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Roy A, Zenker S, Jain S, Afshari R, Oz Y, Zheng Y, Annabi N. A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2404225. [PMID: 38970527 DOI: 10.1002/adma.202404225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.
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Affiliation(s)
- Arpita Roy
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Shea Zenker
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Saumya Jain
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ronak Afshari
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yavuz Oz
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuting Zheng
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Nasim Annabi
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
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Tas B, Walker H, Lawn W, Matcham F, Traykova EV, Evans RAS, Strang J. What impacts the acceptability of wearable devices that detect opioid overdose in people who use opioids? A qualitative study. Drug Alcohol Rev 2024; 43:213-225. [PMID: 37596977 DOI: 10.1111/dar.13737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/21/2023]
Abstract
INTRODUCTION Drug-related deaths involving an opioid are at all-time highs across the United Kingdom. Current overdose antidotes (naloxone) require events to be witnessed and recognised for reversal. Wearable technologies have potential for remote overdose detection or response but their acceptability among people who use opioids (PWUO) is not well understood. This study explored facilitators and barriers to wearable technology acceptability to PWUO. METHODS Twenty-four participants (79% male, average age 46 years) with current (n = 15) and past (n = 9) illicit heroin use and 54% (n = 13) who were engaged in opioid substitution therapy participated in semi-structured interviews (n = 7) and three focus groups (n = 17) in London and Nottingham from March to June 2022. Participants evaluated real devices, discussing characteristics, engagement factors, target populations, implementation strategies and preferences. Conversations were recorded, transcribed and thematically analysed. RESULTS Three themes emerged: device-, person- and environment-specific factors impacting acceptability. Facilitators included inconspicuousness under the device theme and targeting subpopulations of PWUO at the individual theme. Barriers included affordability of devices and limited technology access within the environment theme. Trust in device accuracy for high and overdose differentiation was a crucial facilitator, while trust between technology and PWUO was a significant environmental barrier. DISCUSSION AND CONCLUSIONS Determinants of acceptability can be categorised into device, person and environmental factors. PWUO, on the whole, require devices that are inconspicuous, comfortable, accessible, easy to use, controlled by trustworthy organisations and highly accurate. Device developers must consider how the type of end-user and their environment moderate acceptability of the device.
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Affiliation(s)
- Basak Tas
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hollie Walker
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Will Lawn
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Clinical Psychopharmacology Unit, University College London, London, UK
| | - Faith Matcham
- School of Psychology, University of Sussex, Falmer, UK
| | - Elena V Traykova
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rebecca A S Evans
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John Strang
- National Addiction Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Denmark Hill, London, UK
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Morgan C, Tonkin EL, Masullo A, Jovan F, Sikdar A, Khaire P, Mirmehdi M, McConville R, Tourte GJL, Whone A, Craddock I. A multimodal dataset of real world mobility activities in Parkinson's disease. Sci Data 2023; 10:918. [PMID: 38123584 PMCID: PMC10733419 DOI: 10.1038/s41597-023-02663-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson's disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being "on" or "off" medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.
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Affiliation(s)
- Catherine Morgan
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Southmead Road, Bristol, BS10 5NB, UK
- Translational Health Sciences, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, UK
| | - Emma L Tonkin
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Alessandro Masullo
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
| | - Ferdian Jovan
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK
| | - Arindam Sikdar
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
- Edge Hill University, Ormskirk, UK
| | - Pushpajit Khaire
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
- Datta Meghe Institute of Higher Education and Research, Wardha, India
| | - Majid Mirmehdi
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
| | - Ryan McConville
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
| | - Gregory J L Tourte
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
- Advanced Research Computing, University of Oxford, Oxford, UK
| | - Alan Whone
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Southmead Road, Bristol, BS10 5NB, UK
- Translational Health Sciences, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, UK
| | - Ian Craddock
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK
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Lawson L, Mc Ardle R, Wilson S, Beswick E, Karimi R, Slight SP. Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review. J Med Internet Res 2023; 25:e45658. [PMID: 37490331 PMCID: PMC10410386 DOI: 10.2196/45658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging. OBJECTIVE This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time. METHODS A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains-culturally specific tasks and socialization and communication-were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time. CONCLUSIONS Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861.
