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Journal of NeuroEngineering and Rehabilitation
Published in: Journal of NeuroEngineering and Rehabilitation 1/2017

Open Access 01-12-2017 | Research

Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis

Authors: Dylan Kobsar, Sean T. Osis, Jeffrey E. Boyd, Blayne A. Hettinga, Reed Ferber

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2017

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Abstract

Background

Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort.

Methods

Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components.

Results

The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27).

Conclusions

While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.
Appendix
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Literature
1.
Murphy L, Schwartz TA, Helmick CG, Renner JB, Tudor G, Koch G, et al. Lifetime risk of symptomatic knee osteoarthritis. Arthritis Rheum. 2008;59:1207–13.CrossRefPubMedPubMedCentral
2.
Hunter DJ. Lower extremity osteoarthritis management needs a paradigm shift. Br J Sports Med. 2011;45:283–8.CrossRefPubMed
3.
Fransen M, Mcconnell S, Harmer A, Van Der Esch M, Simic M, Bennell KL. Exercise for osteoarthritis of the knee (review). Cochrane Database Syst Rev. 2015;1:CD004376.PubMed
4.
Buford TW, Pahor M. Making preventive medicine more personalized: implications for exercise-related research. Prev Med (Baltim). 2012;55(1):34–6.CrossRef
5.
Weigl M, Angst F, Aeschlimann A, Lehmann S, Stucki G. Predictors for response to rehabilitation in patients with hip or knee osteoarthritis: a comparison of logistic regression models with three different definitions of responder. Osteoarthr Cartil. 2006;14(7):641–51.CrossRefPubMed
6.
Wright AA, Cook CE, Flynn TW, Baxter GD, Abbott JH. Predictors of response to physical therapy intervention in patients with primary hip osteoarthritis. Phys Ther. 2011;91(4):510–24.CrossRefPubMed
7.
French H, Galvin R. Predictors of short-term outcome to exercise and manual therapy for people with hip osteoarthritis. Phys Ther. 2014;94(1):31–9.CrossRefPubMed
8.
Bennell KL, Dobson F, Roos EM, Skou SK, Hodges P, Wrigley TV, et al. Influence of biomechanical characteristics on pain and function outcomes from exercise in medial knee osteoarthritis and varus malalignment: exploratory analyses from a randomized controlled trial. Arthritis Care Res. 2015;67(9):1281–8.CrossRef
9.
Knoop J, Van Der Leeden M, Roorda LD, Thorstensson CA, Van Der Esch M, Peter WF, et al. Knee joint stabilization therapy in patients with osteoarthritis of the knee and knee instability: subgroup analyses in a randomized, controlled trial. J Rehabil Med. 2014;46:703–7.CrossRefPubMed
10.
Kobsar D, Osis ST, Hettinga BA, Ferber R. Gait biomechanics and patient-reported function as predictors of response to a hip strengthening exercise intervention in patients with knee osteoarthritis. PLoS One. 2015;10(10):e0139923.CrossRefPubMedPubMedCentral
11.
12.
Hendrick P, Boyd T, Low O, Takarangi K, Paterson M, Claydon L, Milosavljevic S. Construct validity of RT3 accelerometer: a comparison of level-ground and treadmill walking at self-selected speeds. J Rehabil Res Dev. 2010;47(2):157–68.CrossRefPubMed
13.
Kobsar D, Osis ST, Phinyomark A, Boyd JE, Ferber R. Reliability of gait analysis using wearable sensors in patients with knee osteoarthritis. J Biomech. 2016;49(16):3977–82.CrossRefPubMed
14.
Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture. 2014;40(1):11–9.CrossRefPubMed
15.
Alviar MJ, Olver J, Brand C, Hale T, Khan F. Do patient-reported outcome measures used in assessing outcomes in rehabilitation after hip and knee arthroplasty capture issues relevant to patients? Results of a systematic review and ICF linking process. J Rehabil Med. 2011;43(10):374–81.CrossRefPubMed
16.
Roos EM, Roos HP, Lohmander LS, Ekdahl C, Beynnon BD. Knee injury and osteoarthritis outcome score (KOOS)--development of a self-administered outcome measure. J Orthop Sports Phys Ther. 1998;28(2):88–96.CrossRefPubMed
17.
Ferber R, Bolgla LA, Earl-Boehm JE, Emery CA, Hamstra-Wright KL. Hip and core versus knee-muscle strengthening for the treatment of patellofemoral pain: a multicentre randomized controlled trial. J Athl Train. 2014;50(4):366–77.CrossRefPubMed
