Average Stride Time Research Articles

Associations of dual-task walking costs with cognition in Parkinson’s disease

BackgroundGait and cognition are closely associated in Parkinson's disease (PD), with specific cognitive domains being associated with different motor symptoms. By identifying gait parameters affected by cognition, clinicians can develop targeted interventions that address cognitive impairment, improve gait, and reduce the risk of injury in PD patients. Research questionWhat gait parameters are affected by cognition in PD patients during dual-task walking, and how are these parameters related to cognitive function as measured by the Montreal Cognitive Assessment (MoCA)? Methods36 patients with available gait data and cognitive assessments were enrolled. Gait data of usual and dual-task walking sessions were recorded using lightweight wireless wearable sensors attached to trunk, lower, and upper extremities. Dual-task costs were calculated from usual and dual-task measures. Statistical analysis included non-parametric tests, Wilcoxon signed-rank test, Spearman’s correlation, and stepwise linear regression models. ResultsWalking speed, cadence, asymmetry in arm swing (ASA), between arms’ amplitude symmetry (BAS), average stride time, and jerk of the acceleration movement of the legs were found to be affected during the dual-task walking session (P<0.05). Spearman’s correlation showed significant correlations between MoCA scores and ASA (ρ=-0.469, P=0.036) and BAS (ρ=-0.448, P=0.036) costs. Stepwise linear regression models found that MoCA scores were significant predictors of BAS and ASA costs (P<0.05). SignificanceThis study found a significant association between global cognitive ability and several gait parameters costs under cognitive load caused by dual-task walking in PD patients. The study identified the gait parameters that were affected by cognitive load and found that MoCA scores were significant predictors of those gait parameters. Identifying gait parameters affected by cognition can lead to more targeted interventions for improving gait and reducing injury risk in PD patients.

Validation of Linear and Nonlinear Gait Variability Measures Derived From a Smartphone System Compared to a Gold-Standard Footswitch System During Overground Walking.

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

The Efficacy of a Human-Ready miniMECP2 Gene Therapy in a Pre-Clinical Model of Rett Syndrome.

Inactivating mutations and the duplication of methyl-CpG binding protein 2 (MeCP2), respectively, mediate Rett syndrome (RTT) and MECP2 duplication syndrome. These disorders underscore the conceptual dose-dependent risk posed by MECP2 gene therapy for mosaic RTT patients. Recently, a miRNA-Responsive Autoregulatory Element (miRARE) mitigated the dose-dependent toxicity posed by self-complementary adeno-associated viral vector serotype 9 (AAV9) miniMECP2 gene therapy (scAAV9/miniMECP2-myc) in mice. Here, we report an efficacy assessment for the human-ready version of this regulated gene therapy (TSHA-102) in male Mecp2-/y knockout (KO) mice after intracerebroventricular (ICV) administration at postnatal day 2 (P2) and after intrathecal (IT) administration at P7, P14 (±immunosuppression), and P28 (±immunosuppression). We also report qPCR studies on KO mice treated at P7-P35; protein analyses in KO mice treated at P38; and a survival safety study in female adult Mecp2-/+ mice. In KO mice, TSHA-102 improved respiration, weight, and survival across multiple doses and treatment ages. TSHA-102 significantly improved the front average stance and swing times relative to the front average stride time after P14 administration of the highest dose for that treatment age. Viral genomic DNA and miniMECP2 mRNA were present in the CNS. MiniMeCP2 protein expression was higher in the KO spinal cord compared to the brain. In female mice, TSHA-102 permitted survivals that were similar to those of vehicle-treated controls. In all, these pivotal data helped to support the regulatory approval to initiate a clinical trial for TSHA-102 in RTT patients (clinical trial identifier number NCT05606614).

The influence of walking speed and effects of signal processing methods on the level of human gait regularity during treadmill walking.

