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Developing a smartphone application, triaxial accelerometer-based, to quantify static and dynamic balance deficits in patients with cerebellar ataxias

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Abstract

Background

Cerebellar ataxia is characterized by difficulty in the planning of movement and lack of anticipatory postural adjustments, which can result in deficits of balance. Being able to have quantitative measurements in clinical practice, to detect any improvements on balance resulting from new rehabilitation treatments or experimental drugs is very important.

Aim

The purpose of this study was to develop an application (APP) able to assess static and dynamic balance in patients with cerebellar ataxias (CA). The APP that works by a wearable device (smartphone) placed at the breastbone level and immobilized by an elastic band, measures the body sway by means of a triaxial accelerometer.

Methods

We investigated 40 CA patients and 80 healthy subjects. All patients were clinically evaluated using the “Berg Balance Scale” (BBS) and the “Scale for the Assessment and Rating of Ataxia” (SARA). Balance impairment was quantitatively assessed using a validated static balance evaluating systems, i.e., Techno-body Pro-Kin footboard. All participants underwent static and dynamic balance assessments using the new APP.

Results

We observed a strong correlation between the APP measurements and the score obtained with the BBS, SARA, and Pro-Kin footboard. The intra-rater reliability and the test–retest reliability of the APP measurements, estimated by intraclass correlation coefficient, were excellent. The standard error of measurement and the minimal detectable change were small. No learning effect was observed.

Conclusions

We can state that the APP is an easy, reliable, and valid evaluating system to quantify the trunk sway in a static position and during the gait.

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References

  1. Di Donato S, Gellera C, Mariotti C (2001) The complex clinical and genetic classification of inherited ataxias II autosomal recessive ataxias. Neurol Sci 22:219–228

    PubMed  Google Scholar 

  2. Embiruçu EK, Martyn ML, Schlesinger D, Kok F (2009) Autosomal recessive ataxias: 20 types and counting. Arq Neuropsiquiatr 67:1143–1156

    PubMed  Google Scholar 

  3. Shakkottai VG, Fogel BL (2013) Clinical neurogenetics: autosomal dominant spinocerebellar ataxia. Neurol Clin 31:987–1007

    PubMed  Google Scholar 

  4. Friedreich N (1863) Uber degenerative Atrophie der spinalen Hinterstrange, Virchow’s Arch. Pathol Anat 26:391–419

    Google Scholar 

  5. Musselman KE, Stoyanov CT, Marasigan R, Jenkins ME, Konczak J, Morton SM et al (2014) Prevalence of ataxia in children: a systematic review. Neurology 82:80–89

    PubMed  PubMed Central  Google Scholar 

  6. Teive HA, Moro A, Moscovich M, Arruda WO, Munhoz RP, Raskin S et al (2015) Ataxia-telangiectasia. A historical review and a proposal for a new designation: ATM syndrome. J Neurol Sci 355(1–2):3–6

    PubMed  PubMed Central  Google Scholar 

  7. Stevanin G, Durr A, Brice A (2002) Spinocerebellar ataxias caused by polyglutamine expansions. Adv Exp Med Biol 516:47–77

    CAS  PubMed  Google Scholar 

  8. Bodranghien F, Bastian A, Casali C, Hallett M, Louis ED, Manto M et al (2016) Consensus paper: revisiting the symptoms and signs of cerebellar syndrome. Cerebellum 15(3):369–391

    PubMed  PubMed Central  Google Scholar 

  9. Patel M, Isaacs CJ, Seyer L, Brigatti K, Gelbard S, Strawer C et al (2016) Progression of Friedreich ataxia: quantitative characterization over 5 years. Ann Clin Transl Neurol 3:684–694

    PubMed  PubMed Central  Google Scholar 

  10. Reetz K, Dogan I, Hilgers RD, Giunti P, Mariotti C, Durr A et al (2016) Biological and clinical characteristics of the European Friedreich’s Ataxia Consortium for Translational Studies (EFACTS) cohort: a cross-sectional analysis of baseline data. Lancet Neurol 15:1346–1354

