Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Experimental Setup and Tasks
2.3. Video Processing
2.4. Accelerometry Processing
2.5. Metrics Calculation
2.5.1. Bimanual Metrics
2.5.2. Unimanual Metrics
2.6. Statistical Analysis
3. Results
3.1. Participant Description
3.2. Metrics Description
3.3. Concurrent Validity and Inter-Rater Reliability
3.4. Discriminative Validity
4. Discussion
4.1. Study Limitations
4.2. Clinical Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Novak, I.; Morgan, C.; Adde, L.; Blackman, J.; Boyd, R.N.; Brunstrom-Hernandez, J.; Cioni, G.; Damiano, D.; Darrah, J.; Eliasson, A.-C.; et al. Early, Accurate Diagnosis and Early Intervention in Cerebral Palsy: Advances in diagnosis and treatment. JAMA Pediatr. 2017, 171, 897–907. [Google Scholar] [CrossRef]
- Cioni, G.; Ferrari, A. The Spastic Forms of Cerebral Palsy: A Guide to the Assessment of Adaptive Functions, 1st ed.; Mailand, S.-V., Ed.; Spinger: Berlin/Heidelberg, Germany, 2010; p. 368. [Google Scholar]
- Pakula, A.T.; Van Naarden Braun, K.; Yeargin-Allsopp, M. Cerebral Palsy: Classification and Epidemiology. Phys. Med. Rehabil. Clin. N. Am. 2009, 20, 425–452. [Google Scholar] [CrossRef]
- Gordon, A.; Bleyenheuft, Y.; Steenbergen, B. Pathophysiology of impaired hand function in children with unilateral cerebral palsy. Dev. Med. Child Neurol. 2013, 55 (Suppl. S4), 32–37. [Google Scholar] [CrossRef] [Green Version]
- Riquelme, I.; Arnould, C.; Hatem, S.M.; Bleyenheuft, Y. The Two-Arm Coordination Test: Maturation of Bimanual Coordination in Typically Developing Children and Deficits in Children with Unilateral Cerebral Palsy. Dev. Neurorehabilit. 2018, 22, 312–320. [Google Scholar] [CrossRef]
- Bailey, R.; Klaesner, J.W.; Lang, C.E. Quantifying Real-World Upper-Limb Activity in Nondisabled Adults and Adults With Chronic Stroke. Neurorehabilit. Neural Repair 2015, 29, 969–978. [Google Scholar] [CrossRef] [Green Version]
- Haaland, K.Y.; Mutha, P.K.; Rinehart, J.K.; Daniels, M.; Cushnyr, B.; Adair, J.C. Relationship Between Arm Usage and Instrumental Activities of Daily Living After Unilateral Stroke. Arch. Phys. Med. Rehabil. 2012, 93, 1957–1962. [Google Scholar] [CrossRef]
- Arnould, C.; Penta, M.; Renders, A.; Thonnard, J.-L. ABILHAND-Kids: A measure of manual ability in children with cerebral palsy. Neurology 2004, 63, 1045–1052. [Google Scholar] [CrossRef]
- Golubović, Š.; Slavković, S. Manual ability and manual dexterity in children with cerebral palsy. Hippokratia 2014, 18, 310–314. [Google Scholar] [PubMed]
- Krumlinde-Sundholm, L.; Holmefur, M.; Kottorp, A.; Eliasson, A.-C. The Assisting Hand Assessment: Current evidence of validity, reliability, and responsiveness to change. Dev. Med. Child Neurol. 2007, 49, 259–264. [Google Scholar] [CrossRef] [PubMed]
- Stone, A.A.; Bachrach, C.A.; Jobe, J.B.; Kurtzman, H.S.; Cain, V.S. The Science of Self-Report: Implications for Research and Practice; Taylor & Francis: Abingdon, UK, 1999. [Google Scholar]
- Adams, S.A.; Matthews, C.E.; Ebbeling, C.B.; Moore, C.G.; Cunningham, J.E.; Fulton, J.; Hebert, J.R. The Effect of Social Desirability and Social Approval on Self-Reports of Physical Activity. Am. J. Epidemiol. 2005, 161, 389–398. [Google Scholar] [CrossRef]
