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Detecting subtle mobility changes among older adults: the Quantitative Timed Up and Go test



The Quantitative Timed Up and Go (QTUG) test uses wearable sensors, containing a triaxial accelerometer and an add-on triaxial gyroscope, to quantify performance during the TUG test with potential to capture more minor changes in mobility.


To examine the responsiveness, minimum detectable change (MDC) and observed effect size of QTUG in a cohort of socially active adults aged 50 years and over participating in a structured community exercise program.


54 participants (91% females, mean age 63.6 ± 6.5 years) completed repeated QTUG testing under single- and dual-task conditions. Responsiveness of the QTUG was assessed by correlation of change in standard TUG with QTUG change (Pearson’s correlation coefficient). MDC and effect sizes (standardized mean difference and Cohen’s d) were also calculated for QTUG.


There was a strong positive correlation between change in the standard TUG and change in QTUG (single task r = 0.91, p < 0.001). MDC in QTUG was calculated as 0.77 (Sd, 1.39; ICC 0.96) seconds (single task) and 2.33 (Sd 2.18; ICC 0.85) seconds (dual task). Several QTUG parameters showed improvements in mean values with small effect sizes (sit -to-stand transition time d = 0.418; walk time d = 0.398; cadence d = 0.306, swing time d = 0.314; step time d = 0.479; gait velocity d = 0.365; time to reach turn d = 0.322) under single-task conditions and with a moderate effect size (d = 0.549) in time taken to turn under the dual-task condition.


Initial evidence of QTUG’s responsiveness to change in mobility in active middle to older age adults has been demonstrated with small to moderate effect sizes observed in specific QTUG parameters.

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  1. 1.

    United Nations (2019) World population ageing. Department of Economic and Social Affairs of the United Nations. United Nations Publications. Accessed Aug 2020

  2. 2.

    Haagsma JA, Olij BF, Majdan M et al (2020) Falls in older aged adults in 22 European countries: incidence, mortality and burden of disease from 1990 to 2017. Inj Prev.

    Article  PubMed  Google Scholar 

  3. 3.

    Lusardi MM, Fritz S, Middleton A et al (2017) Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther 40:1–36.

    Article  PubMed  Google Scholar 

  4. 4.

    Tinetti ME, Speechley M, Ginter SF (1988) Risk factors for falls among elderly persons living in the community. New Engl J Med 319:1701–1707

    CAS  Article  Google Scholar 

  5. 5.

    Veronese N, Maggi S (2018) Epidemiology and social costs of hip fracture. Injury 49:1458–1460.

    Article  PubMed  Google Scholar 

  6. 6.

    Sherrington C, Fairhall NJ, Wallbank GK et al (2019) Exercise for preventing falls in older people living in the community. Cochrane Database Syst Rev 1:CD012424.

    Article  PubMed  Google Scholar 

  7. 7.

    Sherrington C, Tiedemann A (2015) Physiotherapy in the prevention of falls in older people. J Physiother 61:54–60.

    Article  PubMed  Google Scholar 

  8. 8.

    Hopewell S, Adedire O, Copsey BJ et al (2018) Multifactorial and multiple component interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 7:CD012221.

    Article  PubMed  Google Scholar 

  9. 9.

    de Souto BP, Rolland Y, Vellas B et al (2019) Association of long-term exercise training with risk of falls, fractures, hospitalizations, and mortality in older adults: a systematic review and meta-analysis. JAMA Intern Med 179:394–405.

    Article  Google Scholar 

  10. 10.

    Perracini MR, Kristensen MT, Cunningham C (2018) Physiotherapy following fragility fractures. Injury 49:1413–1417.

    Article  PubMed  Google Scholar 

  11. 11.

    Berg K (1989) Measuring balance in the elderly: preliminary development of an instrument. Physiotherapy Can 41:304–311

    Article  Google Scholar 

  12. 12.

    Podsiadlo D, Richardson S (1991) The timed up and go: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148

    CAS  Article  Google Scholar 

  13. 13.

    Wang C-Y, Hsieh C-L, Olson SL et al (2006) Psychometric properties of the berg balance scale in a community-dwelling elderly resident population in Taiwan. J Formos Med Assoc 105:992–1000

    Article  Google Scholar 

  14. 14.

