Journal of Occupational Rehabilitation, Vol 4, No. 3, 1994
Lifestyle Correlates of Carpal Tunnel Syndrome Lisa M. Vogelsang, 1 Robert L. Williams, 2,4 and Kathleen Lawler 3
The potential for predicting membership in a Carpal Tunnel Syndrome group (CTS) vs. a non-CTS group was evaluated for five psychological variables (i.e., life events stress, perceived stress, self-management habits, cognitive self-control skills, and lifestyle organization) and three physical variables (i.e., general physical symptoms, suspected medical risk for CTS, and generic musculoskeletal problems). The subjects included 50 pairs of workers, with each pair having one worker who had CTS and the other who had not. A logistic regression ana~sis indicated that five of the measures (three psychological and two physical) were significant single model predictors of membership in CTS and non-CTS groups. The most efficient multifactor model in predicting CTS appeared to be a combination of measures reflecting generic musculoskeletal problems and lifestyle organization. KEY WORDS: Carpal Tunnel Syndrome; hand injuries; stress; self-management.
INTRODUCTION Carpal Tunnel Syndrome (CTS) is one of the top five injuries that plague industrial workers in the United States. It is a repetitive motion injury that, according to the U.S. Department of Labor, accounts for about half of all reported occupational illnesses (1). This injury is characterized by pain, numbness, tingling, and/or neuropathy in the hands and wrists, typically resulting from impaired sensory conduction in the median nerve at the carpal tunnel in the wrist (2). Carpal Tunnel Syndrome is most likely to occur in occupations that require repetitive, forceful arm and hand movements (2). Occupations assumed to be related to CTS include assembly jobs, sewing jobs, keyboard operation, meat processing jobs, and interpreting for the deaf (1, 3, 4). The percentage of workers in these occupations who eventually develop CTS varies from as low as 5% to greater than 50%. 1The Rehabilitation and Wellness Corporation, Knoxville, Tennessee 37923. 2Department of Educational and Counseling Psychology, The University of Tennessee, Knoxville, Tennessee 37996. 3Department of Psychology, The University of Tennessee, Knoxville, Tennessee 37966. 4Correspondence should be directed to Robert L. Williams, Department of Educational and Counseling Psychology, The University of Tennessee, Knoxville, Tennessee 37996. 141 I053-0487/94/0900-0141507.00/09 1994PlenumPublishingCorporation
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Why do some of the workers in these high-risk occupations never develop CFS? Some of the personal factors that distinguish CTS from non-CTS workers appear to be of a physical nature. For example, structural features of the hands and wrists (e.g., wrists depth/width ratio) as well as the idiosyncratic nature of hand and wrist movements may be factors that can help to distinguish workers who develop CTS from those who do not (5). Other physical factors that may contribute to CTS include tension level in the hands and wrists (6-8), reduced blood circulation to the hands and wrists (9), venous pooling and vascular congestion in the region of the carpal tunnel (5, 10, 11), and more generic physical problems (e.g., arthritis, hyperthyroidism, vascular sclerosis, and hypertension) (12, 13). It may be that an accumulation of many different types of physical symptoms would increase the probability of developing CTS (10). Some personal factors that may increase the probability of CTS are more psychological than physical in nature. Psychological stress is one such factor. The two types of stress that might potentially be implicated in CTS are an accumulation of significant life events (14) and a perception of being overwhelmed by life events (15). Both have been related to a variety of physical symptoms, including low back pain and upper neck and shoulder pain (14-19). Another psychological characteristic that could be related to vulnerability to CTS is general self-management. This construct has previously been linked to selfreported health status (20). Self-management has been defined in terms of lifestyle organization (20), adaptive personal habits (21), and cognitive dimensions such as self-talk and perceived self-efficacy (22). It could be that a well-ordered life characterized by adaptive habits and productive self-talk would make one less vulnerable to a variety of physical problems, including CTS. Some evidence suggests that workers with good health habits may be less prone to CTS than obese and physically inactive workers (10, 23). The fundamental question addressed in this study is whether psychological variables such as stress level and self-management can differentiate workers who develop CTS from those who do not. With relevant demographic characteristics and working conditions being roughly equivalent for CTS and non-CTS subjects, do stress and self-management variables serve as significant predictors of CTS? How does the predictive potential of these psychological variables compare to that of suspected physical and medical conditions that might also be related to CTS?
METHOD Subjects Subjects were selected from occupations that have been documented as having a disproportionately high rate of CTS. The subjects included automotive parts assembly workers, keyboard operators, electronics industry workers, and garment industry workers from East Tennessee. Interpreters for the deaf from the National Center on Deafness at California State University, Northridge and from the
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Lifestyle and CTS Table I. Descriptive Statistics for Matching Variables: CTS and Non-CTS Subjects
Matching variable C ~ subjects~ Age (years) Weight (pounds) Height (inches) Total years (current site) Non-CTS subjectsb Age (years) Weight (pounds) Height (inches) Total years (current site)
M
SD
Min
Max
37.30
7.86
24
56
155.74
43.27
95
280
64.58
2.42
60
71
8.76
6.79
0.5
27
36.40
7.96
23
59
148.90
35.89
105
245
64.82
2.35
60
71
8.84
6.37
1.0
27
an = 50 subjects. bn = 50 subjects.
Knoxville, Tennessee Registry of Interpreters for the Deaf were also included in the sample. The CTS subjects were selected from Workers' Compensation and other records from each industry that indicated a medical diagnosis of CTS by an orthopedic surgeon. The diagnostic criteria most frequently cited included impaired nerve conduction in the hand and wrist, nocturnal pain, numbness, and tingling in the hand and wrist. Some CTS subjects were postsurgical, some presurgical, and some nonsurgical. The non-CTS subjects were randomly selected from the remaining employee pools that best matched the CTS subjects. The sample consisted of 50 pairs of subjects matched as closely as possible on selected demographic criteria. Each pair was matched from the same industry and same job title and also had similar physical characteristics and job histories. The specific matching variables were age, gender, race, height, weight, body type, job duties, and length of time on the job. Body type referred to the somatotype categories of ectomorph, mesomorph, and endomorph. These somatotype categories were determined by visual clinical judgment of wrist size and skeletal structure in combination with lean muscle mass vs. body fat. One member of the pair had CTS within the last 2 years and the other had never had CTS. Thus, 50 subjects with CTS were matched to 50 subjects without CTS (see Table I for a summary of the statistics on matching variables). Each pair was perfectly matched on gender: 47 out of 50 pairs were matched on race; and job duties were either matched exactly or matched on ergonomic risk factors. 5 5Although a formal ergonomie job analysis was not performed, detailed information was obtained through observation of job tasks and supervisors'reports and interviews regarding such ergonomicrisk
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144 INSTRUMENTATION
Social Readjustment Rating Scale (SRRS) Developed by Holmes and Rahe (14), this scale has been a standard in stress research for many years. It has been used to establish relationships between life events and physical and emotional illness and injury (18, 19, 24-26). The format of the SRRS used in this study was the same as that used in Feuerstein et al.'s (19) study on low back pain; that is, people indicated how many times each event had occurred in the past 2 years and that number was multiplied by a rating factor for each event. The resulting numbers were then totalled across events. A cap of ten was arbitrarily put on the number of occurrences of each life event to prevent extreme scores. The ratings for the various life events were the same as those originally established by Holmes and Rahe. On the SRRS the respondent checks only the occurrence of various life events and does not rate the perceived positive or negative valence of those events.
