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Single M, Bruhin LC, Colombo A, Möri K, Gerber SM, Lahr J, Krack P, Klöppel S, Müri RM, Mosimann UP, Nef T. A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:1172. [PMID: 38400329 PMCID: PMC10893300 DOI: 10.3390/s24041172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.
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Affiliation(s)
- Michael Single
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Lena C. Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Aaron Colombo
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Kevin Möri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Stephan M. Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Jacob Lahr
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - Paul Krack
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3012 Bern, Switzerland; (J.L.); (S.K.)
| | - René M. Müri
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Urs P. Mosimann
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, 3012 Bern, Switzerland; (M.S.); (L.C.B.); (A.C.); (K.M.); (S.M.G.); (R.M.M.); (U.P.M.)
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3012 Bern, Switzerland
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2
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Siva P, Wong A, Hewston P, Ioannidis G, Adachi J, Rabinovich A, Lee AW, Papaioannou A. Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty. SENSORS (BASEL, SWITZERLAND) 2024; 24:1056. [PMID: 38400215 PMCID: PMC10891707 DOI: 10.3390/s24041056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length in addition to gait speed is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof-of-concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using a radar point cloud followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment involving 35 frail older adults to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold-standard Zeno Walkway Gait Analysis System, revealing a 4.5 cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k) = 0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong consistency (ICC(3,k) = 0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.
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Affiliation(s)
- Parthipan Siva
- Chirp Inc., Waterloo, ON N2J 4R2, Canada
- Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Alexander Wong
- Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Patricia Hewston
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - George Ioannidis
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Jonathan Adachi
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Alexander Rabinovich
- Department of Surgery, McMaster University, Hamilton, ON L8S 4L8, Canada
- ArthroBiologix Inc., Hamilton, ON L8L 5G4, Canada
| | - Andrea W. Lee
- Hamilton Health Sciences, Hamilton, ON L8N 3Z5, Canada;
| | - Alexandra Papaioannou
- Geras Centre for Aging Research, St. Peter’s Hospital, Hamilton Health Sciences, Hamilton, ON L8M 1W9, Canada; (P.H.); (G.I.); (J.A.); (A.P.)
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
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3
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Hainline G, Hainline RD, Handlery R, Fritz S. A Scoping Review of the Predictive Qualities of Walking Speed in Older Adults. J Geriatr Phys Ther 2023:00139143-990000000-00040. [PMID: 37820357 PMCID: PMC11006824 DOI: 10.1519/jpt.0000000000000398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
BACKGROUND AND PURPOSE Walking speed (WS) is an easily assessable and interpretable functional outcome measure with great utility for the physical therapist providing care to older adults. Since WS was proposed as the sixth vital sign, research into its interpretation and use has flourished. The purpose of this scoping review is to identify the current prognostic value of WS for the older adult. METHODS A scoping review was conducted using PubMed, CINAHL, and SPORTDiscus to find relevant articles highlighting the predictive capabilities of WS for older adults. Titles and abstracts were reviewed to identify relevant articles. Articles were excluded based on the following criteria: sample included both younger and older adults without separate analyses, sample was focused on a particular disease, if the study was published before 2017, or if the study did not report relevant cut points for interpretation of WS. The search returned 1064 results. Following removal of articles not meeting inclusion criteria and critical appraisal, relevant cut points were extracted from 47 original research publications. RESULTS AND DISCUSSION A preliminary review of the included articles showed that WS is a valuable prognostic tool across many health domains, including mental health, mortality, disability, pain, bone and joint health, falls, cognition, physical activity, metabolic health, risk for cardiovascular disease, socialization, and metabolic health. The fastest WS of 1.32 meters per second (m/s) served as a cutoff for decreased risk for incident development of type 2 diabetes, while the slowest WS of less than 0.2 m/s was associated with increased duration of hospitalization. Multiple studies reported on the prognostic value of WS slower than 1.0 m/s. CONCLUSION Although the reported range of predictive WS values was broad, multiple studies found WS of approximately 1.0 m/s to be a useful marker for delineating risk or decline across a variety of health domains. Clinicians may find it useful to use a WS slower than 1.0 m/s as a "yellow flag" to guide evaluation and intervention for their older adult clients.