In recent years the use of sample entropy (SampEn) to evaluate the complexity of the locomotor system in human gait data has gained in popularity. However, it has been suggested that SampEn is sensitive to various input parameters and signal preprocessing methods. This study quantified the effects of different temporal and spatial normalization approaches and various lengths of the template vector (m) on SampEn calculations. The discriminatory ability of SampEn was studied by comparing two walking conditions. Twenty-three participants (seven males, 55.7 ± 8.5years, 165.7 ± 7.9cm, 80.5 ± 16.7kg) walked on a treadmill with preferred (Vpref) and maximum (Vmax) speed. Data were segmented and resampled (SEGM), resampled and spatially normalized (NORM), resampled and detrended (ZERO). For vertical ground reaction force (vGRF) and center of pressure in anterio-posterior direction (COPap), in both walking conditions, SampEn was generally sensitive to the vector length and not to the data processing, except for COPap in ZERO, m = 2, 4. For the COPml SampEn behaved oppositely, it was sensitive to preprocessing method and not to the m length. The regularity of COPap and vGRF in all processed signals increased in Vmax condition. For the COPml only two signals, WHOLE and ZERO, revealed increased complexity caused by more demanding walking conditions. SampEn was able to discriminate between different walking conditions in all analyzed variables, but not in all signals. Depending on evaluated variable, SampEn was susceptible in different way for the m level and processing method. Hence, these should be checked and selected for each variable independently. For future studies evaluating influence of walking velocity on COP and vGRF regularity during treadmill walking it is advised to use raw time series. Furthermore, to maintain template vector which represents biological relevance it is advised to detect highest frequencies present in analyzed signals and evaluate minimal time interval which can reflect change caused by response of a neuromuscular system. During evaluating treadmill walking measured with 100Hz sampling frequency it is recommended to adopt m from 6 to 10, when average stride time is up to about 1s.

Assessment of diurnal variation of stride time variability during continuous, overground walking in healthy young adults

Background: There is emerging evidence that gait variability outcomes provide unique insights regarding the status of an individual’s locomotor control system; however, there is currently limited evidence on the within-day reliability of stride time variability (STV) outcomes, or whether they demonstrate diurnal variation, when measured during continuous, overground walking in healthy young adults. Research Questions: 1) Are STV outcomes measured in the morning and afternoon during continuous, overground walking significantly different in healthy young adults? 2) What is the within-day reliability of STV outcomes measured during continuous, overground walking in healthy young adults?. Methods: Thirty-one healthy young adults (20.8 ± 3.7 years) completed two 10-minute continuous, overground walking trials on the same day (9:00-11:00am and 3:00-5:00pm) at their preferred walking speed. Data from a waist-mounted tri-axial accelerometer were used to determine the series of consecutive stride times for each trial. Results: There were no significant differences between sessions for average walking speed, average stride time, or STV. The within-day reliability was excellent for average walking speed and stride time, and generally poor to fair for STV. Significance: Healthy young adults do not appear to demonstrate diurnal variation in STV outcomes during continuous, overground walking; however, the development of a protocol to improve their reliability, as well as the establishment of normative ranges for such outcomes, would be beneficial to improve their application and interpretation in research and clinical settings.

Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton

Lower limb exoskeletons provide assistance during the gait cycle using a state variable, one in particular is gait phase. This is crucial for the exoskeleton controller to provide the user accurate assistance. Conventional methods often utilize an event marker to estimate gait phase by computing the average stride time. However, this strategy has limitations in adapting to dynamic speeds. We developed a sensor fusion-based neural network model to estimate the gait phase in real-time that can adapt to dynamic speeds ranging from 0.6 to 1.1 m/s. Ten able-bodied subjects walked with an exoskeleton using our estimator and were provided with corresponding torque assistance. Our best performing model had RMSE below 29 ms and 4% for real-time estimation and torque generation, respectively, reducing the estimation error by 36.0% ( ${p} ) and torque error by 40.9% ( ${p} ) compared to conventional methods. Our results indicate that creating a general user-independent model and additionally training on user-specific data outperforms the user-specific model and user-independent model. Our study validates the feasibility of using a sensor fusion-based machine learning model to accurately estimate the user’s gait phase and improve the controllability of a lower limb exoskeleton.

