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Xie L, Hong R, Wu Z, Yue L, Peng K, Li S, Zhang J, Wang X, Jin L, Guan Q. Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease. Aging Clin Exp Res 2023; 35:2507-2516. [PMID: 37639172 DOI: 10.1007/s40520-023-02525-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. AIMS To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. METHODS PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. RESULTS Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00-85.00%, 68.75-100%, and 0.701-0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637-0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832-0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886-0.999, P < 0.001). CONCLUSIONS Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.
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Affiliation(s)
- Ludi Xie
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ronghua Hong
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhuang Wu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Yue
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kangwen Peng
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shuangfang Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingxing Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xijin Wang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingjing Jin
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
- Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Clinical Research Center for Aging and Medicine, Shanghai, China.
| | - Qiang Guan
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
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Velazquez-Diaz D, Arco JE, Ortiz A, Pérez-Cabezas V, Lucena-Anton D, Moral-Munoz JA, Galán-Mercant A. Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review. J Med Internet Res 2023; 25:e47346. [PMID: 37862082 PMCID: PMC10625070 DOI: 10.2196/47346] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/09/2023] [Accepted: 07/27/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). METHODS A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. RESULTS We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. CONCLUSIONS The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.
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Affiliation(s)
- Daniel Velazquez-Diaz
- ExPhy Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cadiz, Cádiz, Spain
- Advent Health Research Institute, Neuroscience Institute, Orlando, FL, United States
| | - Juan E Arco
- Department of Communications Engineering, University of Malaga, Málaga, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andres Ortiz
- Department of Communications Engineering, University of Malaga, Málaga, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Verónica Pérez-Cabezas
- MOVE-IT Research Group, Department of Nursing and Physiotherapy, Faculty of Health Sciences, University of Cádiz, Cádiz, Spain
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
| | - David Lucena-Anton
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
- Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Jose A Moral-Munoz
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
- Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Alejandro Galán-Mercant
- MOVE-IT Research Group, Department of Nursing and Physiotherapy, Faculty of Health Sciences, University of Cádiz, Cádiz, Spain
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
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Vavasour G, Giggins OM, Flood MW, Doyle J, Doheny E, Kelly D. Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity? PLoS One 2023; 18:e0286707. [PMID: 37289776 PMCID: PMC10249831 DOI: 10.1371/journal.pone.0286707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.
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Affiliation(s)
- Grainne Vavasour
- NetwellCASALA, Dundalk Institute of Technology, Co. Louth, Dundalk, Ireland
| | - Oonagh M. Giggins
- NetwellCASALA, Dundalk Institute of Technology, Co. Louth, Dundalk, Ireland
| | | | - Julie Doyle
- NetwellCASALA, Dundalk Institute of Technology, Co. Louth, Dundalk, Ireland
| | - Emer Doheny
- School of Electrical & Electronic Engineering, University College Dublin, Belfield, Ireland
| | - Daniel Kelly
- Faculty of Computing Engineering and The Built Environment, Ulster University, Derry (Londonderry), Northern Ireland
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Teh SK, Rawtaer I, Tan HP. Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis. IEEE J Biomed Health Inform 2022; 26:3638-3648. [PMID: 35737623 DOI: 10.1109/jbhi.2022.3185798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.
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Zhou H, Park C, Poursina O, Zahiri M, Nguyen H, Ruiz IT, Nguyen CK, Bryant MS, Sharafkhaneh A, Bandi VD, Najafi B. Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease. Gerontology 2022; 68:829-839. [PMID: 34844245 PMCID: PMC9148378 DOI: 10.1159/000520401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/22/2021] [Indexed: 01/03/2023] Open
Abstract
INTRODUCTION An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. METHODS Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. RESULTS Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64-1.26, p < 0.010) and all FM metrics (d = 0.45-1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35-0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82-2.12, p < 0.050). CONCLUSION This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.
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Affiliation(s)
- He Zhou
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA,Shenzhen Dengding Biopharma Co., Ltd., Shenzhen, Guangdong, China,Shenzhen Mass Medical Co., Ltd., Shenzhen, Guangdong, China
| | - Catherine Park
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Olia Poursina
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Mohsen Zahiri
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Hung Nguyen
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Ilse Torres Ruiz
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Christina K. Nguyen
- Telehealth Cardio-Pulmonary Rehabilitation program, Medical Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA
| | - Mon S. Bryant
- Telehealth Cardio-Pulmonary Rehabilitation program, Medical Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA,Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, Texas, USA
| | - Amir Sharafkhaneh
- Telehealth Cardio-Pulmonary Rehabilitation program, Medical Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA,Pulmonary, Critical Medicine and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Venkata D Bandi
- Pulmonary, Critical Medicine and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
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Manikkath J, Subramony JA. Toward closed-loop drug delivery: Integrating wearable technologies with transdermal drug delivery systems. Adv Drug Deliv Rev 2021; 179:113997. [PMID: 34634396 DOI: 10.1016/j.addr.2021.113997] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/31/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022]
Abstract
The recent advancement and prevalence of wearable technologies and their ability to make digital measurements of vital signs and wellness parameters have triggered a new paradigm in the management of diseases. Drug delivery as a function of stimuli or response from wearable, closed-loop systems can offer real-time on-demand or preprogrammed drug delivery capability and offer total management of disease states. Here we review the key opportunities in this space for development of closed-loop systems, given the advent of digital wearable technologies. Particular considerations and focus are given to closed-loop systems combined with transdermal drug delivery technologies.
