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Yaqoob I, Gusso S, Simpson M, Meiring RM. Agreement between the activPAL accelerometer and direct observation during a series of gait and sit-to-stand tasks in people living with cervical dystonia. Front Neurol 2024; 15:1286447. [PMID: 38725651 PMCID: PMC11080616 DOI: 10.3389/fneur.2024.1286447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Background Accelerometers are commonly used for the assessment of PA; however, these devices have not been validated in people with dystonia who experience movement limitations. To properly understand movement behaviors and deliver accurate exercise prescription in this population, the validity of these devices must be tested. Objective This study aimed to validate step count and postural transitions detected by the activPAL accelerometer (AP) against direct observation (DO) during two functional assessments: the 30-s sit-to-stand (30STS) and 6-min usual-pace walk tests. Methods: A total of 11 participants with cervical dystonia (CD) (male/female n = 5/6; mean age = 61 years; BMI = 24 kg/m2) performed the 6-min usual pace walking and 30STS while wearing the activPAL. A trained observer counted steps and observed the number of sit-to-stands. Results The average step count detected with AP and DO was 651.8 (218-758) and 654.5 (287-798) respectively. The average transitions detected were 11 (4-16) and 12 (4-17) respectively. Both methods showed good agreement and there was a statistically significant and strong correlation between the two methods, i.e., transitions (r = 0.983, p = 0.0001), and step counts (r = 0.9841, p = 0.0001). Conclusion There is a good agreement between activPAL and direct observation for step counts and transitions between sitting and standing in people living with CD.
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Affiliation(s)
- Irum Yaqoob
- Department of Exercise Sciences, Faculty of Science, The University of Auckland, Auckland, New Zealand
| | - Silmara Gusso
- Department of Exercise Sciences, Faculty of Science, The University of Auckland, Auckland, New Zealand
| | - Mark Simpson
- School of Physiology, University of the Witwatersrand, Johannesburg, South Africa
| | - Rebecca M. Meiring
- Department of Exercise Sciences, Faculty of Science, The University of Auckland, Auckland, New Zealand
- Department of Neurology, Auckland City Hospital, Auckland, New Zealand
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2
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Mazzeo M, Hernan G, Veerubhotla A. Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis. Front Neurosci 2023; 17:1220581. [PMID: 37781244 PMCID: PMC10534037 DOI: 10.3389/fnins.2023.1220581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/23/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. Methods This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. Results This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. Discussion The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.
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Affiliation(s)
| | | | - Akhila Veerubhotla
- Department of Rehabilitation Medicine, New York University - Grossman School of Medicine, New York, NY, United States
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Weber KS, Godkin FE, Cornish BF, McIlroy WE, Van Ooteghem K. Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study. JMIR Form Res 2023; 7:e41685. [PMID: 36920452 PMCID: PMC10131658 DOI: 10.2196/41685] [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: 08/04/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application. OBJECTIVE The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates. METHODS A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking. RESULTS Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t16=4.24, P<.001) and had more variable wrist movement (t16=2.13, P=.049) compared to those in the lowest quartile. CONCLUSIONS The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.
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Affiliation(s)
- Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin F Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [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: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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Celik Y, Aslan MF, Sabanci K, Stuart S, Woo WL, Godfrey A. Improving Inertial Sensor-Based Activity Recognition in Neurological Populations. SENSORS (BASEL, SWITZERLAND) 2022; 22:9891. [PMID: 36560259 PMCID: PMC9783358 DOI: 10.3390/s22249891] [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: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson's disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued.
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Affiliation(s)
- Yunus Celik
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - M. Fatih Aslan
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
| | - Kadir Sabanci
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
| | - Sam Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Wai Lok Woo
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
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Pohl J, Ryser A, Veerbeek JM, Verheyden G, Vogt JE, Luft AR, Easthope CA. Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke. Front Physiol 2022; 13:933987. [PMID: 36225292 PMCID: PMC9549863 DOI: 10.3389/fphys.2022.933987] [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: 05/01/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.
