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Robotic technology quantifies novel perceptual-motor impairments in patients with chronic kidney disease



Neurocognitive impairment is commonly reported in patients with chronic kidney disease (CKD). The precise nature of this impairment is unclear, due to the lack of objective and quantitative assessment tools used. The feasibility of using robotic technology to precisely quantify neurocognitive impairment in patients with CKD is unknown.


Patients with stage 4 and 5 CKD with no previous history of stroke or neurodegenerative disease were eligible for study enrollment. Feasibility was defined as successful study enrollment, high data capture rates (> 90%), and assessment tolerability. Our assessment included a traditional assessment: The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and a robot-based assessment: Kinarm.


Our enrollment rate was 1.6 patients/month. All patients completed the RBANS portion of the assessment, with a 97.8% (range 92–100%) completion rate on Kinarm. Missing data on Kinarm were mainly due to time constraints. Data from 49 CKD patients were analyzed. Kinarm defined more individuals as impaired, compared to RBANS, particularly in the domains of perceptual-motor function (17–49% impairment), complex attention (22–49% impairment), and executive function (29–37.5% impairment). Demographic features (sex and education) predicted performance on some, but not all neurocognitive tasks.


It is feasible to quantify neurocognitive impairments in patients with CKD using robotic technology. Kinarm characterized more patients with CKD as impaired, and importantly identified novel perceptual-motor impairments in these patients, when compared to traditional assessments.

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Availability of data and material

All individual patient data are located in the Supplemental Data.


  1. 1.

    Etgen T, Chonchol M, Förstl H, Sander D (2012) Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. Am J Nephrol 35(5):474–482

    Article  Google Scholar 

  2. 2.

    Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS (2014) Classifying neurocognitive disorders : the DSM—5 approach, no. April 2016, 2014

  3. 3.

    Sanchez-Roman S, Ostrosky-Solis F, Morales-Buenrostro LE, Nogues-Vizcaino MG, Alberu J, McClintock SM (2011) Neurocognitive profile of an adult sample with chronic kidney disease. J Int Neuropsychol Soc 17:80–90

    Article  Google Scholar 

  4. 4.

    Viggiano D et al (2020a) Mechanisms of cognitive dysfunction in CKD. Nat Rev Nephrol 16(8):452–469

    Article  Google Scholar 

  5. 5.

    Vanderlinden JA, Ross-White A, Holden R, Shamseddin MK, Day A, Boyd JG (2019) Quantifying cognitive dysfunction across the spectrum of end-stage kidney disease: a systematic review and meta-analysis. Nephrology 24(1):5–16

    Article  Google Scholar 

  6. 6.

    Viggiano D et al (2020b) Mild cognitive impairment and kidney disease: clinical aspects. Nephrol Dial Transplant 35(1):10–17

    CAS  PubMed  Google Scholar 

  7. 7.

    Baek MJ, Kim K, Park YH, Kim S (2016) The validity and reliability of the Mini-Mental State Examination-2 for detecting mild cognitive impairment and Alzheimer’s disease in a Korean population. PLoS ONE 11(9):e0163792

    Article  Google Scholar 

  8. 8.

    Wouters H, van Gool WA, Schmand B, Zwinderman AH, Lindeboom R (2009) Three sides of the same coin: measuring global cognitive impairment with the MMSE, ADAS-cog and CAMCOG. Int J Geriatr Psychiatry 25(8):770–779

    Article  Google Scholar 

  9. 9.

    Arevalo-Rodriguez et al (2015) Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev

  10. 10.

    Cabel DW, Cisek P, Scott SH (2001) Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. J Neurophysiol 86(4):2102–2108

    CAS  Article  Google Scholar 

  11. 11.

    Desrochers PC, Brunfeldt AT, Kagerer FA (2020) Neurophysiological correlates of adaptation and interference during asymmetrical bimanual movements. Neuroscience 432:30–43

    CAS  Article  Google Scholar 

  12. 12.

    Kinarm End-Point Lab. [Online]. Accessed 24 Jul 2020

  13. 13.

    Tyryshkin K et al (2014) A robotic object hitting task to quantify sensorimotor impairments in participants with stroke. J Neuroeng Rehabil 11:1–12

    Article  Google Scholar 

  14. 14.

    Wood MD et al (2018) Robotic technology provides objective and quanti fi able metrics of neurocognitive functioning in survivors of critical illness: a feasibility study. J Crit Care 48:228–236

    Article  Google Scholar 

  15. 15.

    Debert CT, Herter TM, Scott SH, Dukelow S (2012) Robotic assessment of sensorimotor deficits after traumatic brain injury. J Neurol Phys Ther 36:58–67

    Article  Google Scholar 

  16. 16.

    Simmatis L et al (2019) The feasibility of using robotic technology to quantify sensory, motor, and cognitive impairments associated with ALS. Amyotroph Lateral Scler Front Degener 20(1–2):43–52

    CAS  Article  Google Scholar 

  17. 17.

