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Environment
Original article
Workplace and individual risk factors for carpal tunnel syndrome
  1. Susan Burt1,
  2. Ken Crombie2,
  3. Yan Jin1,
  4. Steve Wurzelbacher1,
  5. Jessica Ramsey3,
  6. James Deddens4
  1. 1Industrywide Studies Branch, Division of Surveillance Hazard Evaluations and Field Studies, National Institute for Occupational Safety and Health, Cincinnati, Ohio, USA
  2. 2US Department of Agriculture, USDA Food Safety and Inspection Service, Beltsville, Maryland, USA
  3. 3Hazard Evaluation and Technical Assistance Branch, Division of Surveillance, Hazard Evaluations and Field Studies, National Institute for Occupational Safety and Health, Cincinnati, Ohio, USA
  4. 4Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA
  1. Correspondence to Susan Burt, Industrywide Studies Branch, Division of Surveillance Hazard Evaluations and Field Studies, National Institute for Occupational Safety and Health, 4676 Columbia Pkwy, Mail Stop R-15, Cincinnati, OH 45226, USA; sburt{at}cdc.gov

Abstract

Objectives To quantify the relationship between workplace physical factors, particularly hand activity level (HAL) and forceful exertion and carpal tunnel syndrome (CTS), while taking into account individual factors. To compare quantitative exposure assessment measures with more practical ratings-based measures.

Methods In a group of healthcare and manufacturing workers, each study participant's job tasks were evaluated for HAL, forceful exertion and other physical stressors and videotaped for further analysis, including frequency and duration of exertion and postural deviation. Electrodiagnostic testing of median and ulnar nerves and questionnaires were administered to all participants. A CTS case required median mononeuropathy and symptoms on hand diagrams in fingers 1–3. Multiple logistic regression models were used to analyse associations between job and individual factors and CTS.

Results Of 477 workers studied, 57 (11.9%) were dominant hand CTS cases. Peak force ≥70% maximum voluntary contraction versus <20% maximum voluntary contraction resulted in an OR of 2.74 (1.32–5.68) for CTS. Among those with a body mass index ≥30, the OR for ≥15 exertions per minute was 3.35 (1.14–9.87). Peak worker ratings of perceived exertion increased the odds for CTS by 1.14 (1.01–1.29) for each unit increase on the 10-point scale. The odds for CTS increased by 1.38 (1.05–1.81) for each unit increase on the HAL 10-point scale among men, but not women. Combined force and HAL values above the ACGIH TLV for HAL resulted in an OR of 2.96 (1.51–5.80) for CTS.

Discussion/Conclusions Quantitative and ratings-based job exposure measures were each associated with CTS. Obesity increased the association between frequency of exertion and CTS.

  • Upper limb musculoskeletal disorders
  • exposure assessment methods
  • ergonomics
  • body mass index
  • gender
  • CTS
  • epidemiology
  • occupational health practice
  • musculoskeletal

https://doi.org/10.1136/oem.2010.063677

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    What this paper adds

    • There is little quantitative exposure–response data that could be used to provide guidance as to the level of exposure to physical work demands that is associated with carpal tunnel syndrome.

    • This study offers detailed quantitative exposure measures of each study participant and case definitions that include clinical electrodiagnostic testing.

    • Quantitative exposure–response data suggest levels of work demands that result in increased occurrence of carpal tunnel syndrome.

    • Practical ratings-based exposure assessment methods are compared with quantitative methods.