Perceived Stress Scale (PSS) This instrument measures the degree to which respondents find their lives unpredictable, uncontrollable, and overloading (15). These variables have been found to be central components of the experience of stress according to the researchers who developed the instrument. Scores on this scale have been shown to be negatively correlated with self-management, life satisfaction, self-efficacy, health habits, health status, optimism, and purpose in life and positively correlated with total anger, anger-in, anger-out, and anger symptoms (27). Test-retest reliability and internal consistency measures for the PSS have ranged from .84 to .86.
Self-Control Schedule (SCS) Several factors are included under one total score for this inventory: (a) the use of cognitive strategies to control physiological and emotional sensations, (b) the application of problem-solving strategies, (c) the perceived ability to delay gratification, and (d) general expectation of self-efficacy (22). Scores on this instrument have been correlated with Rotter's I-E scale (-.40) and with the Irrational Beliefs Test (-.48). A test-retest reliability of .86 and internal consistency measures ranging from .78 to .84 have been reported for the SCS. factors as hand position, repetitiveness, forcefulness, type of manipulations, and actual time intervals on task of the 12-15 job pairs that did not have exact job matches. For example, keyboard operators were at the same institution with identical computers and desks (without wrist rests) and each had height-adjustable chairs. Some of the garment workers were informallytimed in wrist turns per minute to verifysupervisors'job similarityrecommendations.Informalcomparisonsof manipulations and finger pinch requirements were observed for consistency at the electronics and auto assembly plants. Time on task or actual number of hours interpreting per day were compared for interpreters including the type of interpreting (e.g., academic vs. community) and number of hours for other office work or teaching for the two interpreter trainer pairs.
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145 Self-Control Questionnaire (SCQ)
Five factors emerged from the factor analysis of this instrument: weight control, time management, emotional control, financial planning, and social behavior (21). A Cronbach's alpha of .80 was reported for the total scale for an undergraduate and graduate college student sample. Likert's Criterion of Internal Consistency coefficients for the items ranged from .37 to .65. Scores on the instrument were moderately correlated (.33) with the Self-Control Subscale of the California Personality Inventory and with a measure of fitness level (.42).
Lifestyle Approaches (LSA) This instrument primarily measures lifestyle organization (20). The factor analysis and validation assessment led to the inclusion of four factors in the final version of the instrument: performance focus and efficiency, goal directedness, timeliness of task accomplishment, and organization of physical space. The LSA has been positively correlated with self-efficacy, life satisfaction, purpose in life, health status, and health habits, with rs ranging from .18 to .67. Conversely, it has been negatively correlated with an external locus of control (-.17) and perceived stress (-.47). The variable most strongly related to the LSA has been self-efficacy. The Cronbach's alpha internal consistency coefficients have ranged from .64 to .71 for the four factors. The test-retest reliability assessments of the four factors have ranged from .65 to .90, with the composite LSA score yielding a test-retest coefficient of .90 across college student and nonstudent samples.
Inventory of Physical Symptoms The Cohen-Hoberman Inventory of Physical Symptoms (CHIPS) (28) measures 39 common physical symptoms. The items were selected to exclude symptoms of an obviously psychological nature (e.g., felt nervous or depressed); however, the scale does include many physical symptoms that are considered psychosomatic in nature (e.g., headache, weight loss or gain). The CHIPS was found to be significantly correlated with the use of student health services within 5 weeks after the completion of the scale in college student samples. The. CHIPS is moderately correlated (r = .44) with the Center for Epidemiologic Studies Depression Scale. The CHIPS has also evidenced strong internal consistency as measured by Cronbach's alpha (.88).
Related Medical Conditions (RMC) This instrument was developed especially for this study to assist in matching subjects and to provide a location for questions pertaining to medical conditions thought to put individuals at higher risk for CTS. It included three questions related
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to symptoms, diagnosis, and surgery for CTS to insure that non-CTS subjects had never had CTS from other sources, such as from home or a previous job, not documented in Workers' Compensation records. However, these three questions were deleted from the scoring of the inventory, because the principal purpose of the inventory was to assess medical conditions that might be predictive of CTS, but not directly reflected in CTS symptomatology. A second set of questions included medical conditions that have been cited as possibly increasing the risk of CTS development, such as arthritis, hyperthyroidism, hypertension, diabetes mellitus, oophorectomy, and the use of estrogens or birth control pills (12). Questions regarding past and present occurrence of these conditions were used in compiling a Suspected Medical Risk (MR) score for CTS. The remaining questions of the RMC dealt with symptoms, diagnosis, and surgery related to musculoskeletal problems in body regions other than the hands and wrists (e.g., back, neck, shoulders, elbows, knees, or ankles). Because of the previously reported relationship between stress and back, neck, and shoulder pain (17, 18), it was thought that CTS subjects might report a greater occurrence of other orthopedic problems (especially back, shoulder, and neck problems) than would non-CTS subjects. This variable of the RMC was identified as Generic Musculoskeletal Problems (GMP).
RESULTS Comparisons of CTS and Non-CTS Means Table II shows the means of the CTS and non-CTS groups on the different inventories. The inventory means of the two groups were compared via both paired and unpaired t-tests. The variable that most strongly differentiated the two groups was the self-reported occurrence of Generic Musculoskeletal Problems (GMP), with the CTS group reporting significantly (p < .0001) more such problems than the non-CTS group. In addition, both measures of stress (SRRS and PSS), Lifestyle Organization (LSA), and the Cohen-Hoberman Inventory of Physical Symptoms (CHIPS) also significantly differentiated the groups. The CTS group scored significantly (p < .05) lower on the I_SA and higher on the SRRS, PSS, and CHIPS than did the non-CTS group. Only the Self-Control Schedule (SCS), the Self-Control Questionnaire (SCQ), and Suspected Medical Risk (MR) for CTS failed to significantly differentiate the two groups.
