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de Graaf D, de Vries NM, van de Zande T, Schimmel JJP, Shin S, Kowahl N, Barman P, Kapur R, Marks WJ, van 't Hul A, Bloem B. Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e55452. [PMID: 38713508 PMCID: PMC11109858 DOI: 10.2196/55452] [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: 12/13/2023] [Revised: 03/07/2024] [Accepted: 03/11/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. OBJECTIVE We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. METHODS This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. RESULTS In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. CONCLUSIONS The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options. TRIAL REGISTRATION ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/55452.
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Affiliation(s)
- Debbie de Graaf
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Nienke M de Vries
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Tessa van de Zande
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Janneke J P Schimmel
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Sooyoon Shin
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Nathan Kowahl
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Poulami Barman
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Ritu Kapur
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
- Verily Life Sciences, South San Fransisco, CA, United States
| | - William J Marks
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Alex van 't Hul
- Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Respiratory Diseases, Nijmegen, Netherlands
| | - Bastiaan Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
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Lebleu J, Daniels K, Pauwels A, Dekimpe L, Mapinduzi J, Poilvache H, Bonnechère B. Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement. SENSORS (BASEL, SWITZERLAND) 2024; 24:1163. [PMID: 38400321 PMCID: PMC10892564 DOI: 10.3390/s24041163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/20/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.
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Affiliation(s)
- Julien Lebleu
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Kim Daniels
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
| | | | - Lucie Dekimpe
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Jean Mapinduzi
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Filière de Kinésithérapie et Réadaptation, Département des Sciences Clinique, Institut National de la Santé Publique, 6807 Bujumbura, Burundi
| | - Hervé Poilvache
- Orthopedic Surgery Department, CHIREC, 1420 Braine-l’Alleud, Belgium
| | - Bruno Bonnechère
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium
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Lin W, Karahanoglu FI, Psaltos D, Adamowicz L, Santamaria M, Cai X, Demanuele C, Di J. Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers? SENSORS (BASEL, SWITZERLAND) 2023; 23:8542. [PMID: 37896635 PMCID: PMC10611403 DOI: 10.3390/s23208542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023]
Abstract
Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.
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Affiliation(s)
- Wenyi Lin
- Pfizer Inc., Cambridge, MA 02139, USA (C.D.); (J.D.)
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4
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Kittelson AJ, Loyd BJ. Personalized Reference Values for the Two-Minute Walk Test: An Analysis of Cross-Sectional Data From the National Institutes of Health Toolbox Study. Arch Phys Med Rehabil 2023; 104:1418-1424.e1. [PMID: 37037295 PMCID: PMC10524757 DOI: 10.1016/j.apmr.2023.03.021] [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: 08/22/2022] [Revised: 03/09/2023] [Accepted: 03/24/2023] [Indexed: 04/12/2023]
Abstract
OBJECTIVES To develop reference values for the Two-Minute Walk Test (TMWT) via 2 previously untested methods: (1) smooth age-based statistical models and (2) a neighbors-based approach accounting for age, sex, and height. DESIGN Cross-sectional observational study. SETTING National Institutes of Health Toolbox study sites across the United States. PARTICIPANTS A total of 1385 healthy, community dwelling adult participants (age 18-85 years) in the National Institutes of Health Toolbox study were included in this analysis. INTERVENTION None. MAIN OUTCOME MEASURES Reference values for TMWT were generated using 2 approaches: (1) Generalized Additive Models for Location Scale and Shape, wherein TMWT values were modeled as a smooth function of age, and (2) a semiparametric neighbors-based approach. The performance of references values was then adjudicated by examining precision (ie, the average interquartile or interdecile range of reference values), and coverage (ie, the proportion of realized values included within a given inter-percentile interval). Agreement between methods was examined by intraclass correlation coefficient. RESULTS Neighbors-based reference values demonstrated a smaller average interquartile range (149 ft; 95% confidence interval [CI], 146-152 ft), compared with age-based reference values (158 ft; 95% CI, 155-162 ft), but similar average interdecile range (neighbors-based: 369 ft; 95% CI, 360-377 ft; age-based: 374 ft; 95% CI, 366-383 ft). Coverage appeared accurate via both approaches. Agreement between approaches was high (intraclass correlation coefficient=0.96), although differences were apparent on a case-by-case basis. CONCLUSIONS Both age-based and neighbors-based reference values offer viable options for interpreting a person's TMWT performance. In this analysis, the neighbors-based approach (adjusting for height) yielded potentially clinically relevant differences in reference values for persons at extremes of height.
