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Wearable sensors and Mobile Health (mHealth) technologies to assess and promote physical activity in stroke: a narrative review

Published online by Cambridge University Press:  15 February 2016

Shamala Thilarajah*
Affiliation:
School of Exercise Science, Australian Catholic University, Australia Department of Physiotherapy, Singapore General Hospital, Singapore
Ross A Clark
Affiliation:
School of Exercise Science, Australian Catholic University, Australia
Gavin Williams
Affiliation:
Epworth HealthCare, Richmond, Australia The University of Melbourne, Melbourne, Australia La Trobe University, Melbourne, Australia
*
Address for Correspondence: Shamala Thilarajah, School of Exercise Science, Australian Catholic University, Melbourne, Victoria3065 E-mail: [email protected].
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Abstract

Stroke is a leading cause of disability worldwide, with approximately one third of people left with permanent deficits impacting on their function. This may contribute to a physically inactive lifestyle and further associated health issues. Current research suggests that people after stroke are not meeting the recommended levels of physical activity, and are less active than people with other chronic illnesses. Thus, it is important to understand how to support people after stroke to uptake and maintain physical activity. Wearable sensors and mobile health (mHealth) technologies are a potential platform to measure and promote physical activity. Some of these technologies may incorporate behaviour change techniques such as real-time feedback. Although wearable activity trackers and smartphone technology are widely available, the feasibility and applicability of these technologies for people after stroke is unclear. This article reviews the devices available for assessment of physical activity in stroke and discusses the potential for advances in technology to promote physical activity in this population.

Type
Review Article
Copyright
Copyright © Australasian Society for the Study of Brain Impairment 2016 

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References

ACMA. (2013). Australian communications and media authority communications report 2011–12. Retrieved 30th September 2015 from http://www.acma.gov.auGoogle Scholar
Addison, O., Marcus, R. L., Lastayo, P. C., & Ryan, A. S. (2014). Intermuscular fat: a review of the consequences and causes. International Journal of Endocrinology, 2014, 309570-309570.Google Scholar
Ainsworth, B., Cahalin, L., Buman, M., & Ross, R. (2015). The current state of physical activity assessment tools. Progress in Cardiovascular Diseases, 57 (4), 387395.Google Scholar
Alzahrani, M. A., Dean, C. M., & Ada, L. (2009). Ability to negotiate stairs predicts free-living physical activity in community-dwelling people with stroke: an observational study. Australian Journal of Physiotherapy, 55 (4), 277281.Google Scholar
Alzahrani, M. A., Dean, C. M., Ada, L., Dorsch, S., & Canning, C. G. (2011). Mood and balance are associated with free-living physical activity of people after stroke residing in the community. Stroke Research and Treatment, 2012 (2012), 470648470656.Google Scholar
Årsand, E., Muzny, M., Bradway, M., Muzik, J., & Hartvigsen, G. (2015). Performance of the First Combined Smartwatch and Smartphone Diabetes Diary Application Study. Journal of diabetes science and technology, 9 (3), 556563.Google Scholar
Bassett, D. R. Jr, Ainsworth, B. E., Leggett, S. R., Mathien, C. A., Main, J. A., Hunter, D. C., & Duncan, G. E. (1996). Accuracy of five electronic pedometers for measuring distance walked. Medicine and Science in Sports and Exercise, 28 (8), 10711077.Google Scholar
Berlin, J. E., Storti, K. L., & Brach, J. S. (2006). Using activity monitors to measure physical activity in free-living conditions. Physical Therapy, 86 (8), 11371145.Google Scholar
Billinger, S. A., Arena, R., Bernhardt, J., Eng, J. J., Franklin, B. A., Johnson, C. M., . . . Roth, E. J. (2014). Physical activity and exercise recommendations for stroke survivors: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 45, 25322553.Google Scholar