Logistic Regression--Single Variable Model Results The extent to which the various inventories predicted membership in the CTS and non-C-fS groups was assessed via logistic regression analysis. This type of analysis is frequently used when the outcome variable is dichotomous. Although similar
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Table II. Comparison of CTS and Non-CTS Means for all Dependent Measures
Dependent measured SRRS SCS LSA SCQ PSS CHIPS RMC MR GMP
CTS mean
Non-CTS m e a n
750.1 24.4 40.6 47.7 29.7 35.3 12.7 3.5 9.2
522.9 32.6 45.3 50.2 25.6 25.7 8.6 3.2 5.3
Significancelevelb .05 NS .05 NS .05 .05 .0005 NS .0001
aSRRS, Social Readjustment Rating Scale; SCS, Self-Control Schedule; LSA, LifestyleApproaches; SCQ, Self-Control Questionnaire; PSS, Perceived Stress Scales; CHIPS, Cohen-Hoberman Inventory of Physical Symptoms;RMC, Related Medical Conditions; MR, Suspected Medical Risk factors related to CTS; GMP, Generic Musculoskeletal Problems. /'Significance levels cited apply to both paired and unpaired t-tests.
to discriminant function analysis, logistic regression does not assume normality for the predictor variables. The extent to which each inventory distinguished between CTS and non-CTS groups is reflected in Table III with chi-square statistics and p values. Five out of the eight comparison variables significantly discriminated between CSI'S and nonCTS subjects: life events stress, perceived stress, overall physical symptoms, generic musculoskeletal problems, and lifestyle organization. Except for lifestyle organization, the higher the scores on these variables the greater the probability of membership in the CTS group. The two self-control inventories did not significantly differentiate the CTS and non-CTS subjects. Logistic Regression: Selection of Best Model
After determining which variables best predicted membership in the CTS and non-CTS groups, we then assessed whether there was a particular combination of variables that best differentiated the two groups. Thus, a stepwise logistic regression analysis was computed, using all eight variables. This analysis indicated that a twovariable model (Generic Musculoskeletal Problems and Lifestyle Approaches) yielded a level of predictive potential equivalent to or above that of any other model. Computation of standardized estimates, which are similar to standardized Betas in standard regression, indicated that the GMP value was -.49 and the LSA value .15. These values reflect the relative contribution of each variable to the model. A C-Table analysis revealed that this two variable model accurately predicted CTS group membership 72% of the time and incorrectly predicted group membership 27% of the time.
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Table Ill. Chi-Square Statistics and p-Values for Each Inventory as Single Model Predictors for CTS: Unmatcheda Logistic regression Inventory SRRS SCS LSA SCQ PSS CHIPS RMC Medical risk Musculoskeletal problems
X2
p
4.7977* 2.3044 4.8205* 2.1262 4.6063* 5.8086*
0.0285 0.1290 0.0281 0.1448 0.0319 0.0159
0.1680 15.9599"*
0.6819 0.0001
adf = 1 for each inventory score; n = 100 subjects. *p < .05. **p < .001.
Stepwise Logistic Regression with Psychological Variables Because the primary research issue was the potential linkage between the psychological variables and CTS, the authors ran a stepwise logistic regression to determine the best model of the five psychological variables used (SRRS, LSA, PSS, SCS, SCQ). Another reason for separating the psychological variables from the physical was the strength of the physical predictors. The best single predictor of CTS membership was Generic Musculoskeletal Problems (GMP), and the second best single predictor was overall Physical Symptoms (CHIPS). Running the psychological variables alone was also important because the GMP was so strong relative to the LSA in the best two-variable model. The LSA and SRRS were selected as the best model for the psychological variables (p < .05). The standardized estimates for the LSA and SRRS were .18 and .19, respectively. A multivariate analysis across the LSA and SRRS measures yielded a significant difference between the CTS and non-CTS groups (F = 3.57, df = 48, p < .05), thus providing further support for the importance of these variables in differentiating CTS and non-CTS participants. The CTS group was lower than the non-CTS group on LSA, but higher on SRRS.
Post Hoe Analysis: Forced Interaction of LSA • SRRS To determine whether self-management and life events stress independently predict CTS group membership or whether self-management might buffer the effects of stress, an interaction factor was calculated and entered into a subsequent logistic regression analysis. This procedure resulted in the selection of the LSA and
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LSA • SRRS as predictors of group membership (p < .05). The standardized estimate scores were .25 for the LSA and .16 for the LSA x SRRS variable. Stepwise Logistic Regression of Four LSA Factors
Inasmuch as the LSA was the most important psychological variable to emerge from this present study, it was deemed appropriate to test which factor(s) of the LSA was(were) the best predictor(s) of CTS vs. non-CTS membership. Individual and stepwise logistic regression analyses were run to determine which of the four factors differentiated between CTS and non-CTS subjects as single models, as well as to determine the best model of lifestyle organization from the four factors. Both goal directedness and timeliness of task accomplishment from the LSA were significant individual (single model) predictors (p < .005 and .05, respectively) for CTS membership. However, only goal directedness was selected into the final model (p < .005). Therefore, the most important component of lifestyle organization in predicting the occurrence of CTS is the clarity of one's priorities and goals. A tendency toward poorly defined priorities was associated with membership in the CTS group.
DISCUSSION The primary purpose of this research was to determine the association between selected psychological measures and the occurrence of Carpal Tunnel Syndrome. Three psychological variables, perceived stress, life events stress, and lifestyle organization, were significantly different between the CTS and non-CTS groups. Individuals with CTS reported greater stress, both perceived and life events, and poorer lifestyle organization. Further analysis indicated that the major lifestyle organization component related to CTS was goal directedness. Thus, stress and lifestyle organization could emerge as important factors in the prediction of Carpal Tunnel Syndrome. This finding is consistent with the linkage between physical and psychological variables frequently reported in the literature. Stress measures in particular have been related to a variety of orthopedic symptoms that may share some kinship to CTS (18, 19). Although selected psychological dimensions appear to have some potential for predicting CTS, certain physical conditions appear to be more powerful predictors of CTS. Self-report of musculoskeletal problems other than in the wrist was the most powerful predictor of CTS in the current study. This relationship between CTS and more generic musculoskeletal problems has not been extensively reported in the literature. The other physical measures yielded mixed results. The predictive potential of general physical symptoms (CHIPS) was only slightly stronger than that of the stress and lifestyle organization variables. Surprisingly, the medical conditions previously thought to be risk factors for Carpal Tunnel Syndrome were not significantly different for the CTS and non-CTS subjects in this study.