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Affiliation(s)
- Andrew J Kittelson
- School of Physical Therapy and Rehabilitation Science, The University of Montana - Missoula, MT
| | - Brian J Loyd
- School of Physical Therapy and Rehabilitation Science, The University of Montana - Missoula, MT.
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5
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Kontaxis S, Laporta E, Garcia E, Martinis M, Leocani L, Roselli L, Buron MD, Guerrero AI, Zabala A, Cummins N, Vairavan S, Hotopf M, Dobson RJB, Narayan VA, La Porta ML, Costa GD, Magyari M, Sørensen PS, Nos C, Bailon R, Comi G. Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:6017. [PMID: 37447866 DOI: 10.3390/s23136017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/29/2023] [Accepted: 06/10/2023] [Indexed: 07/15/2023]
Abstract
The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.
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Affiliation(s)
- Spyridon Kontaxis
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Estela Laporta
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Esther Garcia
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, 08193 Bellaterra, Spain
| | - Matteo Martinis
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Letizia Leocani
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Lucia Roselli
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Mathias Due Buron
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Ana Zabala
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Nicholas Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | | | - Maria Libera La Porta
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Gloria Dalla Costa
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Melinda Magyari
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Per Soelberg Sørensen
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Carlos Nos
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Raquel Bailon
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Giancarlo Comi
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
- Casa di Cura del Policlinico, 20144 Milan, Italy
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Akingbesote ND, Owusu D, Liu R, Cartmel B, Ferrucci LM, Zupa M, Lustberg MB, Sanft T, Blenman KRM, Irwin ML, Perry RJ. A review of the impact of energy balance on triple-negative breast cancer. J Natl Cancer Inst Monogr 2023; 2023:104-124. [PMID: 37139977 DOI: 10.1093/jncimonographs/lgad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 05/05/2023] Open
Abstract
Cancer cells cannot proliferate without sufficient energy to generate biomass for rapid cell division, as well as to fuel their functions at baseline. For this reason, many recent observational and interventional studies have focused on increasing energy expenditure and/or reducing energy intake during and after cancer treatment. The impact of variance in diet composition and in exercise on cancer outcomes has been detailed extensively elsewhere and is not the primary focus of this review. Instead, in this translational, narrative review we examine studies of how energy balance impacts anticancer immune activation and outcomes in triple-negative breast cancer (TNBC). We discuss preclinical, clinical observational, and the few clinical interventional studies on energy balance in TNBC. We advocate for the implementation of clinical studies to examine how optimizing energy balance-through changes in diet and/or exercise-may optimize the response to immunotherapy in people with TNBC. It is our conviction that by taking a holistic approach that includes energy balance as a key factor to be considered during and after treatment, cancer care may be optimized, and the detrimental effects of cancer treatment and recovery on overall health may be minimized.