AUTOMATIC QUANTIFICATION OF TANDEM WALKING USING A WEARABLE DEVICE: VALIDITY OF THE INSTRUMENTED TANDEM WALK

Tandem walk (TW) is typically assessed by the time to complete the task and the number of missteps, however, these measures suffer from limitations and may not fully capture the range of performance in this task. We developed metrics of TW by using a body-fixed, wearable sensor in young and older adults. Healthy young men (n=40) and older adult men (n=362) were studied. While wearing a 3D accelerometer on their lower back, subjects performed three different tasks: TW, usual-walking, and quiet standing. The extracted measures for TW were: High-to-Low frequency band ratio from the power spectral density from the ML axis [nu], signal vector magnitude[g], step duration[s], sample entropy from ML, AP axis[nu] and CV[%]. All of the TW metrics were significantly different in the young and older men (p<0.001). Older men completed the TW with higher CV, suggesting greater stride-to-stride variability and they walked more slowly, as seen by their step duration. Additionally, the frequency ratio measure suggests that the older adults displayed less complex corrective movements in the ML axis. TW measures were modestly correlated with usual-walking (e.g., average stride time with TW step time, r=0.3; p<0.001) and with quiet standing postural control (e.g., acceleration path length in the ML and AP axis with TW sample entropy in the ML axis, r=0.13; p=0.014). Metrics derived from a wearable device complement conventional TW measures and vary with age. Further work is needed to determine if TW, gait and posture metrics are differentially associated with distinct adverse health outcomes.

Association Between Temporal Spatial Parameters and Overuse Injury History in Runners: A Systematic Review and Meta-analysis.

Temporal spatial parameters during running are measurable outside of clinical and laboratory environments using wearable technology. Data from wearable technology may be useful for injury prevention, however the association of temporal spatial parameters with overuse injury in runners remains unclear. To identify the association between overuse injury and temporal spatial parameters during running. Electronic databases were searched using keywords related to temporal spatial parameters, running, and overuse injury, and authors' personal article collections through hand search. Articles included in this systematic review contained original data, and analytically compared at least one temporal spatial parameter (e.g. cadence) between uninjured and retrospectively or prospectively injured groups of runners. Articles were excluded from this review if they did not meet these criteria or measured temporal spatial parameters via survey. The internal validity of each article was assessed using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Meta-analyses were conducted for temporal spatial parameters if data existed from at least three separate cohorts of the same prospective or retrospective design. Data were pooled and analyzed using an inverse variance fixed-effect model. Thirteen articles which tested a total of 24 temporal spatial parameters during running were included in the review. Meta-analyses were conducted on four temporal spatial parameters using data from eleven retrospective studies. Healthy runners and those with a history of overuse injury had a similar average stride time (mean difference: 0.00 s, 95% CI - 0.01 to 0.01 s), contact time (mean difference: 0.00 s, 95% CI 0.00 to 0.01 s), cadence (mean difference: 0.3 steps per minute (spm), 95% CI - 1.8 to 2.5 spm), and stride length (mean difference 0.00 m, 95% CI - 0.05 to 0.05 m) during running. Data pooled for meta-analyses were limited to retrospective design studies. Studies included in the systematic review had low methodological consistency. Based on pooled results from multiple studies, stride time, contact time, cadence, and stride length averages are not distinguishable between runners either with or without a history of overuse injury. More prospective studies are required to determine the association of temporal spatial parameters with overuse injury development in runners. CRD42018112290.

Preliminary Clinical Application of Textile Insole Sensor for Hemiparetic Gait Pattern Analysis.