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Affiliation(s)
- Johannes Pohl
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Department of Rehabilitation Sciences, KU Leuven—University of Leuven, Leuven, Belgium
- *Correspondence: Johannes Pohl,
| | - Alain Ryser
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | - Geert Verheyden
- Department of Rehabilitation Sciences, KU Leuven—University of Leuven, Leuven, Belgium
| | | | - Andreas Rüdiger Luft
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
| | - Chris Awai Easthope
- Cereneo Foundation, Center for Interdisciplinary Research (CEFIR), Vitznau, Switzerland
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Block VJ, Bove R, Nourbakhsh B. The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review. Front Neurol 2022; 13:878313. [PMID: 35832181 PMCID: PMC9272225 DOI: 10.3389/fneur.2022.878313] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/06/2022] [Indexed: 11/21/2022] Open
Abstract
Fatigue is one of the most common multiple sclerosis (MS) symptoms. Despite this, monitoring and measuring fatigue (subjective lack of energy)- and fatigability (objectively measurable and quantifiable performance decline)- in people with MS have remained challenging. Traditionally, administration of self-report questionnaires during in-person visits has been used to measure fatigue. However, remote measurement and monitoring of fatigue and fatigability have become feasible in the past decade. Traditional questionnaires can be administered through the web in any setting. The ubiquitous availability of smartphones allows for momentary and frequent measurement of MS fatigue in the ecological home-setting. This approach reduces the recall bias inherent in many traditional questionnaires and demonstrates the fluctuation of fatigue that cannot be captured by standard measures. Wearable devices can assess patients' fatigability and activity levels, often influenced by the severity of subjective fatigue. Remote monitoring of fatigue, fatigability, and activity in real-world situations can facilitate quantifying symptom-severity in clinical and research settings. Combining remote measures of fatigue as well as objective fatigability in a single construct, composite score, may provide a more comprehensive outcome. The more granular data obtained through remote monitoring techniques may also help with the development of interventions aimed at improving fatigue and lowering the burden of this disabling symptom.
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Affiliation(s)
- Valerie J. Block
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States,*Correspondence: Valerie J. Block
| | - Riley Bove
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Bardia Nourbakhsh
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
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Shah VV, McNames J, Harker G, Curtze C, Carlson-Kuhta P, Spain RI, El-Gohary M, Mancini M, Horak FB. Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life? Gait Posture 2021; 84:108-113. [PMID: 33302221 PMCID: PMC7946343 DOI: 10.1016/j.gaitpost.2020.11.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 10/21/2020] [Accepted: 11/24/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND There is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living. METHODS 15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC. RESULTS Total number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions. SIGNIFICANCE Quality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,Corresponding author at: Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA. (V.V. Shah)
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA,APDM, Inc., Portland, OR, USA
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
| | | | - Rebecca I. Spain
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,Veterans Affairs Portland Health Care System, Portland, OR, USA
| | | | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA,APDM, Inc., Portland, OR, USA
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Trentzsch K, Weidemann ML, Torp C, Inojosa H, Scholz M, Haase R, Schriefer D, Akgün K, Ziemssen T. The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice. Front Neurosci 2020; 14:582046. [PMID: 33192268 PMCID: PMC7649388 DOI: 10.3389/fnins.2020.582046] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/06/2020] [Indexed: 12/14/2022] Open
Abstract
Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary. At advanced stages of MS with significant walking impairment, this assessment is also necessary to optimize symptomatic treatment, choose the most suitable walking aid and plan individualized rehabilitation. In clinical practice, walking impairment is only assessed at higher levels of the disease using e.g., the Expanded Disability Status Scale (EDSS). In contrast to the EDSS, standardized functional tests such as walking speed, walking endurance and balance as well as walking quality and gait-related patient-reported outcomes allow a more holistic and sensitive assessment of walking impairment. In recent years, the MS Center Dresden has established a standardized monitoring procedure for the routine multidimensional assessment of gait and balance disorders. In the following protocol, we present the techniques and procedures for the analysis of gait and balance of people with MS at the MS Center Dresden. Patients are assessed with a multidimensional gait analysis at least once a year. This enables long-term monitoring of walking impairment, which allows early active intervention regarding further progression of disease and improves the current standard clinical practice.
