Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

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Development and Validation of Two Self-Reported Tools for Insulin Resistance and Hypertension Risk Assessment in A European Cohort: The Feel4Diabetes-Study

Nutrients ◽

10.3390/nu12040960 ◽

2020 ◽

Vol 12 (4) ◽

pp. 960

Author(s):

Spyridon Kanellakis ◽

Christina Mavrogianni ◽

Kalliopi Karatzi ◽

Jaana Lindstrom ◽

Greet Cardon ◽

...

Keyword(s):

Insulin Resistance ◽

Risk Assessment ◽

High Risk ◽

Sensitivity And Specificity ◽

Low Cost ◽

Area Under The Curve ◽

Assessment Tools ◽

Screening Tools ◽

Model Assessment ◽

Sugary Drinks

Early identification of type 2 diabetes mellitus (T2DM) and hypertension (HTN) risk may improve prevention and promote public health. Implementation of self-reported scores for risk assessment provides an alternative cost-effective tool. The study aimed to develop and validate two easy-to-apply screening tools identifying high-risk individuals for insulin resistance (IR) and HTN in a European cohort. Sociodemographic, lifestyle, anthropometric and clinical data obtained from 1581 and 1350 adults (baseline data from the Feel4Diabetes-study) were used for the European IR and the European HTN risk assessment index respectively. Body mass index, waist circumference, sex, age, breakfast consumption, alcohol, legumes and sugary drinks intake, physical activity and sedentary behavior were significantly correlated with Homeostatic Model Assessment of IR (HOMA-IR) and/or HTN and incorporated in the two models. For the IR index, the Area Under the Curve (AUC), sensitivity and specificity for identifying individuals above the 75th and 95th of HOMA-IR percentiles were 0.768 (95%CI: 0.721–0.815), 0.720 and 0.691 and 0.828 (95%CI: 0.766–0.890), 0.696 and 0.778 respectively. For the HTN index, the AUC, sensitivity and specificity were 0.778 (95%CI: 0.680–0.876), 0.667 and 0.797. The developed risk assessment tools are easy-to-apply, valid, and low-cost, identifying European adults at high risk for developing T2DM or having HTN.

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Assessment of Stability of MIMU Probes to Skin-Marker-Based Anatomical Reference Frames During Locomotion Tasks: Effect of Different Locations on the Lower Limb

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.721900 ◽

2021 ◽

Vol 9 ◽

Author(s):

Giovanni Marco Scalera ◽

Maurizio Ferrarin ◽

Alberto Marzegan ◽

Marco Rabuffetti

Keyword(s):

Reference Frames ◽

Low Cost ◽

Wearable Sensors ◽

Ease Of Use ◽

Motor Tasks ◽

Sit To Stand ◽

Bony Landmarks ◽

Skin Marker ◽

Measurement Units ◽

Skeletal Kinematics

Soft tissue artefacts (STAs) undermine the validity of skin-mounted approaches to measure skeletal kinematics. Magneto-inertial measurement units (MIMU) gained popularity due to their low cost and ease of use. Although the reliability of different protocols for marker-based joint kinematics estimation has been widely reported, there are still no indications on where to place MIMU to minimize STA. This study aims to find the most stable positions for MIMU placement, among four positions on the thigh, four on the shank, and three on the foot. Stability was investigated by measuring MIMU movements against an anatomical reference frame, defined according to a standard marker-based approach. To this aim, markers were attached both on the case of each MIMU (technical frame) and on bony landmarks (anatomical frame). For each MIMU, the nine angles between each versor of the technical frame with each versor of the corresponding anatomical frame were computed. The maximum standard deviation of these angles was assumed as the instability index of MIMU-body coupling. Six healthy subjects were asked to perform barefoot gait, step negotiation, and sit-to-stand. Results showed that (1) in the thigh, the frontal position was the most stable in all tasks, especially in gait; (2) in the shank, the proximal position is the least stable, (3) lateral or medial calcaneus and foot dorsum positions showed equivalent stability performances. Further studies should be done before generalizing these conclusions to different motor tasks and MIMU-body fixation methods. The above results are of interest for both MIMU-based gait analysis and rehabilitation approaches using wearable sensors-based biofeedback.

