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A NORMATIVE DATABASE OF ISOKINETIC UPPER-EXTREMITY JOINT STRENGTHS: TOWARDS THE EVALUATION OF DYNAMIC HUMAN PERFORMANCE K.A. KHALAF, M. PARNIANPOUR*
Department of Biomedical Engineering The University of Miami, Florida *Department of Industrial, Welding, & Systems Engineering The Ohio State University, Ohio, U.S.A.
ABSTRACT ABSTRACT The objective of this this study study was to provide provide aa normative normativedatabase databaseofofdynamic dynamicupper-extremity upper-extremity The (shoulder and andelbow) elbow)joint jointstrengths strengthstotofill fillthe thecurrent currentvoid voidininliterature literature multidimensional strength (shoulder forfor multidimensional strength The isokinetic isokineticstrength strengthofofthe theelbow elbowand andshoulder shoulder jointswas wastested tested twentynornor capacity profiles. The joints forfortwenty males and andfemales. females.The Theindependent independentvariables variablesconsisted consistedofof jointangular angular position,joint joint angular mal males joint position, angular velocity, direction of exertion, exertion, and andgender. gender. The Themeasured measuredjoint jointstrength strength(torque, (torque,Nm) Nm) was only was thethe only de-de fined dependent dependentvariable. variable.The Themajority majorityofofexisting existingjoint jointstrength strength prediction models and normative fined prediction models and normative da-da nature. The Thefew fewavailable availabledynamic dynamicmodels modelsare arereported reportedininthe theform formofof tabases are static (isometric) (isometric) in nature. torque as a function functionofofjoint jointangle. angle.Since Sincejoint jointstrength strengthisisa afunction function both joint angular position torque of of both thethe joint angular position and angular angular velocity, velocity, descriptive descriptive models modelsshould shouldtake takethis thisinteraction interactioninto into consideration. The dynamic and consideration. The dynamic joint strengths strengths of of the thesubjects subjects were were studied studied using usingthe theKINCOM125E KIN_COM 125E Plus. A second-order multiple joint Plus. A second-order multiple regressionanalysis analysiswas wasused usedtotomodel modelthe thedynamic dynamic3-D 3-Dstrength strength surface response of each regression surface response of each jointjoint in in each direction direction of of exertion. exertion. Analysis Analysisofofvariance variance(ANOVA) (ANOVA)with with repeatedmeasures measures designwas was used each repeated design used to to for the the effects effects of of gender, gender, angular angularposition, position, angular angular velocity, velocity, and and direction direction on on the the dynamic dynamic test for strengthofofeach eachjoint, joint,joint jointstrength strengthwas was significantly influenced dynamic parameters such as the strength significantly influenced by by dynamic parameters such as the angularvelocity. velocity. The Theinteraction interactionbetween between angular position and velocity was highly significant. angular angular position and velocity was highly significant. 3-D3-D strengthsurface surfacerepresentation representationmay maybebeused usedasasa a"performance "performance capacity envelope" to comprehen strength capacity envelope" to comprehenindividual'sdynamic dynamicjoint jointstrength strengthperformance. performance. sively characterize an individual's Bkmwd Eng Eng Appl App! Basis Basis Comm, Cranm, 2001 2(X)1 (April); (April); 13: 13: 79-92. Biomed Keywords: Performance Performance capacity, capacity, Shoulder, Shoulder. Elbow, Elbow, Rehabilitation, Rehabilitation. Ergonomics, Ergonomics, Dynamic Dynamic strength. Keywords:
1. INTRODUCTION Received: Feb. 12,2001; accepted: March 20, 2001. Correspondence: Kinda Khalaf, Ph.D. Dept. of Biomedical Engineering, The University of Miami, 1251 Memorial Dr., MCA 219A, Coral Gables, Florida, U.S.A. E-mail:
[email protected]
Work-related musculoskeletal injuries are the most frequent cause of chronic or permanent impairm e nt afflicting 19 million people and costing the US approximately $13-20 billion annually (1). While work-related low-back injuries remain the leading
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health hazard that occurs in manual material handling (MMH) activities, both in terms of frequency (33%) and cost (48%), the economic and health impacts of work-related shoulder, elbow, hand, and wrist injuries should not be underestimated (1). For example, The Bureau of Labor Statistics (BLS) reports that in 1994, based on a 250,000 random sample of US private sec tor establishments, 705,800 cases (32%) resulted from overexertion or repetitive motion. 367,424 injuries were due to overexertion in lifting; 65% affected the back and 13% affected the shoulder. Out of 92,576 in juries or illnesses that occurred as a result of repetitive motion, 55% affected the wrist, 7% affected the shoul der, and 6% affected the back (1). The mean cost per case of compensable low-back pain was reported to be $8,321 in 1989 (2). Similarly the mean compensation cost per case of upper-extremity, work-related musculoskeletal disorder (WMSD) was $8,070 in 1993 (2). The accurate assessment of functional muscular strength (maximum torque generation capability of a muscle group performing physiological work in a par ticular functional posture/movement) has been a major concern in the fields of rehabilitation, biomechanics, work physiology, as well as other related research ar eas for many decades. Whether the objectives are to compare the effects of various strength and condition ing programs, to prescribe and document the benefits of a specific therapeutic exercise (3), to prevent man ual material handling (MMH) injuries by allowing ap propriate task assignment based on a worker's strength capabilities (4,5,6), or to document the extent of dis ability (7), the underlying goal is the need for objective and reliable means of quantifying functional muscular performance. Various approaches have been advocated to de velop models that predict muscular forces and mo ments at the neuromuscular level (8,9,10,11,12), and at the musculoskeletal (jo mt ) l e v e l (13,14,15,16,17, 18,19). Neuromuscular