    Introduction

    Carpal tunnel syndrome (CTS) is the most commonly reported entrapment neuropathy of the upper limb.1 2 Its effects on workers can be chronically debilitating both in the workplace and in their personal lives.3 The economic consequences are felt by workers, employers and insurers. CTS is among the greatest driver of workers' compensation costs, lost time, lost productivity and disability.4 5 Many of these disorders could be prevented by identifying hazardous jobs and redesigning job tasks, tools and workstations to reduce the physical stressors. Research has demonstrated associations between repetitive and forceful hand activity, non-neutral wrist postures and hand-arm vibration at work and CTS.6 Individual risk factors for CTS include age, gender, body mass index (BMI), pregnancy and other medical conditions such as diabetes and thyroid disorders.7 A comprehensive review of the literature on work-related musculoskeletal disorders by NIOSH noted that exposure–response relationships were difficult to determine across studies due to a wide range of exposure assessment methods, different health outcome definitions and a restricted range of exposures in the jobs studied and other methodological issues.6 A more recent review suggests that there is still little guidance on the level of exposure to physical work demands that is associated with CTS.8 Limitations of past studies, including a lack of precision of estimation of potentially hazardous exposures, a lack of individual exposure assessment or a limited range of exposure variation, have hindered attempts at exposure–response analysis. The primary aim of this study is to quantify the relationship between recognised workplace physical factors, particularly repetitive hand activity and forceful exertion, and CTS, while taking into account individual factors as well as psychosocial/work organisational factors such as demand versus control, job satisfaction and support from supervisors and coworkers. Another goal is to compare resource-intensive quantitative measures with ratings-based exposure assessment methods that would be more practical in the typical workplace.

    Study population and methods

    Workplaces were selected to represent a range of the two primary exposures of interest—repetitive hand activity and forceful exertion. During initial walkthrough evaluations, ergonomists used checklists to determine exposure categories of jobs (online supplementary appendix). Selected workplaces had jobs that represented at least three of six exposure categories that combined low, medium or high hand activity level (HAL) with low or high force. Potential study plants were identified from those that had volunteered to participate following publication of the study in the Federal Register and communication with occupational safety and health professionals on the National Occupational Research Agenda (NORA I) Musculoskeletal Team. Additional selection criteria included workplaces with at least 100 employees and no anticipated lay-offs or major changes in production or reorganisation. Three worksites were selected: a hospital, a school bus-manufacturing plant and an engine assembly plant.

    The study population included 154 hospital workers from central and sterile supply, laboratory, pharmacy, engineering, surgical, kitchen, laundry and administrative support. In the engine plant, 95 were included from assembly, machining, quality control and manufacturing support. In the bus plant, 215 were included from assembly, subassembly, conveyor, machine fabrication, electrical, upholstery, engineering and transport jobs. All full-time workers with at least 3 months on the job in departments of interest to the study were invited to participate (n=645). Due to the detailed exposure assessment required by the study, we excluded jobs with more than four different tasks, that were not cyclic or that could not easily be videotaped for subsequent analysis (eg, most maintenance jobs). Healthcare providers with direct patient contact were not included because of the difficulty in getting informed consent for videotaping from each patient. The participation rate was 483/645 (74%). Demographics and pertinent prior medical history are shown in table 1.

    View this table:
    Table 1

    Personal characteristics of the study group

    Following approval by the NIOSH Human Subjects Review Board and informed consent, each study participant underwent detailed job exposure assessment including direct observation and videotaping of job tasks, physical examination of the upper limbs and nerve conduction testing of the median and ulnar nerves across the wrist. A detailed questionnaire was administered to collect information on work history, individual factors, work environment, physical activities outside of work and neck, shoulder, arm and hand symptoms, including a hand diagram documenting type and location of symptoms.9–13 Participation in the study took place during working hours and was not compensated. Study participants were notified of their own health assessment results, interpretation and recommendations by mail. Employers were given group results that were not individually identifiable. The exposure assessment team was blinded to study participants' health information, and the health assessment team was blinded to exposure information.

    Exposure assessment

    For the purpose of comparison, two contrasting approaches were taken to exposure assessment in this study—detailed quantitative measures and simpler ratings-based metrics.