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Toward Using a Smartwatch to Monitor Frailty in a Hospital Setting: Using a Single Wrist-Wearable Sensor to Assess Frailty in Bedbound Inpatients

Gerontology ◽

10.1159/000484241 ◽

2017 ◽

Vol 64 (4) ◽

pp. 389-400 ◽

Cited By ~ 8

Author(s):

Hyoki Lee ◽

Bellal Joseph ◽

Ana Enriquez ◽

Bijan Najafi

Keyword(s):

Frailty Index ◽

Hospital Setting ◽

Area Under The Curve ◽

Screening Tools ◽

Recursive Feature Elimination ◽

Physical Frailty ◽

Frailty Phenotype ◽

Frailty Status ◽

Flexion Extension ◽

Sensor Configuration

Background: While various objective tools have been validated for assessing physical frailty in the geriatric population, these are often unsuitable for busy clinics and mobility-impaired patients. Recently, we have developed a frailty meter (FM) using two wearable sensors, which allows capturing key frailty phenotypes (weakness, slowness, and exhaustion), by testing 20-s rapid elbow flexion-extension test. Objective: In this study, we proposed an enhanced automated algorithm to identify frailty using a single wrist-worn sensor. Methods: The data collected from 100 geriatric inpatients (age: 78.9 ± 9.1 years, 49% frail) were reanalyzed to validate the new algorithm. The frailty status of the participants was determined using a validated modified frailty index. Different FM phenotypes (31 features) including velocity of elbow rotation, decline in velocity of elbow rotation over 20 s, range of motion, etc. were extracted. A regression model, bootstrap with 2,000 iterations, and recursive feature elimination technique were used for optimizing the FM parameters and identifying frailty using a single wrist-worn sensor. Results: A strong agreement was observed between two-sensor and wrist-worn sensor configuration (r = 0.87, p < 0.001). Results suggest that the wrist-worn FM with no demographic information still yields a high accuracy of 80.0% (95% CI: 79.7-80.3%) and an area under the curve of 87.7% (95% CI: 87.4-87.9%) to identify frailty status. Results are comparable with two-sensor configuration, where the observed accuracy and area under the curve were 80.6% (95% CI: 80.4-80.9%) and 87.4% (95% CI: 87.1-87.6%), respectively. Conclusion: The simplicity of FM may open new avenues to integrate wearable technology and mobile health to capture frailty status in a busy hospital setting. Furthermore, the reduction of needed sensors to a single wrist-worn sensor allows deployment of the proposed algorithm in the form of a smartwatch application. From the application standpoint, the proposed FM is superior to traditional physical frailty-screening tools in which the walking test is a key frailty phenotype, and thus they cannot be used for bedbound patients or in busy clinics where administration of gait test as a part of routine assessment is impractical.

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An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Sensors ◽

10.3390/s21062050 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2050

Author(s):

Diogo Luís Marques ◽

Henrique Pereira Neiva ◽

Ivan Miguel Pires ◽

Eftim Zdravevski ◽

Martin Mihajlov ◽

...

Keyword(s):

Older Adults ◽

Low Cost ◽

Intraclass Correlation ◽

Smartphone Application ◽

Ease Of Use ◽

Systematic Bias ◽

Validity And Reliability ◽

Stand Test ◽

Sit To Stand ◽

Temporal Variables

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

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Automatic and Real-Time Computation of the 30-Seconds Chair-Stand Test without Professional Supervision for Community-Dwelling Older Adults

Sensors ◽

10.3390/s20205813 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5813

Author(s):

Antonio Cobo ◽

Elena Villalba-Mora ◽

Rodrigo Pérez-Rodríguez ◽

Xavier Ferre ◽

Walter Escalante ◽

...