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Affiliation(s)
- Lauren Lawson
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ríona Mc Ardle
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Sarah Wilson
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Emily Beswick
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Radin Karimi
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Sarah P Slight
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
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Tsai ZR, Kuo CC, Wang CJ, Tsai JJP, Chou HH. Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis. J Pers Med 2023; 13:1181. [PMID: 37511793 PMCID: PMC10381698 DOI: 10.3390/jpm13071181] [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] [Received: 07/05/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Muscle dysfunction, skeletal muscle fibrosis, and disability are associated with weakness in patients with end-stage renal disease. The main purpose of this study was to validate the effectiveness of a proposed system for gait monitoring on short-distance 1.5 m walkways in a dialysis center. Gaits with reduced speed and stride length, long sit-to-stand time (SST), two forward angles, and two unbalanced gait regions are defined in the proposed Kinect v3 gait measurement and analysis system (K3S) and have been considered clinical features in end-stage renal disease (ESRD) associated with poor dialysis outcomes. The stride and pace calibrations of the Kinect v3 system are based on the Zeno Walkway. Its single rating intraclass correlation (ICC) for the stride is 0.990, and its single rating ICC for the pace is 0.920. The SST calibration of Kinect v3 is based on a pressure insole; its single rating ICC for the SST is 0.871. A total of 75 patients on chronic dialysis underwent gait measurement and analysis during walking and weighing actions. After dialysis, patients demonstrated a smaller stride (p < 0.001) and longer SST (p < 0.001). The results demonstrate that patients' physical fitness was greatly reduced after dialysis. This study ensures patients' adequate physical gait strength to cope with the dialysis-associated physical exhaustion risk by tracing gait outliers. As decreased stride and pace are associated with an increased risk of falls, further studies are warranted to evaluate the clinical benefits of monitoring gait with the proposed reliable and valid system in order to reduce fall risk in hemodialysis patients.
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Affiliation(s)
- Zhi-Ren Tsai
- Department of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
- Center for Precision Medicine Research, Asia University, Taichung 41354, Taiwan
| | - Chin-Chi Kuo
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Cheng-Jui Wang
- Department of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan
| | - Jeffrey J P Tsai
- Center for Precision Medicine Research, Asia University, Taichung 41354, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Hsin-Hsu Chou
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Department of Pediatrics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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Morgan C, Masullo A, Mirmehdi M, Isotalus HK, Jovan F, McConville R, Tonkin EL, Whone A, Craddock I. Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity. Digit Biomark 2023; 7:92-103. [PMID: 37588481 PMCID: PMC10425718 DOI: 10.1159/000530953] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/24/2023] [Indexed: 08/18/2023] Open
Abstract
Introduction Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications. Methods Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed. Results 3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant. Conclusion We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Alessandro Masullo
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Majid Mirmehdi
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Hanna Kristiina Isotalus
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Ferdian Jovan
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Ryan McConville
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Emma L. Tonkin
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
| | - Alan Whone
- Translational Health Sciences, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Ian Craddock
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK
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Morgan C, Jameson J, Craddock I, Tonkin EL, Oikonomou G, Isotalus HK, Heidarivincheh F, McConville R, Tourte GJL, Kinnunen KM, Whone A. Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset. Parkinsonism Relat Disord 2022; 105:114-122. [PMID: 36413901 PMCID: PMC10391706 DOI: 10.1016/j.parkreldis.2022.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/02/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD. METHODS 11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters. RESULTS From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living. CONCLUSION This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, UK; Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Southmead Road, Bristol, BS10 5NB, UK.
| | - Jack Jameson
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Southmead Road, Bristol, BS10 5NB, UK.
| | - Ian Craddock
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Emma L Tonkin
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - George Oikonomou
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Hanna Kristiina Isotalus
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Farnoosh Heidarivincheh
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Ryan McConville
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Gregory J L Tourte
- Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, BS1 5DD, UK.
| | - Kirsi M Kinnunen
- Research and Development, IXICO, 4th Floor, Griffin Court, 15 Long Ln, Barbican, London, EC1A 9PN, UK.
| | - Alan Whone
- Translational Health Sciences, University of Bristol, 5 Tyndall Ave, Bristol, BS8 1UD, UK; Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Southmead Road, Bristol, BS10 5NB, UK.