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Of the various self-management measures used in this study, only the LSA proved to be predictive of CTS. In contrast to Nathan et al.'s (23) finding that physical inactivity was associated with the occurrence of CTS, no association was found between personal habits and CTS in the current study. However, the measure of personal habits used in this study was broader and less focused on physical dimensions than that employed by Nathan et aL Cognitive self-management strategies as measured by the SCS also proved unpredictive of CTS. Perhaps the perceptions of self-efficacy, personal thought control, and problem-solving strategies reflected in the SCS were too general to be predictive of tendencies contributing to CTS. After determining that there were significant differences between CTS and non-CTS subjects on certain physical and psychological variables, the authors selected the combination of variables that best predicted membership in the CTS vs. non-CTS group. The most efficient model included two factors: generic musculoskeletal problems and lifestyle organization. However, in this two variable model, the greater portion of the predictive potential was attributable to generic musculoskeletal problems. The best model to predict CTS membership using only the psychological variables was the lifestyle organization and life events stress model. The interaction between these two variables also contributed to group membership prediction. Follow-up analyses indicated that lifestyle organization tended to decrease as life events stress increased in the CTS group (r = -.434). This inverse relationship was not as strong in the non-CTS group (r = -.368). In fact, some of the non-CTS subjects had very high SRRS scores in combination with relatively high scores on the LSA. Even though the present study provides documentation for a relationship between certain psychological variables and CTS, the correlational nature of the study does not permit cause-effect inferences about this relationship. For example, while stress may contribute to CTS, stress would probably also be heightened by CTS. Perhaps a reciprocal relationship exists between the selected psychological variables and CTS. An important research question is whether efforts at reducing stress and improving lifestyle organization skills would decrease the probability of CTS. Another important issue is whether a self-management intervention would increase the effectiveness of medical management of CTS. The principal limitation of the current study is the self-report nature of the assessment instruments used. Consequently, subject responses may reflect a good deal of common method variance. The different measures of stress and self-management likely also have some conceptual overlap. The interdependence of the response measures was confirmed by the rather substantial correlations among many of the instruments. For example, the two psychological instruments most strongly associated with CTS (i.e., LSA and SRRS) yielded a -.44 correlation with each other. Despite its limitations, the study provides some promising leads for ameliorating psychological conditions that may be associated with CTS. Although one cannot always control life events, there are many strategies that can be used to moderate the stress that one may experience from those events. Organizing one's life in a coherent fashion may be one avenue for tempering the stress that could accrue from major life events.
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The present research also suggests that the most important factor in organizing one's life is the development of personal goals. Thus, workers found to be at risk for CTS might benefit from training in the development of dear, attainable, and healthy goals for their work and personal lives. Feuerstein's (29) recent analysis of workstyle factors related to upper extremity disorders suggests that placing too much emphasis on one's work may contribute to disorders such as C-q'S. Prevention training focusing on achieving a healthy balance between work and personal priorities might be especially helpful to individuals who are inclined to see their work as vitally important.
REFERENCES 1. Bureau of Labor Statistics. Occupational injuries and illnesses in the United States by industry, 1989: U.S. Department of Labor, Bulletin 2379, U.S. Government Printing Office, Washington, D.C., 1991. 2. Nathan PA, Meadows KD, Doyle LS. Occupation as a risk factor for impaired sensory conduction of the median nerve at the carpal tunnel. J Hand Surg 1988; 13B: 167-170. 3. Cohn L, Lowry RM, Hart S. Overuse syndromes of the upper extremity in interpreters for the deaf. Orthopedics 1980; 207-209. 4. Meals RA, Payne W, Gaines R. Functional demands and consequences of manual communication. l Hand Surg 1988; 13A: 686-691. 5. Ditmas DM. Patterns of carpal tunnel syndrome. Hand Clin 1993; 9: 241-261. 6. Arndt R. Work pace, stress, and cumulative trauma disorders. J Hand Surg 1987; 12A: 866-869. 7. Lutz G, Hansford T. Cumulative trauma disorder controls: The ergonomics problem at Ethicon, Inc. J Hand Surg 1987; 12A: 863-866. 8. Murphy LR, Hurrell JJ. Machine pacing and occupational stress. In: Schwartz RM, ed. New developments in occupational stress. Cincinnati: UCLA Conference Proceedings, DHEW (NIOSH), 1979, pp. 10-20. 9. Herrick RT, Herrick SK. Thermography in the detection of carpal tunnel syndrome and other compressive neuropathies. J Hand Surg 1987; 12: 943-949. 10. Nathan PA, Keniston RC. Carpal tunnel syndrome and its relation to general physical condition. Hand Clin 1993; 9: 253-261. 11. Thurston AJ, Krause BL. The possible role of vascular congestion in carpal tunnel syndrome. J Hand Surg 1988; 13B: 397-399. 12. Cannon LJ, Bernacki EJ, Walter SD. Personal and occupational factors associated with carpal tunnel syndrome. J Occup Med 1981; 23: 255-258. 13. Fuchs PC, Nathan PA, Myers LD. Synovial histology in carpal tunnel syndrome. J Hand Surg 1991; 16A: 753-758. 14. Holmes TH, Rahe RH. The social readjustment rating scale. J Psychosom Res 1967; 11: 213-218. 15. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress, l Health Soc Behav 1983; 24: 385-396. 16. Adams HE, Feuerstein M, Fowler JL. Migraine headache: Review of parameters, etiology and intervention.-Psychol Bull 1980; 87: 217-237. 17. Feuerstein M, Bush C, Corbisiero T. Stress and chronic headache: A psychophysiology analysis of mechanisms. J Psychosom Res 1982; 26: 167-182. 18. Feuerstein M, Carter RL, Papciak AS. A prospective analysis of stress and fatigue in recurrent low back pain. Pain 1987; 31: 333-344. 19. Feuerstein M, Suit S, Houle M. Environmental stressors and chronic low back pain: Life events, family & work environment. Pain 1985; 22: 295-307. 20. Williams RL, Moore CA, Pettibone TJ, Thomas SP. Construction and validation of a self-report scale of self-management practices. J Res Pers 1992; 26: 216-234. 21. Brandon JE, Oescher J, Loftin JM. The self-control questionnaire: An assessment. Health Val 1980; 14: 3-9. 22. Rosenbaum M. A schedule for assessing self-control behaviors: Preliminary findings. Behav Ther 1980; 11: 109-121.