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Affiliation(s)
- Katrin Trentzsch
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Marie Luise Weidemann
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Charlotte Torp
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Hernan Inojosa
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Maria Scholz
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Rocco Haase
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Dirk Schriefer
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katja Akgün
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Tjalf Ziemssen
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
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10
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Shah VV, McNames J, Mancini M, Carlson-Kuhta P, Spain RI, Nutt JG, El-Gohary M, Curtze C, Horak FB. Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living. J Neurol 2020; 267:1188-1196. [PMID: 31927614 PMCID: PMC7294824 DOI: 10.1007/s00415-020-09696-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/31/2019] [Accepted: 01/03/2020] [Indexed: 12/12/2022]
Abstract
Clinical trials need to specify which specific gait characteristics to monitor as mobility measures for each neurological disorder. As a first step, this study aimed to investigate a set of measures from daily-life monitoring that best discriminate mobility between people with multiple sclerosis (MS) and age-matched healthy control subjects (MS-Ctl) and between people with Parkinson's disease (PD) and age-matched healthy control subjects (PD-Ctl). Further, we investigated how these discriminative measures relate to the disease severity of MS or PD. We recruited 13 people with MS, 21 MS-Ctl, 29 people with idiopathic PD, and 20 PD-Ctl. Subjects wore 3 inertial sensors on their feet and the lumbar back for a week. The Area Under Curves (AUC) from the receiver operator characteristic (ROC) plot was calculated for each measure to determine the objective measures that best separated the MS and PD groups from their respective control cohorts. Adherence wearing the sensors was similar among groups for 58-66 h of recording (p = 0.14). Quantity of mobility (activity measures, such as a median number of strides per gait bout, AUC = 0.93) best discriminated mobility impairments in MS from MS-Ctl. In contrast, quality of mobility (such as turn angle, AUC = 0.90) best discriminated mobility impairments in PD from PD-Ctl. Mobility measures with AUC > 0.80 were correlated with MS and PD clinical scores of disease severity. Thus, measures characterizing mobility impairments differ for MS versus PD during daily life suggesting that mobility measures for clinical trials and clinical practice need to be specific to each neurological disorder.
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Affiliation(s)
- Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
- APDM, Inc., Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Rebecca I Spain
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- Veterans Affairs Portland Health Care System, Portland, OR, USA
| | - John G Nutt
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- APDM, Inc., Portland, OR, USA
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Zilani TA, Al-Turjman F, Khan MB, Zhao N, Yang X. Monitoring Movements of Ataxia Patient by Using UWB Technology. SENSORS (BASEL, SWITZERLAND) 2020; 20:E931. [PMID: 32050576 PMCID: PMC7039007 DOI: 10.3390/s20030931] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/07/2020] [Accepted: 02/07/2020] [Indexed: 01/19/2023]
Abstract
Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.
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Affiliation(s)
- Tanjila Akter Zilani
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Near East University, 99138 Nicosia, Mersin 10, Turkey;
- Research Centre for AI and IoT, Near East University, 99138 Nicosia, Mersin 10, Turkey
| | - Muhammad Bilal Khan
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Nan Zhao
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Xiaodong Yang
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
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12
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Husebo BS, Heintz HL, Berge LI, Owoyemi P, Rahman AT, Vahia IV. Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review. Front Pharmacol 2020; 10:1699. [PMID: 32116687 PMCID: PMC7011129 DOI: 10.3389/fphar.2019.01699] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 12/31/2019] [Indexed: 01/28/2023] Open
Abstract
Background The prevalence of dementia is expected to rapidly increase in the next decades, warranting innovative solutions improving diagnostics, monitoring and resource utilization to facilitate smart housing and living in the nursing home. This systematic review presents a synthesis of research on sensing technology to assess behavioral and psychological symptoms and to monitor treatment response in people with dementia. Methods The literature search included medical peer-reviewed English language publications indexed in Embase, Medline, Cochrane library and Web of Sciences, published up to the 5th of April 2019. Keywords included MESH terms and phrases synonymous with "dementia", "sensor", "patient", "monitoring", "behavior", and "therapy". Studies applying both cross sectional and prospective designs, either as randomized controlled trials, cohort studies, and case-control studies were included. The study was registered in PROSPERO 3rd of May 2019. Results A total of 1,337 potential publications were identified in the search, of which 34 were included in this review after the systematic exclusion process. Studies were classified according to the type of technology used, as (1) wearable sensors, (2) non-wearable motion sensor technologies, and (3) assistive technologies/smart home technologies. Half of the studies investigated how temporarily dense data on motion can be utilized as a proxy for behavior, indicating high validity of using motion data to monitor behavior such as sleep disturbances, agitation and wandering. Further, up to half of the studies represented proof of concept, acceptability and/or feasibility testing. Overall, the technology was regarded as non-intrusive and well accepted. Conclusions Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. To highlight awareness of legal regulations, data risk assessment, and patient and public involvement, we propose a necessary framework for sustainable ethical innovation in healthcare technology. The success of this field will depend on interdisciplinary cooperation and the advance in sustainable ethic innovation. Systematic Review Registration PROSPERO, identifier CRD42019134313.
