- Email: [email protected]
Inter-laboratory repeatability of gait analysis measurements
Gait & Posture 35 (2012) S1–S47
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Abstracts of SIAMOC 2011 Congress
A. Leardini ∗ Istituto Ortopedico Rizzoli, Laboratorio di Analisi del Movimento, Via di Barbiano 1/10, 40136 Bologna, Italy E-mail address: [email protected]. Inter-laboratory repeatability of gait analysis measurements M.G. Benedetti 1 , A. Merlo 1,2 , M. Bacchini 3 , S. Costi 4 , I. 2 5 6 7 Campanini , G.A. Cutti , D. Mazzoli , M. Manca , A. Leardini 1 1 Laboratorio Analisi del Movimento, Istituto Ortopedico Rizzoli, Bologna 2 Laboratorio Analisi del Movimento, SOC Neuroriabilitazione, Osp Correggio, AUSL Reggio Emilia 3 Laboratorio Analisi del Movimento, Fondazione Don Carlo Gnocchi Onlus, Parma 4 Laboratorio Analisi del Movimento del Bambino Disabile, Università degli Studi di Modena e Reggio Emilia 5 Laboratorio Analisi del Movimento, Centro Protesi INAIL, Vigorso di Budrio 6 Laboratorio Analisi del Movimento e Biomeccanica, Sol et Salus, Torre Pedrera 7 Laboratorio Analisi del Movimento, Unità di Medicina Riabilitativa, AUSL Ferrara Introduction: Sharing data among motion analysis laboratories is a top priority in gait analysis research [1], however it may result in a large number of inconsistencies. Repeatability of the typical gait analysis measurements has been assessed over subjects, examiners, sessions, protocols, but rarely over laboratories [2]. Our purpose is to explore the consistency of routine gait analyses on a single healthy subject once performed in different clinical laboratories. Materials and methods: A single healthy subject, 26-year-old, male, 177 cm height, 70 kg weight, was examined within a 1month period in seven different gait analysis laboratories (here acknowledged). These had their own motion analysis systems, and established protocols: 4 ‘conventional’, 2 Total3DGait, 1 CAST. A full kinematics and kinetics assessment was performed at each site by a local examiner with at least 5-year experience in gait analysis. Each lab provided six selected trials and the relevant complete printed report, along with other characteristics (pathway length,
PII of original article: S0966-6362(10)00284-5 ∗ Tel.: +39 51 6366522; fax: +39 51 6366561. 0966-6362/$ – see front matter doi:10.1016/j.gaitpost.2011.09.021
force-plate positions, details on hardware, software releases, kinematics and analog data sampling rate, etc). The consistency of all the measurements was assessed by the coefficient of variation (CV) and the maximum spread, i.e. the worst-case difference. These were used for the anthropometric measurements, spatiotemporal parameters for both the right and the left side, local peaks and mean area from the vertical component of the ground reaction force, local peaks, braking and propulsive areas and their ratio from the fore-aft component, and all joint rotation values in up-right posture (‘offset’). The similarity between two curves was assessed by the coefficient of determination r2 , along with the scaling factor (SF) and the bias term (B), obtained by a robust linear regression, thus separating the effect of the vertical shifts from that of shape variation [3]. For any given gait analysis curve, all possible 21 two-labs comparisons were performed and the obtained coefficients averaged. This procedure was applied to left and right lower limb joint rotations, moments and powers in the sagittal plane. Results: Differences as large as 2–3 cm were found in the anthropometric measurements at the pelvis. CV of the spatiotemporal parameters was in general lower than 6%. Reaction forces were similar, with differences in the order of a few percentage of the body weight. The similarity amongst joint kinematic curves was high, with, on average, r2 > 0.90 in both the sagittal and the frontal (knee excluded) planes and r2 > 0.60 in the transverse plane (knee excluded) with the worst performance at the hip (r2 = 0.30). Pattern similarity was excellent for joint moments at the ankle (r2 = 0.90) and good at knee (r2 = 0.70) and hip (r2 = 0.66) joints. For the latter, comparisons between labs with the same protocols resulted in better similarities (r2 > 0.80). Patterns similarity was very good also for joint power at the ankle (r2 = 0.80) but not satisfactory at the knee and the hip joints (r2 = 0.30 and r2 = 0.40, respectively). Discussion and conclusions: Large consistency was found in joint kinematic and kinetic curves, despite of the large spectrum of differences in the techniques utilized in the laboratories involved. Different protocols can result in similar curves, except for those measurements where knee axes and hip centre of rotation definition is implied. The joint rotations and moments with the most different patterns are those pointed out in a previous work where differences between protocols were identified and pointed out [4]. Relevant variability was observed to be accounted for differences in markers positioning on the thigh, anthropometric measures, and event detection, all dependent on the examiner.
