11 research outputs found

    Movement Variability Increases With Shoulder Pain When Compensatory Strategies of the Upper Body Are Constrained

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    [DE] This cross-sectional study analyzed the influence of chronic shoulder pain (CSP) on movement variability/kinematics during humeral elevation, with the trunk and elbow motions constrained to avoid compensatory strategies. For this purpose, 37 volunteers with CSP as the injured group (IG) and 58 participants with asymptomatic shoulders as the control group (CG) participated in the study. Maximum humeral elevation (Emax), maximum angular velocity (Velmax), variability of the maximum angle (CVEmax), functional variability (Func_var), and approximate entropy (ApEn) were calculated from the kinematic data. Patients' pain was measured on the visual analogue scale (VAS). Compared with the CG, the IG presented lower Emax and Velmax and higher variability (i.e., CVEmax, Func_var, and ApEn). Moderate correlations were achieved for the VAS score and the kinematic variables Emax, Velmax and variability of curve analysis, Func_varm, and ApEn. No significant correlation was found for CVEmax. In conclusion, CSP results in a decrease of angle and velocity and an increased shoulder movement variability when the neuromuscular system cannot use compensatory strategies to avoid painful positions.This work was funded by the Spanish Government and cofinanced by EU FEDER funds (Grant DPI2013-44227-R)Lopez Pascual, J.; Page Del Pozo, AF.; Serra Añó, P. (2017). Movement Variability Increases With Shoulder Pain When Compensatory Strategies of the Upper Body Are Constrained. Journal of Motor Behavior. 1-8. https://doi.org/10.1080/00222895.2017.1371109S1

    Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers

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    Objectives: The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI). Setting: The study was conducted in the Physical Therapy department and the Physical Education and Sports department of the University of Valencia. Methods: A total of 20 volunteers were asked to perform 10 physical activities, lying down, body transfers, moving items, mopping, working on a computer, watching TV, arm-ergometer exercises, passive propulsion, slow propulsion and fast propulsion, while fitted with four accelerometers placed on both wrists, chest and waist. The activities were grouped into five categories: sedentary, locomotion, housework, body transfers and moderate physical activity. Different machine learning algorithms were used to develop individual and group activity classifiers from the acceleration data for different combinations of number and position of the accelerometers. Results: We found that although the accuracy of the classifiers for individual activities was moderate (55-72%), with higher values for a greater number of accelerometers, grouped activities were correctly classified in a high percentage of cases (83.2-93.6%). Conclusions: With only two accelerometers and the quadratic discriminant analysis algorithm we achieved a reasonably accurate group activity recognition system (490%). Such a system with the minimum of intervention would be a valuable tool for studying physical activity in individuals with SCI.X Garcia-Masso gratefully acknowledges the support of the University of Valencia under project UV-INV-PRECOMP13-115364.García-Massó, X.; Serra-Añó P.; Gonzalez, L.; Ye Lin, Y.; Prats-Boluda, G.; Garcia Casado, FJ. (2015). Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers. Spinal Cord. 53(10):772-777. https://doi.org/10.1038/sc.2015.81S7727775310Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC et al. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640–647.Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA . Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755–1759.Manns PJ, Chad KE . Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566–1571.Serra-Añó P, Pellicer-Chenoll M, García-Massó X, Morales J, Giner-Pascual M, González L-M . Effects of resistance training on strength, pain and shoulder functionality in paraplegics. Spinal Cord 2012; 50: 827–831.Slater D, Meade MA . Participation in recreation and sports for persons with spinal cord injury: review and recommendations. NeuroRehabilitation 2004; 19: 121–129.Lee M, Zhu W, Hedrick B, Fernhall B . Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336–343.Lee M, Zhu W, Hedrick B, Fernhall B . Estimating MET values using the ratio of HR for persons with paraplegia. Med Sci Sports Exerc 2010; 42: 985–990.Hayes AM, Myers JN, Ho M, Lee MY, Perkash I, Kiratli BJ . Heart rate as a predictor of energy expenditure in people with spinal cord injury. J Rehabil Res Dev 2005; 42: 617–624.Washburn RA, Zhu W, McAuley E, Frogley M, Figoni SF . The physical activity scale for individuals with physical disabilities: development and evaluation. Arch Phys Med Rehabil 2002; 83: 193–200.Ginis KAM, Latimer AE, Hicks AL, Craven BC . Development and evaluation of an activity measure for people with spinal cord injury. Med Sci Sports Exerc 2005; 37: 1099–1111.Khan AM, Lee Y-K, Lee S, Kim T-S . Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly. Med Biol Eng Comput 2010; 48: 1271–1279.Khan AM, Lee Y-K, Lee SY, Kim T-S . A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed Publ 2010; 14: 1166–1172.Liu S, Gao RX, John D, Staudenmayer J, Freedson PS . SVM-based multi-sensor fusion for free-living physical activity assessment. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc 2011; 2011: 3188–3191.Liu S, Gao RX, John D, Staudenmayer JW, Freedson PS . Multisensor data fusion for physical activity assessment. IEEE Trans Biomed Eng 2012; 59: 687–696.Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P . An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–1307.Trost SG, Wong W-K, Pfeiffer KA, Zheng Y . Artificial neural networks to predict activity type and energy expenditure in youth. Med Sci Sports Exerc 2012; 44: 1801–1809.David Apple MD . Pain above the injury level. Top Spinal Cord Inj Rehabil 2001; 7: 18–29.Subbarao JV, Klopfstein J, Turpin R . Prevalence and impact of wrist and shoulder pain in patients with spinal cord injury. J Spinal Cord Med 1995; 18: 9–13.Postma K, van den Berg-Emons HJG, Bussmann JBJ, Sluis TAR, Bergen MP, Stam HJ . Validity of the detection of wheelchair propulsion as measured with an Activity Monitor in patients with spinal cord injury. Spinal Cord 2005; 43: 550–557.Hiremath SV, Ding D, Farringdon J, Vyas N, Cooper RA . Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury. Spinal Cord 2013; 51: 705–709.Itzkovich M, Gelernter I, Biering-Sorensen F, Weeks C, Laramee MT, Craven BC et al. The Spinal Cord Independence Measure (SCIM) version III: reliability and validity in a multi-center international study. Disabil Rehabil 2007; 29: 1926–1933.García-Massó X, Serra-Añó P, García-Raffi LM, Sánchez-Pérez EA, López-Pascual J, Gonzalez LM . Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury. Spinal Cord 2013; 51: 898–903.Preece SJ, Goulermas JY, Kenney LPJ, Howard D . A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 2009; 56: 871–879.Hurd WJ, Morrow MM, Kaufman KR . Tri-axial accelerometer analysis techniques for evaluating functional use of the extremities. 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    Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury

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    Study design: Cross-sectional validation study. Objectives: The goals of this study were to validate the use of accelerometers by means of multiple linear models (MLMs) to estimate the O2 consumption (VO2) in paraplegic persons and to determine the best placement for accelerometers on the human body. Setting: Non-hospitalized paraplegics’ community. Methods: Twenty participants (age=40.03 years, weight=75.8 kg and height=1.76 m) completed sedentary, propulsion and housework activities for 10 min each. A portable gas analyzer was used to record VO2. Additionally, four accelerometers (placed on the non-dominant chest, non-dominant waist and both wrists) were used to collect second-by-second acceleration signals. Minute-by-minute VO2 (ml kg−1 min−1) collected from minutes 4 to 7 was used as the dependent variable. Thirty-six features extracted from the acceleration signals were used as independent variables. These variables were, for each axis including the resultant vector, the percentiles 10th, 25th, 50th, 75th and 90th; the autocorrelation with lag of 1 s and three variables extracted from wavelet analysis. The independent variables that were determined to be statistically significant using the forward stepwise method were subsequently analyzed using MLMs. Results: The model obtained for the non-dominant wrist was the most accurate (VO2=4.0558−0.0318Y25+0.0107Y90+0.0051YND2−0.0061ZND2+0.0357VR50) with an r-value of 0.86 and a root mean square error of 2.23 ml kg−1 min−1. Conclusions: The use of MLMs is appropriate to estimate VO2 by accelerometer data in paraplegic persons. The model obtained to the non-dominant wrist accelerometer (best placement) data improves the previous models for this population.LM Garcia-Raffi and EA Sanchez-Perez gratefully acknowledge the support of the Ministerio de Economia y Competitividad under project #MTM2012-36740-c02-02. X Garcia-Masso is a Vali + D researcher in training with support from the Generalitat Valenciana.Garcia Masso, X.; Serra Añó, P.; García Raffi, LM.; Sánchez Pérez, EA.; Lopez Pascual, J.; González, L. (2013). Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury. Spinal Cord. 51(12):898-903. https://doi.org/10.1038/sc.2013.85S8989035112Van den Berg-Emons RJ, Bussmann JB, Haisma JA, Sluis TA, van der Woude LH, Bergen MP et al. A prospective study on physical activity levels after spinal cord injury during inpatient rehabilitation and the year after discharge. Arch Phys Med Rehabil 2008; 89: 2094–2101.Jacobs PL, Nash MS . Exercise recommendations for individuals with spinal cord injury. Sports Med 2004; 34: 727–751.Erikssen G . Physical fitness and changes in mortality: the survival of the fittest. Sports Med 2001; 31: 571–576.Warburton DER, Nicol CW, Bredin SSD . Health benefits of physical activity: the evidence. CMAJ 2006; 174: 801–809.Haennel RG, Lemire F . Physical activity to prevent cardiovascular disease. How much is enough? Can Fam Physician 2002; 48: 65–71.Manns PJ, Chad KE . Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566–1571.Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA . Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755–1759.Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC et al. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640–647.Slater D, Meade MA . Participation in recreation and sports for persons with spinal cord injury: review and recommendations. Neurorehabilitation 2004; 19: 121–129.Valanou EM, Bamia C, Trichopoulou A . Methodology of physical-activity and energy-expenditure assessment: a review. J Public Health 2006; 14: 58–65.Liu S, Gao RX, Freedson PS . Computational methods for estimating energy expenditure in human physical activities. Med Sci Sports Exerc 2012; 44: 2138–2146.Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M . Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181–188.Riddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebø L, Sardinha LB et al. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc 2004; 36: 86–92.Hiremath SV, Ding D . Evaluation of activity monitors in manual wheelchair users with paraplegia. J Spinal Cord Med 2011; 34: 110–117.Hiremath SV, Ding D . Evaluation of activity monitors to estimate energy expenditure in manual wheelchair users. 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Med Eng Phys 2010; 32: 1224–1228.Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S, Burtin C, Langer D et al. Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One 2012; 7: e39198.Macfarlane DJ . Automated metabolic gas analysis systems: a review. Sports Med 2001; 31: 841–861.Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P . An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–1307.Daubechies I . Ten Lectures on Wavelets. SIAM, Philadelphia. 1999.Debnat I . Wavelets and Signal Processing. Birkhauser, Boston. 2003.Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE . Energy cost of physical activities in persons with spinal cord injury. Med Sci Sports Exerc 2010; 42: 691–700.Lee M, Zhu W, Hedrick B, Fernhall B . Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336–343.Crouter SE, Clowers KG, Bassett DR Jr . A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 2006; 100: 1324–1331

    Dynamic thoracohumeral kinematics are dependent upon the etiology of the shoulder injury