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Aggar C, Sorwar G, Seton C, Penman O, Ward A. Smart home technology to support older people's quality of life: A longitudinal pilot study. Int J Older People Nurs 2023; 18:e12489. [PMID: 35785517 PMCID: PMC10078149 DOI: 10.1111/opn.12489] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 05/04/2022] [Accepted: 06/07/2022] [Indexed: 01/15/2023]
Abstract
AIM This pilot study aimed to explore the impact of Smart Home technology to support older people's quality of life, particularly for those who live alone. BACKGROUND There has been an increased interest in using innovative technologies and artificial intelligence to enable Smart Home technology to support older people to age independently in their own homes. METHODS This study used a pre-and post-test design. The seven item Personal Wellbeing Index was used to measure participants' subjective quality of life across seven quality of life domains. Participants (n = 60) aged between 68 and 90 years (M = 80.10, SD = 5.56) completed a 12-week personalised Smart Home technology program. RESULTS Approximately half of the participants lived alone (48.3%). Participants' quality of life significantly increased (p = 0.010) after Smart Home use. Two domains, "achieving in life" (p = 0.026) and "future security" (p = 0.004), were also significantly improved after participating in the Smart Home technology program. Improvements in quality of life did not vary as a function of living arrangement (all ps > .152, all η p 2 > .00). CONCLUSION The current study provides preliminary evidence for the role of Smart Home technology in supporting older people's quality of life, particularly their sense of achieving in life and future security.
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Affiliation(s)
- Christina Aggar
- Faculty of Health, Southern Cross University, Bilinga, QLD, Australia
| | - Golam Sorwar
- Faculty of Health, Southern Cross University, Bilinga, QLD, Australia
| | - Carolyn Seton
- Faculty of Health, Southern Cross University, Bilinga, QLD, Australia
| | - Olivia Penman
- Faculty of Health, Southern Cross University, Bilinga, QLD, Australia
| | - Anastasia Ward
- Faculty of Health, Southern Cross University, Bilinga, QLD, Australia.,Feros Care, Tweed Heads, NSW, Australia
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5
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Bernstein JPK, Dorociak K, Mattek N, Leese M, Trapp C, Beattie Z, Kaye J, Hughes A. Unobtrusive, in-home assessment of older adults' everyday activities and health events: associations with cognitive performance over a brief observation period. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2022; 29:781-798. [PMID: 33866939 PMCID: PMC8522171 DOI: 10.1080/13825585.2021.1917503] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 04/11/2021] [Indexed: 12/22/2022]
Abstract
In-home assessment of everyday activities over many months to years may be useful in predicting cognitive decline in older adulthood. This study examined whether a comparatively brief data collection period (3 months) may yield similar diagnostic information. A total of 91 community-dwelling older adults without dementia underwent baseline neuropsychological testing and completed weekly computer-based surveys assessing health-related events/activities. A subset of participants wore fitness tracker watches assessing daily sleep and physical activity patterns, used a sensor-instrumented pillbox, and had their computer use frequency recorded on a daily basis. Similar patterns in computer use, sleep and medication use were noted in comparison to prior literature with more extensive data collection periods. Greater computer use and sleep, as well as self-reported pain and independence, were also linked to better cognition. These activities and symptoms may be useful correlates of cognitive function even when assessed over a relatively brief monitoring period.
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Affiliation(s)
| | - Katherine Dorociak
- Department of Psychology, Palo Alto VA Health Care System, Palo Alto, CA, USA
| | - Nora Mattek
- Oregon Center for Aging & Technology, Portland, OR, USA
| | - Mira Leese
- Department of Psychology, Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Chelsea Trapp
- Department of Psychology, Minneapolis VA Health Care System, Minneapolis, MN, USA
| | | | - Jeffrey Kaye
- Oregon Center for Aging & Technology, Portland, OR, USA
| | - Adriana Hughes
- Oregon Center for Aging & Technology, Portland, OR, USA
- Department of Psychology, Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
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6
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Schütz N, Knobel SEJ, Botros A, Single M, Pais B, Santschi V, Gatica-Perez D, Buluschek P, Urwyler P, Gerber SM, Müri RM, Mosimann UP, Saner H, Nef T. A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. NPJ Digit Med 2022; 5:116. [PMID: 35974156 PMCID: PMC9381599 DOI: 10.1038/s41746-022-00657-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/13/2022] [Indexed: 11/09/2022] Open
Abstract
Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.