Billinger, S. A., Coughenour, E., MacKay-Lyons, M. J., & Ivey, F. M. (2011). Reduced cardiorespiratory fitness after stroke: biological consequences and exercise-induced adaptations. Stroke Research and Treatment, 2012, 959120959131.Google Scholar
Bonato, P. (2009). Advances in wearable technology for rehabilitation. Studies in Health Technology & Informatics, 145, 145159.Google Scholar
Bonomi, A. G., Goris, A., Yin, B., & Westerterp, K. R. (2009). Detection of type, duration, and intensity of physical activity using an accelerometer. Medicine and Science in Sports and Exercise, 41 (9), 17701777.Google Scholar
Borschmann, K. (2011). Exercise protects bone after stroke, or does it? A narrative review of the evidence. Stroke Research and Treatment, 2012, 103697.Google Scholar
Borschmann, K., Pang, M. Y., Bernhardt, J., & Iuliano-Burns, S. (2012). Stepping towards prevention of bone loss after stroke: a systematic review of the skeletal effects of physical activity after stroke. International Journal of Stroke, 7 (4), 330335.Google Scholar
Bort-Roig, J., Gilson, N. D., Puig-Ribera, A., Contreras, R. S., & Trost, S. G. (2014). Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Medicine, 44 (5), 671686.Google Scholar
Butte, N. F., Ekelund, U., & Westerterp, K. R. (2012). Assessing physical activity using wearable monitors: measures of physical activity. Medicine and Science in Sports and Exercise, 44 (1 Suppl 1), S5-12.Google Scholar
Carroll, D. D., Courtney-Long, E. A., Stevens, A. C., Sloan, M. L., Lullo, C., Visser, S. N., . . . Brown, D. R. (2014). Vital signs: disability and physical activity—United States, 2009–2012. MMWR. Morbidity and Mortality Weekly Report, 63 (18), 407413.Google Scholar
Casey, M., Hayes, P. S., Glynn, F., Olaighin, G., Heaney, D., Murphy, A. W., & Glynn, L. G. (2014). Patients' experiences of using a smartphone application to increase physical activity: the SMART MOVE qualitative study in primary care. British Journal of General Practice, 64 (625), e500-508. doi: 10.3399/bjgp14X680989Google Scholar
Chen, K. Y., & Bassett, D. R. J. (2005). The technology of accelerometry-based activity monitors: current and future. Medicine and Science in Sports and Exercise, 37 (11)(Supplement), S490-S500.Google Scholar
Clark, R. A., Weragoda, N., Paterson, K., Telianidis, S., & Williams, G. (2014). A pilot investigation using global positioning systems into the outdoor activity of people with severe traumatic brain injury. Journal of NeuroEngineering and Rehabilitation, 11 (1).Google Scholar
Deloitte. (2014). The smartphone generation gap: over-55? there's no app for that. Retrieved 30th September 2015 from http://www2.deloitte.comGoogle Scholar
Dicianno, B. E., Parmanto, B., Fairman, A. D., Crytzer, T. M., Yu, D. X., Pramana, G., . . . Petrazzi, A. A. (2015). Perspectives on the Evolution of Mobile (mHealth) Technologies and Application to Rehabilitation. Physical Therapy, 95 (3), 397405. doi: 10.2522/ptj.20130534Google Scholar
Elsworth, C., Dawes, H., Winward, C., Howells, K., Collett, J., Dennis, A., . . . Wade, D. (2009). Pedometer step counts in individuals with neurological conditions. Clinical Rehabilitation, 23 (2), 171175.Google Scholar
Endres, M., Gertz, K., Lindauer, U., Katchanov, J., Schultze, J., Schröck, H., . . . Laufs, U. (2003). Mechanisms of stroke protection by physical activity. Annals of Neurology, 54 (5), 582590.Google Scholar
English, C., Manns, P. J., Tucak, C., & Bernhardt, J. (2014). Physical activity and sedentary behaviors in people with stroke living in the community: a systematic review. Physical Therapy, 94 (2), 185196.Google Scholar
English, C., McLennan, H., Thoirs, K., Coates, A., & Bernhardt, J. (2010). Reviews: Loss of skeletal muscle mass after stroke: a systematic review. International Journal of Stroke, 5 (5), 395402.Google Scholar
Field, M. J., Gebruers, N., Shanmuga Sundaram, T., Nicholson, S., & Mead, G. (2013). Physical activity after stroke: a systematic review and meta-analysis. ISRN Stroke, 2013, 113. doi: 10.1155/2013/464176Google Scholar
Fini, N. A., Holland, A. E., Keating, J., Simek, J., & Bernhardt, J. (2014). How is physical activity monitored in people following stroke? Disability and Rehabilitation(0), 115.Google Scholar
