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Lang CE, Hoyt CR, Konrad JD, Bell KR, Marrus N, Bland MD, Lohse KR, Miller AE. Referent data for investigations of upper limb accelerometry: harmonized data from three cohorts of typically-developing children. Front Pediatr 2024; 12:1361757. [PMID: 38496366 PMCID: PMC10940427 DOI: 10.3389/fped.2024.1361757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/23/2024] [Indexed: 03/19/2024] Open
Abstract
Aim The rise of wearable sensing technology shows promise for addressing the challenges of measuring motor behavior in pediatric populations. The current pediatric wearable sensing literature is highly variable with respect to the number of sensors used, sensor placement, wearing time, and how data extracted from the sensors are analyzed. Many studies derive conceptually similar variables via different calculation methods, making it hard to compare across studies and clinical populations. In hopes of moving the field forward, this report provides referent upper limb wearable sensor data from accelerometers on 25 variables in typically-developing children, ages 3-17 years. Methods This is a secondary analysis of data from three pediatric cohorts of children 3-17 years of age. Participants (n = 222) in the cohorts wore bilateral wrist accelerometers for 2-4 days for a total of 622 recording days. Accelerometer data were reprocessed to compute 25 variables that quantified upper limb movement duration, intensity, symmetry, and complexity. Analyses examined the influence of hand dominance, age, gender, reliability, day-to-day stability, and the relationships between variables. Results The majority of variables were similar on the dominant and non-dominant sides, declined slightly with age, and were not different between boys and girls. ICC values were moderate to excellent. Variation within individuals across days generally ranged from 3% to 32%. A web-based R shiny object is available for data viewing. Interpretation With the use of wearable movement sensors increasing rapidly, these data provide key, referent information for researchers as they design studies, and analyze and interpret data from neurodevelopmental and other pediatric clinical populations. These data may be of particularly high value for pediatric rare diseases.
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Affiliation(s)
- Catherine E. Lang
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Catherine R. Hoyt
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
| | - Jeffrey D. Konrad
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
| | - Kayla R. Bell
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
| | - Natasha Marrus
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Marghuretta D. Bland
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Keith R. Lohse
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Allison E. Miller
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
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Kaslow JA, Sokolow AG, Donnelly T, Buchowski MS, Markham LW, Burnette WB, Soslow JH. Spirometry correlates with physical activity in patients with Duchenne muscular dystrophy. Pediatr Pulmonol 2023; 58:1034-1041. [PMID: 36571207 PMCID: PMC10023371 DOI: 10.1002/ppul.26289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/01/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Duchenne muscular dystrophy (DMD) is associated with progressive muscle weakness and respiratory decline. To date, studies have focused on respiratory decline and loss of ambulation as a metric of loss of skeletal muscle strength. However, new functional measures can assess skeletal muscle disease regardless of ambulatory status. The relationship between these tests and concurrent lung function is currently unexplored. OBJECTIVE To assess the correlation between spirometry measurements and functional muscle assessments such as accelerometry and quantitative muscle testing (QMT). METHODS Enrolled patients with DMD underwent accelerometry and QMT at study clinic visits. Any pulmonary function testing within 6 months of visit was obtained from the electronic medical record. The Spearman correlation coefficient was used to assess the relationship between spirometry and functional muscle testing. RESULTS Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1 ) demonstrated the strongest correlation with accelerometry. Both FVC and FEV1 showed a similar relationship to accelerometry when activity was divided into intensity categories, with low intensity and moderate-to-vigorous activity categories showing the strongest correlation. Maximal expiratory pressure (MEP) and FVC showed the most robust correlations with total QMT (p < 0.001 and p < 0.01, respectively). CONCLUSION Lung function, specifically FVC percent predicted and FEV1 %p, shows a good correlation with upper and lower extremity skeletal muscle functional testing such as accelerometry and QMT.
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Affiliation(s)
- Jacob A Kaslow
- Department of Pediatrics, Division of Pediatric Pulmonary, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Andrew G Sokolow
- Department of Pediatrics, Division of Pediatric Pulmonary, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Thomas Donnelly
- Department of Pediatrics, Thomas P Graham Jr. Division of Pediatric Cardiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Maciej S Buchowski
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Larry W Markham
- Department of Pediatrics, Division of Cardiology, Indiana University School of Medicine and Riley Hospital for Children at Indiana University Health, Indianapolis, Indiana, USA
| | - William Bryan Burnette
- Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan H Soslow
- Department of Pediatrics, Thomas P Graham Jr. Division of Pediatric Cardiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Barth J, Lohse KR, Konrad JD, Bland MD, Lang CE. Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits. FRONTIERS IN REHABILITATION SCIENCES 2021; 2. [PMID: 35382114 PMCID: PMC8979497 DOI: 10.3389/fresc.2021.741393] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life. Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits. Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters. Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit. Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.
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Affiliation(s)
- Jessica Barth
- Washington University in St. Louis, Program in Physical Therapy, St. Louis, MO, USA
| | - Keith R Lohse
- Washington University in St. Louis, Program in Physical Therapy, St. Louis, MO, USA
| | - Jeffrey D Konrad
- Washington University in St. Louis, Program in Physical Therapy, St. Louis, MO, USA
| | - Marghuertta D Bland
- Washington University in St. Louis, Program in Physical Therapy, St. Louis, MO, USA.,Washington University in St. Louis, Program in Occupational Therapy, MO, USA.,Washington University in St. Louis, Neurology, MO, USA
| | - Catherine E Lang
- Washington University in St. Louis, Program in Physical Therapy, St. Louis, MO, USA.,Washington University in St. Louis, Program in Occupational Therapy, MO, USA.,Washington University in St. Louis, Neurology, MO, USA
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Lang CE, Barth J, Holleran CL, Konrad JD, Bland MD. Implementation of Wearable Sensing Technology for Movement: Pushing Forward into the Routine Physical Rehabilitation Care Field. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5744. [PMID: 33050368 PMCID: PMC7601835 DOI: 10.3390/s20205744] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 01/01/2023]
Abstract
While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples' homes and communities.
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Affiliation(s)
- Catherine E. Lang
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA; (J.B.); (C.L.H.); (J.D.K.); (M.D.B.)
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63122, USA
| | - Jessica Barth
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA; (J.B.); (C.L.H.); (J.D.K.); (M.D.B.)
| | - Carey L. Holleran
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA; (J.B.); (C.L.H.); (J.D.K.); (M.D.B.)
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63122, USA
| | - Jeff D. Konrad
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA; (J.B.); (C.L.H.); (J.D.K.); (M.D.B.)
| | - Marghuretta D. Bland
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA; (J.B.); (C.L.H.); (J.D.K.); (M.D.B.)
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63122, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63122, USA
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