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Affiliation(s)
- Narayan Schütz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
| | - Samuel E J Knobel
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Angela Botros
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Michael Single
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Bruno Pais
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Valérie Santschi
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Daniel Gatica-Perez
- Idiap Research Institute, Martigny, Switzerland.,School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Prabitha Urwyler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - René M Müri
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
| | - Urs P Mosimann
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Hugo Saner
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Tobias Nef
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
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7
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Bian C, Ye B, Mihailidis A. The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment. SENSORS 2022; 22:s22093532. [PMID: 35591222 PMCID: PMC9099547 DOI: 10.3390/s22093532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/26/2022] [Accepted: 05/02/2022] [Indexed: 01/06/2023]
Abstract
Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland−Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings.
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Affiliation(s)
- Chao Bian
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada;
- Correspondence:
| | - Bing Ye
- Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada;
- Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Alex Mihailidis
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada;
- Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON M5S 1A1, Canada
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8
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Zulfiqar AA, Hajjam M, Hajjam A, Andres E. GER-e-TEC Study: An Innovative Geriatric Risk Remote Monitoring Project. Digit Health 2022. [DOI: 10.36255/exon-publications-digital-health-remote-monitoring] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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9
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de Souto Barreto P, Fabre D, Vellas B, Blain H, Molinier L, Rolland Y. Reduction prevalence of fragility fracture hospitalisation during the COVID-19 lockdown. Arch Osteoporos 2022; 17:68. [PMID: 35437693 PMCID: PMC9015700 DOI: 10.1007/s11657-022-01099-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/15/2022] [Indexed: 02/03/2023]
Abstract
Fracture hospitalizations of people ≥ 65 years old living in France increased annually from 2015 until 2019 (average: 1.8%), until being reduced in 2020 (- 1.4%) with an abrupt decrease during the lockdown period. Decreased exposure to the risk of falling during COVID-19 year 2020 may have reflected in lower incidence of fractures.
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Affiliation(s)
- Philipe de Souto Barreto
- CERPOP UMR1295, University of Toulouse III, Inserm, UPS, Toulouse, France.
- Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France.
- Gérontopôle de Toulouse, Institut du Vieillissement, 37 Allées Jules Guesde, 31000, Toulouse, France.
| | - Didier Fabre
- Department of Medical Information, Toulouse University Hospital (CHU Toulouse), Toulouse, France
| | - Bruno Vellas
- CERPOP UMR1295, University of Toulouse III, Inserm, UPS, Toulouse, France
- Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
| | - Hubert Blain
- Department of Internal Medicine and Geriatrics, MUSE University, Montpellier, France
| | - Laurent Molinier
- Department of Medical Information, Toulouse University Hospital (CHU Toulouse), Toulouse, France
| | - Yves Rolland
- CERPOP UMR1295, University of Toulouse III, Inserm, UPS, Toulouse, France
- Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
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10
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Friedrich B, Lübbe C, Steen EE, Bauer JM, Hein A. Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults. SENSORS (BASEL, SWITZERLAND) 2022; 22:493. [PMID: 35062453 PMCID: PMC8780838 DOI: 10.3390/s22020493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/03/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The OTAGO exercise program is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise program has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.
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Affiliation(s)
- Björn Friedrich
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (C.L.); (E.-E.S.); (A.H.)
| | - Carolin Lübbe
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (C.L.); (E.-E.S.); (A.H.)
| | - Enno-Edzard Steen
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (C.L.); (E.-E.S.); (A.H.)
| | - Jürgen Martin Bauer
- Center for Geriatric Medicine, Agaplesion Bethanien Hospital, University of Heidelberg, Rohrbacher Straße 149, 69126 Heidelberg, Germany;
| | - Andreas Hein
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (C.L.); (E.-E.S.); (A.H.)
