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Are Accelerometers and GPS Devices Valid, Reliable and Feasible Tools for Measurement of Community Ambulation After Stroke?1

Published online by Cambridge University Press:  08 June 2016

Niruthikha Mahendran ,
Suzanne S. Kuys ,
Emma Downie ,
Phoebe Ng  and
Sandra G. Brauer
Niruthikha Mahendran*
Affiliation:
Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia
Suzanne S. Kuys
Affiliation:
School of Physiotherapy, Faculty of Health Sciences, Australian Catholic University, Brisbane, Queensland, Australia Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia
Emma Downie
Affiliation:
Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia
Phoebe Ng
Affiliation:
Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia
Sandra G. Brauer
Affiliation:
Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia
*
Address for correspondence: Dr Niruthikha Mahendran, 12D47, University of Canberra, University Drive, Bruce, ACT 2617. E-mail: [email protected]
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Abstract

Purpose: To determine validity, reliability and feasibility of accelerometers (ActivPAL™, Sensewear Pro2 Armband) and portable global positioning systems (GPS) (Garmin Forerunner 405CX) for community ambulation measurement after stroke.

Methods: Fifteen community-dwelling stroke survivors attended two sessions; completing a 6-minute walk, treadmill walking, and 200-m outdoor circuit. Feasibility was determined by wearing devices over four days. Measures collected included step count, time spent walking, distance, energy expenditure and location. Intra-class correlation coefficients (ICC), Bland–Altman plots and absolute percentage of error (APE) were used to determine validity and reliability.

Results: ActivPAL™ had excellent validity and reliability for most measures (ICC: 0.821–0.999, APE: 0%–11.1%), except for good-excellent findings at speeds < 0.42 m/s (ICC: 0.659–0.894, APE: 1.6%–11.1%). Sensewear had missing values for 23% of recordings and high error for all measures. GPS demonstrated excellent validity and reliability for time spent walking and step count (ICC: 0.805–0.999, APE: 0.9%–10%), and 100% accuracy for location. However, it was not valid or reliable for distance (ICC = −0.139, APE = 23.8%). All devices appeared feasible for community ambulation measurement with assistance for setup and data analysis.

Conclusions: ActivPAL™ and Garmin GPS appear valid, reliable and feasible tools for community ambulation measurement after stroke, except for distance. Sensewear demonstrated poor validity and reliability when worn on the paretic arm.

Keywords

Strokecommunity ambulationwalkingreliability and validityaccelerometerGPS
Type
Themed articles on Stroke
Information
Brain Impairment , Volume 17 , Issue 2: Themed issue on Stroke , September 2016 , pp. 151 - 161
Copyright
Copyright © Australasian Society for the Study of Brain Impairment 2016 

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Footnotes

1

This work has been presented at the International Society of Posture and Gait Research conference, Japan (2013) and Smart Strokes, Australia (2013).