18.
Roos EM, Lohmander LS. The Knee injury and Osteoarthritis Outcome Score (KOOS): from joint injury to osteoarthritis. Health Qual Life Outcomes. 2003;1:64.CrossRefPubMedPubMedCentral
19.
Tundo MD, Lemaire E, Baddour N. Correcting Smartphone orientation for accelerometer-based analysis. MeMeA 2013. IEEE Int Symp Med Meas Appl Proc. 2013;58:62.
20.
Fraccaro P, Walsh L, Doyle J, O’Sullivan D. Real-world gyroscope-based gait event detection and gait feature extraction. eTELEMED 2014, Sixth Int Conf eHealth, Telemedicine, Soc Med. 2014:247–52.
21.
Rampp A, Barth J, Schülein S. Inertial sensor based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng. 2015;62(4):1–8.CrossRef
22.
Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010;11(1):94.CrossRefPubMedPubMedCentral
23.
Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer; 2002.
24.
Phinyomark A, Osis S, Hettinga BA, Kobsar D, Ferber R. Gender differences in gait kinematics for patients with knee osteoarthritis. BMC Musculoskelet Disord. 2016;17(1):157.CrossRefPubMedPubMedCentral
25.
Borra S, Di Ciaccio A. Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Comput. Stat. Data Anal. 2010;54(12):2976–89.CrossRef
26.
Kumari B. Swarnkar T. Filter versus wrapper feature subset selection in large dimensionality micro array: A review. 2011;2(3):1048–53.
27.
Mills K, Hunt MA, Ferber R. Biomechanical deviations during level walking associated with knee osteoarthritis: a systematic review and meta-analysis. Arthritis Care Res (Hoboken). 2013;65(10):1643–65.
28.
Mündermann A, Dyrby CO, Andriacchi TP. Secondary gait changes in patients with medial compartment knee osteoarthritis: increased load at the ankle, knee, and hip during walking. Arthritis Rheum. 2005;52(9):2835–44.CrossRefPubMed
29.
Chan M, Estève D, Fourniols J-Y, Escriba C, Campo E. Smart wearable systems: current status and future challenges. Artif Intell Med. 2012;56(3):137–56.CrossRefPubMed
30.
Watari R, Kobsar D, Phinyomark A, Osis S, Ferber R. Determination of patellofemoral pain sub-groups and development of a method for predicting treatment outcome using running gait kinematics. Clin Biomech (Bristol, Avon). 2016;38:13–21.CrossRef
31.
Leigh RJ, Pohl MB, Ferber R. Does tester experience influence the reliability with which 3D gait kinematics are collected in healthy adults? Physical Therapy in Sport. 2014;15(2):112–6.CrossRefPubMed
32.
Favre J, Aissaoui R, Jolles BM, de Guise JA, Aminian K. Functional calibration procedure for 3D knee joint angle description using inertial sensors. J Biomech. 2009;42(14):2330–5.CrossRefPubMed
33.
Liu T, Inoue Y, Shibata K. Development of a wearable sensor system for quantitative gait analysis. Measurement. 2009;42(7):978–88.CrossRef
34.
Turcot K, Aissaoui R, Boivin K, Pelletier M, Hagemeister N, de Guise JA. New accelerometric method to discriminate between asymptomatic subjects and patients with medial knee osteoarthritis during 3-d gait. IEEE Trans Biomed Eng. 2008;55(4):1415–22.CrossRefPubMed
35.
Preece SJ, Goulermas JY, Kenney LPJ, Howard D, Meijer K, Crompton R. Activity identification using body-mounted sensors-a review of classification techniques. Physiol Meas. 2009;30(4):R1–R33.CrossRefPubMed
36.
Kobsar D, Osis ST, Hettinga BA, Ferber R. Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners. J Biomech. 2014;47(10):2508–11.CrossRefPubMed
37.
Dalton A, Khalil H, Busse M, Rosser A, van Deursen R, Ólaighin G. Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic Huntington’s disease. Gait Posture. 2013;37(1):49–54.CrossRefPubMed
38.
Yang M, Zheng H, Wang H, McClean S, Hall J, Harris N. A machine learning approach to assessing gait patterns for complex regional pain syndrome. Med Eng Phys. 2012;34(6):740–6.CrossRefPubMed
Metadata
Title
Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis
Authors
Dylan Kobsar
Sean T. Osis
Jeffrey E. Boyd
Blayne A. Hettinga
Reed Ferber
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2017
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/s12984-017-0309-z

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