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Affiliation(s)
- Bettina S Husebo
- Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine, University of Bergen, Bergen, Norway.,Department of Nursing Home Medicine, Municipality of Bergen, Bergen, Norway
| | - Hannah L Heintz
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Line I Berge
- Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine, University of Bergen, Bergen, Norway.,NKS Olaviken Gerontopsychiatric Hospital, Bergen, Norway
| | - Praise Owoyemi
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Aniqa T Rahman
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Ipsit V Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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13
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Wiedmann I, Grassi M, Duran I, Lavrador R, Alberg E, Daumer M, Schoenau E, Rittweger J. Accelerometric Gait Analysis Devices in Children-Will They Accept Them? Results From the AVAPed Study. Front Pediatr 2020; 8:574443. [PMID: 33585360 PMCID: PMC7877485 DOI: 10.3389/fped.2020.574443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 12/23/2020] [Indexed: 11/13/2022] Open
Abstract
Aims: To assess children's acceptance to wear a 3D-accelerometer which is attached to the waist under real-world conditions, and also to compare gait speed during supervised testing with the non-supervised gait speed in every-day life. Methods: In a controlled observational, cross sectional study thirty subjects with cerebral palsy (CP), with level I&II of the Gross Motor Function Classification System (GMFCS) and 30 healthy control children (Ctrl), aged 3-12 years, were asked to perform a 1-min-walking test (1 mwt) under laboratory conditions, and to wear an accelerometric device for a 1-week wearing home measurement (1 WHM). Acceptance was measured via wearing time, and by a questionnaire in which subjects rated restrictions in their daily living and wearing comfort. In addition, validity of 3D-accelerometric gait speed was checked through gold standard assessment of gait speed with a mobile perambulator. Results: Wearing time amounted to 10.3 (SD 3.4) hours per day, which was comparable between groups (T = 1.10, P = 0.3). Mode for wearing comfort [CP 1, Range (1,4), Ctrl 1, Range (1,6)] and restriction of daily living [CP 1, Range (1,3), Ctrl 1, Range (1,4)] was comparable between groups. Under laboratory conditions, Ctrl walked faster in the 1 mwt than CP (Ctrl 1.72 ± 0.29 m/s, CP 1.48 ± 0.41 m/s, P = 0.018). Similarly, a statistically significant difference was found when comparing real-world walking speed and laboratory walking speed (CP: 1 mwt 1.48 ± 0.41 m/s, 1 WHM 0.89 ± 0.09 m/s, P = 0.012; Ctrl: 1mwt 1.72 ± 0.29, 1 WHM 0.97 ± 0.06, P < 0.001). Conclusion: 3D-accelerometry is well-enough accepted in a pediatric population of patients with CP and a Ctrl group to allow valid assessments. Assessment outside the laboratory environment yields information about real world activity that was not captured by routine clinical tests. This suggests that assessment of habitual activities by wearable devices reflects the functioning of children in their home environment. This novel information constitutes an important goal for rehabilitation medicine. The study is registered at the German Register of Clinical Trials with the title "Acceptance and Validity of 3D Accelerometric Gait Analysis in Pediatric Patients" (AVAPed; DRKS00011919).
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Affiliation(s)
- Isabella Wiedmann
- Center of Prevention and Rehabilitation, University of Cologne, Cologne, Germany.,Department of Muscle and Bone Metabolism, German Aerospace Center, Institute of Aerospace Medicine, Cologne, Germany.,Department of Applied Health Science, European University of Applied Science, Brühl, Germany
| | - Marcello Grassi
- Sylvia Lawry Center for Multiple Sclerosis, The Human Motion Institute, Munich, Germany.,Trium Analysis Online, Munich, Germany
| | - Ibrahim Duran
- Center of Prevention and Rehabilitation, University of Cologne, Cologne, Germany
| | - Ricardo Lavrador
- Center of Prevention and Rehabilitation, University of Cologne, Cologne, Germany
| | - Evelyn Alberg
- Center of Prevention and Rehabilitation, University of Cologne, Cologne, Germany
| | - Martin Daumer
- Sylvia Lawry Center for Multiple Sclerosis, The Human Motion Institute, Munich, Germany.,Trium Analysis Online, Munich, Germany.,Technical University of Munich, Munich, Germany
| | - Eckhard Schoenau
- Department of Pediatric and Adolescent Medicine, University of Cologne, Cologne, Germany
| | - Jörn Rittweger
- Department of Muscle and Bone Metabolism, German Aerospace Center, Institute of Aerospace Medicine, Cologne, Germany.,Department of Pediatric and Adolescent Medicine, University of Cologne, Cologne, Germany
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14
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Schneider S, Popp WL, Brogioli M, Albisser U, Demkó L, Debecker I, Velstra IM, Gassert R, Curt A. Reliability of Wearable-Sensor-Derived Measures of Physical Activity in Wheelchair-Dependent Spinal Cord Injured Patients. Front Neurol 2018; 9:1039. [PMID: 30619026 PMCID: PMC6295582 DOI: 10.3389/fneur.2018.01039] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 11/19/2018] [Indexed: 01/20/2023] Open
