scholarly journals Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5289
Author(s):  
Catherine Park ◽  
Ramkinker Mishra ◽  
Jonathan Golledge ◽  
Bijan Najafi
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Daily Living ◽  
Recursive Feature Elimination ◽  
Physical Frailty ◽  
Slowing Down ◽  
Percentage Time ◽  
Chronic Stressors ◽  
Digital Biomarkers ◽  
Frailty Criteria

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

The Effect of Functional Circuit Training on Physical Frailty in Frail Older Adults: A Randomized Controlled Trial

2010 ◽  
Vol 18 (4) ◽  
pp. 401-424 ◽  
Author(s):  
Maria Giné-Garriga ◽  
Míriam Guerra ◽  
Esther Pagès ◽  
Todd M. Manini ◽  
Rosario Jiménez ◽  
...  
Keyword(s):  
Older Adults ◽  
Controlled Trial ◽  
Exercise Program ◽  
Community Dwelling ◽  
Control Group ◽  
Physical Frailty ◽  
Circuit Training ◽  
Frail Older Adults ◽  
Randomized Controlled ◽  
Frailty Criteria

The purpose of this study was to evaluate whether a 12-wk functional circuit-training program (FCT) could alter markers of physical frailty in a group of frail community-dwelling adults. Fifty-one individuals (31 women, 20 men), mean age (±SD) 84 (± 2.9) yr, met frailty criteria and were randomly assigned into groups (FCT = 26, control group [CG] = 25). FCT underwent a 12-wk exercise program. CG met once a week for health education meetings. Measures of physical frailty, function, strength, balance, and gait speed were assessed at Weeks 0, 12, and 36. Physical-frailty measures in FCT showed significant (p< .05) improvements relative to those in CG (Barthel Index at Weeks 0 and 36: 73.41 (± 2.35) and 77.0 (± 2.38) for the FCT and 70.79 (± 2.53) and 66.73 (± 2.73) for the CG. These data indicate that an FCT program is effective in improving measures of function and reducing physical frailty among frail older adults.

scholarly journals Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study

2021 ◽  
pp. 44-52
Author(s):  
Karsten Gielis ◽  
Marie-Elena Vanden Abeele ◽  
Katrien Verbert ◽  
Jos Tournoy ◽  
Maarten De Vos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Performance ◽  
Vector Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Digital Biomarkers

Background: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient’s cognition. Objective: To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts. Methods: Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score. Results: The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests. Conclusion: The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations.

scholarly journals Self-Efficacy Proxy Predicts Physical Frailty Incidence Over 8 Years in Non-Institutionalized Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 392-392
Author(s):  
Melissa Hladek ◽  
Jiafeng Zhu ◽  
Brian Buta ◽  
Sarah Szanton ◽  
Karen Bandeen-Roche ◽  
...  
Keyword(s):  
Older Adults ◽  
Activities Of Daily Living ◽  
Sex Education ◽  
Self Efficacy ◽  
Daily Living ◽  
Functional Decline ◽  
Adverse Outcomes ◽  
Physical Frailty ◽  
Frailty Phenotype ◽  
Increased Risk

Abstract Physical frailty is defined as a syndrome of decreased physiologic reserve conferring vulnerability to functional decline, mortality and other adverse outcomes in response to a stressor. One potential modifiable risk factor of frailty is self-efficacy, which is confidence in one’s ability to perform well at a task or domain in life. Self-efficacy is associated with improved health behavior and decreased chronic disease burden but has not been studied extensively in frailty research. Therefore, the purpose of this study was to evaluate a general self-efficacy proxy measure’s ability to predict frailty in a nationally representative sample of older adults using data from the National Health and Aging Trends Study (NHATS) collected from 2011-2018. 4,835 older adults (65+) were dichotomized into low and high self-efficacy groups using the one-item self-efficacy proxy measure in NHATS. The Physical Frailty Phenotype was used to assess frailty. A discrete time hazard model was used to obtain incident hazard ratios of frailty in two models. Model 1 was adjusted for age, race, sex, education and income. Model 2 contained Model 1 covariates and activities of daily living and co-morbidities. We found that low self-efficacy predicted a 41% increased risk of developing frailty over 8 years after adjustment for socio-demographics (P&lt;0.0001) and a 27% risk of incident frailty after further adjustment for activities of daily living and co-morbidities (P=0.004). This study provides preliminary evidence that self-efficacy may be a key modifiable element to incorporate into multi-modal frailty interventions.

Can Social Resources Explain the Limitations in the Activities of Daily Living of Older Adults Classified by the Phenotype of Physical Frailty?