    PubMed  Google Scholar 

  11. Diallo A, Jacobi H, Cook A, Labrum R, Durr A, Brice A et al (2018) Survival in patients with spinocerebellar ataxias types 1,2,3 and 6 (EUROSCA): a longitudinal cohort study. Lancet Neurol 17:327–334

    PubMed  Google Scholar 

  12. Schmitz-Hübsch T, du Montcel ST, Balik L, Berciano J, Boesch S, Depondt C et al (2006) Scale for the assessment and rating of ataxia: development of a new clinical scale. Neurology 66:1717–1720

    PubMed  Google Scholar 

  13. Schmitz-Hübsch T, Tezenas-Du-Montcel S, Baliko L, Boesch S, Bonato S, Fancellu R et al (2005) Reliability and validity of the international cooperative ataxia rating scale: a study in 156 spinocerebellar ataxia patients. Mov Disord 21:699–704

    Google Scholar 

  14. Tison F, Yekhlef F (2002) Application of the international cooperative ataxia scale rating in multiple system atrophy. Mov Disord 17(6):1248–1254

    PubMed  Google Scholar 

  15. Winser SJ, Smith CM, Hale LA, Claydon LS, Whitney SL (2018) Clinical assessment of balance using BBS and SARAbal in cerebellar ataxia: synthesis of findings of a psychometric property analysis. Hong Kong Physiother J 38(1):53–61

    PubMed  PubMed Central  Google Scholar 

  16. Winser SJ, Smith CM, Hale LA, Claydon LS, Whitney SL, Mehta P (2015) Systematic review of the psychometric properties of balance measures for cerebellar ataxia. Clin Rehabil 29:69–79

    PubMed  Google Scholar 

  17. Winser SJ, Smith C, Hale LA, Claydon LS, Whitney SL (2015) Balance outcome measures in cerebellar ataxia: a Delphi survey. Disabil Rehabil 37:165–170

    PubMed  Google Scholar 

  18. Berg K, Wood-Dauphinee S, Williams JI (1995) The balance scale: reliability assessment with elderly residents and patients with an acute stroke. Scand J Rehabil Med 27(1):27–36

    CAS  PubMed  Google Scholar 

  19. Berg K, Wood-Dauphinee S, Williams JI (1995) The balance scale: reliability assessment for elderly residents and patients with an acute stroke. Scand J Rehabil Med 27:27–36

    CAS  PubMed  Google Scholar 

  20. Berg K, Maki B, Williams JI, Holliday P, Wood-Dauphinee S (1992) A comparison of clinical and laboratory measures of postural balance in an elderly population. Arch Phys Med Rehabil 73:1073–1083

    CAS  PubMed  Google Scholar 

  21. Berg K, Wood-Dauphinee S, Williams JI, Gayton D (1989) Measuring balance in the elderly: preliminary development of an instrument. Physiother Can 41:304–311

    Google Scholar 

  22. Downs S, Marquez J, Chiarelli P (2013) The Berg Balance Scale has high intra- and inter-rater reliability but absolute reliability varies across the scale: a systematic review. J Physiother 59(2):93–99

    PubMed  Google Scholar 

  23. Saether R, Helbostad JL, Riphagen II, Vik T (2013) Clinical tools to assess balance in children and adults with cerebral palsy: a systematic review. Dev Med Child Neurol 55(11):988–999

    PubMed  Google Scholar 

  24. Sorrentino G, Sale P, Solaro C, Rabini A, Cerri CG, Ferriero G (2018) Clinical measurement tools to assess trunk performance after stroke: a systematic review. Eur J Phys Rehabil Med 54(5):772–784

    PubMed  Google Scholar 

  25. Verheyden G, Nieuwboer A, Van de Winckel A, De Weerdt W (2007) Clinical tools to measure trunk performance after stroke: a systematic review of the literature. Clin Rehabil 21(5):387–394

    PubMed  Google Scholar 

  26. Winser SJ, Smith CM, Hale LA, Claydon LS, Whitney SL, Mehta P (2015) Systematic review of the psychometric properties of balance measures for cerebellar ataxia. Clin Rehabil 29(1):69–79