    On-site data collection

    For each study participant, an NIOSH ergonomist rated the HAL of each task using the HAL 10-point visual analogue scale14 and determined whether the task reached a threshold of forceful exertion, defined as at least 8.9 N (2 lb) of pinch force or 44.5 N (10 lb) of power grip force. For forceful exertions, the ergonomist recorded additional observational exposure data including observer and worker ratings of perceived exertion (RPE) using a modified Borg CR-10 scale,15 grip type, contact stress and the presence or absence of hand-arm vibration. Workers were also asked to mimic the level of force and type of grip they used for specific tasks immediately after performing the task, using a power grip (Jamar, Bolingbrook, Illinois, USA) or pinch grip (Baseline, White Plains, New York, USA) dynamometer, and ergonomists recorded these force-matching readings in pounds.16 17 Each task was also videotaped using two cameras at different angles for later analysis. Single-task jobs were videotaped for at least 15 min; for multi-task jobs, each task was videotaped for at least 10 min. These exposure assessment methods are as described by Bao et al.18

    Video-based exposure assessment

    Task analysis

    Five-minute segments of digitised video were randomly selected for analysis for each single-task job and 3 min for each multi-task job. Trained analysts then used the Multi-Media Video Task Analysis (MVTA) system19 to mark the start and end of each exertion following written criteria to facilitate applying the study definition of an exertion as at least 5% of maximum voluntary contraction (MVC) and each forceful exertion, defined during field observation. Frequency, duration and per cent time spent in regular and forceful exertions were calculated, and field measurement data on RPE and force-matching values were linked to each forceful exertion.

    Posture analysis

    NIOSH researchers developed a computer-assisted, video-based method to quantify the upper limb postural demands of job tasks. Trained raters viewed selected still frames from the same job task video segments selected for MVTA analysis above and recorded postures on each of 15 scales, using onscreen posture illustrations as a guide. Seventy-five still frames were analysed for each single-task job or 60 still frames for each task if the job involved multiple tasks. Ratings by physical therapy undergraduates with 3 h of training by an ergonomist resulted in intraclass correlation coefficients (ICCs) ranging from 0.41 to 0.85; 11 of the 15 scales had ICCs>0.70.20

    Job-level exposure variables were subsequently created by combining exposure data across tasks for each study participant to represent their entire job. Job-level peak variables represent the peak of all tasks. Job-level time weighted average HAL ratings were weighted by the per cent time spent in each task. Raw force measurements (pounds) from field observations were normalised as a per cent of the individual worker's MVC (%MVC), matching type of grip (pinch or power).

    Health assessment

    All nerve conduction testing was performed by the same experienced registered electrodiagnostic technician according to published guidelines.21 Median and ulnar sensory and median motor latencies and amplitudes were determined on both hands using standard techniques of supramaximal percutaneous nerve stimulation and surface recording, using XLTEK NeuroMax1002 equipment (Oakville, Ontario, Canada). Skin temperature was measured using a digital skin thermometer, and if necessary, the hand was warmed to obtain a temperature >32°C. Determinations of median mononeuropathy were provided by the technician following predetermined criteria (box 1). In addition, NIOSH researchers blinded to the technician's results applied the same criteria using a SAS program and results were compared. Finally, nerve conduction tracings were reviewed by a neurologist to identify potential problems. Issues were resolved by adopting decision rules, taking into account, for example, age and length of hands.

    Box 1

    Electrodiagnostic criteria for median mononeuropathy: criteria A, and B or C

    • Criteria A. Slowed latency in median nerve

      • Wrist to index finger sensory latency >3.7 ms or

      • mid-palm to wrist sensory latency >2.2 ms or

      • motor latency >4.4 ms

    • Criteria B. Normal distal ulnar nerve latency

      • Wrist to little finger sensory latency ≤3.7 ms

    • Criteria C. Distal median nerve latency>distal ulnar latency

      • Median wrist to index finger—ulnar wrist to little finger latency difference >1.0 ms or

      • median mid-palm to wrist—ulnar mid-palm to wrist latency difference >0.5 ms

    ms, milliseconds.

