Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson's disease?
Abstract
INTRODUCTION: The Timed Up and Go (TUG) test is a widely used measure of mobility and fall risk in older adults and in Parkinson's disease (PD). We tested the hypothesis that body-fixed accelerometers can provide insight into TUG performance in PD patients. METHODS: We examined 17 patients with PD (Hoehn and Yahr score: 2.7+/-0.7; ON state) and 15 age-matched healthy controls; mean ages were 66.8+/-5.9 years, 67.6+/-9.6 years, respectively. Subjects wore a 3D-accelerometer (ADXL330, Analog Devices) on the lower back while performing the TUG test. Sit-to-Stand and Stand-to-Sit times were extracted from the anterior-posterior (AP) signal. Parameters included Sit-to-Stand, Stand-to-Sit durations, amplitude range (Range) and slopes (Jerk). Acceleration median and standard deviation (SD) were also calculated. RESULTS: Stopwatch-based TUG duration tended to be higher for the PD patients compared to the control group, although not significantly (p=0.08). In contrast, the TUG duration that was extracted from the acceleration signal was significantly (p<0.02) higher in the PD group compared to the control group. Many acceleration-parameters were also significantly different (p<0.05) between groups; most were not correlated with TUG duration. CONCLUSIONS: Accelerometer-derived parameters are sensitive to group differences, indicating that PD patients have poorer mobility during specific aspects of the TUG. In addition to test duration, these measures may serve as complementary and objective bio-markers of PD to augment the evaluation of disease progression and the response to therapeutic interventions.
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Salarian et al and Weiss et al have shown that instrumented versions of the timed up-and-go test lead to improved sensitivity to gait impairments due to Parkinson's disease compared to observation methods.
“They have shown that instrumented tests lead to an improved sensitivity to gait impairments compared to observation methods.”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [104]
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Evaluation of an inertial sensor system for analysis of timed-up-and-go under dual-task demands
Coulthard, J. T., Treen, T. T., Oates, A., & Lanovaz, J. L. (2015) · Gait & Posture · doi:10.1016/j.gaitpost.2015.03.009 · Cited by 44 · see citing works
“The TUG is one of the many functional tests that can be measured easily with these new technologies [1,10–17].”
BackgroundBuchman, A. S., Leurgans, S. E., Weiss, A., VanderHorst, V., Mirelman, A., Dawe, R. J., Barnes, L. L., Wilson, R. S., Hausdorff, J. M., & Bennett, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086262 · Cited by 43 · see citing works
“Then we employed graphical and analytical techniques to analyze each of these performance and derived 31 gait measures from the 5 mobility subtasks as detailed in Appendix S1[16,18].”
Self-citationMilman, U., Atias, H., Weiss, A., Mirelman, A., & Hausdorff, J. M. (2014) · Journal of Parkinson s Disease · doi:10.3233/jpd-130321 · Cited by 41 · see citing works
“, sit-to-stand, turn), and to further characterize performance using previously described methods [30, 31, 48].”
Self-citationDimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test
Palmerini, L., Mellone, S., Rocchi, L., & Chiari, L. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091814 · Cited by 39 · see citing works
“Lately instrumented versions of the test are being considered [2], [3] where inertial sensors are used to quantitatively assess the characteristics of motion.”
BackgroundMethodBonnyaud, C., Pradon, D., Vuillerme, N., Bensmaïl, D., & Roche, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129821 · Cited by 39 · see citing works
“Several studies have evaluated the TUG test using accelerometers in patients with Parkinson’s disease and healthy subjects [10, 11, 12].”
BackgroundWang, H., Zhao, Y., Yu, L., Liu, J., Zwetsloot, I. M., Cabrera, J., & Tsui, K. (2020) · Journal of Medical Internet Research · doi:10.2196/19223 · Cited by 40 · see citing works
Hill, E. J., Mangleburg, C. G., Alfradique‐Dunham, I., Ripperger, B., Stillwell, A., Saade, H., Rao, S., Fagbongbe, O., von Coelln, R., Tarakad, A., et al. (2021) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2021.02.006 · Cited by 38 · see citing works
Motion Sensors to Assess and Monitor Medical and Surgical Management of Parkinson Disease
Lieber, B. A., Taylor, B. E. S., Appelboom, G., McKhann, G. M., & Connolly, E. S. (2015) · World Neurosurgery · doi:10.1016/j.wneu.2015.03.024 · Cited by 37 · see citing works
“By aiding in the assessment of initial levodopa responsiveness, later motor fluctuations, and dyskinesias, accelerometers have the potential to play a crucial role in guiding selection and optimal timing for DBS treatment [29-40] (Table 1).”