Post-stroke gait dysfunction occurs at a very high prevalence. A practical method to quantitatively analyze the characteristics of hemiparetic gait is needed in both clinical and community settings. This study developed a 10-channeled textile capacitive pressure sensing insole (TCPSI) with a real-time monitoring system and tested its performance through hemiparetic gait pattern analysis. Thirty-five subjects (18 hemiparetic, 17 healthy) walked down a 40-m long corridor at a comfortable speed while wearing TCPSI inside the shoe. For gait analysis, the percentage of the plantar pressure difference (PPD), the step count, the stride time, the coefficient of variation, and the phase coordination index (PCI) were used. The results of the stroke patients showed a threefold higher PPD, a higher step count (41.61 ± 10.7), a longer average stride time on the affected side, a lower mean plantar pressure on the affected side, higher plantar pressure in the toe area and the lateral side of the foot, and a threefold higher PCI (hemi: 19.50 ± 13.86%, healthy: 5.62 ± 5.05%) compared to healthy subjects. This study confirmed that TCPSI is a promising tool for distinguishing hemiparetic gait patterns and thus may be used as a wearable gait function evaluation tool, the external feedback gait training device, and a simple gait pattern analyzer for both hemiparetic patients and healthy individuals.

Smartphone App-Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability.

BackgroundWalking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and “dual-task” walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings.ObjectiveThe objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings.MethodsWe created an iPhone app that used the phone’s motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user’s pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard–instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times—a clinically meaningful metric of locomotor control—from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time (ie, percentage change from normal to dual-task conditions) from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses.ResultsAcross all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (P<.001, r2=.98). These correlations were independent of walking condition and pocket tightness. App- and GAITRite-derived stride-time dual-task costs were also highly correlated (P<.001, r2=.95). The error of app-derived stride times (mean 16.9, SD 9.0 ms) was unaffected by the magnitude of stride time, walking condition, or pocket tightness. For both normal and dual-task trials, average stride times derived from app walking trials demonstrated excellent test-retest reliability within and between both laboratory and home-based assessments (intraclass correlation coefficient range .82-.94).ConclusionsThe iPhone app we created enabled valid and reliable assessment of stride timing—with the smartphone in the pocket—during both normal and dual-task walking and within both laboratory and nonlaboratory environments. Additional work is warranted to expand the functionality of this tool to older adults and other patient populations.

Stride-Time Variability and Fall Risk in Persons with Multiple Sclerosis.

Gait variability is associated with falls in clinical populations. However, gait variability's link to falls in persons with Multiple Sclerosis (PwMS) is not well established. This investigation examined the relationship between stride-time variability, fall risk, and physiological fall risk factors in PwMS. 17 PwMS (62.8 ± 7.4 years) and 17 age-matched controls (62.8 ± 5.9 years) performed the 6-minute walk test. Stride-time was assessed with accelerometers attached to the participants' shanks. Stride-time variability was measured by interstride coefficient of variation (CV) of stride-time. The participant's fall risk was measured by the short form physiological profile assessment (PPA). A Spearman correlation analysis was used to determine the relationship between variables. Increased fall risk was strongly associated with increased stride-time CV in both PwMS (ρ = 0.71, p < 0.01) and the controls (ρ = 0.67, p < 0.01). Fall risk was not correlated with average stride-time (p > 0.05). In PwMS, stride-time CV was related to postural sway (ρ = 0.74, p < 0.01) while in the control group, it was related to proprioception (ρ = 0.61, p < 0.01) and postural sway (ρ = 0.78, p < 0.01). Current observations suggest that gait variability is maybe more sensitive marker of fall risk than average gait parameters in PwMS. It was also noted that postural sway may be potentially targeted to modify gait variability in PwMS.

Sensitivity of the Wolf’s and Rosenstein’s Algorithms to Evaluate Local Dynamic Stability from Small Gait Data Sets: Response to Commentaries by Bruijn et al.