S2
A. Leardini / Gait & Posture 35 (2012) S1–S47
References [1] Engsberg, et al. Gait and clinical movement analysis research priorities: 2007 Update from the research committee of the gait and clinical movement analysis society. Gait & Posture 2009;29:169–71. [2] Gorton, et al. Assessment of the kinematic variability among 12 motion analysis laboratories. Gait & Posture 2009;29:398–402. [3] Iosa, et al. A linear method for curve comparison in gait data. Gait & Posture 2009;30(S53):4. [4] Ferrari, et al. Quantitative comparison of five current protocols in gait analysis. Gait & Posture 2008;28(2):207–16.
doi:10.1016/j.gaitpost.2011.09.022 Inter-operator variability in the measurement of ‘pelvic height’. Preliminary study and comparison between measurement devices A. Merlo ∗ , M. Ferrari, S. Scaltriti, B. Damiano, A. Fusolini, R. Iotti, I. Campanini LAM – Movement Analysis Laboratory, Neurorehabilitation Unit – AUSL di Reggio Emilia, Correggio Hosp., Italy Introduction: In 45 Italian laboratories out of 60 registered by the Siamoc on its website, the model described in [1] is used to perform gait analysis of patients. A variable used in the regression equations of this model is the measure of the so-called pelvis height (pelH). More properly, pelH is the antero-posterior component of the distance between a point approximating the hip centre and the homolateral ASIS. This point is obtained by the operator based on the position of the prominence of the great trocanter with the patient supine and the leg in neutral position. To the authors’ knowledge, this measurement is carried out in gait laboratories by means of a Martin pelvimeter or of a caliper used in association with a ruler placed horizontally over the two ASIS. No standard procedure is used across laboratories. The large inter-operator variability in the identification of the anatomical landmarks (AL) used in the measurement of pelH has been described in the literature [2]. After AL identification, a further error, not yet investigated in the literature, could be introduced during the execution of the measurement, which might be difficult during the clinical practice, i.e. with subjects with a prominent abdomen. In this preliminary work, we present a comparison of the interoperator variability in the measurement of pelH, when assessed by a standard caliper (SC) and a novel device, purpose-designed and built, referred to as Anatomical Caliper (AC). Materials and methods: Five operators of the same laboratiry measured, independently, pelH in two subjects, one normal weight (BMI = 23) and one overweight (BMI = 30), by using SC and AC in random order. AL were previously identified and marked on subjects’ skin. Both the standard deviation and the largest difference among operators (worst case) were used to assess variability. Results: For the first subject (BMI = 23) pelH was 111 ± 6 mm with SC and 115 ± 3 with AC. The largest difference among operators was 14 mm with SC and 7 mm with AC. For the second subject (BMI = 30) pelH was 117 ± 6 mm with SC and 114 ± 3 with AC. The largest difference among operators was 13 mm with SC and 7 mm with AC. Discussion: The measure of pelH, obtained after having identified the ALs and fixed the lower limb position, may vary among operators by more than 1 cm and may represent an additional source of error in the execution of gait analysis. This source of error should be minimized in the daily routine, because an error of +1 cm in the measure of pelH determines an error in the location of hip joint error of about 1 cm backwards, with a consequent delay, i.e., in the inversion of the hip sagittal
moment from flexion-to-extension in the order of 10% of the stride duration [3]. The use of the Anatomical Caliper, in this preliminary work, has halved both the standard deviation and the largest difference in the measurements of the five operators on both subjects. We are carrying on a study on a wider sample, also supported by the use of radiographic data to control for the concurrent validity of the measures.