BackgroundTransition Between the Timed up and Go Turn to Sit Subtasks: Is Timing Everything?
Weiss, A., Mirelman, A., Giladi, N., Barnes, L. L., Bennett, D. A., Buchman, A. S., & Hausdorff, J. M. (2016) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2016.06.025 · Cited by 36 · see citing works
“Results—Subjects employed two different strategies when they transitioned from turning to sitting: (1) Distinct Transition strategy (DTS): 816 participants (77.”
Self-citationGreene, B. R., Caulfield, B., Lamichhane, D., Bond, W. F., Svendsen, J. D., Zurski, C., & Pratt, D. (2018) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668317750811 · Cited by 35 · see citing works
Weiss, A., Herman, T., Mirelman, A., Shema-Shiratzky, S., Giladi, N., Barnes, L. L., Bennett, D. A., Buchman, A. S., & Hausdorff, J. M. (2018) · Gait & Posture · doi:10.1016/j.gaitpost.2018.10.018 · Cited by 34 · see citing works
Self-citationNégrini, S., Serpelloni, M., Amici, C., Gobbo, M., Silvestro, C., Buraschi, R., Borboni, A., Crovato, D., & Lopomo, N. F. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-58877-3_40 · Cited by 33 · see citing works
Effects of Hemodialysis Therapy on Sit-to-Walk Characteristics in End Stage Renal Disease Patients
Soangra, R., Lockhart, T. E., Lach, J., & Abdel‐Rahman, E. M. (2012) · Annals of Biomedical Engineering · doi:10.1007/s10439-012-0701-6 · Cited by 33 · see citing works
“21,47 Developed by Mathias et al., 38 the TUG test is a well-known clinical test for mobility and fall risk, and is considered a reliable and valid test for quantifying functional mobility.”
Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults
von Coelln, R., Dawe, R. J., Leurgans, S. E., Curran, T. A., Truty, T., Yu, L., Barnes, L. L., Shulman, J. M., Shulman, L. M., Bennett, D. A., et al. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.06.012 · Cited by 32 · see citing works
“The TUG is composed of 4 different movements or subtasks.[21, 22] This study examined 3 TUG subtasks including: 1) transition from sit to stand (S1), 2) transition from stand to sit (S2), and 3) turning subtasks and did not include the walk components.”
MethodSelf-citationYu, L., Zhao, Y., Wang, H., Sun, T., Murphy, T. E., & Tsui, K. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01463-4 · Cited by 34 · see citing works
“A detail discussion can be found in [29].”
BackgroundInfluentialLee, S.-H., Yeh, S., Chan, R., Chen, S., Yang, G., & Zheng, L. (2016) · BioMed Research International · doi:10.1155/2016/7075464 · Cited by 31 · see citing works
“Motor Ingredients Derived from a Wearable Sensor-Based”
Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease
Greene, B. R., Premoli, I., McManus, K., McGrath, D., & Caulfield, B. M. (2021) · Sensors · doi:10.3390/s22010054 · Cited by 31 · see citing works
“Several studies have examined the value of instrumented gait and mobility tests in the assessment of Parkinson’s [22,29,48,49].”