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11
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Huang H, Chen Z, Cao S, Xiao M, Xie L, Zhao Q. Adoption Intention and Factors Influencing the Use of Gerontechnology in Chinese Community-Dwelling Older Adults: A Mixed-Methods Study. Front Public Health 2021; 9:687048. [PMID: 34604153 PMCID: PMC8484701 DOI: 10.3389/fpubh.2021.687048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: To explore the Chinese community-dwelling intention of older adults to adopt gerontechnology and its influencing factors. Design: A mixed-methods sequential explanatory design with an inductive approach was employed. In phase 1, a self-made questionnaire was administered from August 2018 to December 2019. Multifactor logistic regression was used to analyze the adoption intention and factors influencing the use of gerontechnology. In phase 2, participants completed a semistructured interview to explore the adoption intention of a specific form of gerontechnology, Smart Aged Care Platform, from May to July 2020. Setting: Twelve communities in three districts of Chongqing, China. Participants: Community-dwelling older adults were included. Results: A total of 1,180 older adults completed the quantitative study; two-thirds of them (68.7%) showed adoption intention toward gerontechnology. Nineteen participants (10 users and nine nonusers) completed the qualitative study and four themes were explored. Through a summarized understanding of the qualitative and quantitative data, a conceptual model of influencing factors, namely, predictive, enabling, and need factors, was constructed. Conclusions: This study reveals that most Chinese community-dwelling older adults welcome the emergence of new technologies. However, there was a significant difference in the adoption intention of gerontechnology in Chinese community-dwelling older adults based on their sociodemographic and psychographic characteristics. Our findings extend previous technology acceptance models and theories and contribute to the existing resource base.
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Affiliation(s)
- Huanhuan Huang
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhiyu Chen
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Orthopedic, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Songmei Cao
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liling Xie
- Department of Nursing, The First Branch of First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qinghua Zhao
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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12
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Contactless Gait Assessment in Home-like Environments. SENSORS 2021; 21:s21186205. [PMID: 34577412 PMCID: PMC8473097 DOI: 10.3390/s21186205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/25/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r≥0.98,R2≥0.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments.
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Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms. SENSORS 2021; 21:s21061957. [PMID: 33799526 PMCID: PMC7999588 DOI: 10.3390/s21061957] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 11/17/2022]
Abstract
Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians’ assessment toolkit and improve fall prevention.
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14
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Yoon S, Jung HW, Jung H, Kim K, Hong SK, Roh H, Oh BM. Development and Validation of 2D-LiDAR-Based Gait Analysis Instrument and Algorithm. SENSORS 2021; 21:s21020414. [PMID: 33430161 PMCID: PMC7826665 DOI: 10.3390/s21020414] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/16/2022]
Abstract
Acquiring gait parameters from usual walking is important to predict clinical outcomes including life expectancy, risk of fall, and neurocognitive performance in older people. We developed a novel gait analysis tool that is small, less-intrusive and is based on two-dimensional light detection and ranging (2D-LiDAR) technology. Using an object-tracking algorithm, we conducted a validation study of the spatiotemporal tracking of ankle locations of young, healthy participants (n = 4) by comparing our tool and a stereo camera with the motion capture system as a gold standard modality. We also assessed parameters including step length, step width, cadence, and gait speed. The 2D-LiDAR system showed a much better accuracy than that of a stereo camera system, where mean absolute errors were 46.2 ± 17.8 mm and 116.3 ± 69.6 mm, respectively. Gait parameters from the 2D-LiDAR system were in good agreement with those from the motion capture system (r = 0.955 for step length, r = 0.911 for cadence). Simultaneous tracking of multiple targets by the 2D-LiDAR system was also demonstrated. The novel system might be useful in space and resource constrained clinical practice for older adults.