Assessing gait stability using the Largest Lyapunov Exponent (k1) has become popular, especially because it may be a key measure in evaluating gait abnormalities in patient populations. However, clinical settings usually involve having small gait data sets and accurate determination of k1 estimates from such sets is difficult. In an effort to address this issue, Cignetti et al. recently identified that k1 estimates using the algorithm of Wolf et al. (W-algorithm) were more sensitive than those using the algorithm of Rosenstein et al. (R-algorithm) in order to capture age-related decline in gait stability from small data sets. Thus, they advocated the use of the former algorithm. Some concerns about the study were expressed afterwards by Bruijn et al. and we welcome the opportunity to discuss them in the present letter. Bruijn et al. expressed four concerns about the validity of the methods used by Cignetti et al. that could have biased the results. First, they indicate that although speed difference between young adults (YA) and older adults (OA) was not significant, it does not exclude speed as a confounder of the aging effect on gait stability. Although we agree that a perfect matching of YA and OA with respect to speed would definitely avoid confounding, such matching is highly unlikely as YA walk usually faster than OA. Accordingly, matching statistically the two groups in terms of average speed appears to be the best compromise between ecological validity and methodological validity. However, a mean to further avoid the confounding of speed on k1 is to evaluate group difference by using analyses of covariance (ANCOVAs) instead of using analyses of variance (ANOVAs), thus controlling for speed effect. As reported in Table 1, results from ANCOVAs run on the data sets of Cignetti et al. confirmed previous results obtained using ANOVAs. In particular, with respect to the main effect of age, k1 remained significantly larger in OA as compared to YA regardless the size of the data set (i.e., 3600, 7200, and 10,800 data points) when using the W-algorithm. Such result was obtained only for the largest data set (i.e., 10,800 data points) when using the R-algorithm. Therefore, the difference in k1 between YA and OA reported by Cignetti et al. is not biased by the inter-group difference in walking speed. Second, Bruijn et al. argued that the time series of YA might have counted more strides than those of OA due to shorter stride time, increasing artificially k1. However, the stride time was the same in YA and OA with mean ± standard error values of 1.27 ± 0.03 and 1.26 ± 0.05 s, respectively (Table 2). These data are in agreement with previous studies that reported similar values of stride time in both YA and OA populations. Accordingly, k1 exponents were estimated in the study of Cignetti et al. from a similar number of strides for both groups. Specifically, the time series with 3600 data points contained 47 strides in both YA and OA, the time series with 7200 data points contained 94 strides in YA and 95 strides in OA, and the time series with 10,800 data points contained 141 strides in YA and 143 strides in OA (Table 2). Hence, Bruijn et al. were mistaken in assuming that an intergroup difference in stride time could have biased the difference in k1 between YA and OA in Cignetti et al.’s study. A third concern expressed by Bruijn et al., closely related to the previous one, relates with the fact that Cignetti et al. did not normalize time using average stride time when estimating k1 with the W-algorithm, Address correspondence to Fabien Cignetti, Nebraska Biomechanics Core Facility, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE 68182-0216, USA. Electronic mail: fabien. cignetti@univ-amu.fr Annals of Biomedical Engineering, Vol. 40, No. 12, December 2012 ( 2012) pp. 2507–2509 DOI: 10.1007/s10439-012-0665-6

2.10 Walking is more like catching than tapping: gait in the elderly as a complex cognitive task

Walking is generally viewed as an automated, over-learned, rhythmic motor task and may even be considered the lower-limb analog of rhythmic finger tapping, another automated motor task. Thus, one might hypothesize that walking would be associated with a simple rhythmic task like tapping rather than with a complex motor task like catching. Surprisingly, however, we find that among older adults, routine walking has more in common with complex motor tasks, like catching a moving object, than it does with tapping. Tapping performance, including both the average tapping interval and the variability of tapping interval, was not significantly associated with any gait parameter (gait speed, average stride time and stride time variability). In contrast, catch game performance was significantly associated with measures of walking, suggesting that walking is more like catching than it is like tapping. For example, participants with a higher gait speed tended to have lower times to first move when catching, better catching accuracy, and less catching errors. Stride time variability was significantly associated with each of the measures of catching. Participants with a lower stride time variability (a more steady gait) had better catching accuracy, lower time to first move, fewer direction changes when moving the cursor to catch the falling object, and less catching errors. To understand this association, we compared walking performance to performance on the Stroop test, a classic measure of executive function, and tests of memory. Walking was associated with higher-level cognitive resources, specifically, executive function, but not with memory or cognitive function in general. For example, a lower (better) stride time variability was significantly associated with higher (better) scores on the Stroop test, but not with tests of memory. Similarly, when participants were stratified based on their performance on the Stroop test and tests of memory, stride time variability was dependent on the former, but not the latter. These findings underscore the interconnectedness of gait and cognitive function, indicate that even routine walking is a complex cognitive task that is associated with higher-level cognitive function, and suggest an alternative approach to the treatment of gait and fall risk in the elderly.