Keyword(s):

Older Adults ◽

Older People ◽

Low Cost ◽

Community Dwelling ◽

Chair Stand ◽

Stand Test ◽

Professional Supervision ◽

Chair Stand Test ◽

Sit To Stand ◽

Community Dwelling Older Adults

The present paper describes a system for older people to self-administer the 30-s chair stand test (CST) at home without supervision. The system comprises a low-cost sensor to count sit-to-stand (SiSt) transitions, and an Android application to guide older people through the procedure. Two observational studies were conducted to test (i) the sensor in a supervised environment (n = 7; m = 83.29 years old, sd = 4.19; 5 female), and (ii) the complete system in an unsupervised one (n = 7; age 64–74 years old; 3 female). The participants in the supervised test were asked to perform a 30-s CST with the sensor, while a member of the research team manually counted valid transitions. Automatic and manual counts were perfectly correlated (Pearson’s r = 1, p = 0.00). Even though the sample was small, none of the signals around the critical score were affected by harmful noise; p (harmless noise) = 1, 95% CI = (0.98, 1). The participants in the unsupervised test used the system in their homes for a month. None of them dropped out, and they reported it to be easy to use, comfortable, and easy to understand. Thus, the system is suitable to be used by older adults in their homes without professional supervision.

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Functional movement compensations persist in individuals with hip osteoarthritis performing the five times sit-to-stand test 1 year after total hip arthroplasty

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-020-01663-0 ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Anna-Clara Esbjörnsson ◽

Josefine E. Naili

Keyword(s):

Total Hip Arthroplasty ◽

Hip Arthroplasty ◽

Center Of Mass ◽

Area Under The Curve ◽

Hip Osteoarthritis ◽

Functional Movement ◽

Stand Test ◽

Total Hip ◽

Sit To Stand ◽

Anterior Posterior

Abstract Background Methods to quantify and evaluate function are important for development of specific rehabilitation interventions. This study aimed to evaluate functional movement compensation in individuals with hip osteoarthritis performing the five times sit-to-stand test and change following total hip arthroplasty. To this end, trajectories of the body’s center of mass in the medial-lateral and anterior-posterior dimensions were quantified prior to and 1 year after total hip arthroplasty and compared to a healthy control group. Methods Twenty-eight individuals with hip osteoarthritis and 21 matched healthy controls were enrolled in this prospective study. Within 1 month prior to and 1 year after total hip arthroplasty, performance on the five times sit-to-stand test was evaluated using three-dimensional motion analysis and perceived pain using a visual analog scale. The center of mass trajectories for the medial-lateral and the anterior-posterior dimensions were identified, and the area under the curve was calculated, respectively. Repeated measures ANOVA were used to evaluate differences in the area under the curve, between pre- and postoperative performance, and between participants with hip osteoarthritis and controls. Results Preoperatively, individuals with hip osteoarthritis displayed a larger contralateral shift (p < 0.001) and forward displacement of the center of mass (p = 0.022) compared to controls. After surgery, deviations in both dimensions were reduced (medial-lateral p = 0.013; anterior-posterior p = 0.009). However, as compared to controls, the contralateral shift of the center of mass remained larger (p = 0.010), indicative of persistent asymmetric limb loading. Perceived pain was significantly reduced postoperatively (p < 0.001). Conclusions By quantifying the center of mass trajectory during five times sit-to-stand test performance, functional movement compensations could be detected and evaluated over time. Prior to total hip arthroplasty, individuals with hip osteoarthritis presented with an increased contralateral shift and forward displacement of the center of mass, representing a strategy to reduce pain by unloading the affected hip and reducing required hip and knee extension moments. After surgery, individuals with total hip arthroplasty displayed a persistent increased contralateral shift as compared to controls. This finding has implications for rehabilitation, where more focus must be directed towards normalizing loading of the limbs.

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Toward Remote Assessment of Physical Frailty Using Sensor-based Sit-to-stand Test

Journal of Surgical Research ◽

10.1016/j.jss.2021.01.023 ◽

2021 ◽

Vol 263 ◽

pp. 130-139

Author(s):

Catherine Park ◽

Amir Sharafkhaneh ◽

Mon S. Bryant ◽

Christina Nguyen ◽

Ilse Torres ◽

...