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Affiliation(s)
- Seongjun Yoon
- Dyphi Research Institute, Dyphi Inc., Daejeon 34068, Korea;
| | - Hee-Won Jung
- Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Korea;
- Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 03080, Korea
| | - Heeyoune Jung
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Gyeonggi-do 12564, Korea; (H.J.); (S.-K.H.)
| | - Keewon Kim
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea;
| | - Suk-Koo Hong
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Gyeonggi-do 12564, Korea; (H.J.); (S.-K.H.)
| | - Hyunchul Roh
- Dyphi Research Institute, Dyphi Inc., Daejeon 34068, Korea;
- Correspondence: (H.R.); (B.-M.O.)
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Gyeonggi-do 12564, Korea; (H.J.); (S.-K.H.)
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea;
- Correspondence: (H.R.); (B.-M.O.)
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15
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Beattie Z, Miller LM, Almirola C, Au-Yeung WTM, Bernard H, Cosgrove KE, Dodge HH, Gamboa CJ, Golonka O, Gothard S, Harbison S, Irish S, Kornfeld J, Lee J, Marcoe J, Mattek NC, Quinn C, Reynolds C, Riley T, Rodrigues N, Sharma N, Siqueland MA, Thomas NW, Truty T, Wall R, Wild K, Wu CY, Karlawish J, Silverberg NB, Barnes LL, Czaja S, Silbert LC, Kaye J. The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community. Digit Biomark 2020; 4:100-118. [PMID: 33442584 DOI: 10.1159/000512208] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/09/2020] [Indexed: 12/11/2022] Open
Abstract
Introduction Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. Methods CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants' homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. Results The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160-169 min), physical mobility (e.g., mean daily transitions between rooms = 96-155), sleep (e.g., mean nightly sleep duration = 6.3-7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15-45%) were collected across cohorts. Conclusion The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.
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Affiliation(s)
- Zachary Beattie
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Lyndsey M Miller
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,School of Nursing, Oregon Health & Science University, Portland, Oregon, USA
| | - Carlos Almirola
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Wan-Tai M Au-Yeung
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Hannah Bernard
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Kevin E Cosgrove
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Hiroko H Dodge
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Charlene J Gamboa
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Ona Golonka
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Sarah Gothard
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Sam Harbison
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Stephanie Irish
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Judith Kornfeld
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Jonathan Lee
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Jennifer Marcoe
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Nora C Mattek
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Charlie Quinn
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Christina Reynolds
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Thomas Riley
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Nathaniel Rodrigues
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Nicole Sharma
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Mary Alice Siqueland
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Neil W Thomas
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Bruyère Research Institute, Ottawa, Ontario, Canada
| | - Timothy Truty
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Rachel Wall
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Portland Veterans Affairs Medical Center, Portland, Oregon, USA
| | - Katherine Wild
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Chao-Yi Wu
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Jason Karlawish
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nina B Silverberg
- Division of Neuroscience, National Institute on Aging, National Institute of Health, Bethesda, Maryland, USA
| | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Sara Czaja
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA.,Center on Aging and Behavioral Research, Division of Geriatrics and Palliative Medicine, Weil Cornell Medicine, New York, New York, USA
| | - Lisa C Silbert
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Portland Veterans Affairs Medical Center, Portland, Oregon, USA
| | - Jeffrey Kaye
- Oregon Center for Aging & Technology, Oregon Health & Science University, Portland, Oregon, USA.,National Institute on Aging, Layton Aging & Alzheimer's Disease Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
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16