Walking is more like catching than tapping: gait in the elderly as a complex cognitive task

Walking is generally viewed as an automated, over-learned, rhythmic motor task and may even be considered the lower-limb analog of rhythmic finger tapping, another automated motor task. Thus, one might hypothesize that walking would be associated with a simple rhythmic task like tapping rather than with a complex motor task like catching. Surprisingly, however, we find that among older adults, routine walking has more in common with complex motor tasks, like catching a moving object, than it does with tapping. Tapping performance, including both the average tapping interval and the variability of tapping interval, was not significantly associated with any gait parameter (gait speed, average stride time and stride time variability). In contrast, catch game performance was significantly associated with measures of walking, suggesting that walking is more like catching than it is like tapping. For example, participants with a higher gait speed tended to have lower times to first move when catching, better catching accuracy, and less catching errors. Stride time variability was significantly associated with each of the measures of catching. Participants with a lower stride time variability (a more steady gait) had better catching accuracy, lower time to first move, fewer direction changes when moving the cursor to catch the falling object, and less catching errors. To understand this association, we compared walking performance to performance on the Stroop test, a classic measure of executive function, and tests of memory. Walking was associated with higher-level cognitive resources, specifically, executive function, but not with memory or cognitive function in general. For example, a lower (better) stride time variability was significantly associated with higher (better) scores on the Stroop test, but not with tests of memory. Similarly, when participants were stratified based on their performance on the Stroop test and tests of memory, stride time variability was dependent on the former, but not the latter. These findings underscore the interconnectedness of gait and cognitive function, indicate that even routine walking is a complex cognitive task that is associated with higher-level cognitive function, and suggest an alternative approach to the treatment of gait and fall risk in the elderly.

Gait dynamics in Parkinson's disease: relationship to Parkinsonian features, falls and response to levodopa

Objectives: Patients with Parkinson's disease (PD) have an increased risk of falling that has yet to be fully explained. To better understand the gait disturbance in PD and the factors that contribute to falls, we quantitatively evaluated: (1) the relationship between gait variability (a marker of fall risk in other populations), fall history, and other parkinsonian features, and (2) the effects of levodopa on these relationships. Methods: The average stride time and stride-to-stride variability were measured using force-sensitive insoles during comfortable walking. Fall frequency, motor control, function, and mental health were measured using the Unified Parkinson's Disease Rating Scale (UPDRS), the Mini-Mental State Exam (MMSE), and the timed motor tests of the Core Assessment Program for Intracerebral Transplantations (CAPIT) in 32 subjects with idiopathic PD, in an “off” (unmedicated) state and again in an “on” (medicated) state. Results: Average stride time was not associated with any UPDRS or CAPIT measure and was similar in fallers and non-fallers in “off” and “on” states (p>0.27). Stride time variability was significantly associated with fall frequency as well as with total scores on the CAPIT and the UPDRS, ADL abilities, and motor function. Stride time variability and falls were not related to tremor, rigidity or bradykinesia in the “off” state. 41% of subjects reported one or more falls. Stride time variability was 8.8±7.9% in fallers and 4.2±1.3% in non-fallers (p<0.009). Stride time variability significantly improved in response to levodopa, both in fallers and non-fallers, but remained increased in fallers (vs. non-fallers). Conclusions: The patho-physiology responsible for impaired stride-to-stride regulation of gait timing is apparently independent of other cardinal features of PD, i.e., tremor, rigidity, or bradykinesia, but is responsive to levodopa. Stride-to-stride variability is especially impaired among PD subjects with a history of falls, suggesting, for the first time, the possibility of exaggerated impairment of internal clock function in PD fallers.