    PubMed  Google Scholar 

  27. Milne SC, Murphy A, Georgiou-Karistianis N, Yiu EM, Delatycki MB, Corben LA (2018) Psychometric properties of outcome measures evaluating decline in gait in cerebellar ataxia: a systematic review. Gait Posture 61:149–162

    PubMed  Google Scholar 

  28. Pérennou D, Decavel P, Manckoundia P, Penven Y, Mourey F, Launay F et al (2005) Evaluation of balance in neurologic and geriatric disorders. Ann Readapt Med Phys 48(6):317–335

    PubMed  Google Scholar 

  29. Yelnik A, Bonan I (2008) Clinical tools for assessing balance disorders. Neurophysiol Clin 38(6):439–445

    CAS  PubMed  Google Scholar 

  30. Petró B, Papachatzopoulou A, Kiss RM (2017) Devices and tasks involved in the objective assessment of standing dynamic balancing—a systematic literature review. PLoS One 12(9):e0185188

    PubMed  PubMed Central  Google Scholar 

  31. Smith C, Claydon LS, Hale L, Winser S (2013) Outcome measures for the assessment of balance and posture control in cerebellar ataxia. Phys Ther Rev 18(2):117

    Google Scholar 

  32. Proessel F, Swanson CW, Rudroff T, Fling BW, Tracy BL (2018) Good agreement between smart device and inertial sensor-based gait parameters during a 6-min walk. Gait Posture 64:63–67

    Google Scholar 

  33. Shirai S, Yabe I, Matsushima M, Ito YM, Yoeyamsa M, Sasaki H (2015) Quantitative evaluation of gait ataxia by accelerometers. J Neurol Sci 358(1–2):253–258

    PubMed  Google Scholar 

  34. Okajima Y, Chino N, Noda Y, Takahashi H (1990) Accelerometric evaluation of ataxic gait: therapeutic uses of weighting and elastic bandage. Int Disabil Stud 12(4):165–168

    CAS  PubMed  Google Scholar 

  35. Patterson JA, Amick RZ, Thummar T, Rogers ME (2014) Validation of measures from the smartphone sway balance application: a pilot study. Int J Sports Phys Ther 9(2):135–139

    PubMed  PubMed Central  Google Scholar 

  36. Galán-Mercant A, Barón-López FJ, Labajos-Manzanares MT, Cuesta-Vargas AI (2014) Reliability and criterion-related validity with a smartphone used in timed-up-and-go test. Biomed Eng Online 13:156

    PubMed  PubMed Central  Google Scholar 

  37. Daneault JF, Carignan B, Codère CÉ, Sadikot AF, Duval C (2013) Using a smart phone as a standalone platform for detection and monitoring of pathological tremors. Front Hum Neurosci 6:357

    PubMed  PubMed Central  Google Scholar 

  38. Clark RA, Bryant AL, Pua Y, McCrory P, Bennell KM (2010) Hunt validity and reliability of the Nintendo Wii balance board for assessment of standing balance. Gait Posture 31:307–310

    PubMed  Google Scholar 

  39. Mauch M, Kälin X (2011) Reliability of the ProKin Type B line system (TechnoBod) balance system https://www.woodway.com/sites/default/files/manuals/reliability_prokinb_line_system.pdf. Accessed 06 Jan 2018

  40. Saadat M, Salehi R, Negahban H, Jafar Shaterzadeh M, Mehravar M, Hessam M (2018) Postural stability in patients with non-specific chronic neck pain: a comparative study with healthy people. Med J Islam Repub Iran 32:33

    PubMed  PubMed Central  Google Scholar 

  41. Cattaneo D, Jonsdottir J (2009) Sensory impairments in quiet standing in subjects with multiple sclerosis. Mult Scler J 15(1):59–67

    CAS  Google Scholar 

  42. Zhao W, You H, Jiang S, Zhang H, Yang Y, Zhang M (2019) Effect of Pro-kin visual feedback balance training system on gait stability in patients with cerebral small vessel disease. Medicine 98(7):e14503

    PubMed  PubMed Central  Google Scholar 

  43. You H, Zhang H, Liu J, Han T, Zhang M, Zhao W, Jiang S (2017) Effect of balance training with Pro-kin system on balance in patients with white matter lesions. Medicine 96(51):e9057