    Semrau JS et al (2019) Quantified pre-operative neurological dysfunction predicts outcome after coronary artery bypass surgery. Aging Clin Exp Res 32:289–297

    Article  Google Scholar 

  18. 18.

    Randolph C et al (1998) The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropyschol 3395:310–319

    Article  Google Scholar 

  19. 19.

    User Guides and Documentation. [Online]. Accessed 24 Jul 2020

  20. 20.

    Simmatis LER, Early S, Moore KD, Appaqaq S, Scott SH (2020) Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing. J Neuroeng Rehabil 17(1):86

    Article  Google Scholar 

  21. 21.

    Glickman ME, Rao SR, Schultz MR (2014) False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol 67(8):850–857

    Article  Google Scholar 

  22. 22.

    van Hooren SAH, Valentijn AM, Bosma H, Ponds RWHM, van Boxtel MPJ, Jolles J (2007) Cognitive functioning in healthy older adults aged 64–81: a cohort study into the effects of age, sex, and education. Aging Neuropsychol Cogn 14(1):40–54

    Article  Google Scholar 

  23. 23.

    Weiner DE, Seliger SL (2014) Cognitive and physical function in chronic kidney disease. Curr Opin Nephrol Hypertens 23(3):291–297

    Article  Google Scholar 

  24. 24.

    van den Berg E, Kloppenborg RP, Kessels RPC, Kappelle LJ, Biessels GJ (2009) Type 2 diabetes mellitus, hypertension, dyslipidemia and obesity: a systematic comparison of their impact on cognition. Biochim Biophys Acta Mol Basis Dis 1792(5):470–481

    Article  Google Scholar 

  25. 25.

    Vanderploeg RD, Axelrod BN, Sherer M, Scott J, Adams RL (1997) The importance of demographic adjustments on neuropsychological test performance: a response to Reitan and Wolfson (1995). Clin Neuropsychol 11(2):210–217

    Article  Google Scholar 

  26. 26.

    R Core Team (2016) R: a language and environment. Google Scholar. [Online]. Accessed 24 Jul 2020

  27. 27.

    Hellberg M, Hoglund P, Svensson P, Abdulahi H, Clyne N (2017) Decline in measured glomerular filtration rate is associated with a decrease in endurance, strength, balance and fine motor skills. Nephrology 22(7):513–519

    CAS  Article  Google Scholar 

  28. 28.

    Ng CL, Ho DD, Chow SP (1999) The Moberg pickup test: results of testing with a standard protocol. J Hand Ther 12(4):309–312

    CAS  Article  Google Scholar 

  29. 29.

    Erken E et al (2019) Impaired cognition in hemodialysis patients: the Montreal Cognitive Assessment (MoCA) and important clues for testing. Clin Nephrol 91(5):275–283

    CAS  Article  Google Scholar 

  30. 30.

    Kepecs DM, Glick L, Silver SA, Yuen DA (2018) Does chronic kidney disease: induced cognitive impairment affect driving safety? Can J Kid Health Dis 5

  31. 31.

    Vancouver JB, Thompson CM, Williams AA (2001) The changing signs in the relationships among self-efficacy, personal goals, and performance. J Appl Psychol 86(4):605–620

    CAS  Article  Google Scholar 

  32. 32.

    Kluger AN, DeNisi A (1996) The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol Bull 119(2):254–284

    Article  Google Scholar 

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This work was funded by the Queen’s University Department of Medicine Innovation Fund. We would like to thank the patients for volunteering their time, along with Kim Moore and Simone Appaqaq for conducting the neurocognitive testing.


This work was funded by the Queen’s University Department of Medicine Innovation Fund.

Author information




JGB and RH: designed the study; SS: developed and designed Kinarm; JV: gathered consent, analyzed, interpreted, and plotted the data, drafted and revised the manuscript; JGB, RH, and SS: were involved in revising the manuscript. All authors approved the final submission.

Corresponding author

Correspondence to John Gordon Boyd.

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Conflict of interest

Stephen Scott is the co-founder and CSO of Kinarm that commercializes the robotic technology used in the present study. Jessica Vanderlinden, J. Gordon Boyd and Rachel Holden have no conflicts of interest to report.

Ethical approval

The study was approved by Queen’s University and Affiliated Hospitals Health Sciences Research Ethics Board.

Consent to participant

Each patient gave informed consent prior to any assessment or data collection.

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Vanderlinden, J.A., Holden, R.M., Scott, S.H. et al. Robotic technology quantifies novel perceptual-motor impairments in patients with chronic kidney disease. J Nephrol 34, 1243–1256 (2021).

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  • Chronic kidney disease (CKD)
  • The Repeatable Battery for the Neuropsychological Assessment (RBANS)
  • Kinarm
  • Neurocognitive impairment