Fulk, G. D., Combs, S. A., Danks, K. A., Nirider, C. D., Raja, B., & Reisman, D. S. (2014). Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Physical Therapy, 94 (2), 222229.Google Scholar
Glynn, L. G., Hayes, P. S., Casey, M., Glynn, F., Alvarez-Iglesias, A., Newell, J., . . . Murphy, A. W. (2014). Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. British Journal of General Practice, 64 (624), e384-e391.Google Scholar
Haeuber, E., Shaughnessy, M., Forrester, L. W., Coleman, K. L., & Macko, R. F. (2004). Accelerometer monitoring of home-and community-based ambulatory activity after stroke. Archives of Physical Medicine and Rehabilitation, 85 (12), 19972001.Google Scholar
Hafer-Macko, C. E., Ryan, A. S., Ivey, F. M., & Macko, R. F. (2008). Skeletal muscle changes after hemiparetic stroke and potential beneficial effects of exercise intervention strategies. Journal of Rehabilitation Research and Development, 45 (2), 261.Google Scholar
Hale, L. A., Pal, J., & Becker, I. (2008). Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Archives of Physical Medicine and Rehabilitation, 89 (9), 17651771.Google Scholar
Haskell, W. L., Lee, I.-M., Pate, R. R., Powell, K. E., Blair, S. N., Franklin, B. A., . . . Bauman, A. (2007). Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation, 116 (9), 10811090.Google Scholar
Infographic: 2013 mobile growth statistics. (2013). Retrieved 30th September 2015 from http://www.digitalbuzzblog.com/infographic-2013-mobile-growth-statistics/Google Scholar
Ivey, F. M., Ryan, A. S., Hafer-Macko, C. E., Goldberg, A. P., & Macko, R. F. (2007). Treadmill aerobic training improves glucose tolerance and indices of insulin sensitivity in disabled stroke survivors: a preliminary report. Stroke, 38 (10), 27522758.Google Scholar
Karabulut, M., Crouter, S. E., & Bassett, D. R. Jr (2005). Comparison of two waist-mounted and two ankle-mounted electronic pedometers. European Journal of Applied Physiology, 95 (4), 335343.Google Scholar
Kuys, S. S., Clark, C., & Morris, N. (2014). Portable multisensor activity monitor (SenseWear) lacks accuracy in energy expenditure measurement during treadmill walking following stroke. International Journal of Neurorehabilitation, 1 (1), 1000101-1000101-1000101-1000105.Google Scholar
Liu, L., Miguel Cruz, A., Rios Rincon, A., Buttar, V., Ranson, Q., & Goertzen, D. (2014). What factors determine therapists' acceptance of new technologies for rehabilitation-a study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disability and Rehabilitation (0), 19.Google Scholar
Mackey, D. C., Manini, T. M., Schoeller, D. A., Koster, A., Glynn, N. W., Goodpaster, B. H., . . . Cummings, S. R. (2011). Validation of an armband to measure daily energy expenditure in older adults. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 66A (10), 11081113.Google Scholar
Manns, P. J., & Haennel, R. G. (2012). SenseWear armband and stroke: validity of energy expenditure and step count measurement during walking. Stroke Research and Treatment, 2012, 18.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, 18. doi: 10.5402/2012/823180Google Scholar
Melanson, E. L., Knoll, J. R., Bell, M. L., Donahoo, W. T., Hill, J., Nysse, L. J., . . . Levine, J. A. (2004). Commercially available pedometers: considerations for accurate step counting. Preventive Medicine, 39 (2), 361368.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), 16601662.Google Scholar
Morillo, L. M. S., Gonzalez-Abril, L., Ramirez, J. A. O., & de la Concepcion, M. A. A. (2015). Low energy physical activity recognition system on smartphones. Sensors, 15 (3), 51635196.Google Scholar
Morris, J. H., MacGillivray, S., & Mcfarlane, S. (2014). Interventions to promote long-term participation in physical activity after stroke: a systematic review of the literature. Archives of Physical Medicine and Rehabilitation, 95 (5), 956967.Google Scholar
Motl, R. W., McAuley, E., Snook, E. M., & Scott, J. A. (2005). Accuracy of two electronic pedometers for measuring steps taken under controlled conditions among ambulatory individuals with multiple sclerosis. Multiple Sclerosis, 11 (3), 343345.Google Scholar