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Ruiz-Ruiz L, Jimenez AR, Garcia-Villamil G, Seco F. Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters. SENSORS 2021; 21:s21206918. [PMID: 34696131 PMCID: PMC8538337 DOI: 10.3390/s21206918] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/14/2021] [Indexed: 12/15/2022]
Abstract
In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.
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Vavasour G, Giggins OM, Doyle J, Kelly D. How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review. J Neuroeng Rehabil 2021; 18:112. [PMID: 34238323 PMCID: PMC8268245 DOI: 10.1186/s12984-021-00909-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/28/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world's population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide. AIM The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults. METHOD Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS). RESULTS Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty. CONCLUSIONS Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.
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Affiliation(s)
- Grainne Vavasour
- NetwellCASALA, Dundalk Institute of Technology. Co, Louth, A91 K584, Ireland.
| | - Oonagh M Giggins
- NetwellCASALA, Dundalk Institute of Technology. Co, Louth, A91 K584, Ireland
| | - Julie Doyle
- NetwellCASALA, Dundalk Institute of Technology. Co, Louth, A91 K584, Ireland
| | - Daniel Kelly
- Ulster University Faculty of Computing Engineering and The Built Environment, Derry(Londonderry), BT48 7JL, Northern Ireland
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Drăgulinescu A, Drăgulinescu AM, Zincă G, Bucur D, Feieș V, Neagu DM. Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4316. [PMID: 32748872 PMCID: PMC7435916 DOI: 10.3390/s20154316] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/23/2020] [Accepted: 07/28/2020] [Indexed: 12/25/2022]
Abstract
The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.
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Affiliation(s)
- Andrei Drăgulinescu
- Electronics Technology and Reliability Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Ana-Maria Drăgulinescu
- Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Gabriela Zincă
- Automation and Industrial Informatics Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Doina Bucur
- Mechatronics Department, Faculty of Mechanical Engineering and Mechatronics, Biomedical Engineering and Biotechnology Department, Faculty of Medical Engineering, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Valentin Feieș
- Electronics Technology and Reliability Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
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E-Knitted Textile with Polymer Optical Fibers for Friction and Pressure Monitoring in Socks. SENSORS 2019; 19:s19133011. [PMID: 31288468 PMCID: PMC6651217 DOI: 10.3390/s19133011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 12/23/2022]
Abstract
The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.
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Panhwar YN, Naghdy F, Naghdy G, Stirling D, Potter J. Assessment of frailty: a survey of quantitative and clinical methods. BMC Biomed Eng 2019; 1:7. [PMID: 32903310 PMCID: PMC7422496 DOI: 10.1186/s42490-019-0007-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 02/25/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. RESULTS The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. CONCLUSION Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.
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Affiliation(s)
| | | | | | | | - Janette Potter
- University of Wollongong, Wollongong, Australia
- Illawarra Health and Medical Research Institute, Wollongong, Australia
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Dobkin BH, Martinez C. Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice. Curr Neurol Neurosci Rep 2018; 18:87. [PMID: 30293160 DOI: 10.1007/s11910-018-0896-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE OF REVIEW Measurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use. RECENT FINDINGS Commercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.
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Affiliation(s)
- Bruce H Dobkin
- Geffen School of Medicine at UCLA, Department of Neurology, Reed Neurologic Research Center, 710 Westwood Plaza, Los Angeles, CA, 90095-1769, USA.
| | - Clarisa Martinez
- Geffen School of Medicine at UCLA, Department of Neurology, Reed Neurologic Research Center, 710 Westwood Plaza, Los Angeles, CA, 90095-1769, USA
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Cohen AB, Mathews SC. The Digital Outcome Measure. Digit Biomark 2018; 2:94-105. [PMID: 32095761 PMCID: PMC7015352 DOI: 10.1159/000492396] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 07/23/2018] [Indexed: 01/04/2023] Open
Abstract
Improving clinical outcomes remains the gold standard in advancing healthcare. Focusing on outcomes holds the potential to unite all clinical stakeholders including payers, industry, providers, and patients. Yet, the dominant ways in which outcomes are captured, provider-collected or patient-reported, have significant limitations. The emerging field of biosensors and wearables, which aims to capture many types of health data, holds promise to specifically capture outcomes while complementing existing outcome collection methods. A digital outcome measure, unlike a traditional provider-collected or patient-reported outcome measure, depends less on active patient or provider participation. Thus, digital outcome measures may be more amenable to standardization as well as greater collection consistency, frequency, and accuracy.
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Affiliation(s)
- Adam B. Cohen
- The Johns Hopkins University Applied Physics Lab, Health Technologies, National Health Mission Area, Laurel, Maryland, USA
- Department of Neurology, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Simon C. Mathews
- Armstrong Institute for Patient Safety and Quality, Baltimore, Maryland, USA
- Division of Gastroenterology, Department of Internal Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, USA
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