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Affiliation(s)
- Ngozi D Akingbesote
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Department of Cellular & Molecular Physiology, Yale University, New Haven, CT, USA
| | - Dennis Owusu
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Department of Cellular & Molecular Physiology, Yale University, New Haven, CT, USA
- Kwame Nkrumah University of Science and Technology, Kumasi, Ashanti Region, Ghana
| | - Ryan Liu
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Department of Cellular & Molecular Physiology, Yale University, New Haven, CT, USA
- Cedar Park High School, Cedar Park, TX, USA
| | - Brenda Cartmel
- Yale School of Public Health, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
| | - Leah M Ferrucci
- Yale School of Public Health, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
| | | | - Maryam B Lustberg
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
| | - Tara Sanft
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
| | - Kim R M Blenman
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Melinda L Irwin
- Yale School of Public Health, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
| | - Rachel J Perry
- Department of Internal Medicine, Yale University, New Haven, CT, USA
- Department of Cellular & Molecular Physiology, Yale University, New Haven, CT, USA
- Yale Cancer Center, New Haven, CT, USA
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7
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Sun S, Folarin AA, Zhang Y, Cummins N, Liu S, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Dalla Costa G, Leocani L, Sørensen PS, Magyari M, Guerrero AI, Zabalza A, Vairavan S, Bailon R, Simblett S, Myin-Germeys I, Rintala A, Wykes T, Narayan VA, Hotopf M, Comi G, Dobson RJ. The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107204. [PMID: 36371974 DOI: 10.1016/j.cmpb.2022.107204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/27/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions. METHODS In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS). RESULTS The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT. CONCLUSIONS This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.
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Affiliation(s)
- Shaoxiong Sun
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Amos A Folarin
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | - Yuezhou Zhang
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicholas Cummins
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Shuo Liu
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany
| | - Callum Stewart
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yatharth Ranjan
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petroula Laiou
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Heet Sankesara
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Letizia Leocani
- Vita-Salute University and Experimental Neurophysiology Unit, Institute of Experimental Neurology-INSPE, Scientific Institute San Raffaele, Milan, Italy
| | - Per Soelberg Sørensen
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Raquel Bailon
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain; Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Inez Myin-Germeys
- Department of Neurosciences, Centre for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Aki Rintala
- Department of Neurosciences, Centre for Contextual Psychiatry, KU Leuven, Leuven, Belgium; Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Giancarlo Comi
- Vita Salute San Raffaele University, Milan, Italy; Casa di Cura Privata del Policlinico, Milan, Italy
| | - Richard Jb Dobson
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK.
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8
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Antikainen E, Njoum H, Kudelka J, Branco D, Rehman RZU, Macrae V, Davies K, Hildesheim H, Emmert K, Reilmann R, Janneke van der Woude C, Maetzler W, Ng WF, O’Donnell P, Van Gassen G, Baribaud F, Pandis I, Manyakov NV, van Gils M, Ahmaniemi T, Chatterjee M. Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study. Front Physiol 2022; 13:968185. [PMID: 36452041 PMCID: PMC9702812 DOI: 10.3389/fphys.2022.968185] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 10/31/2022] [Indexed: 08/07/2023] Open
Abstract
Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.
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Affiliation(s)
- Emmi Antikainen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | | | - Jennifer Kudelka
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Diogo Branco
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Victoria Macrae
- NIHR Newcastle Biomedical Research Centre and NIHR Newcastle Clinical Research Facility, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Kristen Davies
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Hanna Hildesheim
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Kirsten Emmert
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Ralf Reilmann
- George-Huntington-Institute, University of Münster, Münster, Germany
- Department of Clinical Radiology, University of Münster, Münster, Germany
- Department of Neurodegenerative Diseases and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | | | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Wan-Fai Ng
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- NIHR Newcastle Biomedical Research Centre and NIHR Newcastle Clinical Research Facility, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Patricio O’Donnell
- Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, United States
| | | | | | | | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Teemu Ahmaniemi
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
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Butkuviene M, Tamuleviciute-Prasciene E, Beigiene A, Barasaite V, Sokas D, Kubilius R, Petrenas A. Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery. IEEE J Biomed Health Inform 2022; 26:4426-4435. [PMID: 35700246 DOI: 10.1109/jbhi.2022.3181738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.
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