- Kantak, S.; Jax, S.; Wittenberg, G. Bimanual coordination: A missing piece of arm rehabilitation after stroke. Restor. Neurol. Neurosci. 2017, 35, 347–364. [Google Scholar] [CrossRef]
- Wolfson, A.M.; Doctor, J.N.; Burns, S.P. Clinician judgments of functional outcomes: How bias and perceived accuracy affect rating. Arch. Phys. Med. Rehabil. 2000, 81, 1567–1574. [Google Scholar] [CrossRef]
- Mahtani, K.; Spencer, E.A.; Brassey, J.; Heneghan, C. Catalogue of bias: Observer bias. BMJ Evid.-Based Med. 2018, 23, 23–24. [Google Scholar] [CrossRef]
- Godfrey, A.; Conway, R.; Meagher, D.; Ólaighin, G. Direct measurement of human movement by accelerometry. Med. Eng. Phys. 2008, 30, 1364–1386. [Google Scholar] [CrossRef]
- Fairclough, S.J.; Noonan, R.; Rowlands, A.V.; VAN Hees, V.; Knowles, Z.; Boddy, L.M. Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers. Med. Sci. Sports Exerc. 2016, 48, 245–253. [Google Scholar] [CrossRef]
- Scott, J.; Rowlands, A.; Cliff, D.; Morgan, P.J.; Plotnikoff, R.; Lubans, D.R. Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents. J. Sci. Med. Sport 2017, 20, 1101–1106. [Google Scholar] [CrossRef] [Green Version]
- Noorkõiv, M.; Rodgers, H.; Price, C.I. Accelerometer measurement of upper extremity movement after stroke: A systematic review of clinical studies. J. Neuroeng. Rehabil. 2014, 11, 144. [Google Scholar] [CrossRef] [Green Version]
- Beani, E.; Maselli, M.; Sicola, E.; Perazza, S.; Cecchi, F.; Dario, P.; Braito, I.; Boyd, R.; Cioni, G.; Sgandurra, G. Actigraph assessment for measuring upper limb activity in unilateral cerebral palsy. J. Neuroeng. Rehabil. 2019, 16, 30. [Google Scholar] [CrossRef] [Green Version]
- Rabuffetti, M.; Meriggi, P.; Pagliari, C.; Bartolomeo, P.; Ferrarin, M. Differential actigraphy for monitoring asymmetry in upper limb motor activities. Physiol. Meas. 2016, 37, 1798–1812. [Google Scholar] [CrossRef]
- Hayward, K.S.; Eng, J.J.; Boyd, L.A.; Lakhani, B.; Bernhardt, J.; Lang, C.E. Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke. Brain Impair. 2016, 17, 16–33. [Google Scholar] [CrossRef] [Green Version]
- Bailey, R.; Klaesner, J.W.; Lang, C.E. An Accelerometry-Based Methodology for Assessment of Real-World Bilateral Upper Extremity Activity. PLoS ONE 2014, 9, e103135. [Google Scholar] [CrossRef]
- Hoyt, C.R.; Van, A.N.; Ortega, M.; Koller, J.M.; Everett, E.A.; Nguyen, A.L.; Lang, C.E.; Schlaggar, B.L.; Dosenbach, N.U.F. Detection of Pediatric Upper Extremity Motor Activity and Deficits With Accelerometry. JAMA Netw. Open 2019, 2, e192970. [Google Scholar] [CrossRef]
- Lang, C.E.; Waddell, K.J.; Klaesner, J.W.; Bland, M.D. A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers. J. Vis. Exp. 2017, 55673, e55673. [Google Scholar] [CrossRef] [Green Version]
- Michielsen, M.E.; Selles, R.W.; Stam, H.J.; Ribbers, G.; Bussmann, J.B. Quantifying Nonuse in Chronic Stroke Patients: A Study Into Paretic, Nonparetic, and Bimanual Upper-Limb Use in Daily Life. Arch. Phys. Med. Rehabil. 2012, 93, 1975–1981. [Google Scholar] [CrossRef]
- Goodwin, B.M.; Sabelhaus, E.K.; Pan, Y.-C.; Bjornson, K.F.; Pham, K.L.D.; Walker, W.O.; Steele, K.M. Accelerometer Measurements Indicate That Arm Movements of Children With Cerebral Palsy Do Not Increase After Constraint-Induced Movement Therapy (CIMT). Am. J. Occup. Ther. 2020, 74, 7405205100p1–7405205100p9. [Google Scholar] [CrossRef]