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23. Nathan PA, Keniston RC, Myers LD, Meadows KD. Obesity as a risk factor for slowing sensory conduction of the median nerve in industry: A cross-sectional and longitudinal study involving 429 workers. J Occup Med 1992; 34: 379-383. 24. Coddington RD. The significance of life events as etiologic factors in diseases of children: A survey of professional workers. J Psychosom Res 1972; 16: 7-18. 25. Paykel EG. Recent life events and clinical depression. In: Gunderson EKE, Rahe RH, eds. Life stress and illness. Springfield: Charles C. Thomas Publishing, 1974: 134-163. 26. Rahe RH. The pathway between subjects' recent life changes and their near-future illness reports and methodological issues. In: Dohrenwend BS, Dohrenwend BP, eds. Stressful life events: Their nature and effects. New York: John Wiley & sons, 1974, pp. 73-86. 27. Williams RL, Long JD. Manage your life: Instructor's resource manual with test items (4th Ed.). Boston: Houghton Mifflin Company, 1991. 28. Cohen S, Hoberman HM. Positive events and social supports as buffers of life change stress. JAppl Psychol 1983; 13: 99-125. 29. Feuerstein, M. Workstyle: Definition, empirical support and implications for prevention, evaluation and rehabilitation of occupational upper extremity disorders. In: Moon SD, Sauter S, eds. Beyond biomechanics: Psychosocial factors in office-related cumulative trauma disorders. London: Taylor and Francis (in press).
Lifestyle Correlates of Carpal Tunnel Syndrome Lisa M. Vogelsang, 1 Robert L. Williams, 2,4 and Kathleen Lawler 3
The potential for predicting membership in a Carpal Tunnel Syndrome group (CTS) vs. a non-CTS group was evaluated for five psychological variables (i.e., life events stress, perceived stress, self-management habits, cognitive self-control skills, and lifestyle organization) and three physical variables (i.e., general physical symptoms, suspected medical risk for CTS, and generic musculoskeletal problems). The subjects included 50 pairs of workers, with each pair having one worker who had CTS and the other who had not. A logistic regression ana~sis indicated that five of the measures (three psychological and two physical) were significant single model predictors of membership in CTS and non-CTS groups. The most efficient multifactor model in predicting CTS appeared to be a combination of measures reflecting generic musculoskeletal problems and lifestyle organization. KEY WORDS: Carpal Tunnel Syndrome; hand injuries; stress; self-management.
INTRODUCTION Carpal Tunnel Syndrome (CTS) is one of the top five injuries that plague industrial workers in the United States. It is a repetitive motion injury that, according to the U.S. Department of Labor, accounts for about half of all reported occupational illnesses (1). This injury is characterized by pain, numbness, tingling, and/or neuropathy in the hands and wrists, typically resulting from impaired sensory conduction in the median nerve at the carpal tunnel in the wrist (2). Carpal Tunnel Syndrome is most likely to occur in occupations that require repetitive, forceful arm and hand movements (2). Occupations assumed to be related to CTS include assembly jobs, sewing jobs, keyboard operation, meat processing jobs, and interpreting for the deaf (1, 3, 4). The percentage of workers in these occupations who eventually develop CTS varies from as low as 5% to greater than 50%. 1The Rehabilitation and Wellness Corporation, Knoxville, Tennessee 37923. 2Department of Educational and Counseling Psychology, The University of Tennessee, Knoxville, Tennessee 37996. 3Department of Psychology, The University of Tennessee, Knoxville, Tennessee 37966. 4Correspondence should be directed to Robert L. Williams, Department of Educational and Counseling Psychology, The University of Tennessee, Knoxville, Tennessee 37996. 141 I053-0487/94/0900-0141507.00/09 1994PlenumPublishingCorporation
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Why do some of the workers in these high-risk occupations never develop CFS? Some of the personal factors that distinguish CTS from non-CTS workers appear to be of a physical nature. For example, structural features of the hands and wrists (e.g., wrists depth/width ratio) as well as the idiosyncratic nature of hand and wrist movements may be factors that can help to distinguish workers who develop CTS from those who do not (5). Other physical factors that may contribute to CTS include tension level in the hands and wrists (6-8), reduced blood circulation to the hands and wrists (9), venous pooling and vascular congestion in the region of the carpal tunnel (5, 10, 11), and more generic physical problems (e.g., arthritis, hyperthyroidism, vascular sclerosis, and hypertension) (12, 13). It may be that an accumulation of many different types of physical symptoms would increase the probability of developing CTS (10). Some personal factors that may increase the probability of CTS are more psychological than physical in nature. Psychological stress is one such factor. The two types of stress that might potentially be implicated in CTS are an accumulation of significant life events (14) and a perception of being overwhelmed by life events (15). Both have been related to a variety of physical symptoms, including low back pain and upper neck and shoulder pain (14-19). Another psychological characteristic that could be related to vulnerability to CTS is general self-management. This construct has previously been linked to selfreported health status (20). Self-management has been defined in terms of lifestyle organization (20), adaptive personal habits (21), and cognitive dimensions such as self-talk and perceived self-efficacy (22). It could be that a well-ordered life characterized by adaptive habits and productive self-talk would make one less vulnerable to a variety of physical problems, including CTS. Some evidence suggests that workers with good health habits may be less prone to CTS than obese and physically inactive workers (10, 23). The fundamental question addressed in this study is whether psychological variables such as stress level and self-management can differentiate workers who develop CTS from those who do not. With relevant demographic characteristics and working conditions being roughly equivalent for CTS and non-CTS subjects, do stress and self-management variables serve as significant predictors of CTS? How does the predictive potential of these psychological variables compare to that of suspected physical and medical conditions that might also be related to CTS?
METHOD Subjects Subjects were selected from occupations that have been documented as having a disproportionately high rate of CTS. The subjects included automotive parts assembly workers, keyboard operators, electronics industry workers, and garment industry workers from East Tennessee. Interpreters for the deaf from the National Center on Deafness at California State University, Northridge and from the
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Lifestyle and CTS Table I. Descriptive Statistics for Matching Variables: CTS and Non-CTS Subjects
Matching variable C ~ subjects~ Age (years) Weight (pounds) Height (inches) Total years (current site) Non-CTS subjectsb Age (years) Weight (pounds) Height (inches) Total years (current site)
M
SD
Min
Max
37.30
7.86
24
56
155.74
43.27
95
280
64.58
2.42
60
71
8.76
6.79
0.5
27
36.40
7.96
23
59
148.90
35.89
105
245
64.82
2.35
60
71
8.84
6.37
1.0
27
an = 50 subjects. bn = 50 subjects.
