Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review

Denise M Peters, Emma S O'Brien, Kira E Kamrud, Shawn M Roberts, Talia A Rooney, Kristen P Thibodeau, Swapna Balakrishnan, Nancy Gell, Sambit Mohapatra, Denise M Peters, Emma S O'Brien, Kira E Kamrud, Shawn M Roberts, Talia A Rooney, Kristen P Thibodeau, Swapna Balakrishnan, Nancy Gell, Sambit Mohapatra

Abstract

Background: Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

Methods: We performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

Results: The majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

Conclusion: The use of various wearable technologies has enabled researchers and clinicians to monitor patients' activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients' lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Keywords: Gait; Mobility; Rehabilitation; Sensors; Stroke; Wearable.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flowchart for systematic review

References

    1. Hayhurst C. Physical therapy and the internet of things: american physical therapy association; 2016. .
    1. Riebe DEJ, Liguori G, Magal M. Acsm's guidelines for exercise testing and prescription. 10. Philadelphia: Wolters Kluwer; 2018.
    1. Allet L, Knols RH, Shirato K, Bruin EDD. Wearable systems for monitoring mobility-related activities in chronic disease: a systematic review. Sensors. 2010;10(10):9026–9052. doi: 10.3390/s101009026.
    1. Choi J, Lee JH, Vittinghoff E, Fukuoka Y. Mhealth physical activity intervention: a randomized pilot study in physically inactive pregnant women. Matern Child Health J. 2016;20(5):1091–1101. doi: 10.1007/s10995-015-1895-7.
    1. Gell N, Grover WK, Humble M, Sexton M, Dittus K. Efficacy, feasibility, and acceptability of a novel technology-based intervention to support physical activity in cancer survivors. Supportive Care Cancer. 2016;25:4.
    1. Haeuber E, Shaughnessy M, Forrester LW, Coleman KL, Macko RF. Accelerometer monitoring of home- and community-based ambulatory activity after stroke. Arch Phys Med Rehabil. 2004;85(12):1997–2001. doi: 10.1016/j.apmr.2003.11.035.
    1. Harbottle V, Bennett J, Duong C, Foster H, Mcerlane F. Feasibility of wearable technologies in children and young people with juvenile idiopathic arthritis. Rheumatology. 2018 doi: 10.1007/s43441-021-00278-9.
    1. Hubble RP, Naughton GA, Silburn PA, Cole MH. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review. Plos One. 2015;10(4):E0123705. doi: 10.1371/journal.pone.0123705.
    1. Mcmahon SK, Lewis B, Oakes M, Guan W, Wyman JF, Rothman AJ. Older adults' experiences using a commercially available monitor to self-track their physical activity. JMIR Mhealth Uhealth. 2016;4(2):E35. doi: 10.2196/mhealth.5120.
    1. Napolitano MA, Borradaile KE, Lewis BA, Whiteley JA, Longval JL, Parisi AF, et al. Accelerometer use in a physical activity intervention trial. Contemp Clin Trials. 2010;31(6):514–23. doi: 10.1016/j.cct.2010.08.004.
    1. Schaffer K, Panneerselvam N, Loh KP, Herrmann R, Kleckner IR, Dunne RF, et al. Systematic review of randomized controlled trials of exercise interventions using digital activity trackers in patients with cancer. J Natl Compr Canc Netw. 2019;17(1):57–63. doi: 10.6004/jnccn.2018.7082.
    1. Weiss A, Brozgol M, Dorfman M, Herman T, Shema S, Giladi N, et al. Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair. 2013;27(8):742–752. doi: 10.1177/1545968313491004.
    1. Weiss A, Herman T, Giladi N, Hausdorff JM. New evidence for gait abnormalities among Parkinson's disease patients who suffer from freezing of gait: insights using a body-fixed sensor worn for 3 days. J Neural Transm (Vienna) 2015;122(3):403–410. doi: 10.1007/s00702-014-1279-y.