Mudge, S., Stott, N. S., & Walt, S. E. (2007). Criterion validity of the StepWatch Activity Monitor as a measure of walking activity in patients after stroke. Archives of Physical Medicine and Rehabilitation, 88 (12), 17101715.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), 108114.Google Scholar
Pak, P., Jawed, H., Tirone, C., Lamb, B., Cott, C., Brunton, K., . . . Inness, E. L. (2015). Incorporating research technology into the clinical assessment of balance and mobility: perspectives of physiotherapists and people with stroke. Physiotherapy Canada, 67 (1), 18.Google Scholar
Pambianco, G., Wing, R. R., & Robertson, R. (1990). Accuracy and reliability of the Caltrac accelerometer for estimating energy expenditure. Medicine and Science in Sports and Exercise, 22 (6), 858862.Google Scholar
Pang, M. Y., Charlesworth, S. A., Lau, R. W., & Chung, R. C. (2013). Using aerobic exercise to improve health outcomes and quality of life in stroke: evidence-based exercise prescription recommendations. Cerebrovascular Diseases, 35 (1), 722.Google Scholar
Rawassizadeh, R., Price, B. A., & Petre, M. (2014). Wearables: has the age of smartwatches finally arrived? Communications of the ACM, 58 (1), 4547.Google Scholar
Rodríguez, D. A., Brown, A. L., & Troped, P. J. (2005). Portable global positioning units to complement accelerometry-based physical activity monitors. Medicine and Science in Sports and Exercise, 37 (11 Suppl), S572-581.Google Scholar
Rogers, W. A., & Fisk, A. D. (2010). Toward a psychological science of advanced technology design for older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, gbq065.Google Scholar
Rothney, M. P., Schaefer, E. V., Neumann, M. M., Choi, L., & Chen, K. Y. (2008). Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers. Obesity, 16 (8), 19461952.Google Scholar
Samson, K. (2015). Wearing the detectives: wristbands, smartwatches, and other wearable devices allow for more real-time monitoring of seizures and other neurologic symptoms—and, possibly, more precise treatment. Neurology Now, 11 (4), 3436.Google Scholar
Scherbakov, N., & Doehner, W. (2011). Sarcopenia in stroke—facts and numbers on muscle loss accounting for disability after stroke. Journal of Cachexia, Sarcopenia and Muscle, 2 (1), 58.Google Scholar
Schneider, P. L., Crouter, S. E., Lukajic, O., & Bassett, D. R. (2003). Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Medicine and Science in Sports and Exercise, 35 (10), 17791784.Google Scholar
Sharma, V., Mankodiya, K., De La Torre, F., Zhang, A., Ryan, N., Ton, T. G., . . . Jain, S. (2014). SPARK: personalized Parkinson disease interventions through synergy between a smartphone and a smartwatch Design, User Experience, and Usability. User Experience Design for Everyday Life Applications and Services (pp. 103114): Springer.Google Scholar
Smith, A. C., Saunders, D. H., & Mead, G. (2012). Cardiorespiratory fitness after stroke: a systematic review. International Journal of Stroke, 7 (6), 499510.Google Scholar
Vähä-Ypyä, H., Vasankari, T., Husu, P., Suni, J., & Sievänen, H. (2015). A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clinical Physiology and Functional Imaging, 35 (1), 6470.Google Scholar
Vanroy, C., Vissers, D., Cras, P., Beyne, S., Feys, H., Vanlandewijck, Y., & Truijen, S. (2013). Physical activity monitoring in stroke: SenseWear Pro2 Activity accelerometer versus Yamax Digi-Walker SW-200 Pedometer. Disability and Rehabilitation, 36 (20), 16951703.Google Scholar
Veerbeek, M. A., Voshaar, R. C. O., & Pot, A. M. (2012). Clinicians’ perspectives on a web-based system for routine outcome monitoring in old-age psychiatry in the Netherlands. Journal of Medical Internet Research, 14 (3).Google Scholar
The World in 2014: ICT Facts and Figures. (2014). Retrieved 31st March 2015 from http://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspxGoogle Scholar
Yang, C.-H., Maher, J. P., & Conroy, D. E. (2015). Implementation of behavior change techniques in mobile applications for physical activity. American Journal of Preventive Medicine, 48 (4), 452455.Google Scholar
Zhang, K., Werner, P., Sun, M., Pi-Sunyer, F. X., & Boozer, C. N. (2003). Measurement of human daily physical activity. Obesity Research, 11 (1), 3340.Google Scholar