Knoxville, Tennessee Registry of Interpreters for the Deaf were also included in the sample. The CTS subjects were selected from Workers' Compensation and other records from each industry that indicated a medical diagnosis of CTS by an orthopedic surgeon. The diagnostic criteria most frequently cited included impaired nerve conduction in the hand and wrist, nocturnal pain, numbness, and tingling in the hand and wrist. Some CTS subjects were postsurgical, some presurgical, and some nonsurgical. The non-CTS subjects were randomly selected from the remaining employee pools that best matched the CTS subjects. The sample consisted of 50 pairs of subjects matched as closely as possible on selected demographic criteria. Each pair was matched from the same industry and same job title and also had similar physical characteristics and job histories. The specific matching variables were age, gender, race, height, weight, body type, job duties, and length of time on the job. Body type referred to the somatotype categories of ectomorph, mesomorph, and endomorph. These somatotype categories were determined by visual clinical judgment of wrist size and skeletal structure in combination with lean muscle mass vs. body fat. One member of the pair had CTS within the last 2 years and the other had never had CTS. Thus, 50 subjects with CTS were matched to 50 subjects without CTS (see Table I for a summary of the statistics on matching variables). Each pair was perfectly matched on gender: 47 out of 50 pairs were matched on race; and job duties were either matched exactly or matched on ergonomic risk factors. 5 5Although a formal ergonomie job analysis was not performed, detailed information was obtained through observation of job tasks and supervisors'reports and interviews regarding such ergonomicrisk
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144 INSTRUMENTATION
Social Readjustment Rating Scale (SRRS) Developed by Holmes and Rahe (14), this scale has been a standard in stress research for many years. It has been used to establish relationships between life events and physical and emotional illness and injury (18, 19, 24-26). The format of the SRRS used in this study was the same as that used in Feuerstein et al.'s (19) study on low back pain; that is, people indicated how many times each event had occurred in the past 2 years and that number was multiplied by a rating factor for each event. The resulting numbers were then totalled across events. A cap of ten was arbitrarily put on the number of occurrences of each life event to prevent extreme scores. The ratings for the various life events were the same as those originally established by Holmes and Rahe. On the SRRS the respondent checks only the occurrence of various life events and does not rate the perceived positive or negative valence of those events.
Perceived Stress Scale (PSS) This instrument measures the degree to which respondents find their lives unpredictable, uncontrollable, and overloading (15). These variables have been found to be central components of the experience of stress according to the researchers who developed the instrument. Scores on this scale have been shown to be negatively correlated with self-management, life satisfaction, self-efficacy, health habits, health status, optimism, and purpose in life and positively correlated with total anger, anger-in, anger-out, and anger symptoms (27). Test-retest reliability and internal consistency measures for the PSS have ranged from .84 to .86.
Self-Control Schedule (SCS) Several factors are included under one total score for this inventory: (a) the use of cognitive strategies to control physiological and emotional sensations, (b) the application of problem-solving strategies, (c) the perceived ability to delay gratification, and (d) general expectation of self-efficacy (22). Scores on this instrument have been correlated with Rotter's I-E scale (-.40) and with the Irrational Beliefs Test (-.48). A test-retest reliability of .86 and internal consistency measures ranging from .78 to .84 have been reported for the SCS. factors as hand position, repetitiveness, forcefulness, type of manipulations, and actual time intervals on task of the 12-15 job pairs that did not have exact job matches. For example, keyboard operators were at the same institution with identical computers and desks (without wrist rests) and each had height-adjustable chairs. Some of the garment workers were informallytimed in wrist turns per minute to verifysupervisors'job similarityrecommendations.Informalcomparisonsof manipulations and finger pinch requirements were observed for consistency at the electronics and auto assembly plants. Time on task or actual number of hours interpreting per day were compared for interpreters including the type of interpreting (e.g., academic vs. community) and number of hours for other office work or teaching for the two interpreter trainer pairs.
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145 Self-Control Questionnaire (SCQ)
Five factors emerged from the factor analysis of this instrument: weight control, time management, emotional control, financial planning, and social behavior (21). A Cronbach's alpha of .80 was reported for the total scale for an undergraduate and graduate college student sample. Likert's Criterion of Internal Consistency coefficients for the items ranged from .37 to .65. Scores on the instrument were moderately correlated (.33) with the Self-Control Subscale of the California Personality Inventory and with a measure of fitness level (.42).
Lifestyle Approaches (LSA) This instrument primarily measures lifestyle organization (20). The factor analysis and validation assessment led to the inclusion of four factors in the final version of the instrument: performance focus and efficiency, goal directedness, timeliness of task accomplishment, and organization of physical space. The LSA has been positively correlated with self-efficacy, life satisfaction, purpose in life, health status, and health habits, with rs ranging from .18 to .67. Conversely, it has been negatively correlated with an external locus of control (-.17) and perceived stress (-.47). The variable most strongly related to the LSA has been self-efficacy. The Cronbach's alpha internal consistency coefficients have ranged from .64 to .71 for the four factors. The test-retest reliability assessments of the four factors have ranged from .65 to .90, with the composite LSA score yielding a test-retest coefficient of .90 across college student and nonstudent samples.
Inventory of Physical Symptoms The Cohen-Hoberman Inventory of Physical Symptoms (CHIPS) (28) measures 39 common physical symptoms. The items were selected to exclude symptoms of an obviously psychological nature (e.g., felt nervous or depressed); however, the scale does include many physical symptoms that are considered psychosomatic in nature (e.g., headache, weight loss or gain). The CHIPS was found to be significantly correlated with the use of student health services within 5 weeks after the completion of the scale in college student samples. The. CHIPS is moderately correlated (r = .44) with the Center for Epidemiologic Studies Depression Scale. The CHIPS has also evidenced strong internal consistency as measured by Cronbach's alpha (.88).