References [1] Davis, et al. Human Mov Sci 1991;10:575–87. [2] Della Croce, et al. Gait Posture 2005;21:226–37. [3] Stagni, et al. J Biomech 2000;33(11):1479–87.
doi:10.1016/j.gaitpost.2011.09.023 Fall risk after stroke: Do stabilometric measures add to the predictive value of clinical information? J. Jonsdottir, D. Cattaneo, R. Parelli, M. Meotti, A. Montesano LaRiCE, Neuroriabilitazione, Fondazione Don Gnocchi Background and purpose: It is important that we can identify persons who are at risk of falling. For that we need insight into the underlying pathological and functional impairments. While balance functions are often assessed with clinical scales, stabilometric measures may give further insight into the specific abnormalities in postural control and the consequential imbalance. This is a retrospective study to investigate whether stabilometric assessment has added predictive value compared with clinical measures for identifying persons with a history of falls in a population with stroke [1]. Methods: Fifty-five first ever stroke survivors were evaluated in a logistic regression analysis [2]. Model 1 (N = 55) was based on outcomes of various clinical assessment scales frequently used to measure dynamic and static balance in the stroke population. Model 2 was the same as Model 1 but was based on a reduced number of subjects (N = 43). In Model 3 that was the same as Model 2 (N = 43) stabilometric variables were added to the clinical scales. Data collection: After informed consent and after all assessments of static and dynamic balance had been carried out in one session, fall history was collected retrospectively for the six months before inclusion in the study. Persons with 2 or more falls were categorized as fallers. The following clinical variables discriminated between fallers and non fallers (P ≤ 0.15) and were used in all 3 models: Berg Balance Scale (/BBS), Dynamic Gait Index, Timed Up and Go, Activities Balance Scale, Barthel Index. Stabilometric measures collected on a Technobody platform included in model 3 were: sway medio-lateral (Sway ML) and velocity of sway antero-posterior (Vel AP) during quiet standing with eyes open Data analysis: Clinical variables shown to be significantly associated with falling were used as independent candidate variables for both the full and the reduced multivariate logistic regression model (Model 1 and 2) whereas stabilometric variables associated with falling were added to Model 2 to form Model 3. The significance level was set at 0.10 to account for the low number of fallers and for the importance of identifying fallers. Results: Out of 55 subjects, mean age 61.8 ± 13.2 years, onset 2.69 ± 4.84 years, 11 were identified as fallers. Of the 43 subjects modeled in the reduced logistic Models 2 and 3, mean age 62.72 ± 12.11, onset 2.76 ± 0.14, 9 were fallers. There were no significant differences between non fallers and fallers in age and onset. See Table 1 for validity of models. BBS was a significant predictor of being a faller in Model 1 while not in reduced Models 2 and 3. Reduced Model 2 was not significant but became significant when
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Abstracts of SIAMOC 2011 Congress
A. Leardini ∗ Istituto Ortopedico Rizzoli, Laboratorio di Analisi del Movimento, Via di Barbiano 1/10, 40136 Bologna, Italy E-mail address: [email protected]. Inter-laboratory repeatability of gait analysis measurements M.G. Benedetti 1 , A. Merlo 1,2 , M. Bacchini 3 , S. Costi 4 , I. 2 5 6 7 Campanini , G.A. Cutti , D. Mazzoli , M. Manca , A. Leardini 1 1 Laboratorio Analisi del Movimento, Istituto Ortopedico Rizzoli, Bologna 2 Laboratorio Analisi del Movimento, SOC Neuroriabilitazione, Osp Correggio, AUSL Reggio Emilia 3 Laboratorio Analisi del Movimento, Fondazione Don Carlo Gnocchi Onlus, Parma 4 Laboratorio Analisi del Movimento del Bambino Disabile, Università degli Studi di Modena e Reggio Emilia 5 Laboratorio Analisi del Movimento, Centro Protesi INAIL, Vigorso di Budrio 6 Laboratorio Analisi del Movimento e Biomeccanica, Sol et Salus, Torre Pedrera 7 Laboratorio Analisi del Movimento, Unità di Medicina Riabilitativa, AUSL Ferrara Introduction: Sharing data among motion analysis laboratories is a top priority in gait analysis research [1], however it may result in a large number of inconsistencies. Repeatability of the typical