    Downs S, Marquez J, Chiarelli P (2014) Normative scores on the Berg Balance Scale decline after age 70 years in healthy community-dwelling people: a systematic review. J Physiother 60:85–89.

    Article  PubMed  Google Scholar 

  15. 15.

    Tomas-Carus P, Biehl-Printes C, Pereira C et al (2019) Dual task performance and history of falls in community-dwelling older adults. Exp Gerontol 120:35–39

    Article  Google Scholar 

  16. 16.

    Sheehan KJ, Greene BR, Cunningham C et al (2014) Early identification of declining balance in higher functioning older adults, an inertial sensor based method. Gait Posture 39:1034–1039.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Lundin H, Sääf M, Strender L-E et al (2017) Gait speed and one-leg standing time each add to the predictive ability of FRAX. Osteoporos Int 28:179–187.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Barry E, Galvin R, Keogh C et al (2014) Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta-analysis. BMC Geriatr 4:14.

    Article  Google Scholar 

  19. 19.

    Steffen TM, Hacker TA, Mollinger L (2002) Age-and gender-related test performance in community-dwelling elderly people: six-minute walk test, berg balance scale, timed up & go test, and gait speeds. Phys Ther 2002:128–137

    Article  Google Scholar 

  20. 20.

    Bohannon RW (2006) Reference values for the timed up and go test: a descriptive meta-analysis. J Geriatr Phys Ther 29:64–68

    Article  Google Scholar 

  21. 21.

    Schoene D, Wu SMS, Mikolaizak AS et al (2013) Discriminative ability and predictive validity of the timed up and go test in identifying older people who fall: systematic review and meta-analysis. J Am Geriatr Soc 61:202–208.

    Article  PubMed  Google Scholar 

  22. 22.

    Greene BR, Redmond SJ, Caulfield B (2017) Fall risk assessment through automatic combination of clinical fall risk factors and body-worn sensor data. IEEE J Biomed Health Inform 21:725–731.

    Article  PubMed  Google Scholar 

  23. 23.

    Greene BR, Caulfield B, Lamichhane D et al (2018) Longitudinal assessment of falls in patients with Parkinson’s disease using inertial sensors and the Timed Up and Go test. J Rehabil Assist Technol Eng.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Hofheinz M, Mibs M (2016) The prognostic validity of the timed up and go test with a dual task for predicting the risk of falls in the elderly. Gerontol Geriatr Med.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Smith E, Walsh L, Doyle J et al (2017) Effect of a dual task on quantitative Timed Up and Go performance in community-dwelling older adults: a preliminary study. Geriatr Gerontol Int 17:1176–1182.

    Article  PubMed  Google Scholar 

  26. 26.

    Smith E, Cusack T, Blake C (2016) The effect of a dual task on gait speed in community dwelling older adults: a systematic review and meta-analysis. Gait Posture 44:250–258.

    Article  PubMed  Google Scholar 

  27. 27.

    Caronni A, Picardi M, Aristidou E et al (2019) How do patients improve their timed up and go test? Responsiveness to rehabilitation of the TUG test in elderly neurologicalpatients. Gait Posture 70:33–38.

    Article  PubMed  Google Scholar 

  28. 28.

    Salarian A, Horak FB, Zampieri C et al (2010) iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng 18:303–310.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Dite W, Temple VA (2002) Development of a clinical measure of turning for older adults. Am J Phys Med Rehabil 81:857–866

    Article  Google Scholar 

  30. 30.

    Najafi B, Aminian K, Loew F et al (2002) Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng 49:843–851

    Article  Google Scholar 

  31. 31.

    Cheng P-T, Liaw M-Y, Wong M-K et al (1998) The sit-to-stand movement in stroke patients and its correlation with falling. Arch Phys Med Rehab 79:1043–1046

    CAS  Article  Google Scholar 

  32. 32.

    Smith E, Walsh L, Doyle J et al (2016) The reliability of the quantitative timed up and go test (QTUG) measured over five consecutive days under single and dual-task conditions in community dwelling older adults. Gait Posture 43:239–244.

    Article  PubMed  Google Scholar 

  33. 33.

    QTUG for assessing fallsrisk and frailty. Medtech innovation briefing [MIB73] Published date:July 2016

  34. 34.