Related Medical Conditions (RMC) This instrument was developed especially for this study to assist in matching subjects and to provide a location for questions pertaining to medical conditions thought to put individuals at higher risk for CTS. It included three questions related
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to symptoms, diagnosis, and surgery for CTS to insure that non-CTS subjects had never had CTS from other sources, such as from home or a previous job, not documented in Workers' Compensation records. However, these three questions were deleted from the scoring of the inventory, because the principal purpose of the inventory was to assess medical conditions that might be predictive of CTS, but not directly reflected in CTS symptomatology. A second set of questions included medical conditions that have been cited as possibly increasing the risk of CTS development, such as arthritis, hyperthyroidism, hypertension, diabetes mellitus, oophorectomy, and the use of estrogens or birth control pills (12). Questions regarding past and present occurrence of these conditions were used in compiling a Suspected Medical Risk (MR) score for CTS. The remaining questions of the RMC dealt with symptoms, diagnosis, and surgery related to musculoskeletal problems in body regions other than the hands and wrists (e.g., back, neck, shoulders, elbows, knees, or ankles). Because of the previously reported relationship between stress and back, neck, and shoulder pain (17, 18), it was thought that CTS subjects might report a greater occurrence of other orthopedic problems (especially back, shoulder, and neck problems) than would non-CTS subjects. This variable of the RMC was identified as Generic Musculoskeletal Problems (GMP).
RESULTS Comparisons of CTS and Non-CTS Means Table II shows the means of the CTS and non-CTS groups on the different inventories. The inventory means of the two groups were compared via both paired and unpaired t-tests. The variable that most strongly differentiated the two groups was the self-reported occurrence of Generic Musculoskeletal Problems (GMP), with the CTS group reporting significantly (p < .0001) more such problems than the non-CTS group. In addition, both measures of stress (SRRS and PSS), Lifestyle Organization (LSA), and the Cohen-Hoberman Inventory of Physical Symptoms (CHIPS) also significantly differentiated the groups. The CTS group scored significantly (p < .05) lower on the I_SA and higher on the SRRS, PSS, and CHIPS than did the non-CTS group. Only the Self-Control Schedule (SCS), the Self-Control Questionnaire (SCQ), and Suspected Medical Risk (MR) for CTS failed to significantly differentiate the two groups.
Logistic Regression--Single Variable Model Results The extent to which the various inventories predicted membership in the CTS and non-C-fS groups was assessed via logistic regression analysis. This type of analysis is frequently used when the outcome variable is dichotomous. Although similar
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Table II. Comparison of CTS and Non-CTS Means for all Dependent Measures
Dependent measured SRRS SCS LSA SCQ PSS CHIPS RMC MR GMP
CTS mean
Non-CTS m e a n
750.1 24.4 40.6 47.7 29.7 35.3 12.7 3.5 9.2
522.9 32.6 45.3 50.2 25.6 25.7 8.6 3.2 5.3
Significancelevelb .05 NS .05 NS .05 .05 .0005 NS .0001
aSRRS, Social Readjustment Rating Scale; SCS, Self-Control Schedule; LSA, LifestyleApproaches; SCQ, Self-Control Questionnaire; PSS, Perceived Stress Scales; CHIPS, Cohen-Hoberman Inventory of Physical Symptoms;RMC, Related Medical Conditions; MR, Suspected Medical Risk factors related to CTS; GMP, Generic Musculoskeletal Problems. /'Significance levels cited apply to both paired and unpaired t-tests.
to discriminant function analysis, logistic regression does not assume normality for the predictor variables. The extent to which each inventory distinguished between CTS and non-CTS groups is reflected in Table III with chi-square statistics and p values. Five out of the eight comparison variables significantly discriminated between CSI'S and nonCTS subjects: life events stress, perceived stress, overall physical symptoms, generic musculoskeletal problems, and lifestyle organization. Except for lifestyle organization, the higher the scores on these variables the greater the probability of membership in the CTS group. The two self-control inventories did not significantly differentiate the CTS and non-CTS subjects. Logistic Regression: Selection of Best Model
After determining which variables best predicted membership in the CTS and non-CTS groups, we then assessed whether there was a particular combination of variables that best differentiated the two groups. Thus, a stepwise logistic regression analysis was computed, using all eight variables. This analysis indicated that a twovariable model (Generic Musculoskeletal Problems and Lifestyle Approaches) yielded a level of predictive potential equivalent to or above that of any other model. Computation of standardized estimates, which are similar to standardized Betas in standard regression, indicated that the GMP value was -.49 and the LSA value .15. These values reflect the relative contribution of each variable to the model. A C-Table analysis revealed that this two variable model accurately predicted CTS group membership 72% of the time and incorrectly predicted group membership 27% of the time.
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Table Ill. Chi-Square Statistics and p-Values for Each Inventory as Single Model Predictors for CTS: Unmatcheda Logistic regression Inventory SRRS SCS LSA SCQ PSS CHIPS RMC Medical risk Musculoskeletal problems
X2
p
4.7977* 2.3044 4.8205* 2.1262 4.6063* 5.8086*
0.0285 0.1290 0.0281 0.1448 0.0319 0.0159
0.1680 15.9599"*
0.6819 0.0001
adf = 1 for each inventory score; n = 100 subjects. *p < .05. **p < .001.
Stepwise Logistic Regression with Psychological Variables Because the primary research issue was the potential linkage between the psychological variables and CTS, the authors ran a stepwise logistic regression to determine the best model of the five psychological variables used (SRRS, LSA, PSS, SCS, SCQ). Another reason for separating the psychological variables from the physical was the strength of the physical predictors. The best single predictor of CTS membership was Generic Musculoskeletal Problems (GMP), and the second best single predictor was overall Physical Symptoms (CHIPS). Running the psychological variables alone was also important because the GMP was so strong relative to the LSA in the best two-variable model. The LSA and SRRS were selected as the best model for the psychological variables (p < .05). The standardized estimates for the LSA and SRRS were .18 and .19, respectively. A multivariate analysis across the LSA and SRRS measures yielded a significant difference between the CTS and non-CTS groups (F = 3.57, df = 48, p < .05), thus providing further support for the importance of these variables in differentiating CTS and non-CTS participants. The CTS group was lower than the non-CTS group on LSA, but higher on SRRS.