Keyword(s):

Physical Frailty ◽

Stand Test ◽

Sit To Stand ◽

Remote Assessment

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Ultrafast Preliminary Screening of COVID-19 by Machine Learning Analysis of Exhaled NO

10.21203/rs.3.rs-56416/v2 ◽

2021 ◽

Author(s):

Li Yang ◽

Jing Ma ◽

Wei Zhou ◽

Lei Sun ◽

Dong Zhai ◽

...

Keyword(s):

Machine Learning ◽

Low Cost ◽

Screening Method ◽

Area Under The Curve ◽

Diagnostic Methods ◽

Antibody Detection ◽

Nucleic Acid Detection ◽

Preliminary Screening ◽

Large Numbers ◽

Significant Difference

Abstract Background A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. Current diagnostic methods mainly include nucleic acid detection, antibody detection, antigen detection, and chest computed tomography (CT) imaging. Although these methods are crucial for the diagnosis of COVID-19, there is a lack of a rapid and economical method for preliminary screening COVID-19.Methods We measured the FeNO concentrations of 103 subjects without COVID-19 and 46 patients with COVID-19. Using machine learning (ML) method, we build a ML model based on fractional exhaled nitric oxide (FeNO) concentration and features of age, and body size for rapid preliminary screening COVID-19 suspects with low-cost.Findings The statistical analysis t-test show that there is a significant difference between the FeNO of healthy people and patients with COVID-19. The ML model can screen out the patients with COVID-19 or other diseases, which show abnormal FeNO distributions. An area under the curve of 0.982 and a sensitivity 0.917 have been achieved for preliminary screening COVID-19 suspects. This non-invasive detection method which takes in two minutes and costs less than a dollar could provide a direction for the control of the rapid spread COVID-19.Interpretation During the COVID-19 pandemic, large numbers and extensive testing of COVID-19 patients remains a problem. Public healthy efforts to limit SARS-CoV-2 spread need to find a more economical and faster screening method.

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Machine learning approach for early detection of autism by combining questionnaire and home video screening

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy039 ◽

2018 ◽

Vol 25 (8) ◽

pp. 1000-1007 ◽

Cited By ~ 23

Author(s):

Halim Abbas ◽

Ford Garberson ◽

Eric Glover ◽

Dennis P Wall

Keyword(s):

Machine Learning ◽

Clinical Study ◽

Early Detection ◽

Low Cost ◽

Clinical Analysis ◽

Autism Spectrum ◽

Training Data ◽

Screening Tools ◽

Home Video ◽

Home Videos

Abstract Background Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool. Methods Two algorithms are trained to identify autism, one based on short, structured parent-reported questionnaires and the other on tagging key behaviors from short, semi-structured home videos of children. A combination algorithm is then used to combine the results into a single assessment of higher accuracy. To overcome the scarcity, sparsity, and imbalance of training data, we apply novel feature selection, feature engineering, and feature encoding techniques. We allow for inconclusive determination where appropriate in order to boost screening accuracy when conclusive. The performance is then validated in a controlled clinical study. Results A multi-center clinical study of n = 162 children is performed to ascertain the performance of these algorithms and their combination. We demonstrate a significant accuracy improvement over standard screening tools in measurements of AUC, sensitivity, and specificity. Conclusion These findings suggest that a mobile, machine learning process is a reliable method for detection of autism outside of clinical settings. A variety of confounding factors in the clinical analysis are discussed along with the solutions engineered into the algorithms. Final results are statistically limited and will benefit from future clinical studies to extend the sample size.

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Bayesian Network as a Decision Tool for Predicting ALS Disease

Brain Sciences ◽

10.3390/brainsci11020150 ◽

2021 ◽

Vol 11 (2) ◽

pp. 150

Author(s):

Hasan Aykut Karaboga ◽

Aslihan Gunel ◽

Senay Vural Korkut ◽

Ibrahim Demir ◽

Resit Celik

Keyword(s):

Machine Learning ◽

Bayesian Networks ◽

Neurological Diseases ◽

Low Cost ◽

Early Period ◽

Area Under The Curve ◽

Learning Methods ◽

Machine Learning Methods ◽

Comparison Results ◽

Als Patients

Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person’s other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson’s patients, it is higher in the ALS patients than all control groups.

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