References

Almeida, G.J.M., Wasko, M.C.M., Jeong, K., Moore, C.G., & Piva, S.R. (2011). Physical activity measured by the SenseWear Armband in women with rheumatoid arthritis. Physical Therapy, 91 (9), 1367–1376.CrossRefGoogle ScholarPubMed
Andre, D., Pelletier, R., Farringdon, J., Safier, S., Talbot, W., Stone, R., & Teller, A. (2006). The development of the SenseWear® armband, a revolutionary energy assessment device to assess physical activity and lifestyle. 2013, Retrieved from http://powerhousegymaiea.dotfit.com/sites/63/templates/categories/images/178/Dev_SenseWear_article.pdf.Google Scholar
Bayat, R., Barbeau, H., & Lamontagne, A. (2005). Speed and temporal-distance adaptations during treadmill and overground walking following stroke. Neurorehabilitation & Neural Repair, 19 (2), 115–124.Google Scholar
Bijleveld-Uitman, M., van de Port, I., & Kwakkel, G. (2013). Is gait speed or walking distance a better predictor for community walking after stroke? Journal of Rehabilitation Medicine, 45 (6), 535–540.CrossRefGoogle ScholarPubMed
Bland, J.M., & Altman, D.G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1 (8), 307–310.CrossRefGoogle ScholarPubMed
Brouwer, B., Parvataneni, K., & Olney, S.J. (2009). A comparison of gait biomechanics and metabolic requirements of overground and treadmill walking in people with stroke. Clinical Biomechanics, 24, 729–734.CrossRefGoogle ScholarPubMed
Dahlgren, G., Carlsson, D., Moorhead, A., Häger-Ross, C., & McDonough, S.M. (2010). Test-retest reliability of step counts with the ActivPAL™ device in common daily activities. Gait & Posture, 32 (3), 386–390.CrossRefGoogle ScholarPubMed
English, C., Manns, P.J., Tucak, C., & Bernhardt, J. (2014). Physical activity and sedentary behaviors in community-dwelling stroke survivors: A systematic review. Physical Therapy, 94 (2), 185–196.Google Scholar
Enright, P.L. (2003). The six-minute walk test. Respiratory Care, 48 (8), 783–785.Google Scholar
Evans, C.C., Hanke, T.A., Zielke, D., Keller, S., & Ruroede, K. (2012). Monitoring community mobility with global positioning system technology after a stroke: A case study. Journal of Neurologic Physical Therapy, 36 (2), 68–78.Google Scholar
Fini, N.A., Holland, A.E., Keating, J., Simek, J., & Bernhardt, J. (2015). How is physical activity monitored in people following stroke? Disability and rehabilitation, 37 (19), 1717–1731.CrossRefGoogle Scholar
Folstein, M., Folstein, S., & McHugh, P. (1975). Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle Scholar
Gebruers, N., Vanroy, C., Truijen, S., Engelborghs, S., & De Deyn, P.P. (2010). Monitoring of physical activity after stroke: A systematic review of accelerometry-based measures. Archives of Physical Medicine and Rehabilitation, 91 (2), 288–297.Google Scholar
Grant, P.M., Dall, P.M., Mitchell, S.L., & Granat, M.H. (2008). Activity-monitor accuracy in measuring step number and cadence in community-dwelling older adults. Journal of Aging & Physical Activity, 16 (2), 201–214.Google Scholar
Jakicic, J.M., Marcus, M., Gallagher, K.I., Randall, C., Thomas, E., Goss, F.L., & Robertson, R.J. (2004). Evaluation of the SenseWear Pro Armband to assess energy expenditure during exercise. Medicine & Science in Sports & Exercise, 36 (5), 897–904.Google Scholar
Keenan, M.A., Perry, J., & Jordan, C. (1984). Factors affecting balance and ambulation following stroke. Clinical Orthopaedics & Related Research, 182, 165–171.Google Scholar
Le Faucheur, A., Abraham, P., Jaquinandi, V., Bouye, P., Saumet, J.L., & Noury-Desvaux, B. (2007). Study of human outdoor walking with a low-cost GPS and simple spreadsheet analysis. Medicine & Science in Sports & Exercise, 39 (9), 1570–1578.CrossRefGoogle ScholarPubMed
Le Faucheur, A., Abraham, P., Jaquinandi, V., Bouye, P., Saumet, J.L., & Noury-Desvaux, B. (2008). Measurement of walking distance and speed in patients with peripheral arterial disease: A novel method using a global positioning system. Circulation, 117 (7), 897–904.CrossRefGoogle ScholarPubMed
Lord, S.E., McPherson, K., McNaughton, H.K., Rochester, L., & Weatherall, M. (2004). Community ambulation after stroke: How important and obtainable is it and what measures appear predictive? Archives of Physical Medicine and Rehabilitation, 85 (2), 234–239.Google Scholar
Maddison, R., & Ni Mhurchu, C.N. (2009). Global positioning system: A new opportunity in physical activity measurement. International Journal of Behavioral Nutrition and Physical Activity, 6, 73–80.Google Scholar