Post Hoe Analysis: Forced Interaction of LSA • SRRS To determine whether self-management and life events stress independently predict CTS group membership or whether self-management might buffer the effects of stress, an interaction factor was calculated and entered into a subsequent logistic regression analysis. This procedure resulted in the selection of the LSA and
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LSA • SRRS as predictors of group membership (p < .05). The standardized estimate scores were .25 for the LSA and .16 for the LSA x SRRS variable. Stepwise Logistic Regression of Four LSA Factors
Inasmuch as the LSA was the most important psychological variable to emerge from this present study, it was deemed appropriate to test which factor(s) of the LSA was(were) the best predictor(s) of CTS vs. non-CTS membership. Individual and stepwise logistic regression analyses were run to determine which of the four factors differentiated between CTS and non-CTS subjects as single models, as well as to determine the best model of lifestyle organization from the four factors. Both goal directedness and timeliness of task accomplishment from the LSA were significant individual (single model) predictors (p < .005 and .05, respectively) for CTS membership. However, only goal directedness was selected into the final model (p < .005). Therefore, the most important component of lifestyle organization in predicting the occurrence of CTS is the clarity of one's priorities and goals. A tendency toward poorly defined priorities was associated with membership in the CTS group.
DISCUSSION The primary purpose of this research was to determine the association between selected psychological measures and the occurrence of Carpal Tunnel Syndrome. Three psychological variables, perceived stress, life events stress, and lifestyle organization, were significantly different between the CTS and non-CTS groups. Individuals with CTS reported greater stress, both perceived and life events, and poorer lifestyle organization. Further analysis indicated that the major lifestyle organization component related to CTS was goal directedness. Thus, stress and lifestyle organization could emerge as important factors in the prediction of Carpal Tunnel Syndrome. This finding is consistent with the linkage between physical and psychological variables frequently reported in the literature. Stress measures in particular have been related to a variety of orthopedic symptoms that may share some kinship to CTS (18, 19). Although selected psychological dimensions appear to have some potential for predicting CTS, certain physical conditions appear to be more powerful predictors of CTS. Self-report of musculoskeletal problems other than in the wrist was the most powerful predictor of CTS in the current study. This relationship between CTS and more generic musculoskeletal problems has not been extensively reported in the literature. The other physical measures yielded mixed results. The predictive potential of general physical symptoms (CHIPS) was only slightly stronger than that of the stress and lifestyle organization variables. Surprisingly, the medical conditions previously thought to be risk factors for Carpal Tunnel Syndrome were not significantly different for the CTS and non-CTS subjects in this study.
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Of the various self-management measures used in this study, only the LSA proved to be predictive of CTS. In contrast to Nathan et al.'s (23) finding that physical inactivity was associated with the occurrence of CTS, no association was found between personal habits and CTS in the current study. However, the measure of personal habits used in this study was broader and less focused on physical dimensions than that employed by Nathan et aL Cognitive self-management strategies as measured by the SCS also proved unpredictive of CTS. Perhaps the perceptions of self-efficacy, personal thought control, and problem-solving strategies reflected in the SCS were too general to be predictive of tendencies contributing to CTS. After determining that there were significant differences between CTS and non-CTS subjects on certain physical and psychological variables, the authors selected the combination of variables that best predicted membership in the CTS vs. non-CTS group. The most efficient model included two factors: generic musculoskeletal problems and lifestyle organization. However, in this two variable model, the greater portion of the predictive potential was attributable to generic musculoskeletal problems. The best model to predict CTS membership using only the psychological variables was the lifestyle organization and life events stress model. The interaction between these two variables also contributed to group membership prediction. Follow-up analyses indicated that lifestyle organization tended to decrease as life events stress increased in the CTS group (r = -.434). This inverse relationship was not as strong in the non-CTS group (r = -.368). In fact, some of the non-CTS subjects had very high SRRS scores in combination with relatively high scores on the LSA. Even though the present study provides documentation for a relationship between certain psychological variables and CTS, the correlational nature of the study does not permit cause-effect inferences about this relationship. For example, while stress may contribute to CTS, stress would probably also be heightened by CTS. Perhaps a reciprocal relationship exists between the selected psychological variables and CTS. An important research question is whether efforts at reducing stress and improving lifestyle organization skills would decrease the probability of CTS. Another important issue is whether a self-management intervention would increase the effectiveness of medical management of CTS. The principal limitation of the current study is the self-report nature of the assessment instruments used. Consequently, subject responses may reflect a good deal of common method variance. The different measures of stress and self-management likely also have some conceptual overlap. The interdependence of the response measures was confirmed by the rather substantial correlations among many of the instruments. For example, the two psychological instruments most strongly associated with CTS (i.e., LSA and SRRS) yielded a -.44 correlation with each other. Despite its limitations, the study provides some promising leads for ameliorating psychological conditions that may be associated with CTS. Although one cannot always control life events, there are many strategies that can be used to moderate the stress that one may experience from those events. Organizing one's life in a coherent fashion may be one avenue for tempering the stress that could accrue from major life events.
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The present research also suggests that the most important factor in organizing one's life is the development of personal goals. Thus, workers found to be at risk for CTS might benefit from training in the development of dear, attainable, and healthy goals for their work and personal lives. Feuerstein's (29) recent analysis of workstyle factors related to upper extremity disorders suggests that placing too much emphasis on one's work may contribute to disorders such as C-q'S. Prevention training focusing on achieving a healthy balance between work and personal priorities might be especially helpful to individuals who are inclined to see their work as vitally important.
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23. Nathan PA, Keniston RC, Myers LD, Meadows KD. Obesity as a risk factor for slowing sensory conduction of the median nerve in industry: A cross-sectional and longitudinal study involving 429 workers. J Occup Med 1992; 34: 379-383. 24. Coddington RD. The significance of life events as etiologic factors in diseases of children: A survey of professional workers. J Psychosom Res 1972; 16: 7-18. 25. Paykel EG. Recent life events and clinical depression. In: Gunderson EKE, Rahe RH, eds. Life stress and illness. Springfield: Charles C. Thomas Publishing, 1974: 134-163. 26. Rahe RH. The pathway between subjects' recent life changes and their near-future illness reports and methodological issues. In: Dohrenwend BS, Dohrenwend BP, eds. Stressful life events: Their nature and effects. New York: John Wiley & sons, 1974, pp. 73-86. 27. Williams RL, Long JD. Manage your life: Instructor's resource manual with test items (4th Ed.). Boston: Houghton Mifflin Company, 1991. 28. Cohen S, Hoberman HM. Positive events and social supports as buffers of life change stress. JAppl Psychol 1983; 13: 99-125. 29. Feuerstein, M. Workstyle: Definition, empirical support and implications for prevention, evaluation and rehabilitation of occupational upper extremity disorders. In: Moon SD, Sauter S, eds. Beyond biomechanics: Psychosocial factors in office-related cumulative trauma disorders. London: Taylor and Francis (in press).