gait analysis measurements has been assessed over subjects, examiners, sessions, protocols, but rarely over laboratories [2]. Our purpose is to explore the consistency of routine gait analyses on a single healthy subject once performed in different clinical laboratories. Materials and methods: A single healthy subject, 26-year-old, male, 177 cm height, 70 kg weight, was examined within a 1month period in seven different gait analysis laboratories (here acknowledged). These had their own motion analysis systems, and established protocols: 4 ‘conventional’, 2 Total3DGait, 1 CAST. A full kinematics and kinetics assessment was performed at each site by a local examiner with at least 5-year experience in gait analysis. Each lab provided six selected trials and the relevant complete printed report, along with other characteristics (pathway length,
PII of original article: S0966-6362(10)00284-5 ∗ Tel.: +39 51 6366522; fax: +39 51 6366561. 0966-6362/$ – see front matter doi:10.1016/j.gaitpost.2011.09.021
force-plate positions, details on hardware, software releases, kinematics and analog data sampling rate, etc). The consistency of all the measurements was assessed by the coefficient of variation (CV) and the maximum spread, i.e. the worst-case difference. These were used for the anthropometric measurements, spatiotemporal parameters for both the right and the left side, local peaks and mean area from the vertical component of the ground reaction force, local peaks, braking and propulsive areas and their ratio from the fore-aft component, and all joint rotation values in up-right posture (‘offset’). The similarity between two curves was assessed by the coefficient of determination r2 , along with the scaling factor (SF) and the bias term (B), obtained by a robust linear regression, thus separating the effect of the vertical shifts from that of shape variation [3]. For any given gait analysis curve, all possible 21 two-labs comparisons were performed and the obtained coefficients averaged. This procedure was applied to left and right lower limb joint rotations, moments and powers in the sagittal plane. Results: Differences as large as 2–3 cm were found in the anthropometric measurements at the pelvis. CV of the spatiotemporal parameters was in general lower than 6%. Reaction forces were similar, with differences in the order of a few percentage of the body weight. The similarity amongst joint kinematic curves was high, with, on average, r2 > 0.90 in both the sagittal and the frontal (knee excluded) planes and r2 > 0.60 in the transverse plane (knee excluded) with the worst performance at the hip (r2 = 0.30). Pattern similarity was excellent for joint moments at the ankle (r2 = 0.90) and good at knee (r2 = 0.70) and hip (r2 = 0.66) joints. For the latter, comparisons between labs with the same protocols resulted in better similarities (r2 > 0.80). Patterns similarity was very good also for joint power at the ankle (r2 = 0.80) but not satisfactory at the knee and the hip joints (r2 = 0.30 and r2 = 0.40, respectively). Discussion and conclusions: Large consistency was found in joint kinematic and kinetic curves, despite of the large spectrum of differences in the techniques utilized in the laboratories involved. Different protocols can result in similar curves, except for those measurements where knee axes and hip centre of rotation definition is implied. The joint rotations and moments with the most different patterns are those pointed out in a previous work where differences between protocols were identified and pointed out [4]. Relevant variability was observed to be accounted for differences in markers positioning on the thigh, anthropometric measures, and event detection, all dependent on the examiner.
S2
A. Leardini / Gait & Posture 35 (2012) S1–S47
References [1] Engsberg, et al. Gait and clinical movement analysis research priorities: 2007 Update from the research committee of the gait and clinical movement analysis society. Gait & Posture 2009;29:169–71. [2] Gorton, et al. Assessment of the kinematic variability among 12 motion analysis laboratories. Gait & Posture 2009;29:398–402. [3] Iosa, et al. A linear method for curve comparison in gait data. Gait & Posture 2009;30(S53):4. [4] Ferrari, et al. Quantitative comparison of five current protocols in gait analysis. Gait & Posture 2008;28(2):207–16.