Abstract
Physical activity (PA) has been shown to have a positive influence on functional recovery in patients after a spinal cord injury (SCI). Hence, it can act as a confounder in clinical intervention studies. Wearable sensors are used to quantify PA in various neurological conditions. However, there is a lack of knowledge about the inter-day reliability of PA measures. The objective of this study was to investigate the single-day reliability of various PA measures in patients with a SCI and to propose recommendations on how many days of PA measurements are required to obtain reliable results. For this, PA of 63 wheelchair-dependent patients with a SCI were measured using wearable sensors. Patients of all age ranges (49.3 ± 16.6 years) and levels of injury (from C1 to L2, ASIA A-D) were included for this study and assessed at three to four different time periods during inpatient rehabilitation (2 weeks, 1 month, 3 months, and if applicable 6 months after injury) and after in-patient rehabilitation in their home-environment (at least 6 months after injury). The metrics of interest were total activity counts, PA intensity levels, metrics of wheeling quantity and metrics of movement quality. Activity counts showed consistently high single-day reliabilities, while measures of PA intensity levels considerably varied depending on the rehabilitation progress. Single-day reliabilities of metrics of movement quantity decreased with rehabilitation progress, while metrics of movement quality increased. To achieve a mean reliability of 0.8, we found that three continuous recording days are required for out-patients, and 2 days for in-patients. Furthermore, the results show similar weekday and weekend wheeling activity for in- and out-patients. To our knowledge, this is the first study to investigate the reliability of an extended set of sensor-based measures of PA in both acute and chronic wheelchair-dependent SCI patients. The results provide recommendations for sensor-based assessments of PA in clinical SCI studies.
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Affiliation(s)
- Sophie Schneider
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Werner L. Popp
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Michael Brogioli
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Urs Albisser
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - László Demkó
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Isabelle Debecker
- REHAB Basel, Clinic for Neurorehabilitation and Paraplegiology, Basel, Switzerland
| | | | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
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15
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Jayaraman C, Mummidisetty CK, Mannix-Slobig A, McGee Koch L, Jayaraman A. Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors. J Neuroeng Rehabil 2018. [PMID: 29534737 PMCID: PMC5850975 DOI: 10.1186/s12984-018-0358-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied. Methods 28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements. Results The sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms. Conclusions Implementing population specific customized algorithms accounting for the influences of sensor location, type and activity characteristics for estimating physical activity metrics in individuals with stroke and iSCI could be beneficial. Electronic supplementary material The online version of this article (10.1186/s12984-018-0358-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chandrasekaran Jayaraman
- Shirley Ryan AbilityLab, Center for Bionic Medicine, Chicago, IL, USA.,Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | | | - Lori McGee Koch
- Shirley Ryan AbilityLab, Center for Bionic Medicine, Chicago, IL, USA
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Center for Bionic Medicine, Chicago, IL, USA. .,Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. .,Northwestern University, Departments of Physical Medicine & Rehabilitation and Medical Social Sciences, Shirley Ryan Abilitylab, 355 E Erie St., Chicago, IL, 60611, USA.
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16
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Kovalchick C, Sirkar R, Regele OB, Kourtis LC, Schiller M, Wolpert H, Alden RG, Jones GB, Wright JM. Can composite digital monitoring biomarkers come of age? A framework for utilization. J Clin Transl Sci 2017; 1:373-380. [PMID: 29707260 PMCID: PMC5916505 DOI: 10.1017/cts.2018.4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 01/16/2018] [Accepted: 01/19/2018] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard. METHODS A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare. RESULTS The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated. CONCLUSIONS The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.
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Affiliation(s)
| | - Rhea Sirkar
- Eli Lilly Innovation Center, Cambridge, MA, USA
| | | | | | | | | | | | - Graham B. Jones
- Clinical & Translational Science Institute, Tufts University Medical Center, Boston, MA, USA
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