BackgroundTulipani, L. J., Meyer, B., Allen, D., Solomon, A. J., & McGinnis, R. S. (2022) · Gait & Posture · doi:10.1016/j.gaitpost.2022.02.016 · Cited by 32 · see citing works
Geritz, J., Mätzold, S., Steffen, M., Pilotto, A., Corrà, M. F., Moscovich, M., Rizzetti, M. C., Borroni, B., Padovani, A., Alpes, A., et al. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-1445-z · Cited by 30 · see citing works
Dagan, M., Herman, T., Bernad‐Elazari, H., Gazit, E., Maidan, I., Giladi, N., Mirelman, A., Manor, B. D., & Hausdorff, J. M. (2020) · Journal of Neurology · doi:10.1007/s00415-020-10089-x · Cited by 29 · see citing works
Self-citationMangia, A. L., Cortesi, M., Fantozzi, S., Giovanardi, A., Borra, D., & Gatta, G. (2017) · Sensors · doi:10.3390/s17040927 · Cited by 30 · see citing works
The reliability and validity of the L-test in people with Parkinson’s disease
Haas, B. M., Clarke, E., Elver, L., Gowman, E., Mortimer, E., & Byrd, E. (2017) · Physiotherapy · doi:10.1016/j.physio.2017.11.218 · Cited by 29 · see citing works
Sit-to-walk performance in Parkinson's disease: A comparison between faller and non-faller patients
Palmisano, C., Brandt, G. A., Pozzi, N. G., Leporini, A., Maltese, V., Canessa, A., Volkmann, J., Pezzoli, G., Frigo, C., & Isaias, I. U. (2019) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2019.03.002 · Cited by 29 · see citing works
Quantitative clinical assessment of motor function during and following LSVT-BIG® therapy
Flood, M. W., O’Callaghan, B., Diamond, P., Liegey, J., Hughes, G., & Lowery, M. M. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00729-8 · Cited by 29 · see citing works
Manaf, H., Justine, M., & Omar, M. (2014) · Stroke Research and Treatment · doi:10.1155/2014/439304 · Cited by 29 · see citing works
“The time taken to complete the TUG test demonstrates a strong correlation with laboratory measurement [30] and clinically based assessment such as BBS.”
MethodAge-associated changes in head jerk while walking reveal altered dynamic stability in older people
Brodie, M. A. D., Menz, H. B., & Lord, S. R. (2013) · Experimental Brain Research · doi:10.1007/s00221-013-3719-6 · Cited by 28 · see citing works
Picardi, M., Redaelli, V., Antoniotti, P., Pintavalle, G., Aristidou, E., Sterpi, I., Meloni, M., Corbo, M., & Caronni, A. (2020) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2020.105177 · Cited by 28 · see citing works
Pilot Study of the EncephaLog Smartphone Application for Gait Analysis
Tchelet, K., Stark-Inbar, A., & Yekutieli, Z. (2019) · Sensors · doi:10.3390/s19235179 · Cited by 27 · see citing works
“iTUG’s higher accuracy in comparison to subjective evaluation has proven to be more sensitive to pathologies, useful in fall risk prediction [12,19,23,24,26,27], helpful in the evaluation of responses to therapeutic interventions [17,26], and can even provide cognitive related indicators [24,26,28].”
BackgroundGalán-Mercant, A., & Cuesta‐Vargas, A. (2013) · BioMed Research International · doi:10.1155/2013/279197 · Cited by 23 · see citing works
“In this study, the frail elderly showed low magnitudes in the kinematic values with low variability (very small standard deviations) compared to the controls, the same as the subjects affected by Parkinson’s disease [16, 25, 26], the elderly with a high risk of falls [2] and the frail elderly in a previous study [13].”
ResultConi, A., Ancum, J. M. V., Bergquist, R., Mikolaizak, A. S., Mellone, S., Chiari, L., Maier, A. B., & Pijnappels, M. A. G. M. (2019) · Sensors · doi:10.3390/s19030449 · Cited by 23 · see citing works
A Two-Dimensional Feature Space-Based Approach for Human Locomotion Recognition
Chinimilli, P. T., Redkar, S., & Sugar, T. G. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2895289 · Cited by 23 · see citing works
“accelerometers were used to enhance the utility of Timed Up and Go (TUG) test when evaluating PD patients [7].”
MethodOtis, M. J.-D., Ayena, J. C., Tremblay, L. E., Fortin, P. E., & Ménélas, B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162107 · Cited by 22 · see citing works
Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
Adamowicz, L., Karahanoğlu, F. I., Cicalo, C., Zhang, H., Demanuele, C., Santamaria, M., Cai, X., & Patel, S. (2020) · Sensors · doi:10.3390/s20226618 · Cited by 22 · see citing works
“In addition, multi-parametric approaches [16,19,20,23,24,26] relying on features that capture aspects associated with STS transfer quality (e.”