doi:10.1016/j.gaitpost.2011.09.022 Inter-operator variability in the measurement of ‘pelvic height’. Preliminary study and comparison between measurement devices A. Merlo ∗ , M. Ferrari, S. Scaltriti, B. Damiano, A. Fusolini, R. Iotti, I. Campanini LAM – Movement Analysis Laboratory, Neurorehabilitation Unit – AUSL di Reggio Emilia, Correggio Hosp., Italy Introduction: In 45 Italian laboratories out of 60 registered by the Siamoc on its website, the model described in [1] is used to perform gait analysis of patients. A variable used in the regression equations of this model is the measure of the so-called pelvis height (pelH). More properly, pelH is the antero-posterior component of the distance between a point approximating the hip centre and the homolateral ASIS. This point is obtained by the operator based on the position of the prominence of the great trocanter with the patient supine and the leg in neutral position. To the authors’ knowledge, this measurement is carried out in gait laboratories by means of a Martin pelvimeter or of a caliper used in association with a ruler placed horizontally over the two ASIS. No standard procedure is used across laboratories. The large inter-operator variability in the identification of the anatomical landmarks (AL) used in the measurement of pelH has been described in the literature [2]. After AL identification, a further error, not yet investigated in the literature, could be introduced during the execution of the measurement, which might be difficult during the clinical practice, i.e. with subjects with a prominent abdomen. In this preliminary work, we present a comparison of the interoperator variability in the measurement of pelH, when assessed by a standard caliper (SC) and a novel device, purpose-designed and built, referred to as Anatomical Caliper (AC). Materials and methods: Five operators of the same laboratiry measured, independently, pelH in two subjects, one normal weight (BMI = 23) and one overweight (BMI = 30), by using SC and AC in random order. AL were previously identified and marked on subjects’ skin. Both the standard deviation and the largest difference among operators (worst case) were used to assess variability. Results: For the first subject (BMI = 23) pelH was 111 ± 6 mm with SC and 115 ± 3 with AC. The largest difference among operators was 14 mm with SC and 7 mm with AC. For the second subject (BMI = 30) pelH was 117 ± 6 mm with SC and 114 ± 3 with AC. The largest difference among operators was 13 mm with SC and 7 mm with AC. Discussion: The measure of pelH, obtained after having identified the ALs and fixed the lower limb position, may vary among operators by more than 1 cm and may represent an additional source of error in the execution of gait analysis. This source of error should be minimized in the daily routine, because an error of +1 cm in the measure of pelH determines an error in the location of hip joint error of about 1 cm backwards, with a consequent delay, i.e., in the inversion of the hip sagittal
moment from flexion-to-extension in the order of 10% of the stride duration [3]. The use of the Anatomical Caliper, in this preliminary work, has halved both the standard deviation and the largest difference in the measurements of the five operators on both subjects. We are carrying on a study on a wider sample, also supported by the use of radiographic data to control for the concurrent validity of the measures.
References [1] Davis, et al. Human Mov Sci 1991;10:575–87. [2] Della Croce, et al. Gait Posture 2005;21:226–37. [3] Stagni, et al. J Biomech 2000;33(11):1479–87.
doi:10.1016/j.gaitpost.2011.09.023 Fall risk after stroke: Do stabilometric measures add to the predictive value of clinical information? J. Jonsdottir, D. Cattaneo, R. Parelli, M. Meotti, A. Montesano LaRiCE, Neuroriabilitazione, Fondazione Don Gnocchi Background and purpose: It is important that we can identify persons who are at risk of falling. For that we need insight into the underlying pathological and functional impairments. While balance functions are often assessed with clinical scales, stabilometric measures may give further insight into the specific abnormalities in postural control and the consequential imbalance. This is a retrospective study to investigate whether stabilometric assessment has added predictive value compared with clinical measures for identifying persons with a history of falls in a population with stroke [1]. Methods: Fifty-five first ever stroke survivors were evaluated in a logistic regression analysis [2]. Model 1 (N = 55) was based on outcomes of various clinical assessment scales frequently used to measure dynamic and static balance in the stroke population. Model 2 was the same as Model 1 but was based on a reduced number of subjects (N = 43). In Model 3 that was the same as Model 2 (N = 43) stabilometric variables were added to the clinical scales. Data collection: After informed consent and after all assessments of static and dynamic balance had been carried out in one session, fall history was collected retrospectively for the six months before inclusion in the study. Persons with 2 or more falls were categorized as fallers. The following clinical variables discriminated between fallers and non fallers (P ≤ 0.15) and were used in all 3 models: Berg Balance Scale (/BBS), Dynamic Gait Index, Timed Up and Go, Activities Balance Scale, Barthel Index. Stabilometric measures collected on a Technobody platform included in model 3 were: sway medio-lateral (Sway ML) and velocity of sway antero-posterior (Vel AP) during quiet standing with eyes open Data analysis: Clinical variables shown to be significantly associated with falling were used as independent candidate variables for both the full and the reduced multivariate logistic regression model (Model 1 and 2) whereas stabilometric variables associated with falling were added to Model 2 to form Model 3. The significance level was set at 0.10 to account for the low number of fallers and for the importance of identifying fallers. Results: Out of 55 subjects, mean age 61.8 ± 13.2 years, onset 2.69 ± 4.84 years, 11 were identified as fallers. Of the 43 subjects modeled in the reduced logistic Models 2 and 3, mean age 62.72 ± 12.11, onset 2.76 ± 0.14, 9 were fallers. There were no significant differences between non fallers and fallers in age and onset. See Table 1 for validity of models. BBS was a significant predictor of being a faller in Model 1 while not in reduced Models 2 and 3. Reduced Model 2 was not significant but became significant when