    1. Weiss A, Mirelman A, Buchman AS, Bennett DA, Hausdorff JM. Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go. PLoS ONE. 2013;8(7):e68885. doi: 10.1371/journal.pone.0068885.
    1. Fini NA, Holland AE, Keating J, Simek J, Bernhardt J. How physically active are people following stroke? Systematic review and quantitative synthesis. Phys Ther. 2017;97(7):707–717. doi: 10.1093/ptj/pzx038.
    1. Degroote L, De Bourdeaudhuij I, Verloigne M, Poppe L, Crombez G. The accuracy of smart devices for measuring physical activity in daily life: validation study. JMIR Mhealth Uhealth. 2018;6(12):e10972. doi: 10.2196/10972.
    1. Tedesco S, Sica M, Ancillao A, Timmons S, Barton J, O’flynn B. Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults. Plos One. 2019;14(5):e0216891. doi: 10.1371/journal.pone.0216891.
    1. Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, et al. Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J Med Internet Res. 2018;20(3):e110. doi: 10.2196/jmir.9157.
    1. Tshiswaka ID, Bennett C, Franklin C. Effects of walking trainings on walking function among stroke survivors: a systematic review. Int J Rehabil Res. 2018;41(1):1–13. doi: 10.1097/MRR.0000000000000250.
    1. Taylor-Piliae RE, Latt LD, Hepworth JT, Coull BM. Predictors of gait velocity among community-dwelling stroke survivors. Gait Posture. 2012;35(3):395–399. doi: 10.1016/j.gaitpost.2011.10.358.
    1. Hill K, Ellis P, Bernhardt J, Maggs P, Hull S. Balance and mobility outcomes for stroke patients: a comprehensive audit. Aust J Physiother. 1997;43(3):173–180. doi: 10.1016/S0004-9514(14)60408-6.
    1. Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS. Recovery of walking function in stroke patients: the copenhagen stroke study. Arch Phys Med Rehabil. 1995;76(1):27–32. doi: 10.1016/S0003-9993(95)80038-7.
    1. Parvataneni K, Olney SJ, Brouwer B. Changes in muscle group work associated with changes in gait speed of persons with stroke. Clin Biomech (Bristol, Avon) 2007;22(7):813–820. doi: 10.1016/j.clinbiomech.2007.03.006.
    1. Wonsetler EC, Bowden MG. A systematic review of mechanisms of gait speed change post-stroke. Part 1: spatiotemporal parameters and asymmetry ratios. Top Stroke Rehabil. 2017;24(6):435–46. doi: 10.1080/10749357.2017.1285746.
    1. Schmid A, Duncan PW, Studenski S, Lai SM, Richards L, Perera S, et al. Improvements in speed-based gait classifications are meaningful. Stroke. 2007;38(7):2096–2100. doi: 10.1161/STROKEAHA.106.475921.
    1. Fritz SL, Peters DM, Greene JV. Measuring walking speed: clinical feasibility and reliability. Topics Geriatric Rehabil. 2012;28(2):91–96. doi: 10.1097/TGR.0b013e31823d9c22.
    1. Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314–322. doi: 10.1123/japa.2013-0236.
    1. Fritz S, Lusardi M. White paper: "walking speed: the sixth vital sign". J Geriatr Phys Ther. 2009;32(2):46–49. doi: 10.1519/00139143-200932020-00002.
    1. Kim CM, Eng JJ. Magnitude and pattern of 3d kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004;20(2):140–146. doi: 10.1016/j.gaitpost.2003.07.002.
    1. Cho KH, Lee WH. Changes of spatio-temporal gait parameters according to experience falls in post-stroke patients. Phys Ther Rehabil Sci. 2012;1(1):22–27.
    1. Goldie PA, Matyas TA, Evans OM. Gait after stroke: initial deficit and changes in temporal patterns for each gait phase. Arch Phys Med Rehabil. 2001;82(8):1057–1065. doi: 10.1053/apmr.2001.25085.
