
- Brown University, Providence, RI, USA
| | | | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | | | - Bruce J. Gluckman
- Departments of Engineering Science & Mechanics, Neurosurgery, and Biomedical Engineering, Penn State University, University Park, PA, USA
| | | | - Milena K. Pavlova
- Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Gordon F. Buchanan
- Department of Neurology and Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
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9
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Neinstein AB, Masharani U. Letter to the Editor: "Approach to the Patient with Thyrotoxicosis Using Telemedicine". J Clin Endocrinol Metab 2021; 106:e1060-e1061. [PMID: 33038240 DOI: 10.1210/clinem/dgaa723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 10/06/2020] [Indexed: 11/19/2022]
Affiliation(s)
- Aaron B Neinstein
- Department of Medicine, University of California, San Francisco, San Francisco, California
- UCSF Center for Digital Health Innovation, San Francisco, California
| | - Umesh Masharani
- Department of Medicine, University of California, San Francisco, San Francisco, California
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Steinberger E, Pilz S, Trummer C, Theiler-Schwetz V, Reichhartinger M, Benninger T, Pandis M, Malle O, Keppel MH, Verheyen N, Grübler MR, Voelkl J, Meinitzer A, März W. Associations of Thyroid Hormones and Resting Heart Rate in Patients Referred to Coronary Angiography. Horm Metab Res 2020; 52:850-855. [PMID: 32886945 DOI: 10.1055/a-1232-7292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Resting heart rate (RHR) is associated with increased risk of cardiovascular morbidity and mortality. Thyroid hormones exert several effects on the cardiovascular system, but the relation between thyroid function and RHR remains to be further established. We evaluated whether measures of thyroid hormone status are associated with RHR in patients referred to coronary angiography. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxin (FT4), and RHR were determined in 2795 participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) Study. Median (25th to 75th percentile) serum concentrations were 1.25 (0.76-1.92) mU/l for TSH, 4.8 (4.2-5.3) pmol/l for FT3 and 17.1 (15.4-19.0) pmol/l for FT4, and mean (±standard deviation) RHR was 68.8 (±11.7) beats/min. Comparing the highest versus the lowest quartile, RHR (beats/min) was significantly higher in the fourth FT4 quartile [3.48, 95% confidence interval (CI): 2.23-4.73; p <0.001] and in the fourth FT3 quartile (2.30, 95% CI: 1.06-3.55; p <0.001), but there was no significant difference for TSH quartiles. In multiple linear regression analyses adjusting for various potential confounders, FT3 and FT4 were significant predictors of RHR (p <0.001 for both). In subgroups restricted to TSH, FT3, and FT4 values within the reference range, both FT3 and FT4 remained significant predictors of RHR (p <0.001 for all). In conclusion, in patients referred to coronary angiography, FT3 and FT4 but not TSH were positively associated with RHR. The relationship between free thyroid hormones and RHR warrants further investigations regarding its diagnostic and therapeutic implications.
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Affiliation(s)
- Eva Steinberger
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Stefan Pilz
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Christian Trummer
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Verena Theiler-Schwetz
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | | | - Thomas Benninger
- Institute of Automation and Control, Graz University of Technology, Graz, Austria
| | - Marlene Pandis
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Oliver Malle
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Martin H Keppel
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Nicolas Verheyen
- Department of Cardiology, Medical University of Graz, Graz, Austria
| | - Martin R Grübler
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Jakob Voelkl
- Institute for Physiology and Pathophysiology, Johannes Kepler University Linz, Linz, Austria
- DZHK (German Centre for Cardiovascular Research), Berlin, Germany
- Department of Nephrology and Medical Intensive Care, Charite - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Meinitzer
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Winfried März
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Holding GmbH, Mannheim, Germany
- Medical Clinic 5, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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11
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Sun J, Liu Y. Using Smart Bracelets to Assess Heart Rate Among Students During Physical Education Lessons: Feasibility, Reliability, and Validity Study. JMIR Mhealth Uhealth 2020; 8:e17699. [PMID: 32663136 PMCID: PMC7439147 DOI: 10.2196/17699] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 05/02/2020] [Accepted: 06/14/2020] [Indexed: 12/18/2022] Open
Abstract
Background An increasing number of wrist-worn wearables are being examined in the context of health care. However, studies of their use during physical education (PE) lessons remain scarce. Objective We aim to examine the reliability and validity of the Fizzo Smart Bracelet (Fizzo) in measuring heart rate (HR) in the laboratory and during PE lessons. Methods In Study 1, 11 healthy subjects (median age 22.0 years, IQR 3.75 years) twice completed a test that involved running on a treadmill at 6 km/h for 12 minutes and 12 km/h for 5 minutes. During the test, participants wore two Fizzo devices, one each on their left and right wrists, to measure their HR. At the same time, the Polar Team2 Pro (Polar), which is worn on the chest, was used as the standard. In Study 2, we went to 10 schools and measured the HR of 24 students (median age 14.0 years, IQR 2.0 years) during PE lessons. During the PE lessons, each student wore a Polar device on their chest and a Fizzo on their right wrist to measure HR data. At the end of the PE lessons, the students and their teachers completed a questionnaire where they assessed the feasibility of Fizzo. The measurements taken by the left wrist Fizzo and the right wrist Fizzo were compared to estimate reliability, while the Fizzo measurements were compared to the Polar measurements to estimate validity. To measure reliability, intraclass correlation coefficients (ICC), mean difference (MD), standard error of measurement (SEM), and mean absolute percentage errors (MAPE) were used. To measure validity, ICC, limits of agreement (LOA), and MAPE were calculated and Bland-Altman plots were constructed. Percentage values were used to estimate the feasibility of Fizzo. Results The Fizzo showed excellent reliability and validity in the laboratory and moderate validity in a PE lesson setting. In Study 1, reliability was excellent (ICC>0.97; MD<0.7; SEM<0.56; MAPE<1.45%). The validity as determined by comparing the left wrist Fizzo and right wrist Fizzo was excellent (ICC>0.98; MAPE<1.85%). Bland-Altman plots showed a strong correlation between left wrist Fizzo measurements (bias=0.48, LOA=–3.94 to 4.89 beats per minute) and right wrist Fizzo measurements (bias=0.56, LOA=–4.60 to 5.72 beats per minute). In Study 2, the validity of the Fizzo was lower compared to that found in Study 1 but still moderate (ICC>0.70; MAPE<9.0%). The Fizzo showed broader LOA in the Bland-Altman plots during the PE lessons (bias=–2.60, LOA=–38.89 to 33.69 beats per minute). Most participants considered the Fizzo very comfortable and easy to put on. All teachers thought the Fizzo was helpful. Conclusions When participants ran on a treadmill in the laboratory, both left and right wrist Fizzo measurements were accurate. The validity of the Fizzo was lower in PE lessons but still reached a moderate level. The Fizzo is feasible for use during PE lessons.
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Affiliation(s)
- Jiangang Sun
- School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai, China
| | - Yang Liu
- School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai, China.,Shanghai Research Center for Physical Fitness and Health of Children and Adolescents, Shanghai University of Sport, Shanghai, China
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Moon JH, Steinhubl SR. Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease. Endocrinol Metab (Seoul) 2019; 34:124-131. [PMID: 31257740 PMCID: PMC6599900 DOI: 10.3803/enm.2019.34.2.124] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 05/23/2019] [Accepted: 05/27/2019] [Indexed: 01/28/2023] Open
Abstract
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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Affiliation(s)
- Jae Hoon Moon
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
| | - Steven R Steinhubl
- Department of Molecular Medicine, Scripps Research Translational Institute, La Jolla, CA, USA.
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