BackgroundMethodUsing multi-dimensional dynamic time warping for TUG test instrumentation with inertial sensors
Al-Jawad, A., Adame, M. R., Romanovas, M., Hobert, M. A., Maetzler, W., Traechtler, M., Möeller, K., & Manoli, Y. (2012) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2012.6343011 · Cited by 22 · see citing works
Walgaard, S., Faber, G. S., van Lummel, R. C., van Dieën, J. H., & Kingma, I. (2016) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2016.03.010 · Cited by 22 · see citing works
“…during chair rise (Giansanti and Maccioni, 2006; Millor et al., 2013; Schwenk et al., 2012; Van Lummel et al., 2012; Van Lummel et al., 2013; Weiss et al., 2011, 2010) and have been validated against other instrumentation for the sit-to-stand task (Boonstra et al., 2006; Janssen et al.,…”
Muralidharan, V., Balasubramani, P. P., Chakravarthy, V. S., Gilat, M., Lewis, S. J. G., & Moustafa, A. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00649 · Cited by 21 · see citing works
“…examples include tasks such as the attention network task (ANT), the virtual reality gait task (Shine et al., 2013a), “timed up and go” task (TUG) (Weiss et al., 2010; Herman et al., 2011), object avoidance (Snijders et al., 2010; Pieruccini-Faria et al., 2014), and dual tasking (Yogev et al.,…”
InfluentialHsu, Y., Zhao, Y., Huang, K.-H., Wu, Y., Cabrera, J., Sun, T., & Tsui, K. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2987623 · Cited by 21 · see citing works
Evans, T., Jefferson, A., Byrnes, M. L., Walters, S., Ghosh, S., Mastaglia, F. L., Power, B., & Anderton, R. S. (2017) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2017.01.050 · Cited by 21 · see citing works
A Comparison of Four Approaches to Evaluate the Sit-to-Stand Movement
Shukla, B. K., Jain, H., Vijay, V., Yadav, S. K., Mathur, A., & Hewson, D. J. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2987357 · Cited by 22 · see citing works
“Such tests have used a range of techniques including body-worn accelerometers [10], [11], pressure sensors [12], and visual sensors, often using multiple cameras [13], [14].”
BackgroundAutomatic measurement of fall risk indicators in timed up and go test
Dubois, A., Bihl, T., & Bresciani, J. (2018) · Informatics for Health and Social Care · doi:10.1080/17538157.2018.1496089 · Cited by 20 · see citing works
de Vries, N. M., Śmiłowska, K., Hummelink, J., Abramiuc, B., van Gilst, M. M., Bloem, B. R., de With, P. H. N., & Overeem, S. (2019) · BMC Neurology · doi:10.1186/s12883-019-1301-y · Cited by 20 · see citing works
- Authors
- Weiss, A., Herman, T., Plotnik, M., Brozgol, M., Maidan, I., Giladi, N., Gurevich, T., & Hausdorff, J. M.
- Affiliations
- Tel Aviv Sourasky Medical Center (IL)
- Tel Aviv University (IL)
- Harvard University (US)
- Corresponding author
- Hausdorff, Jeffrey M.
- ORCID
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2009-11-25
- Volume
- 32
- Issue
- 2
- Pages
- 119-125
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W2139868675
- Cited by
- 207
- References
- 23
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 7 · 2023: 11 · 2022: 10 · 2021: 8 · 2020: 15 · 2019: 14 · 2018: 11 · 2017: 18 · 2016: 19 · 2015: 20 · 2014: 16 · 2013: 19 · 2012: 10
- FWCI
- 22.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- European Commission
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- Parkinson's disease, Timed Up and Go test, accelerometer, mobility assessment, fall risk, sit-to-stand, stand-to-sit, acceleration parameter, jerk, objective biomarkers, disease progression
- MeSH terms
- Accidental Falls, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Motor Activity, Parkinson Disease, Posture, Time Factors, Case-Control Studies, Risk Assessment
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