    1. Jonsdottir J, Recalcati M, Rabuffetti M, Casiraghi A, Boccardi S, Ferrarin M. Functional resources to increase gait speed in people with stroke: strategies adopted compared to healthy controls. Gait Posture. 2009;29(3):355–359. doi: 10.1016/j.gaitpost.2009.01.008.
    1. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2020 update: a report from the American heart association. Circulation. 2020;141(9):e139–e596. doi: 10.1161/CIR.0000000000000757.
    1. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil. 2012;9:21. doi: 10.1186/1743-0003-9-21.
    1. Porciuncula F, Roto AV, Kumar D, Davis I, Roy S, Walsh CJ, et al. Wearable movement sensors for rehabilitation: a focused review of technological and clinical advances. Pm R. 2018;10(9 Suppl 2):S220–S232. doi: 10.1016/j.pmrj.2018.06.013.
    1. Mansfield A, Wong JS, Bryce J, Brunton K, Inness EL, Knorr S, et al. Use of accelerometer-based feedback of walking activity for appraising progress with walking-related goals in inpatient stroke rehabilitation: a randomized controlled trial. Neurorehabil Neural Repair. 2015;29(9):847–857. doi: 10.1177/1545968314567968.
    1. Post-Stroke Rehabilitation Guideline Panel. Gresham Ge. Post-Stroke Rehabilitation. Gaithersburg, Md.: Aspen Publishers; 1996. Xviii, Pp. 248.
    1. Petraglia F, Scarcella L, Pedrazzi G, Brancato L, Puers R, Costantino C. Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. Eur J Phys Rehabil Med. 2019;55(2):265–280. doi: 10.23736/S1973-9087.18.05306-6.
    1. Muro-De-La-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems. Highlighting Clin Appl Sensors (Basel) 2014;14(2):3362–3394. doi: 10.3390/s140203362.
    1. Trojaniello D, Ravaschio A, Hausdorff JM, Cereatti A. Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects. Gait Posture. 2015;42(3):310–316. doi: 10.1016/j.gaitpost.2015.06.008.
    1. Clay L, Webb M, Hargest C, Adhia DB. Gait quality and velocity influences activity tracker accuracy in individuals post-stroke. Top Stroke Rehabil. 2019;26(6):412–417. doi: 10.1080/10749357.2019.1623474.
    1. Taraldsen K, Askim T, Sletvold O, Einarsen EK, Bjastad KG, Indredavik B, et al. Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function. Phys Ther. 2011;91(2):277–285. doi: 10.2522/ptj.20100159.
    1. Kim K, Kim YM, Kim EK. Correlation between the activities of daily living of stroke patients in a community setting and their quality of life. J Phys Ther Sci. 2014;26(3):417–9. doi: 10.1589/jpts.26.417.
    1. Ramos-Lima MJM, Brasileiro IC, Lima TL, Braga-Neto P. Quality of life after stroke: impact of clinical and sociodemographic factors. Clinics (Sao Paulo) 2018;73:E418. doi: 10.6061/clinics/2017/e418.
    1. Van Mierlo ML, Van Heugten CM, Post MW, Hajos TR, Kappelle LJ, Visser-Meily JM. Quality of life during the first two years post stroke: The Restore4stroke Cohort Study. Cerebrovasc Dis. 2016;41(1–2):19–26. doi: 10.1159/000441197.
    1. Yang YN, Kim BR, Uhm KE, Kim SJ, Lee S, Oh-Park M, et al. Life space assessment in stroke patients. Ann Rehabil Med. 2017;41(5):761–768. doi: 10.5535/arm.2017.41.5.761.
    1. Khan KS, Kunz R, Kleijnen J, Antes G. Five steps to conducting a systematic review. J R Soc Med. 2003;96(3):118–21. doi: 10.1177/014107680309600304.
    1. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (Prisma-P) 2015 statement. Syst Rev. 2015;4:1. doi: 10.1186/2046-4053-4-1.
    1. Pedro Scale (English): Institute For Musculoskeletal Health, School Of Public Health, University Of Sydney; (Updated April 6 2020). .
    1. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The strengthening the reporting of observational studies in epidemiology (strobe) statement: guidelines for reporting observational studies. Plos Med. 2007;4(10):E296. doi: 10.1371/journal.pmed.0040296.
