Physical Therapy Provider Continuity Predicts Functional Improvements in Inpatient Rehabilitation.

BACKGROUND AND PURPOSE: Health care continuity has been linked to improved patient outcomes in a variety of professions and settings. Patients in inpatient rehabilitation receive a consistent dosage of physical therapy (PT) treatment; however, the providing physical therapist may vary. Despite the potential influence of PT provider continuity on functional outcomes in the inpatient rehabilitation setting, this association has not yet been studied. METHODS: An observational retrospective chart review was conducted on 555 discharged inpatient rehabilitation patients. The relationship between the number of PT providers from whom a patient received care and Quality Indicator (QI) Mobility discharge scores was examined with Pearson product-moment correlation coefficients, initially with the entire patient group and secondarily with distinct diagnostic groups. Data from subgroups for whom a significant relationship was established were then included in a hierarchical linear regression analysis accounting for relevant covariates. RESULTS: The number of PT providers correlated negatively with QI Mobility discharge scores ( r = -0.41, P ≤ 0.001). When controlling for QI Mobility admission scores, the "Stroke" (partial r = -0.17, P = 0.02), "Spinal Cord Injury" (partial r = -0.28, P = 0.002), and "Other Neuromuscular" (partial r = -0.35, P = 0.03) groups demonstrated significant inverse relationships. A hierarchical linear regression incorporating these 3 diagnostic groups revealed that the number of PT providers remained a significant predictor of QI Mobility discharge scores ( B = -1.50, P ≤ 0.001) when accounting for covariates. DISCUSSION AND CONCLUSIONS: PT provider continuity is related to the functional improvement of neurologically impaired patients in inpatient rehabilitation.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A405 , which discusses the findings of this work in a narrative format).

Comparing adaptive fractal and detrended fluctuation analyses of stride time variability: Tests of equivalence.

BACKGROUND: Fractal analyses quantify self-similarities in stride-to-stride fluctuations over different time scales. Fractal exponents can be measured with adaptive fractal analysis (AFA) or detrended fluctuation analysis (DFA), though measurements obtained with the algorithms have not been directly compared. RESEARCH QUESTION: Are stride time fractal exponents measured with AFA and DFA algorithms equivalent? METHODS: Data from 50 participants with Parkinson's Disease (n = 15), age-similar healthy adults (n = 15) and healthy young adults (n = 20) were analyzed in this cross-sectional, observational study. Participants completed 6-min walks at self-selected speeds overground on a straight walkway and on a treadmill. Stride times were measured with inertial measurement units. Fractal exponents in stride time data were processed using AFA and DFA algorithms and compared with two one-sided tests of equivalence. Mixed ANOVAs were used to compare exponents between groups and conditions. RESULTS: Fractal exponents computed with AFA and DFA were equivalent neither in the overground (0.796 & 0.830, respectively, p = .587) nor treadmill conditions (0.806 & 0.882, respectively, p = .122). Fractal exponents measured with DFA were higher than when measured with AFA. Standard errors were 22% lower when measured with AFA. Additionally, a group × condition interaction was statistically significant when fractal exponents were processed with the AFA algorithm (F(2,47) = 11.696, p < .001), whereas the group × condition interaction was not statistically significant when DFA exponents were compared (F(2, 47) = 2.144, p = .129). SIGNIFICANCE: AFA and DFA do not produce equivalent estimates of the fractal exponent α in stride time dynamics. Estimates of the fractal exponent α obtained with AFA or DFA algorithms therefore should not be used interchangeably. Standard errors were lower when derived with AFA. Fractal exponents calculated with AFA may be more sensitive to conditions that influence stride time fractal dynamics than are measures calculated with DFA.

Treadmill walking alters stride time dynamics in Parkinson's disease.

