Database Open Access
Tai Chi, Physiological Complexity, and Healthy Aging: Gait and EMG data
Published: Dec. 30, 2020. Version: 1.0.1
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Wayne, P., Gow, B., Hausdorff, J., Peng, C., Lipsitz, L., Ahn, A., Novak, V., & Manor, B. (2020). Tai Chi, Physiological Complexity, and Healthy Aging: Gait and EMG data (version 1.0.1). PhysioNet. https://doi.org/10.13026/51h0-rs07.
Wayne PM, Manor B, Novak V, et al. A Systems Biology Approach to Studying Tai Chi, Physiological Complexity and Healthy Aging: Design and Rationale of a Pragmatic Randomized Controlled Trial. Contemporary clinical trials. 2013;34(1):21-34. doi:10.1016/j.cct.2012.09.006.
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
This dataset contains gait and electromyography (EMG) data collected during a hybrid study that included a two-arm randomized clinical trial (RCT) along with an additional observational comparison group. The RCT arm investigated the short-term effects of a Tai Chi intervention on 60 Tai Chi healthy naïve subjects, aged 50-79 years, living within the Greater Boston area, and reporting no regular Tai Chi practice within the past 5 years. Subjects were either randomized to 6 months of Tai Chi or usual care. Subjects came in for a baseline visit along with 3 month and 6 month follow-ups. The observational comparison group consisted of 27 healthy Tai Chi experts, aged 50-79 years, currently engaged in an active Tai Chi training regimen, each with at least 5 years of Tai Chi experience. The expert group was tested only at their baseline visit. The gait and EMG data were simultaneously recorded during walking at the subjects preferred speed for 10 minutes under the single-task condition and for 90 seconds under the dual-task condition. Our aim for this study was to use gait and EMG data to gain a better understanding of the mobility changes associated with Tai Chi training.
Previous studies have shown the beneficial effects of Tai Chi on falls [1-4], balance [1, 5-8], and gait [9-11]. Some evidence suggests that the beneficial effect of Tai Chi on mobility may be more pronounced under cognitive dual-task conditions, such as performing serial subtractions while walking [11-12].
Gait stride time variability (calculated from stride to stride intervals) and speed are established measures of mobility health [13-15]. Complexity based metrics such as detrended fluctuation analysis have shown promise for assessing mobility health but have not been widely used to assess changes in gait due to Tai Chi training [16-17]. The collection of 10 minutes of gait data (under the single-task condition) is enough for the computation of detrended fluctuation analysis which can reveal long-term memory effects across the span of hundreds of strides .
Additionally, few studies have investigated the muscle activation patterns associated with Tai Chi training in an effort to understand the mechanisms behind Tai Chi’s effects on mobility. Muscle co-contraction measured using electromyography (EMG), and defined as the simultaneous activation of agonist and antagonist muscle groups, can serve as an informative clinical marker of mobility health . The data from this project allows for exploration of whether co-contraction of muscle groups in the lower extremities may be an underlying mechanism associated with mobility improvements from Tai Chi training.
This project contains gait and electromyography (EMG) data from a hybrid study that included a two-arm randomized clinical trial (RCT) along with an additional observational comparison group. The RCT arm investigated the short-term effects of a Tai Chi intervention on 60 Tai Chi healthy naïve subjects, aged 50-79 years, living within the Greater Boston area, and reporting no regular Tai Chi practice within the past 5 years. Subjects were either randomized to 6 months of Tai Chi or usual care. Subjects came in for a baseline visit along with 3 month and 6 month follow-ups. The observational comparison group consisted of 27 healthy Tai Chi experts, aged 50-79 years, currently engaged in an active Tai Chi training regimen, each with at least 5 years of Tai Chi experience. The expert group was tested only at their baseline visit. The gait and EMG data were simultaneously recorded during walking at the subjects preferred speed for 10 minutes under the single-task condition and for 90 seconds under the dual-task condition.
To collect gait stride-to-stride timing information, wireless force-sensitive resistor footswitches were placed under the subject’s toes and heels to record toe and heel strikes. This gait data was collected with the ME6000 (Mega Elektronika, Inc) data acquisition monitor. Data was recorded for 10 minutes while walking normally (single-task) and for 90 seconds while walking and performing verbalized serial subtractions (dual-task). Gait speed was calculated based on the distance the subjects walked over a given period of time (10 minutes or 90 seconds). Surface EMG of the anterior tibialis and lateral gastrocnemius muscles was recorded simultaneously with the gait data. The EMG data was collected with a Noraxon data acquisition system (Noraxon, Scottsdale, USA) with the selectable low pass filter set to 500Hz. The EMG and gait footswitch data were both sampled at 1500Hz.
The Institutional Review Boards at Beth Israel Deaconess Medical Center and Brigham and Women’s Hospital, Boston, Massachusetts, approved this study. The randomized clinical trial was registered at ClinicalTrials.gov (NCT01340365).
The files are split by task, with the files under the Single-task folder containing data from 10 minutes of normal walking and the files under the Dual-task folder containing 90 seconds of walking while performing serial subtractions by 3. The data files are provided in standard WFDB format, named:
SXXX_KT_VY(_PZ) - for non-experts
SXXX_KT_master(_PZ) - for experts
XXX is the subject number,
S for the single-task (ST) recording and
D for the dual-task (DT) recording,
Y is the visit number, and
Z is the recording part for instances that had to be recorded in two segments.