    1. Dorsch AK, Thomas S, Xu X, Kaiser W, Dobkin BH, Investigators S. Sirract: an international randomized clinical trial of activity feedback during inpatient stroke rehabilitation enabled by wireless sensing. Neurorehabil Neural Repair. 2015;29(5):407–415. doi: 10.1177/1545968314550369.
    1. English C, Healy GN, Olds T, Parfitt G, Borkoles E, Coates A, et al. Reducing sitting time after stroke: a phase II safety and feasibility randomized controlled trial. Arch Phys Med Rehabil. 2016;97(2):273–280. doi: 10.1016/j.apmr.2015.10.094.
    1. Givon N, Zeilig G, Weingarden HRD. Video-games used in a group setting is feasible and effective to improve indicators of physical activity in individuals with chronic stroke: a randomized controlled trial. Clin Rehabil. 2016;30(4):383–392. doi: 10.1177/0269215515584382.
    1. Danks KA, Pohlig R, Reisman DS. Combining fast-walking training and a step activity monitoring program to improve daily walking activity after stroke: a preliminary study. Arch Phys Med Rehabil. 2016;97(9 Suppl):S185–S193. doi: 10.1016/j.apmr.2016.01.039.
    1. Kanai M, Izawa KP, Kobayashi M, Onishi A, Kubo H, Nozoe M, et al. Effect of accelerometer-based feedback on physical activity in hospitalized patients with ischemic stroke: a randomized controlled trial. Clin Rehabil. 2018;32(8):1047–1056. doi: 10.1177/0269215518755841.
    1. Prajapati SK, Gage WH, Brooks D, Black SE, Mcilroy WE. A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabil Neural Repair. 2011;25(1):6–14. doi: 10.1177/1545968310374189.
    1. Tramontano M, Morone G, Curcio A, Temperoni G, Medici A, Morelli D, et al. Maintaining gait stability during dual walking task: effects of age and neurological disorders. Eur J Phys Rehabil Med. 2017;53(1):7–13.
    1. Wang C, Kim Y, Shin H, Min SD. Preliminary clinical application of textile insole sensor for hemiparetic gait pattern analysis. Sensors (Basel). 2019;19(18):3950. doi: 10.3390/s19183950.
    1. Seo M, Shin MJ, Park TS, Park JH. Clinometric gait analysis using smart insoles in patients with hemiplegia after stroke: pilot study. Jmir Mhealth Uhealth. 2020;8(9):e22208. doi: 10.2196/22208.
    1. Paul L, Wyke S, Brewster S, Sattar N, Gill JM, Alexander G, et al. Increasing physical activity in stroke survivors using starfish, an interactive mobile phone application: a pilot study. Top Stroke Rehabil. 2016;23(3):170–177. doi: 10.1080/10749357.2015.1122266.
    1. Shin SY, Lee RK, Spicer P, Sulzer J. Quantifying dosage of physical therapy using lower body kinematics: a longitudinal pilot study on early post-stroke individuals. J Neuroeng Rehabil. 2020;17(1):15. doi: 10.1186/s12984-020-0655-0.
    1. Howick J, Chalmers I, Glasziou P, Greenhalgh T, Heneghan C, Liberati A, et al. Explanation Of The 2011 Oxford Centre For Evidence-Based Medicine (Ocebm) Levels Of Evidence (Background Document): Oxford Centre For Evidence-Based Medicine. .
    1. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS. Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther. 2014;94(2):222–229. doi: 10.2522/ptj.20120525.
    1. Godfrey A, Hetherington V, Shum H, Bonato P, Lovell NH, Stuart S. From A to Z: Wearable technology explained. Maturitas. 2018;113:40–47. doi: 10.1016/j.maturitas.2018.04.012.
    1. Parker J, Powell L, Mawson S. Effectiveness of upper limb wearable technology for improving activity and participation in adult stroke survivors: systematic review. J Med Internet Res. 2020;22(1):e15981. doi: 10.2196/15981.

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