    Case definition for CTS

    All of the following criteria were required22:

    1. Met the electrodiagnostic criteria for median mononeuropathy in box 1

    2. On the questionnaire, symptoms of numbness, tingling, burning or pain occurring more than three times or lasting 7 days or longer in the previous 12 months were recorded

    3. On the hand diagram, the above symptoms were recorded in the median nerve distribution (fingers 1–3).

    Data analysis

    t Tests and χ2 statistics were calculated to identify univariate associations between CTS and individual characteristics (gender, age, etc), job exposure variables (HAL, exertion level, degree of postural deviation, etc) and work organisational/psychosocial variables (job satisfaction, support at work, job control and demand, etc) in our study.

    Separate multiple logistic regression (MLR) analyses were conducted for detailed quantitative exposure assessment measures (those that required analysis of videotaped job tasks and instrumentation) and more practical ratings-based methods (observational ratings of HAL and RPE). The purpose of this approach is to compare the simpler more practical methods with the more resource-intensive research quality measures. Age, gender and BMI were included in initial MLR models along with the primary workplace exposure variables. The initial MLR model using quantitative exposure measures included the following variables: exertions per minute (counts from MVTA analysis), peak force (peak force-matching value measured in pounds by dynamometer, expressed as %MVC for each participant, matched on type of grip—power or pinch), vibration (ergonomist's observation noting presence or absence of hand-arm vibration), wrist flexion (per cent time spent in each of three ordinal categories of deviation, defined by percentage of the total range of motion: 0–20%, 21–40%, >40%), BMI (weight in kg÷height in m2), age in years and gender.

    A similar ratings-based MLR model was created, substituting RPE and HAL ratings for the quantitative job exposure measures above. The following variables were entered into the initial ratings-based model: HAL (time weighted average HAL rating by ergonomist), peak worker RPE (peak worker rating of perceived exertion on a modified Borg scale), vibration (ergonomist's observation noting presence or absence of hand-arm vibration), age, BMI and gender.

    CTS analysis included 464 subjects, since 19 were excluded from analysis due to abnormal nerve conduction results that were not median mononeuropathy (n=13), no nerve conduction test on the dominant hand (n=5) or incomplete nerve conduction test (n=1). Confounding (defined as ≥10% change in the coefficient) and effect modification (two-way interaction p<0.05) of each workplace exposure variable by the other variables in the model were evaluated. This information, along with statistical significance (p<0.05), and the explanatory power of the model (−2 log likelihood) determined which variables remained in final models. Additional potential covariates from questionnaires, including work organisation and psychosocial factors such as job demand versus control (job strain) that were individually associated with CTS in this study at the p<0.1 level, were then tested for possible inclusion in MLR models (online supplementary appendix). The additional potential covariates that were tested in the MLR models were as follows: resource control, overtime, general health for age, job satisfaction, high blood pressure and arthritis.

    A third model combined HAL ratings and force-matching peak values to obtain a variable with three categories that represent job exposures that are below the action limit, between the action limit and the threshold limit or above the ACGIH TLV (American Conference of Governmental Industrial Hygienists Threshold Limit Value) for HAL, using the following formulas23 24:

    TLV ratio (TLR)=(Force)/((−0.78)×(HAL)+7.78))

    Action limit ratio (ALR)=(Force)/((−0.56)×(HAL)+5.56))

    If ALR<1, then the overall rating=1

    If ALR≥1 and TLR<1, then the overall rating=2

    If TLR≥1, then the overall rating=3.

    Results

    There were 57 prevalent dominant hand CTS cases (11.9%), 32 (15.5%) among women and 25 (9.7%) among men. Individual factors and physical job exposures by CTS case status are presented in table 2. By worksite, the hospital had 4.6% CTS cases, while the engine plant had 19.0% and the bus plant had 14.9%.

    View this table:
    Table 2

    Individual factors and job exposure ranges and means (or %) by CTS case status

    Quantitative exposure model

    In the quantitative exposure model, peak force, frequency of exertion and BMI were associated with CTS, and there was an interaction between BMI and frequency of exertion (table 3). High frequency of exertion (≥15 per minute) resulted in a tripling of the odds for CTS among the obese, while obesity resulted in a doubling of the odds for CTS among those with at least 15 exertions per minute. Overtime, general health for age, job satisfaction, high blood pressure and arthritis were not associated with CTS when tested in final quantitative models.

