scholarly journals Network Medicine: Harnessing the Potential of Digital Biomarkers

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Jones GB ◽  
Wright JM
Keyword(s):  
Network Medicine ◽  
Digital Biomarkers

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
He Zhou ◽  
Catherine Park ◽  
Mohammad Shahbazi ◽  
Michele K. York ◽  
Mark E. Kunik ◽  
...  
Keyword(s):  
Older Adults ◽  
Steady State ◽  
Dual Task ◽  
Gait Speed ◽  
Stride Length ◽  
Cognitive Frailty ◽  
Single Task ◽  
Double Support ◽  
Gait Parameters ◽  
Digital Biomarkers

<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m<sup>2</sup>) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

scholarly journals Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Brinnae Bent ◽  
Peter J. Cho ◽  
Maria Henriquez ◽  
April Wittmann ◽  
Connie Thacker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Data Driven ◽  
Self Management ◽  
Noninvasive Method ◽  
Medical Interventions ◽  
Interstitial Glucose ◽  
Using Data ◽  
Glucose Prediction ◽  
Digital Biomarkers

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

scholarly journals Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3139
Author(s):  
Julian Varghese ◽  
Catharina Marie van Alen ◽  
Michael Fujarski ◽  
Georg Stefan Schlake ◽  
Julitta Sucker ◽  
...  
Keyword(s):  
Low Noise ◽  
Disease Classification ◽  
Multilayer Perceptrons ◽  
Sensor Validation ◽  
Diagnostic Potential ◽  
A Prospective Study ◽  
Learning Architectures ◽  
Diagnosis Classification ◽  
Healthy Participants ◽  
Digital Biomarkers

Smartwatches provide technology-based assessments in Parkinson’s Disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

scholarly journals NeuroTec Sitem-Insel Bern: Closing the Last Mile in Neurology

2021 ◽  
Vol 5 (2) ◽  
pp. 13
Author(s):  
Kaspar A. Schindler ◽  
Tobias Nef ◽  
Maxime O. Baud ◽  
Athina Tzovara ◽  
Gürkan Yilmaz ◽  
...  
Keyword(s):  
Diagnostic Tests ◽  
Current Model ◽  
Disease Process ◽  
Therapeutic Interventions ◽  
Sleep Studies ◽  
Last Mile ◽  
Specialized Equipment ◽  
The One ◽  
Treatment Procedures ◽  
Digital Biomarkers

Neurology is focused on a model where patients receive their care through repeated visits to clinics and doctor’s offices. Diagnostic tests often require expensive and specialized equipment that are only available in clinics. However, this current model has significant drawbacks. First, diagnostic tests, such as daytime EEG and sleep studies, occur under artificial conditions in the clinic, which may mask or wrongly emphasize clinically important features. Second, early detection and high-quality management of chronic neurological disorders require repeat measurements to accurately capture the dynamics of the disease process, which is impractical to execute in the clinic for economical and logistical reasons. Third, clinic visits remain inaccessible to many patients due to geographical and economical circumstances. Fourth, global disruptions to daily life, such as the one caused by COVID-19, can seriously harm patients if access to in-person clinical visits for diagnostic and treatment purposes is throttled. Thus, translating diagnostic and treatment procedures to patients’ homes will convey multiple substantial benefits and has the potential to substantially improve clinical outcomes while reducing cost. NeuroTec was founded to accelerate the re-imagining of neurology and to promote the convergence of technological, scientific, medical and societal processes. The goal is to identify and validate new digital biomarkers that can close the last mile in neurology by enabling the translation of personalized diagnostics and therapeutic interventions from the clinic to the patient’s home.

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

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