    PubMed  PubMed Central  Google Scholar 

  44. Portney LG, Watkins MP (2009) Foundations of clinical research: applications to practice. Pearson/Prentice Hall, Upper Saddle River

    Google Scholar 

  45. Portney LG, Watkins MP (2000) Foundations of clinical research: applications to practice. Pearson/Prentice Hall, Upper Saddle River

    Google Scholar 

  46. Wamper KE, Sierevelt IN, Poolman RW, Bhandari M, Haverkamp D (2010) The Harris hip score: do ceiling effects limit its usefulness in orthopedics? Acta Orthop 81(6):703–707

    PubMed  PubMed Central  Google Scholar 

  47. McHorney CA, Tarlov AR (1995) Individual-patient monitoring in clinical practice: are available health status surveys adequate? Qual Life Res 4(4):293–307

    CAS  PubMed  Google Scholar 

  48. Everitt BS (2002) The Cambridge dictionary of statistics, 2nd edn. CUP, Cambridge

    Google Scholar 

  49. Cone BL, Levy SS, Goble DJ (2015) Wii Fit exer-game training improves sensory weighting and dynamic balance in healthy young adults. Gait Posture 41(2):711–715

    PubMed  Google Scholar 

  50. Goble DJ, Cone BL, Fling BW (2014) Using the Wii Fit as a tool for balance assessment and neurorehabilitation: the first half decade of “Wii-search”. J Neuroeng Rehabil 11:12

    PubMed  PubMed Central  Google Scholar 

  51. Foo J, Paterson K, Williams G, Clark R (2013) Low-cost evaluation and real-time feedback of static and dynamic weight bearing asymmetry in patients undergoing in-patient physiotherapy rehabilitation for neurological conditions. J Neuroeng Rehabil 10:74

    PubMed  PubMed Central  Google Scholar 

  52. Peterson S (2018) Telerehabilitation booster sessions and remote patient monitoring in the management of chronic low back pain: a case series. Physiother Theory Pract 34(5):393–402

    PubMed  Google Scholar 

  53. Lee Y, Yeh H, Kim KH, Choi O (2017) A real-time fall detection system based on the acceleration sensor of smartphone. Int J Control Autom 10(1):315–326

    Google Scholar 

  54. Mao A, Ma X, He Y, Luo J (2017) Highly portable, sensor-based system for human fall monitoring. Sensors (Basel) 17(9):2096

    Google Scholar 

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Authors and Affiliations

Authors

Contributions

GA contributed to structuring study design, developing the application, subject recruitment, data collection, data review, data analysis, data interpretation, writing and revising the manuscript. CM contributed to structuring study design, data interpretation, writing and revising the manuscript. RA contributed to developing the application. GD contributed to writing the manuscript, statistical data analysis, and revising the manuscript. CG contributed to recruiting patients and data collection. FP contributed to structuring study design, data analysis, and data interpretation. CC contributed to structuring study design, subject recruitment, data analysis, data interpretation, writing and revising the manuscript.

Corresponding author

Correspondence to Giuseppe Arcuria.

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Conflicts of interest

The manuscript has been approved by all the authors. The authors declare that they have no financial or any kind of relationships that might lead to a conflict of interests.

Ethical standards

This study was conducted in the Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, at “Marco Pasquali” Institute—ICOT (Latina), from January 2018 to October 2018. All patients who participated in the study were informed of the use of measurements for research purposes and gave their written informed consent. All the procedures performed were in accordance with the ethical standards of the institutional and national research committee. The regulations of our institution concerning intellectual property have been respected. The ethical standards, laid down in 1964 Declaration of Helsinki and its later amendments, were respected.

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Arcuria, G., Marcotulli, C., Amuso, R. et al. Developing a smartphone application, triaxial accelerometer-based, to quantify static and dynamic balance deficits in patients with cerebellar ataxias. J Neurol 267, 625–639 (2020). https://doi.org/10.1007/s00415-019-09570-z

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  • DOI: https://doi.org/10.1007/s00415-019-09570-z

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