    View this table:
    Table 3

    Multiple logistic regression models—CTS ORs

    Ratings-based exposure model

    Peak worker RPE, BMI and female gender were associated with CTS; HAL was associated with CTS among men but not women (table 3).

    Although overtime, perceived general health and health for age, job satisfaction, resource control, arthritis and high blood pressure were associated with CTS in univariate analyses (online supplementary appendix), overtime was the only one of these potential covariates to remain significant in models that included individual physical job exposure variables. However, since all but two cases worked overtime, it was not included in the model.

    ACGIH HAL TLV model

    HAL values at or above the TLV resulted in a tripling of the odds for CTS; the action-level odds were approximately doubled, but the confidence interval included one (table 3). Arthritis, high blood pressure and female gender each approximately doubled the odds for CTS in this model, but the confidence interval for gender included one.

    Discussion

    The prevalence of CTS in this study (12%) is within the range of 5–15% reported in previous workplace studies.25–28 Higher peak force demands resulted in increased odds for CTS, whether quantitative measures or observational ratings were used to assess jobs. The effect of repetitive hand activity on CTS was modified by personal factors in both the quantitative and ratings-based models. Peak force appeared to have a linear association with CTS. When trichotomised, the high category of peak force (≥70% MVC) resulted in an OR of 2.7 for CTS compared with the low category of <20% MVC. In our study population, 70% MVC translates to 320 N (72 lb) of power grip force or 76 N (17 lb) of pinch force for a 50th percentile worker, 391 N (88 lb) power grip force or 85 N (19 lb) pinch grip force for 50th percentile men and 231 N (52 lb) power grip force or 58 N (13 lb) pinch grip force for 50th percentile women.

    ACGIH TLV for HAL values at or above the TLV resulted in a tripling of the odds for CTS; the action-level odds were approximately doubled, but the confidence interval included one. These results are similar to those reported in a recent longitudinal study.29

    The developers of the HAL scale intended that its application would be limited to single-task jobs and that the ratings would be determined by group consensus.14 However, this was not practical for our study. Instead, our HAL ratings were made by a single ergonomist and we included multi-task jobs. Despite this, higher HAL was associated with CTS among men in our study. Our quantitative measure of repetitiveness, the number of exertions per minute, was associated with increased odds for CTS among the obese. BMI was also independently associated with CTS as reported in previous studies. A large study of factors influencing nerve conduction supports this, although nerve conduction velocity was reported to increase with obesity in other peripheral nerves.30 Proposed mechanisms have not been fully developed, but some researchers suggest fatty tissue and/or swelling in the carpal tunnel impinges on the median nerve.31 We identified an interaction between BMI and frequency of exertion resulting in increased odds for CTS if both obesity and higher frequency of exertion were present. Obesity doubled the odds for CTS among those in the high frequency of exertion group (≥15 per minute), while high frequency of exertion tripled the odds for CTS among the obese. A previous study reported a positive interaction between obesity and length of employment.32 More research is needed to elucidate the association between obesity and CTS and interactions with workplace risk factors.

    When ratings were used to represent job exposures, gender differences were observed. This might be explained by the observation that HAL and RPE varied by gender. We observed that women in our study group tended to have lower force but higher hand activity tasks, while men had the opposite. This pattern has been reported by other researchers in the past.33 34 The size of the sample did not allow for separate analyses for men and women.

    Gender was not associated with CTS in models with detailed quantitative exposure measures, but it was in models with ratings-based exposure measures. It is possible that when cruder measures of exposure are substituted, resulting in more exposure misclassification, there is a greater likelihood that gender will act as a surrogate for exposure. Previous research suggests that as the quality of exposure characterisation increases in workplace studies, observed gender differences decrease.35 36

    Scales created from the Work Environment questionnaire (psychological demands, decision latitude, job strain, task control, mental demands, supervisor support, coworker support, group pressure and depression) resulted in no associations with CTS, with the exception of the resource control scale (online supplementary appendix). In univariate analysis, a single item on that scale, the inability to take a break at will, was associated with CTS. Although work environment factors have frequently been reported to be associated with upper limb musculoskeletal disorders in general, other studies have also reported a lack of association between common work environment risk factors and CTS.8

