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Walking performance: correlation between energy cost of walking and walking participation. new statistical approach concerning outcome measurement.

Franceschini M, Rampello A, Agosti M, Massucci M, Bovolenta F, Sale P - PLoS ONE (2013)

Bottom Line: The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability.This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%.We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it.

View Article: PubMed Central - PubMed

Affiliation: Department of NeuroRehabilitation IRCCS San Raffale, Pisana, Rome.

ABSTRACT
Walking ability, though important for quality of life and participation in social and economic activities, can be adversely affected by neurological disorders, such as Spinal Cord Injury, Stroke, Multiple Sclerosis or Traumatic Brain Injury. The aim of this study is to evaluate if the energy cost of walking (CW), in a mixed group of chronic patients with neurological diseases almost 6 months after discharge from rehabilitation wards, can predict the walking performance and any walking restriction on community activities, as indicated by Walking Handicap Scale categories (WHS). One hundred and seven subjects were included in the study, 31 suffering from Stroke, 26 from Spinal Cord Injury and 50 from Multiple Sclerosis. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%. Our research reveal that, in our subjects, CW is the only predictor of the walking performance of in the community, to be compared with the score of WHS. We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it. In particular, these values could be used to predict the ability to walk in the community when discharged from the rehabilitation units, and to adjust the rehabilitative treatment to improve the performance.

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Interactive dot diagram of cut-off point of the energy cost of walking.(CW = Cost of walking).
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pone-0056669-g002: Interactive dot diagram of cut-off point of the energy cost of walking.(CW = Cost of walking).

Mentions: One hundred and seven subjects were included in the study, 61 (57%) were males and 46 (43%) females: thirty-one (29%) suffering from Stroke, 26 (24.3%) from SCI and 50 (46.7%) from MS. The sample average age was 49.79±14.70 years (Stroke 62.03±11.77 years; SCI 44.92±15.56 years; MS 44.74±11.23 years), with a range of 20–84 years. Regarding clinical walking evaluation, the sample average of WHS score was 3.97±1.06 (range of 1–5), with 37 subjects whose scores were under 3, and 70 subjects whose scores were above 3 on the WHS. Walking Distance average was 203.35±129.14 meters, with a range of 12.5–528 meters; the average velocity was 33.93±21.57 m/min, with a range of 2.08–88 m/min. The mean energy consumption was 10.81±2.81 (mlO2*Kg−1*Kg1) and the mean value of cost of walking was.51±.515 (mlO2*Kg−1*min−1). For each pathology, a summary description of the collected data of the descriptive analysis of the sample and of the performance on the walking distance (WD) (m and % of predicted value), velocity (m/min), VO2 consumption, energy cost of walking (CW) with a reference to the significant range, is provided in Table 1 and Table 2. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability (Table 3). The model presents a sufficient capacity of classification for each subject included in our sample (83.18% of cases). In our sample, due to the fact that both PPV and NPV are related to the sensitivity and the specificity of the test, and that they also depend on the prevalence of the disease in the population, these data, in our case, do not exist in the literature. The equation for the probability of classification model based on the measurement of energy cost (CW), which allowed us to determine the probability of "Walking Restriction in participation" for each specific value of the Energy Cost, is the following: . The examples for CW = 0.39 is is the following: . Finally, with the receiver-operating characteristic (ROC) analyses and Youden Index application, we have defined another model to identify a cut-off value of energy cost of walking that can predict the membership of each patient to one or other categories of dichotomy WHS. This model generated a cut-off value of.40 that is able to classify correctly the cases with a percentage of 85.05% (Fig. 1–2, table 4–5).


Walking performance: correlation between energy cost of walking and walking participation. new statistical approach concerning outcome measurement.

Franceschini M, Rampello A, Agosti M, Massucci M, Bovolenta F, Sale P - PLoS ONE (2013)

Interactive dot diagram of cut-off point of the energy cost of walking.(CW = Cost of walking).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585321&req=5

pone-0056669-g002: Interactive dot diagram of cut-off point of the energy cost of walking.(CW = Cost of walking).
Mentions: One hundred and seven subjects were included in the study, 61 (57%) were males and 46 (43%) females: thirty-one (29%) suffering from Stroke, 26 (24.3%) from SCI and 50 (46.7%) from MS. The sample average age was 49.79±14.70 years (Stroke 62.03±11.77 years; SCI 44.92±15.56 years; MS 44.74±11.23 years), with a range of 20–84 years. Regarding clinical walking evaluation, the sample average of WHS score was 3.97±1.06 (range of 1–5), with 37 subjects whose scores were under 3, and 70 subjects whose scores were above 3 on the WHS. Walking Distance average was 203.35±129.14 meters, with a range of 12.5–528 meters; the average velocity was 33.93±21.57 m/min, with a range of 2.08–88 m/min. The mean energy consumption was 10.81±2.81 (mlO2*Kg−1*Kg1) and the mean value of cost of walking was.51±.515 (mlO2*Kg−1*min−1). For each pathology, a summary description of the collected data of the descriptive analysis of the sample and of the performance on the walking distance (WD) (m and % of predicted value), velocity (m/min), VO2 consumption, energy cost of walking (CW) with a reference to the significant range, is provided in Table 1 and Table 2. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability (Table 3). The model presents a sufficient capacity of classification for each subject included in our sample (83.18% of cases). In our sample, due to the fact that both PPV and NPV are related to the sensitivity and the specificity of the test, and that they also depend on the prevalence of the disease in the population, these data, in our case, do not exist in the literature. The equation for the probability of classification model based on the measurement of energy cost (CW), which allowed us to determine the probability of "Walking Restriction in participation" for each specific value of the Energy Cost, is the following: . The examples for CW = 0.39 is is the following: . Finally, with the receiver-operating characteristic (ROC) analyses and Youden Index application, we have defined another model to identify a cut-off value of energy cost of walking that can predict the membership of each patient to one or other categories of dichotomy WHS. This model generated a cut-off value of.40 that is able to classify correctly the cases with a percentage of 85.05% (Fig. 1–2, table 4–5).

Bottom Line: The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability.This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%.We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it.

View Article: PubMed Central - PubMed

Affiliation: Department of NeuroRehabilitation IRCCS San Raffale, Pisana, Rome.

ABSTRACT
Walking ability, though important for quality of life and participation in social and economic activities, can be adversely affected by neurological disorders, such as Spinal Cord Injury, Stroke, Multiple Sclerosis or Traumatic Brain Injury. The aim of this study is to evaluate if the energy cost of walking (CW), in a mixed group of chronic patients with neurological diseases almost 6 months after discharge from rehabilitation wards, can predict the walking performance and any walking restriction on community activities, as indicated by Walking Handicap Scale categories (WHS). One hundred and seven subjects were included in the study, 31 suffering from Stroke, 26 from Spinal Cord Injury and 50 from Multiple Sclerosis. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%. Our research reveal that, in our subjects, CW is the only predictor of the walking performance of in the community, to be compared with the score of WHS. We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it. In particular, these values could be used to predict the ability to walk in the community when discharged from the rehabilitation units, and to adjust the rehabilitative treatment to improve the performance.

Show MeSH
Related in: MedlinePlus