- Sokal, B.; Uswatte, G.; Vogtle, L.; Byrom, E.; Barman, J. Everyday movement and use of the arms: Relationship in children with hemiparesis differs from adults. J. Pediatr. Rehabil. Med. 2015, 8, 197–206. [Google Scholar] [CrossRef] [Green Version]
- Ando, N.; Ueda, S. Functional deterioration in adults with cerebral palsy. Clin. Rehabil. 2000, 14, 300–306. [Google Scholar] [CrossRef]
- Jahnsen, R.; Villien, L.; Egeland, T.; Stanghelle, J.K.; Holm, I. Locomotion skills in adults with cerebral palsy. Clin. Rehabil. 2004, 18, 309–316. [Google Scholar] [CrossRef]
- Dawe, J.; Yang, J.; Fehlings, D.; Likitlersuang, J.; Rumney, P.; Zariffa, J.; Musselman, K.E. Validating Accelerometry as a Measure of Arm Movement for Children With Hemiplegic Cerebral Palsy. Phys. Ther. 2019, 99, 721–729. [Google Scholar] [CrossRef]
- Vega-González, A.; Bain, B.J.; Dall, P.M.; Granat, M.H. Continuous monitoring of upper-limb activity in a free-living environment: A validation study. Med. Biol. Eng. Comput. 2007, 45, 947–956. [Google Scholar] [CrossRef]
- Teufl, S.; Preston, J.; Van Wijck, F.; Stansfield, B. Objective identification of upper limb tremor in multiple sclerosis using a wrist-worn motion sensor: Establishing validity and reliability. Br. J. Occup. Ther. 2017, 80, 596–602. [Google Scholar] [CrossRef] [Green Version]
- Janssen, W.G.M.; Bussmann, J.B.J.; Horemans, H.L.D.; Stam, H.J. Validity of accelerometry in assessing the duration of the sit-to-stand movement. Med. Biol. Eng. Comput. 2008, 46, 879–887. [Google Scholar] [CrossRef] [PubMed]
- Gebruers, N.; Vanroy, C.; Truijen, S.; Engelborghs, S.; De Deyn, P.P. Monitoring of Physical Activity After Stroke: A Systematic Review of Accelerometry-Based Measures. Arch. Phys. Med. Rehabil. 2010, 91, 288–297. [Google Scholar] [CrossRef] [PubMed]
- Bonett, D.G. Sample size requirements for estimating intraclass correlations with desired precision. Stat. Med. 2002, 21, 1331–1335. [Google Scholar] [CrossRef]
- Eliasson, A.-C.; Krumlinde-Sundholm, L.; Rösblad, B.; Beckung, E.; Arner, M.; Öhrvall, A.-M.; Rosenbaum, P. The Manual Ability Classification System (MACS) for children with cerebral palsy: Scale development and evidence of validity and reliability. Dev. Med. Child Neurol. 2006, 48, 549–554. [Google Scholar] [CrossRef]
- Poitras, I.; Clouâtre, J.; Bouyer, L.J.; Routhier, F.; Mercier, C.; Campeau-Lecours, A. Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors. Sensors 2020, 20, 6767. [Google Scholar] [CrossRef]
- Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med. 2016, 15, 155–163. [Google Scholar] [CrossRef] [Green Version]
- Brunner, E.; Bathke, A.; Konietschke, F. Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs: Using R and SAS; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
- Benjamini, Y.; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 2001, 29, 1165–1188. [Google Scholar] [CrossRef]
- Claridge, E.A.; Mcphee, P.G.; Timmons, B.W.; Ginis, K.A.M.; Macdonald, M.J.; Gorter, J.W. Quantification of Physical Activity and Sedentary Time in Adults with Cerebral Palsy. Med. Sci. Sports Exerc. 2015, 47, 1719–1726. [Google Scholar] [CrossRef]
- O’Neil, M.E.; Fragala-Pinkham, M.; Lennon, N.; George, A.; Forman, J.; Trost, S.G. Reliability and Validity of Objective Measures of Physical Activity in Youth With Cerebral Palsy Who Are Ambulatory. Phys. Ther. 2016, 96, 37–45. [Google Scholar] [CrossRef] [Green Version]