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    [EN] Obtaining kinematic patterns that depend on the shoulder injury may be important when planning rehabilitation. The main goal of this study is to explore whether the kinematic patterns of continuous and repetitive shoulder elevation motions are different according to the type of shoulder injury in question, specifically tendinopathy or rotator cuff tear, and to analyze the influence of the load handled during its assessment. For this purpose, 19 individuals with tendinopathy and 9 with rotator cuff tear performed a repetitive scaption movement that was assessed with stereophotogrammetry. Furthermore, static range of motion (ROM) and isometric strength were evaluated with a goniometer and a dynamometer, respectively. Dynamic measurements of maximum elevation (Emax), variablility of the maximum angle (VMA), maximum angular velocity (Velmax), and time to maximum velocity (tmaxvel) were found to be significantly different between the tendinopathy group (TG) and the rotator cuff tear group (RTCG). No differences were found in the ROM assessed with goniometry and the isometric strength. The effect of increasing the load placed in the hand during the scaption movement led to significant differences in Emax, VMA, tmaxvel and repeatability. Therefore, only the dynamic variables showed sufficient capability of detecting differences in functional performance associated with structural shoulder injury. The differences observed in the kinematic variables between patients with tendinopathy and rotator cuff tear seem to be related to alterations in thoracohumeral rhythm and neuromuscular control. Kinematic analysis may contribute to a better understanding of the functional impact of shoulder injuries, which would help in the assessment and treatment of shoulder pain.This work was funded by the Spanish Government, Secretaria de Estado de Investigacion, Desarrollo e Innovacion, and co-financed by EU FEDER funds (Grant DPI2013-44227-R). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Lopez Pascual, J.; Page Del Pozo, AF.; Serra Añó, P. (2017). Dynamic thoracohumeral kinematics are dependent upon the etiology of the shoulder injury. PLoS ONE. 12(8). https://doi.org/10.1371/journal.pone.0183954S12

    Comparison of conventional hamstring/quadriceps ratio between genders in level-matched soccer players

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    Objectives: The main goal of our study is to compare the hamstring/quadriceps (H/Q) ratio at different knee angles between level-matched male and female soccer players and to determine whether differences in the H/Q ratio exist between the dominant and the non-dominant leg. Methods: A cross-sectional study design was used to compare an isometric H/Q ratio and functional isokinetic ratio (between hamstring in eccentric contraction and quadriceps in concentric contraction) between males (n = 14) and females (n = 14). These ratios were studied at two different speeds of movement (60° s−1 and 180° s−1) and in five different positions (40°, 50°, 60°, 70° and 80° degrees of knee flexion). Results: Our results showed no differences in the H/Q ratio between genders. The ratio in the dominant leg showed an average of 9% higher values. The ratios were an average of 53.4% lower in positions near flexion than in positions near extension. Conclusions: For both men and women, the results showed lower ratios in the non-dominant leg compared to the dominant leg. At higher velocities, the force ratios were higher, while in more knee-flexed positions, the ratios were lower. Finally, we did not find differences in ratios between men and women

    Postural and cortical responses following visual occlusion in standing and sitting tasks

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    © 2017 Springer-Verlag Berlin HeidelbergPerturbation-evoked responses (PERs) to a physical perturbation of postural stability have been detected using electroencephalography (EEG). Components of these responses are hypothesized to demonstrate the detection (P1) and evaluation (N1) of postural instability. Despite the important contribution of the visual system to postural control, PERs to a visual perturbation of posture have yet to be reported. Ten healthy young adults were exposed to unpredictable visual occlusion mediated through liquid crystal glasses under two conditions of postural demand: quiet standing and quiet sitting. The participants’ PERs and postural responses were recorded and differences between conditions assessed using Wilcoxon signed-rank tests. In response to unpredictable visual occlusion, both P1 and N1 components of the PER were observed in both postural conditions. The amplitude of the P1 response remained consistent between postural conditions ((Formula presented.), (Formula presented.)), whereas N1 amplitude and postural responses were significantly smaller in the sitting condition ((Formula presented.), (Formula presented.)). This is the first study to demonstrate cortical responses to visual perturbation of posture. The responses to postural perturbation by sudden visual occlusion are similar in nature to that seen in relation to a physical perturbation. In addition, the amplitude of the N1 response is not only consistent with the relative magnitude of the perturbation, but also the underlying postural set, with a larger N1 seen in standing relative to sitting. The study informs the relative importance of vision to postural stability, postural set and provides a protocol to objectively assess sensory-based postural disorders
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