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Results of the "GER-e-TEC" Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes. J Clin Med 2020; 9:jcm9123836. [PMID: 33256080 PMCID: PMC7761279 DOI: 10.3390/jcm9123836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/17/2020] [Accepted: 11/24/2020] [Indexed: 01/22/2023] Open
Abstract
Introduction: Telemedicine is believed to be helpful in managing patients suffering from chronic diseases, in particular elderly patients with numerous accompanying conditions. This was the basis for the “GERIATRICS and e-Technology (GER-e-TEC) study”, which was an experiment involving the use of the smart MyPredi™ e-platform to automatically detect the exacerbation of geriatric syndromes. Methods: The MyPredi™ platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. These alerts are issued in the event that the health of a patient deteriorates due to an exacerbation of their chronic diseases. An experiment was conducted between 24 September 2019 and 24 November 2019 to test this alert system. During this time, the platform was used on patients being monitored in an internal medicine unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, and positive and negative predictive values with respect to clinical data. The results of the experiment are provided below. Results: A total of 36 patients were monitored remotely, 21 of whom were male. The mean age of the patients was 81.4 years. The patients used the telemedicine solution for an average of 22.1 days. The telemedicine solution took a total of 147,703 measurements while monitoring the geriatric risks of the entire patient group. An average of 226 measurements were taken per patient per day. The telemedicine solution generated a total of 1611 alerts while assessing the geriatric risks of the entire patient group. For each geriatric risk, an average of 45 alerts were emitted per patient, with 16 of these alerts classified as “low”, 12 classified as “medium”, and 20 classified as “critical”. In terms of sensitivity, the results were 100% for all geriatric risks and extremely satisfactory in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts had an impact on the duration of hospitalization due to decompensated heart failure, a deterioration in the general condition, and other reasons. Conclusion: The MyPredi™ telemedicine system allows the generation of automatic, non-intrusive alerts when the health of a patient deteriorates due to risks associated with geriatric syndromes.
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Ullal A, Su BY, Enayati M, Skubic M, Despins L, Popescu M, Keller J. Non-invasive monitoring of vital signs for older adults using recliner chairs. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00503-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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18
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Evers LJ, Raykov YP, Krijthe JH, Silva de Lima AL, Badawy R, Claes K, Heskes TM, Little MA, Meinders MJ, Bloem BR. Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study. J Med Internet Res 2020; 22:e19068. [PMID: 33034562 PMCID: PMC7584982 DOI: 10.2196/19068] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/10/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022] Open
Abstract
Background Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.
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Affiliation(s)
- Luc Jw Evers
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.,Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Yordan P Raykov
- Department of Mathematics, School of Engineering and Applied Sciences, Aston University, Birmingham, United Kingdom
| | - Jesse H Krijthe
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Ana Lígia Silva de Lima
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Reham Badawy
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | | | - Tom M Heskes
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Max A Little
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Marjan J Meinders
- Scientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bastiaan R Bloem
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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19
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Paraschiv-Ionescu A, Soltani A, Aminian K. Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4596-4599. [PMID: 33019017 DOI: 10.1109/embc44109.2020.9176281] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Walking speed (WS) is recognized as an important dimension of functional health and a candidate endpoint for clinical trials. To be adopted as a powerful outcome measure in clinical assessment, WS should be estimated pervasively and accurately in the real-life context. Although current state of the art points to possible solutions, e.g., by using pairing of wearable sensors with dedicated algorithms, the accuracy and robustness of existing algorithms in challenging situations should be carefully considered. This study highlights the main methodological issues for WS estimation using single inertial sensor fixed on trunk (chest/low back) and data recorded in a sample of stroke patients with impaired mobility.