- Uswatte, G.; Giuliani, C.; Winstein, C.; Zeringue, A.; Hobbs, L.; Wolf, S.L. Validity of Accelerometry for Monitoring Real-World Arm Activity in Patients With Subacute Stroke: Evidence From the Extremity Constraint-Induced Therapy Evaluation Trial. Arch. Phys. Med. Rehabil. 2006, 87, 1340–1345. [Google Scholar] [CrossRef] [PubMed]
- Uswatte, G.; Taub, E.; Morris, D.; Light, K.; Thompson, P.A. The Motor Activity Log-28: Assessing daily use of the hemiparetic arm after stroke. Neurology 2006, 67, 1189–1194. [Google Scholar] [CrossRef] [PubMed]
- Bambach, K.; Elder, N.; Gregory, M.; Southerland, L. Are geriatric screening tools too time consuming for the emergency department? A workflow time study. Proc. J. Am. Geriatr. Soc. 2019, 67, S293. [Google Scholar]
- Rich, T.L.; Menk, J.S.; Rudser, K.D.; Feyma, T.; Gillick, B.T. Less-Affected Hand Function in Children With Hemiparetic Unilateral Cerebral Palsy: A Comparison Study With Typically Developing Peers. Neurorehabilit. Neural Repair 2017, 31, 965–976. [Google Scholar] [CrossRef]
- Demers, M.; Levin, M.F. Do Activity Level Outcome Measures Commonly Used in Neurological Practice Assess Upper-Limb Movement Quality? Neurorehabilit. Neural Repair 2017, 31, 623–637. [Google Scholar] [CrossRef] [Green Version]
Task | Description |
---|---|
Cleaning the table | Turning on a tap, filling a bowl of water, turning off the tap, wringing out a towel, and cleaning the table. |
Making coffee | Opening a container, picking up a spoon, putting two spoonfuls of coffee in a cup, and closing the jar. |
Setting the table | Setting a table for two, with two forks, two knives, two plates, and two glasses. |
Pouring a glass of water | Turning on the tap, filling up a pitcher, turning off the tap, and pouring a glass of water. |
Folding towels | Folding two large towels and stacking them on top of each other. |
Putting toothpaste on a toothbrush | Opening a tube of toothpaste, putting a small amount of toothpaste on a toothbrush, putting the cap back on the toothpaste, and putting both objects on the table. |
CTRL (n = 11) | CP (n = 11) | |
---|---|---|
Age (mean ± SD years) | 27.8 ± 6.6 | 35.9 ± 13.3 |
Female | 8 (73%) | 7 (64%) |
Right-handed | 10 (91%) | 4 (36%) |
Side of hemiplegia | - | 8 (73%) |
MACS levels | - | I = 4 (36.5%) II = 4 (36.5%) III = 3 (27%) |
Metric | Comparison | ICC | 95% Confidence Interval | p-Value |
---|---|---|---|---|
UR | Accelerometry vs. Rater 1 (Experienced) | 0.97 | 0.96–0.98 | <0.001 |
Accelerometry vs. Rater 2 (Naive) | 0.74 | 0.66–0.81 | <0.001 | |
Rater 1 (Experienced) vs. Rater 2 (Naïve) | 0.69 | 0.66–0.72 | <0.001 | |
percentage of dominant use | Accelerometry vs. Rater 1 (Experienced) | 0.92 | 0.88–0.95 | <0.001 |
Accelerometry vs. Rater 2 (Naive) | 0.78 | 0.69–0.84 | <0.001 | |
Rater 1 vs. Rater 2 | 0.87 | 0.82–0.91 | <0.001 | |
percentage non dominant use | Accelerometry vs. Rater 1 (Experienced) | 0.88 | 0.84–0.92 | <0.001 |
Accelerometry vs. Rater 2 (Naive) | 0.61 | 0.47–0.71 | <0.001 | |
Rater 1 (Experienced) vs. Rater 2 (Naive) | 0.70 | 0.60–0.78 | <0.001 | |
percentage bimanual use | Accelerometry vs. Rater 1 (Experienced) | 0.85 | 0.75–0.91 | <0.001 |
