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1.
Preprint in English | medRxiv | ID: ppmedrxiv-21261626

ABSTRACT

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1,118 reported testing positive and 7,032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC=0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-21256482

ABSTRACT

Two mRNA vaccines and one adenovirus-based vaccine against SARS CoV-2 are currently being distributed at scale in the United States. Objective evidence of a specific individuals physiologic response to that vaccine are not routinely tracked but may offer insights into the acute immune response and personal and/or vaccine characteristics associated with that. We explored this possibility using a smartphone app-based research platform developed early in the pandemic that enabled volunteers (38,911 individuals between 25 March 2020 and 4 April 2021) to share their smartwatch and activity tracker data, as well as self-report, when appropriate, any symptoms, COVID-19 test results and vaccination dates and type. Of 4,110 individuals who reported at least one mRNA vaccination dose, 3,312 provided adequate resting heart rate data from the peri-vaccine period for analysis. We found changes in resting heart rate with respect to an individual baseline increased the days after vaccination, peaked on day 2, and returned to normal on day 6, with a much stronger effect after second dose with respect to first dose (average changes 1.6 versus 0.5 beats per minute). The changes were more pronounced for individuals who received the Moderna vaccine (on both doses), those who previously tested positive to COVID-19 (on dose 1), and for individuals aged <40 years, after adjusting for possible confounding factors. Taking advantage of continuous passive data from personal sensors could potentially enable the identification of a digital fingerprint of inflammation, which might prove useful as a surrogate for vaccine-induced immune response.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-20141333

ABSTRACT

Traditional screening for COVID-19 typically includes survey questions about symptoms, travel history, and sometimes temperature measurements. We explored whether longitudinal, personal sensor data can help identify subtle changes which may indicate an infection, such as COVID-19. To do this we developed an app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results from participants living in the US. We assessed whether symptoms and sensor data could differentiate COVID-19 positive versus negative cases in symptomatic individuals. Between March 25 and June 7, 2020, we enrolled 30,529 participants, of whom 3,811 reported symptoms, 54 reported testing positive for COVID-19, and 279 negative. We found that a combination of symptom and sensor data resulted in an AUC=0.80 [0.73 - 0.86] which was significantly better (p < 0.01) than a model which just considered symptoms alone (AUC=0.71 [0.63 - 0.79]) in the discrimination between symptomatic individuals positive or negative for COVID-19. Such orthogonal, continuous, passively captured data may be complementary to virus testing that is generally a one-off, or infrequent, sampling assay.

4.
Article in English | WPRIM (Western Pacific) | ID: wpr-898027

ABSTRACT

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

5.
Article in English | WPRIM (Western Pacific) | ID: wpr-890323

ABSTRACT

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

6.
Article in English | WPRIM (Western Pacific) | ID: wpr-763703

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.


Subject(s)
Artificial Intelligence , Biopsy, Fine-Needle , Classification , Diagnosis , Hyperthyroidism , Hypothyroidism , Learning , Machine Learning , Thyroid Diseases , Thyroid Gland , Thyroid Neoplasms , Thyroid Nodule
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