    Posture was not associated with CTS in our study, although it has been cited as a risk factor for CTS in some studies.6 8 37 It has been demonstrated in past studies that wrist flexion and extension lead to increased carpal tunnel pressure, a proposed mechanism leading to CTS.38 Although our computer-assisted posture analysis method represents an improvement over earlier direct observation methods,39 there was still relatively low interobserver agreement (κ=0.42–0.78) and more missed ratings of wrist postures compared with larger joint movements such as arm elevation in our study (κ=0.72–0.84). In our data, we observed that CTS cases were more likely to be in the neutral posture category. This could be due to misclassification of wrist postures or possibly due to the fact that cases had higher force demands, and it is difficult to perform high-force tasks in non-neutral postures that result in a biomechanical disadvantage. The difficulty of evaluating wrist postures from videotaped job tasks has been noted in previous studies. Instrumentation methods might be more accurate for characterising wrist postures if they can be adapted to large field studies.

    The strengths of this study include individual, detailed quantitative exposure assessment measures paired with simple observational metrics for comparison, nerve conduction testing on all study subjects and detailed consideration of individual risk factors. The primary limitation of the study is that it was cross-sectional, which does not allow us to demonstrate that job exposures occurred prior to the outcome. Other concerns include whether the exposure might be modified as a result of the health outcome and whether exposure is measured at a relevant time period. However, our study participants were primarily long-term employees and most had been in their current jobs for several years, which reduces the likelihood that the outcome occurred prior to job physical exposures. Our questionnaire asked whether workers had moved to their current job due to symptoms. Preplacement screening is a common practice in the USA and would tend to exclude new hires with CTS from jobs with high force and hand activity demands, arguing against CTS preceding job exposure. There are now a number of prospective studies that show that job physical exposures precede the incidence of CTS in workplaces. Cross-sectional data do not allow an assessment of survivor bias, which, if it were present, would tend to underestimate observed associations with workplace stressors.

    Conclusions

    This study used detailed quantitative measures to characterise the exposure of individual study participants to physical job demands such as forceful exertion and repetitiveness. A job requirement for a peak exertion of 70% MVC or more or 15 or more exertions per minute among those who were obese was associated with CTS. Although caution must be used in inferences drawn from cross-sectional studies, longitudinal studies support the findings that repetitive forceful job tasks are associated with CTS.40 Designing jobs to limit physical job demands to lower levels should result in fewer cases of CTS. When commonly used rating scales for perceived exertion were substituted for detailed quantitative measures of forceful exertion in this study, similar associations with CTS were observed. We previously identified a correlation between HAL ratings and the number of exertions counted from video recorded job tasks.24 This suggests that simple and inexpensive methods might be substituted for resource-intensive quantitative exposure assessment to prioritise job tasks for interventions to reduce exposures and prevent CTS.

    Acknowledgments

    The authors are grateful to Larry Fine for guidance during the developmental phase of the study, Paul Dowdy for nerve conduction testing, Robert Werner for review of nerve conduction test results, Nancy Gell for coding hand symptom diagrams, Barbara Silverstein for review of the analysis and manuscript, Fred Gerr for review of the manuscript and Stephen Bao for consultation on exposure assessment. Additional thanks go to Lian Luo, Diane Wormus, Steve Allee, Diane Papes, Jean Geiman, Kim Jenkins, Denise Giglio, Avima Ruder, Bill Ehling and Chip Lehman.

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    Footnotes

    • Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

    • This work was written by officers or employees of the US government as part of their official duties, and therefore, it is not subject to US copyright.

    • Funding This study was funded by the Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, as part of the National Occupational Research Agenda (NORA I) research program in 2000.

    • Competing interests None.

    • Patient consent Obtained.

    • Ethics approval This study was conducted with the approval of the NIOSH Human Subjects Review Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.