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20
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Winkler T, Duda GN. Predicting Health with Function - How Can Biomechanics "Ride the Tiger"? J Cachexia Sarcopenia Muscle 2020; 11:1161-1163. [PMID: 32677344 PMCID: PMC7567153 DOI: 10.1002/jcsm.12576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Tobias Winkler
- Julius Wolff Institute, Center for Musculoskeletal Surgery, Berlin Institute of Health Center for Regenerative Therapies, Charité - Universitaetsmedizin Berlin, Berlin, Germany
| | - Georg N Duda
- Julius Wolff Institute, Center for Musculoskeletal Surgery, Berlin Institute of Health Center for Regenerative Therapies, Charité - Universitaetsmedizin Berlin, Berlin, Germany
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21
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Piau A, Steinmeyer Z, Charlon Y, Courbet L, Rialle V, Lepage B, Campo E, Nourhashemi F. A smart shoe insole to monitor frail older adults walking speed: results of two evaluation phases completed in a living-lab and through a 12-week pilot study (Preprint). JMIR Mhealth Uhealth 2019; 9:e15641. [PMID: 36260404 PMCID: PMC8406107 DOI: 10.2196/15641] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 02/02/2021] [Accepted: 04/14/2021] [Indexed: 12/13/2022] Open
Abstract
Background Recent World Health Organization reports propose wearable devices to collect information on activity and walking speed as innovative health indicators. However, mainstream consumer-grade tracking devices and smartphone apps are often inaccurate and require long-term acceptability assessment. Objective Our aim is to assess the user acceptability of an instrumented shoe insole in frail older adults. This device monitors participants’ walking speed and differentiates active walking from shuffling after step length calibration. Methods A multiphase evaluation has been designed: 9 older adults were evaluated in a living lab for a day, 3 older adults were evaluated at home for a month, and a prospective randomized trial included 35 older adults at home for 3 months. A qualitative research design using face-to-face and phone semistructured interviews was performed. Our hypothesis was that this shoe insole was acceptable in monitoring long-term outdoor and indoor walking. The primary outcome was participants' acceptability, measured by a qualitative questionnaire and average time of insole wearing per day. The secondary outcome described physical frailty evolution in both groups. Results Living lab results confirmed the importance of a multiphase design study with participant involvement. Participants proposed insole modifications. Overall acceptability had mixed results: low scores for reliability (2.1 out of 6) and high scores for usability (4.3 out of 6) outcomes. The calibration phase raised no particular concern. During the field test, a majority of participants (mean age 79 years) were very (10/16) or quite satisfied (3/16) with the insole's comfort at the end of the follow-up. Participant insole acceptability evolved as follows: 63% (12/19) at 1 month, 50% (9/18) at 2 months, and 75% (12/16) at 3 months. A total of 9 participants in the intervention group discontinued the intervention because of technical issues. All participants equipped for more than a week reported wearing the insole every day at 1 month, 83% (15/18) at 2 months, and 94% (15/16) at 3 months for 5.8, 6.3, and 5.1 hours per day, respectively. Insole data confirmed that participants effectively wore the insole without significant decline during follow-up for an average of 13.5 days per 4 months and 5.6 hours per day. For secondary end points, the change in frailty parameters or quality of life did not differ for those randomly assigned to the intervention group compared to usual care. Conclusions Our study reports acceptability data on an instrumented insole in indoor and outdoor walking with remote monitoring in frail older adults under real-life conditions. To date, there is limited data in this population set. This thin instrumentation, including a flexible battery, was a technical challenge and seems to provide an acceptable solution over time that is valued by participants. However, users still raised certain acceptability issues. Given the growing interest in wearable health care devices, these results will be useful for future developments. Trial Registration ClinicalTrials.gov NCT02316600; https://clinicaltrials.gov/ct2/show/NCT02316600
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Affiliation(s)
- Antoine Piau
- Gerontopole, University Hospital of Toulouse, Toulouse, France
- Laboratoire d'analyse et d'architecture des systèmes, Centre national de la Recherche Scientifique, Toulouse, France
- Unité 1295, Institut National de la Santé Et de la Recherche Médicale, Toulouse, France
| | - Zara Steinmeyer
- Gerontopole, University Hospital of Toulouse, Toulouse, France
| | - Yoann Charlon
- Laboratoire d'analyse et d'architecture des systèmes, Centre national de la Recherche Scientifique, Toulouse, France
| | - Laetitia Courbet
- Autonomie, Gérontologie, E-santé, Imagerie et Société, Grenoble Alps University, Grenoble, France
| | - Vincent Rialle
- Autonomie, Gérontologie, E-santé, Imagerie et Société, Grenoble Alps University, Grenoble, France
| | - Benoit Lepage
- Department of Medical Information, University Hospital of Toulouse, Toulouse, France
| | - Eric Campo
- Laboratoire d'analyse et d'architecture des systèmes, Centre national de la Recherche Scientifique, Toulouse, France
| | - Fati Nourhashemi
- Gerontopole, University Hospital of Toulouse, Toulouse, France
- Unité 1295, Institut National de la Santé Et de la Recherche Médicale, Toulouse, France
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