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Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. J Med Internet Res 2022; 24:e35951. [PMID: 35617003 PMCID: PMC9185357 DOI: 10.2196/35951] [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: 12/23/2021] [Revised: 02/14/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
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Affiliation(s)
- Ieuan Clay
- Digital Medicine Society, Boston, MA, United States
| | | | | | | | | | | | | | | | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Benjamin Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | | | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
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Peraza LR, Kinnunen KM, McNaney R, Craddock IJ, Whone AL, Morgan C, Joules R, Wolz R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2021; 21:8286. [PMID: 34960379 PMCID: PMC8707484 DOI: 10.3390/s21248286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/06/2021] [Accepted: 12/08/2021] [Indexed: 12/29/2022]
Abstract
The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events-bout segmentation, initial contact (IC), and final contact (FC)-from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson's disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56-64.66 and 40.19-72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06-48.42, 40.19-72.70 and 36.06-60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.
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Affiliation(s)
- Luis R. Peraza
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | | | - Roisin McNaney
- Department of Human Centred Computing, Monash University, Clayton, VIC 3800, Australia;
| | - Ian J. Craddock
- Electrical and Electronic Engineering, School of Computer Science, University of Bristol, Bristol BS8 1QU, UK;
| | - Alan L. Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Richard Joules
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | - Robin Wolz
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
- Department of Computing, Imperial College London, London SW7 2AZ, UK
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Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities. SENSORS 2021; 21:s21124133. [PMID: 34208690 PMCID: PMC8235443 DOI: 10.3390/s21124133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.
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12
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Smith MD, Brazier DE, Henderson EJ. Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson's Disease. Neuropsychiatr Dis Treat 2021; 17:2965-2985. [PMID: 34584414 PMCID: PMC8464370 DOI: 10.2147/ndt.s304567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/25/2021] [Indexed: 12/31/2022] Open
Abstract
Gait dysfunction is a key defining feature of Parkinson's disease (PD), and is associated with symptoms of freezing and an increased risk of falls. In this narrative review, we cover the putative mechanisms of gait dysfunction in PD, the assessment of gait abnormalities, and the management of symptoms caused by the inherent difficulty in walking. Our understanding of the causes of gait problems in PD has progressed in recent times, moving from neurocognitive theory to correlates of affected neuronal pathways. In particular, this can be shown to correspond with abnormalities in responses to dual-task paradigms and dysfunction in cholinergic signaling. Great progress has been made in the sophistication and precision of gait assessment; however, it has firmly remained in the research domain. There is significant momentum behind wearable technologies that can be used by patients in their own environment, acting as digital biomarkers that can not only reflect progression but also independently discriminate PD from non-PD individuals. The treatment of gait dysfunction has historically relied on physical therapies and training combined with a view to mitigating the impact of such consequences as falls. Pharmacological therapies that are the mainstay of treatment in PD have tended to address symptoms like bradykinesia; however, optimization of dopaminergic therapies likely has a positive effect on quality of gait. Other targets have been assessed with the goal of improving gait, of which medications that improve cholinergic signaling appear most promising. Neuromodulation techniques are increasingly used in the form of deep-brain stimulation; however, standard targets, such as the globus pallidus interna, have a modest effect on gait. Considerable benefit has been seen through targeting the pedunculopontine nucleus, and a dual-target approach may be warranted. Stimulation of the spinal cord and brain through direct or magnetic approaches has been assessed, but requires further evidence.
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Affiliation(s)
- Matthew D Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Older People's Unit, Royal United Hospital NHS Foundation Trust, Bath, UK
| | - Danielle E Brazier
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emily J Henderson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Older People's Unit, Royal United Hospital NHS Foundation Trust, Bath, UK
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