Group allocation by Subject ID with age, gender, education, body mass index (BMI), visit, trail making time, category fluency, hallway length, number of laps, and distance walked, can be found in the accompanying file:
A previous publication used detrended fluctuation analysis to investigate the long-range scaling in gait stride times . This study employed intention to treat analysis and included all of the single-task gait data provided here. Information on detrended fluctuation analysis along with code can found here: https://www.physionet.org/content/dfa/1.0.0/.
Another study analyzed co-contraction from electromyography data and stride time variability and gait speed under single- and dual-task conditions [in-press]. This publication didn't include subjects who were: lost to follow up (5 non-experts), missing EMG data (4 non-experts), or had corrupt footswitch data (2 non-experts). See the
EMG_Subset.csv file for the subjects included in the analysis for this publication.
Both of these studies made cross-sectional comparisons between Tai Chi experts and Tai Chi naive subjects at their baseline visit along with comparisons between the naive subjects randomized to Tai Chi training versus usual care. Additional insight into the effects of Tai Chi training may be garnered by using other analysis techniques on this gait and electromyography data. The recordings are at the original sampling rate of 1500 Hz but can downsampled as appropriate.
This updated release includes the 90 seconds of walking data collected when subjects were performing a dual-task (serial subtractions). This release also includes the electromyography (EMG) data collected during the single- and dual-task walking.
We thank Jacquelyn Walsh, Matthew Lough, Danielle Berkowitz and Mary Quilty for research assistance.
Conflicts of Interest
Peter Wayne is the founder and sole owner of the Tree of Life Tai Chi Center. Peter Wayne's interests were reviewed and managed by the Brigham and Women's Hospital and Partner's HealthCare in accordance with their conflict of interest policies. The other author(s) have no conflicts of interest to declare.
- Wolf SL, Barnhart HX, Ellison GL, Coogler CE, Atlanta FICSIT Group. The effect of Tai Chi Quan and computerized balance training on postural stability in older subjects. Physical therapy. 1997 Apr 1;77(4):371-81.
- Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, Lamb SE. Interventions for preventing falls in older people living in the community. Cochrane database of systematic reviews. 2012(9).
- Harmer PA, Li F. Tai Chi and falls prevention in older people. InTai Chi Chuan 2008 (Vol. 52, pp. 124-134). Karger Publishers.
- Liu H, Frank A. Tai chi as a balance improvement exercise for older adults: a systematic review. Journal of geriatric physical therapy. 2010 Jul 1;33(3):103-9.
- Yan JH. Tai Chi practice improves senior citizens’ balance and arm movement control. Journal of Aging and Physical Activity. 1998 Jul;6(3):271-84.
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- Wang C, Collet JP, Lau J. The effect of Tai Chi on health outcomes in patients with chronic conditions: a systematic review. Archives of internal medicine. 2004 Mar 8;164(5):493-501.
- Wu G, Zhao F, Zhou X, Wei L. Improvement of isokinetic knee extensor strength and reduction of postural sway in the elderly from long-term Tai Chi exercise. Archives of physical medicine and rehabilitation. 2002 Oct 1;83(10):1364-9.
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- Li F, Harmer P, Fitzgerald K, Eckstrom E, Stock R, Galver J, Maddalozzo G, Batya SS. Tai chi and postural stability in patients with Parkinson's disease. New England Journal of Medicine. 2012 Feb 9;366(6):511-9.
- Wayne PM, Hausdorff JM, Lough M, Gow BJ, Lipsitz L, Novak V, Macklin EA, Peng CK, Manor B. Tai chi training may reduce dual task gait variability, a potential mediator of fall risk, in healthy older adults: cross-sectional and randomized trial studies. Frontiers in human neuroscience. 2015 Jun 9;9:332.
- Vergara-Diaz G, Osypiuk K, Hausdorff JM, Bonato P, Gow BJ, Miranda JG, Sudarsky LR, Tarsy D, Fox MD, Gardiner P, Thomas CA. Tai Chi for reducing dual-task gait variability, a potential mediator of fall risk in Parkinson’s disease: a pilot randomized controlled trial. Global advances in health and medicine. 2018 May;7:2164956118775385.
- Hausdorff JM, Edelberg HK, Mitchell SL, Goldberger AL, Wei JY. Increased gait unsteadiness in community-dwelling elderly fallers. Archives of physical medicine and rehabilitation. 1997 Mar 1;78(3):278-83.
- Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Archives of physical medicine and rehabilitation. 2001 Aug 1;82(8):1050-6.
- Maki BE. Gait changes in older adults: predictors of falls or indicators of fear?. Journal of the American geriatrics society. 1997 Mar;45(3):313-20.
- Hausdorff JM. Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Human movement science. 2007 Aug 1;26(4):555-89.
- Hausdorff JM. Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2009 Jun 29;19(2):026113.
- Busse ME, Wiles CM, van Deursen RW. Co-activation: its association with weakness and specific neurological pathology. Journal of neuroengineering and rehabilitation. 2006 Dec 1;3(1):26.
- Gow BJ, Hausdorff JM, Manor B, Lipsitz LA, Macklin EA, Bonato P, Novak V, Peng CK, Ahn AC, Wayne PM. Can Tai Chi training impact fractal stride time dynamics, an index of gait health, in older adults? Cross-sectional and randomized trial studies. PloS one. 2017 Oct 11;12(10):e0186212.
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Open Data Commons Attribution License v1.0
Total uncompressed size: 2.3 GB.
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wget -r -N -c -np https://physionet.org/files/taichidb/1.0.1/
|EMG_Subset.csv (download)||17.1 KB||2020-12-10|
|LICENSE.txt (download)||19.9 KB||2020-12-23|
|SHA256SUMS.txt (download)||72.0 KB||2020-12-30|
|TCPCHA_Subjects.csv (download)||26.9 KB||2020-12-10|