BACKGROUND: Treadmill training may be used to improve gait rhythmicity in people with Parkinson's disease. Treadmills, however, alter dynamical stride time fluctuations in healthy adults in a manner that mimics pathologic states, indicating the stride-to-stride fluctuations that characterize healthy gait are constrained. It is unclear if treadmills similarly alter dynamic gait properties in Parkinson's disease. RESEARCH QUESTION: Do stride time fractal dynamics in individuals with Parkinson's disease differ between treadmill and overground walking? METHODS: Fifteen participants with Parkinson's disease and 15 healthy age-similar adults walked for 6 min in a conventional overground condition and on a treadmill while wearing inertial measurement units. Gait speed, stride times and stride time variability were measured. Fractal exponents (α) were computed with adaptive fractal analysis. Inferential statistics were analyzed with mixed model analyses of variance and post hoc simple effects tests. RESULTS: Mean gait speeds decreased and stride times increased on the treadmill but did not differ between the Parkinson's and control groups. Stride time variability was greater in the Parkinson's than control group in both conditions. Most relevant to our research question, stride time fractal exponents were greater on the treadmill (mean α = .910) than overground (mean α = .797) in individuals with Parkinson's disease, but not in healthy controls. SIGNIFICANCE: The fractal scaling exponent α emanating from stride time fluctuations during treadmill walking increased toward a 1/f signal of α = 1.0 that has been interpreted as an optimal structural variability for gait. The clinical implication is that treadmill training may promote more efficient walking dynamics in people with Parkinson's disease than conventional overground training.

Complexity, fractal dynamics and determinism in treadmill ambulation: Implications for clinical biomechanists.

BACKGROUND: Reduced inter-stride complexity during ambulation may represent a pathologic state. Evidence is emerging that treadmill training for rehabilitative purposes may constrain the locomotor system and alter gait dynamics in a way that mimics pathological states. The purpose of this study was to examine the dynamical system components of gait complexity, fractal dynamics and determinism during treadmill ambulation. METHODS: Twenty healthy participants aged 23.8 (1.2) years walked at preferred walking speeds for 6min on a motorized treadmill and overground while wearing APDM 6 Opal inertial monitors. Stride times, stride lengths and peak sagittal plane trunk velocities were measured. Mean values and estimates of complexity, fractal dynamics and determinism were calculated for each parameter. Data were compared between overground and treadmill walking conditions. FINDINGS: Mean values for each gait parameter were statistically equivalent between overground and treadmill ambulation (P>0.05). Through nonlinear analyses, however, we found that complexity in stride time signals (P<0.001), and long-range correlations in stride time and stride length signals (P=0.005 and P=0.024, respectively), were reduced on the treadmill. INTERPRETATION: Treadmill ambulation induces more predictable inter-stride time dynamics and constrains fluctuations in stride times and stride lengths, which may alter feedback from destabilizing perturbations normally experienced by the locomotor control system during overground ambulation. Treadmill ambulation, therefore, may provide less opportunity for experiencing the adaptability necessary to successfully ambulate overground. Investigators and clinicians should be aware that treadmill ambulation will alter dynamic gait characteristics.

A comparison of variability in spatiotemporal gait parameters between treadmill and overground walking conditions.

Motorized treadmills are commonly used in biomechanical and clinical studies of human walking. Whether treadmill walking induces identical motor responses to overground walking, however, is equivocal. The purpose of this study was to examine differences in the spatiotemporal gait parameters of the lower extremities and trunk during treadmill and overground walking using comparison of mean and variability values. Twenty healthy participants (age 23.8±1.2 years) walked for 6min on a treadmill and overground while wearing APDM 6 Opal inertial monitors. Stride length, stride time, stride velocity, cadence, stance phase percentage, and peak sagittal and frontal plane trunk velocities were measured. Mean values were calculated for each parameter as well as estimates of short- (SD1) and long-term variability (SD2) using Poincaré analyses. The mean, SD1, and SD2 values were compared between overground and treadmill walking conditions with paired t-tests (α=0.05) and with effect size estimates using Cohen's d statistic. Mean values for each of the gait parameters were statistically equivalent between treadmill and overground walking (p>0.05). The SD1 and SD2 values representing short- and long-term variability were considerably reduced (p<0.05) on the treadmill as compared to overground walking. This demonstrates the importance of consideration of gait variability when using treadmills for research or clinical purposes. Treadmill training may induce invariant gait patterns, posing difficulty in translating locomotor skills gained on a treadmill to overground walking conditions.