    Mokkink LB, Vet HC, Prinsen CA, et al. (2019) COSMIN design checklist for patient reported outcome measurement instruments - User Manual. Available from: Accessed Aug 2020

  35. 35.

    Cunningham C, Mc Carthy U, Blake C. UCD Better Bones Manual. Accessed Aug 2020

  36. 36.

    Greene BR, Doheny EP, Walsh C et al (2012) Evaluation of falls risk in community-dwelling older adults using body-worn sensors. Gerontology 58:472–480.

    Article  PubMed  Google Scholar 

  37. 37.

    Greene BR, Foran TG, Mc Grath D et al (2012) A comparison of algorithms for body worn sensor based spatiotemporal gait parameters to the GAITRite electronic walkway. J Appl Biomec 28:349–355

    Article  Google Scholar 

  38. 38.

    McGrath D, Greene BR, Doheny EP et al (2011) Reliability of quantitative TUG measures of mobility for use in falls risk assessment. Conf Proc IEEE Eng Med Biol Soc.

    Article  Google Scholar 

  39. 39.

    Curb JD, Ceria-Ulep CD, Rodriguez BL et al (2006) Performance-based measures of physical function for high-function populations. J Am Geriatr Soc 54:737–742

    Article  Google Scholar 

  40. 40.

    Greene BR, McGrath D, O’Neill R et al (2010) An adaptive gyroscope-based algorithm for temporal gait analysis. Med BiolEng and Comput 48:1251–1260.

    Article  Google Scholar 

  41. 41.

    Greene BR, McGrath D, Caulfield B (2014) A comparison of cross-sectional and prospective algorithms for falls risk assessment. Conf Proc IEEE Eng Med Biol Soc.

    Article  Google Scholar 

  42. 42.

    Coulthard JT, Treen TT, Oates AR et al (2015) Evaluation of an inertial sensor system for analysis of timed-up-and-go under dual-task demands. Gait Posture 41:882–887.

    Article  PubMed  Google Scholar 

  43. 43.

    Kuo AD (2001) A simple model of bipedal walking predicts the preferred speed-step length relationship. J Biomech Eng 123:264–9.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Booth V, Hood V, Kearney F (2016) Interventions incorporating physical and cognitive elements to reduce falls risk in cognitively impaired older adults: a systematic review. JBI Database Syst Rev Implement Rep 14:110–135.

    Article  Google Scholar 

  45. 45.

    Lord SR, Close JCT (2018) New horizons in falls prevention. Age Ageing 47:492–498.

    Article  PubMed  Google Scholar 

  46. 46.

    Chen HY, Tang PF (2016) Factors contributing to single- and dual-task timed “up & go” test performance in middle-aged and older adults who are active and dwell in the community. Phys Ther 96:284–292.

    Article  PubMed  Google Scholar 

  47. 47.

    Rodrigues I, Armstrong J, Adachi J et al (2017) Facilitators and barriers to exercise adherence in patients with osteopenia and osteoporosis: a systematic review. Osteoporos Int 28:735–745.

    CAS  Article  PubMed  Google Scholar 

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To all the Better Bones Program Participants who consented to be part of this study.



Author information




Study Concept and Design: ES, CB; Data Acquisition: ES, CC, UMCP; Data Analysis: CB, ES; Data Interpretation: ES, CB, BG, CC, UMCP; Paper write up, Critical Revision and Approval: ES, CB, BG, CC, UMCP. The first draft of the paper was written by ES. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Caitriona Cunningham.

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Conflict of interest

B.R. Greene has a commercial interest in Kinesis Heath Technologies, the developers of the QTUG technology used in this study. Research was led by independent UCD researchers. BR Greene was not involved in data analysis. No funding provided.

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University College Dublin (UCD) Human Research Ethics Committee.

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Written consent of participants. This work has not been published in any previous journal. Related abstracts have been presented via oral presentation at conferences (eg. Irish Osteoporosis Society) but with no published conference proceedings or journal outputs.

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Smith, E., Cunningham, C., Greene, B.R. et al. Detecting subtle mobility changes among older adults: the Quantitative Timed Up and Go test. Aging Clin Exp Res 33, 2157–2164 (2021).

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  • QTUG
  • Responsiveness
  • Aging
  • Exercise
  • Osteoporosis
  • Minimal detectable change