Accelerometry vs. Rater 2 (Naive) | 0.65 | 0.44–0.78 | <0.001 | |
Rater 1 vs. Rater 2 | 0.85 | 0.78–0.90 | <0.001 |
Metrics | Pool of Data | Effect | p-Value without Correction | p-Value with Correction | ANOVA Type Statistic (ATS) |
---|---|---|---|---|---|
UR | All data | Group | <0.001 | <0.001 | 33.5 |
Task | <0.001 | <0.01 | 5.5 | ||
Group XTask | 0.47 | - | - | ||
Task 3 | Group | <0.01 | 0.06 | 9.7 | |
Task 5 | Group | <0.001 | <0.001 | 42.4 | |
Task 6 | Group | <0.01 | <0.05 | 10.9 | |
Task 3 vs. 5 | Group | <0.001 | <0.001 | 29.5 | |
Task | <0.01 | 0.08 | 11.2 | ||
Group X Task | 0.89 | - | - | ||
Task 3 vs. 6 | Group | <0.001 | <0.01 | 19.5 | |
Task | 0.66 | - | - | ||
Group XTask | 0.72 | - | - | ||
Task 5 vs. 6 | Group | <0.001 | <0.001 | 27.8 | |
Task | <0.05 | 0.76 | 5.5 | ||
Group X Task | 0.89 | - | - | ||
percentage of dominant use | All data | Group | <0.001 | <0.001 | 34.0 |
Task | <0.001 | <0.001 | 10.1 | ||
Group X Task | 0.34 | - | 1.2 | ||
Task 3 | Group | <0.01 | <0.05 | 14.8 | |
Task 5 | Group | <0.001 | <0.01 | 38.7 | |
Task 6 | Group | <0.01 | 0.14 | 10.2 | |
Task 3 vs. 5 | Group | <0.001 | <0.001 | 34.8 | |
Task | <0.001 | <0.01 | 23.4 | ||
Group X Task | 0.70 | - | - | ||
Task 3 vs 6 | Group | <0.001 | <0.001 | 23.8 | |
Task | 0.28 | - | - | ||
Group XTask | 0.95 | - | - | ||
Task 5 vs. 6 | Group | <0.001 | <0.001 | 33.9 | |
Task | <0.001 | <0.001 | 35.3 | ||
Group X Task | 0.34 | - | - | ||
percentage of non dominant use | All data | Group | <0.001 | <0.001 | 26.2 |
Task | <0.05 | 0.50 | - | ||
Group X Task | 0.24 | - | - | ||
Task2 | Group | <0.001 | <0.05 | 19.8 | |
Task 3 | Group | <0.05 | 0.82 | 6.5 | |
Task 5 | Group | <0.05 | 1 | 4.7 | |
Task 6 | Group | <0.05 | 0.62 | 7.1 | |
Task 2 vs. 3 | Group | <0.01 | <0.01 | 18.7 | |
Task | <0.05 | 0.57 | 6.7 | ||
Group X Task | 0.19 | - | - | ||
Task 2 vs 5 | Group | <0.001 | <0.001 | 24.7 | |
Task | 0.52 | - | - | ||
Group X Task | <0.05 | 1 | 4.4 | ||
Task 2 vs. 6 | Group | <0.001 | <0.001 | 23.6 | |
Task | 0.15 | - | - | ||
Group X Task | 0.47 | - | - | ||
Task 3 vs. 5 | Group | <0.01 | 0.21 | 8.8 | |
Task | <0.001 | <0.05 | 15.1 | ||
Group X task | 0.63 | - | - | ||
Task 3 vs. 6 | Group | <0.01 | 0.06 | 11.9 | |
Task | 0.23 | - | - | ||
Group X task | 0.72 | - | - | ||
Task 5 vs. 6 | Group | <0.01 | 0.07 | 11.8 | |
Task | 0.05 | 1 | 4.1 | ||
Group X Task | 0.37 | - | - | ||
percentage of bimanual use | All data | Group | <0.01 | <0.01 | 13.9 |
Task | <0.001 | <0.001 | 13.6 | ||
Group X Task | 0.75 | - | - | ||
Task 3 | Group | 0.05 | 1 | 4.4 | |
Task 5 | Group | <0.05 | 0.22 | 8.1 | |
Task 3 vs. 5 | Group | <0.01 | <0.05 | 12.6 | |
Task | <0.001 | <0.001 | 45.0 | ||
Group X Task | 0.40 | - | - |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Poitras, I.; Clouâtre, J.; Campeau-Lecours, A.; Mercier, C. Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy. Sensors 2022, 22, 1022. https://doi.org/10.3390/s22031022
Poitras I, Clouâtre J, Campeau-Lecours A, Mercier C. Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy. Sensors. 2022; 22(3):1022. https://doi.org/10.3390/s22031022
Chicago/Turabian StylePoitras, Isabelle, Jade Clouâtre, Alexandre Campeau-Lecours, and Catherine Mercier. 2022. "Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy" Sensors 22, no. 3: 1022. https://doi.org/10.3390/s22031022