2022 ◽  
pp. 073346482110642
Author(s):  
Claudia Venturini ◽  
Bruno de Souza Moreira ◽  
Eduardo Ferriolli ◽  
Anita Liberalesso Neri ◽  
Roberto Alves. Lourenço ◽  
...  
Keyword(s):  
Older Adults ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Living Alone ◽  
Linear Regression Models ◽  
Mediating Effects ◽  
Physical Frailty ◽  
Personal Network ◽  
Mediation Effects ◽  
The Relationship

The objective is to investigate the mediating roles of living alone and personal network in the relationship between physical frailty and activities of daily living (ADL) limitations among older adults. 2271 individuals were classified as vulnerable (pre-frail or frail) or robust. Mediating variables were living alone and personal network. Katz Index and Lawton-Brody scale were used to assess ADL. Mediating effects were analyzed with beta coefficients from linear regression models using the bootstrapping method. Mediation analysis showed significant mediating effects of living alone (β = .011; 95% CI = .004; .018) and personal network (β = .005; 95% CI = .001; .010) on the relationship between physical frailty and basic ADL limitations. Mediation effects of living alone and personal network on the relationship between physical frailty and instrumental ADL limitations were β = −.074 (95% CI=−.101; −.046) and β = −.044 (95% CI = −.076; −.020), respectively. Physically vulnerable older adults who lived alone or had poor personal network were more dependent on basic and instrumental ADL.

scholarly journals Machine learning identifies novel markers predicting functional decline in older adults

2020 ◽  
Author(s):  
Kate Valerio ◽  
Sarah Prieto ◽  
Alexander N. Hasselbach ◽  
Jena N. Moody ◽  
Scott M. Hayes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Functional Decline ◽  
Area Under The Curve ◽  
Everyday Cognition ◽  
The Everyday ◽  
Individual Variables ◽  
Fluorodeoxyglucose Pet

The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments, and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prior research suggests that cognitive measures, neuroimaging, and fluid-based biomarkers predict functional decline. However, a priori selection of variables can lead to the over-valuation of certain variables and exclusion of others that may be predictive. In the present study, we used machine learning techniques to select a wide range of baseline variables that best predicted functional decline in two years in individuals from the Alzheimer’s Disease Neuroimaging Initiative dataset. The sample included 398 individuals characterized as cognitively normal or mild cognitive impairment. Support vector machine classification algorithms were used to identify the most predictive modality from five different data modality types (demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive, and genetic/fluid-based biomarkers). In addition, variable selection identified individual variables across all modalities that best predicted functional decline in a testing sample. Of the five modalities examined, neurocognitive measures demonstrated the best accuracy in predicting functional decline (accuracy = 74.2%; area under the curve = 0.77), followed by fluorodeoxyglucose-PET (accuracy = 70.8%; area under the curve = 0.66). The individual variables with the greatest discriminatory ability for predicting functional decline included partner report of language in the Everyday Cognition questionnaire, the ADAS13, and activity of the left angular gyrus using fluorodeoxyglucose-PET. These three variables collectively explained 32% of the total variance in functional decline. Taken together, the machine learning model identified novel biomarkers that may be involved in the processing, retrieval, and conceptual integration of semantic information and which predict functional decline two years after assessment. These findings may be used to explore the clinical utility of the Everyday Cognition as a non-invasive, cost and time effective tool to predict future functional decline.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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.

Frailty, with or without Cognitive Impairment, is a Strong Predictor of Recurrent Falls in a US Population-Representative Sample of Older Adults

2021 ◽  
Author(s):  
Mei-Ling Ge ◽  
Eleanor M Simonsick ◽  
Bi-Rong Dong ◽  
Judith D Kasper ◽  
Qian-Li Xue
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Strong Predictor ◽  
Word Recall ◽  
Physical Frailty ◽  
Frailty Phenotype ◽  
Clock Drawing Test ◽  
Recurrent Falls ◽  
Health Aging ◽  
Marginal Means

Abstract Background Physical frailty and cognitive impairment have been separately associated with falls. The purpose of the study is to examine the associations of physical frailty and cognitive impairment separately and jointly with incident recurrent falls among older adults. Methods The analysis included 6000 older adults in community or non-nursing home residential care settings who were ≥65 years and participated in the National Health Aging Trends Study (NHATS). Frailty was assessed using the physical frailty phenotype; cognitive impairment was defined by bottom quintile of clock drawing test or immediate and delayed 10-word recall, or self/proxy-report of diagnosis of dementia, or AD8 score≥ 2. The marginal means/rates models were used to analyze the associations of frailty (by the physical frailty phenotype) and cognitive impairment with recurrent falls over 6 years follow-up (2012-2017). Results Of the 6000 older adults, 1,787 (29.8%) had cognitive impairment only, 334 (5.6%) had frailty only, 615 (10.3%) had both, and 3,264 (54.4%) had neither. After adjusting for age, sex, race, education, living alone, obesity, disease burden, and mobility disability, those with frailty (with or without cognitive impairment) at baseline had higher rates of recurrent falls than those without cognitive impairment and frailty (frailty only: Rate ratio (RR)=1.31, 95% confidence interval (CI)=1.18-1.44; both: RR=1.28, 95% CI=1.17-1.40). The association was marginally significant for those with cognitive impairment only (RR=1.07, 95% CI=1.00-1.13). Conclusions Frailty and cognitive impairment were independently associated with recurrent falls in non-institutionalized older adults. There was a lack of synergistic effect between frailty and cognitive impairment.

scholarly journals Technology Used to Recognize Activities of Daily Living in Community-Dwelling Older Adults

2020 ◽  
Vol 18 (1) ◽  
pp. 163
Author(s):  
Nicola Camp ◽  
Martin Lewis ◽  
Kirsty Hunter ◽  
Julie Johnston ◽  
Massimiliano Zecca ◽  
...  
Keyword(s):  
Older Adults ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Wearable Technology ◽  
Community Dwelling ◽  
Use Of Technology ◽  
Key Stakeholders ◽  
Environmental Technologies ◽  
Current Monitoring ◽  
Future Work

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable.

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