models, in which "cause-andeffect" relationships exist, provide more insight into the neuromotor strategies underlying human motion. They also have the ability to incorporate the basic properties of musculotendon units and musculoskeletal geometry as a function of joint angular position and velocity (9,10,20). Unfortunately, up to date, reasona bly accurate neuromuscular models are far too com plex for industrial and/or clinical applications (since they require the solution of ordinary or partial differen tial equations), and require parameters that are often quite difficult to obtain. The various assumptions re garding the complex muscle architecture and proper ties, and muscle excitation levels introduce further complexity and potential errors (8,20). Alternatively, several strength prediction models have been developed and implemented at the joint level (13,14,16,17,21). The fundamental concept be hind these models is that net muscular strength may be
characterized using the measured moments at the joint level without concern of the internal neuromuscular dynamics and load sharing amongst individual muscu lotendon units (9,20,22). Similar to the concept of the "feasible region" employed in system optimization, these "descriptive" models attempt to predict a "maximum performance capacity envelope" to charac terize an individual's joint strength performance (23). From an ergonomic and biomechanics task-analysis perspective, these models have been considered in valuable due to their conceptual simplicity, since they often result in regression models which can be de scribed by algebraic equations. They have been used over the past few decades in the design and analysis of industrial MMH tasks, and in torque-driven simulation applications of physical activities (24,25). Inherent limitations include the inability to study the roles of individual muscles, as well as the complexity of incor porating the effect of multiarticular muscles (muscles spanning more than one joint). Considering that each modeling approach has its limitations, the imple mented model is usually highly dependent on the re search objectives and the type of task under study (6,20). With the advent of isokinetic exercise about three decades ago (26), and the realization that dynamic test ing of muscle function is necessary for the objective evaluation of most human movements, isokinetic test ing protocols have been extensively used for muscu loskeletal performance assessment (7,27,28,29,30, 31,32). While numerous studies provided clinicians and practitioners with average and maximum joint strength information, the void still exists for a stan dardized joint strength prediction model, which com prehensively satisfies the needs of many potential us ers of the resulting strength measures (28,33,34). The majority of existing models and normative databases are static (isometric) in nature. Furthermore, most dy namic joint strength prediction models of maximal torque levels are reported in the form of torque as a function of joint angle (28,32,37). Such models have been related to the variations of the muscles moment arms as a function of jomt angles, and the lengthtension relationships, well documented by physiologi cal studies of muscle mechanics (10,11,20,38,39). In reality, joint strength is a function of both the joint an gular position and angular velocity since the force that a muscle group generates about a joint depends not only on the relative lengths of the contributing muscles, but also on the rate with which the muscle length changes (34,40,41). Descriptive models of strength should take this interaction into consideration for more accurate quantitative characterization of musculoskele tal performance (19,21,34,42). In our previous study (43), a normative database of 3-D dynamic surface responses of joint flexion and extension strength was developed for the lower ex-
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tremity joints: ankle, knee and hip. The objective of the present study is to expand this work to include the elbow and shoulder upper extremity joints. In addition, the effects of the joint angular position, joint angular velocity, direction of exertion, and gender on joint strength will be investigated for the five joints. The underlying intent is to provide ergonomists, clinicians, and practitioners with a better tool for predicting joint strength capacity as compared to the traditional use of a single value representation for joint strength without consideration of the inherent tension-length-velocity relationship for contractile machinery and the dynamic change of moment arm of muscles as a function of an gular position. The strength profiles can be combined with task demand parameters in order to provide ap propriate task assignment based on an individual's physical capabilities (44). Such data representation can be of further use is in the formulation of biomechanical simulation models (24,25,45,46).
2. METHODS
2.1 Subjects Twenty healthy males and females participated in this study. The mean (s.d.) age, mass, and stature were 26.2 (3.8) years, 85.1 (14.0) kg, and 178.6 (10.7) cm for the males (N=10), and 24.2 (2.6) years, 58.3 (7.2) kg, and 165.3 (8.4) cm for the females (N=10) respec tively. The detailed measures of population anthro pometry are provided in Table 1. None of the subjects reported a history of musculoskeletal disorders or pain in the previous year. The subjects were briefed on the study's goals and procedures prior to signing an in formed consent form approved by the human subjects committee.
2.2 Apparatus The dynamic joint strengths of the subjects were studied for the elbow and shoulder joints using the KINCOM 125E Plus muscle testing and training sys tem from Chattecx. Corp. (Chattanooga, TN). The KIN_COM is a closed loop system, which consists of a servomotor-controlled rotary arm equipped with an attached user-positioned load cell. A joint-specific at tachment comfortably fastens the subject's limb to the
Table 1. The mean (s.d.) age (yr), mass (kg), and stature (cm) for 20 subjects (males: ml-mlO; females: fll£20), and for the male population, female population, and combined population. Subject ml m2 m3 m4 m5 m6 m7 m8 m9 m10 f11 f12 f13 f14 f15 f16 fl7 f18 f19 120 Males