Manns, P.J., & Haennel, R. (2012). SenseWear armband and stroke: Validity of energy expenditure and step count measurement during walking. Stroke Research and Treatment, 2012, 1–8.Google Scholar
Manns, P.J., Tomczak, C.R., Jelani, A., & Haennel, R.G. (2010). Oxygen uptake kinetics: Associations with ambulatory activity and physical functional performance in stroke survivors. Journal of Rehabilitation Medicine, 42 (3), 259–264.Google Scholar
McCluskey, A., Ada, L., Dean, C.M., & Vargas, J. (2012). Feasibility and validity of a wearable GPS device for measuring outings after stroke. ISRN Rehabilitation, 2012 (Article ID 823180), pp. 1–8.Google Scholar
Moore, S.A., Hallsworth, K., Bluck, L.J., Ford, G.A., Rochester, L., & Trenell, M.I. (2012). Measuring energy expenditure after stroke: Validation of a portable device. Stroke, 43 (6), 1660–1662.Google Scholar
Moore, S.A., Hallsworth, K., Plotz, T., Ford, G.A., Rochester, L., & Trenell, M.I. (2013). Physical activity, sedentary behaviour and metabolic control following stroke: A cross-sectional and longitudinal study. PLoS One, 8 (1), e55263.Google Scholar
Murphy, S.L. (2009). Review of physical activity measurement using accelerometers in older adults: Considerations for research design and conduct. Preventive Medicine, 48 (2), 108–114.Google Scholar
Pandey, A., Bretz, M., Jensen, A., Henasey, D., Troung, A., Lushbough, C., . . . Levine, , S.R. (2013). Abstract TP317: Mobile technology profile of stroke survivors and caregivers: Preliminary results from a national survey. Stroke, 44 (2), Abstract TP317.Google Scholar
Patla, A.E., & Shumway-Cook, A. (1999). Dimensions of mobility: Defining the complexity and difficulty associated with community mobility. Journal of Aging and Physical Activity, 7, 7–19.Google Scholar
Perry, J., Garrett, M., Gronley, J.K., & Mulroy, S.J. (1995). Classification of walking handicap in the stroke population. Stroke, 26 (6), 982–989.CrossRefGoogle ScholarPubMed
Rankin, G., & Stokes, M. (1998). Reliability of assessment tools in rehabilitation: An illustration of appropriate statistical analyses. Clinical Rehabilitation, 12 (3), 187–199.CrossRefGoogle ScholarPubMed
Robinson, C.A., Shumway-Cook, A., Matsuda, P.N., & Ciol, M.A. (2011). Understanding physical factors associated with participation in community ambulation following stroke. Disability & Rehabilitation, 33 (12), 1033–1042.Google Scholar
Rodriguez, D.A., Brown, A.L., & Troped, P.J. (2005). Portable global positioning units to complement accelerometry-based physical activity monitors. Medicine & Science in Sports & Exercise, 37 (Suppl. 11), S572–581.Google Scholar
Roos, M.A., Rudolph, K.S., & Reisman, D.S. (2012). The structure of walking activity in people after stroke compared with older adults without disability: A cross-sectional study. Physical Therapy, 92 (9), 1141–1147.CrossRefGoogle ScholarPubMed
Ryan, C.G., Grant, P.M., Tigbe, W.W., & Granat, M.H. (2006). The validity and reliability of a novel activity monitor as a measure of walking. British Journal of Sports Medicine, 40, 779–784.CrossRefGoogle ScholarPubMed
Shumway-Cook, A., Patla, A.E., Stewart, A., Ferrucci, L., Ciol, M.A., & Guralnik, J.M. (2002). Environmental demands associated with community mobility in older adults with and without mobility disabilities. Physical Therapy, 82 (7), 670–681.CrossRefGoogle ScholarPubMed
Taraldsen, K., Askim, T., Sletvold, O., Einarsen, E.K., Bjastad, K.G., Indredavik, B., & Helbostad, J.L. (2011). Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function. Physical Therapy, 91 (2), 277–285.Google Scholar
Vanroy, C., Vissers, D., Cras, P., Beyne, S., Feys, H., Vanlandewijck, Y., & Truijen, S. (2014). Physical activity monitoring in stroke: SenseWear Pro2 activity accelerometer versus yamax digi-walker SW-200 pedometer. Disability & Rehabilitation, 36 (20), 1695–1703.Google Scholar
Viosca, E., Lafuente, R., Martinez, J., Almagro, P., Gracia, A., & Gonzalez, C. (2005). Walking recovery after an acute stroke: Assessment with a new functional classification and the barthel index. Archives of Physical Medicine Rehabilitation, 86 (6), 1239–1244.Google Scholar
Wagenaar, R.C., & Beek, W.J. (1992). Hemiplegic gait: A kinematic analysis using walking speed as a bias. Journal of Biomechanics, 25 (9), 1007–1015.Google Scholar
Webber, S.C., & Porter, M.M. (2009). Monitoring mobility in older adults using global positioning system (GPS) watches and accelerometers: A feasibility study. Journal of Aging & Physical Activity, 17 (4), 455–467.Google Scholar
Weir, J.P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. Journal of Strength and Conditioning Research, 19 (1), 231–240.Google Scholar