The Strain Index and ACGIH TLV for HAL: risk of trigger digit in the WISTAH prospective cohort

Musculoskeletal complaints, physical work demands, and functional capacity in individuals with a brachial plexus injury: An exploratory study

BACKGROUND

Musculoskeletal complaints (MSCs) may be more common in individuals with brachial plexus injury (BPI), whose physical work demands exceed their functional capacity (FC).

OBJECTIVES

(a) To assess the concurrent validity of five methods for measuring upper extremity work demands and the Dictionary of Occupational Titles (DOT). (b) To explore the relations between MSCs, physical work demands, and FC in individuals with BPI.

METHODS

This study had a descriptive correlational design. Physical work demands of 16 individuals with BPI (12 males, 6 one-handed workers) were assessed during work using five assessment methods and the DOT. Spearman correlation coefficients between work demand methods were determined. FC was assessed using the functional capacity evaluation one-handed (FCE-OH). A questionnaire was used to examine MSCs. The relationship between MSCs, physical work demands and FC was analyzed visually, using Spearman correlation coefficients, and by comparing FCE-OH results to FCE reference values.

RESULTS

Spearman correlation coefficients for the DOT and four out of five assessment methods for determining work demands on upper extremities were significant and moderate (four combinations: r = 0.65-0.79) to strong (five combinations: r = 0.81-0.94). Correlations of the fifth method with the other methods were weak to fair. No significant relationships were found between MSCs, physical work demands and FCE-OH results.

CONCLUSION

The relationships between MSCs, physical work demands, and FC are evidently complex and require further investigation. In this small sample the concurrent validity of the DOT and four methods for determining work demands on upper extremities was moderate to good.

Comparison of the observer, single-frame video and computer vision hand activity levels

Observer, manual single-frame video, and automated computer vision measures of the Hand Activity Level (HAL) were compared. HAL can be measured three ways: (1) observer rating (HALO), (2) calculated from single-frame multimedia video task analysis for measuring frequency (F) and duty cycle (D) (HALF), or (3) from automated computer vision (HALC). This study analysed videos collected from three prospective cohort studies to ascertain HALO, HALF, and HALC for 419 industrial videos. Although the differences for the three methods were relatively small on average (<1), they were statistically significant (p < .001). A difference between the HALC and HALF ratings within ±1 point on the HAL scale was the most consistent, where more than two thirds (68%) of all the cases were within that range and had a linear regression through the mean coefficient of 1.03 (R2 = 0.89). The results suggest that the computer vision methodology yields comparable results as single-frame video analysis.Practitioner summary: The ACGIH Hand Activity Level (HAL) was obtained for 419 industrial tasks using three methods: observation, calculated using single-frame video analysis and computer vision. The computer vision methodology produced results that were comparable to single-frame video analysis.

Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

The Prevalence and Risk Factors for Trigger Digits in a Random Sampling of a Japanese Population Registry between 50 and 89 Years of Age

Background: Trigger digit(s) (TD) is one of the most common disorders of the hand in the elderly population. The aim of this study is to determine the prevalence and identify the risk factors for TD in an elderly Japanese population. Methods: We randomly sampled 1,297 subjects between the ages of 50 and 89 years from the population registry of a town in Japan. About 413 subjects agreed to participate in the study, and all were examined for the presence of TD. Subjects were divided into three groups namely history of treatment for TD in the past (PTD), current evidence of TD (CTD) or both (BTD). The prevalence of TD was weighted by age according to the composition of the Japanese population. Age, female gender, obesity, hard manual work, exposure to vibration tools, sports activity, smoking, alcohol, wrist fracture, hypertension, hypothyroidism, diabetes mellitus, rheumatoid arthritis and carpal tunnel syndrome were assessed as risk factors for TD using univariate and multivariate logistic regression analysis. Results: Forty subjects had TD. This included 18, 19 and 3 subjects with PTD, CTD and BTD, respectively. The weighted prevalence of TD was 9.7% (female, 14.3%; male, 4.4%) in the Japanese population aged 50-89 years. Age 70-79 and female gender were identified as risk factors for TD. Conclusions: The random sampling of a Japanese population registry between the ages of 50 and 89 years revealed the prevalence of TD as 9.7% and identified age between 70 and 79 and female gender as risk factors for developing TD. Level of Evidence: Level II (Therapeutic).

Diabetes Mellitus as a Risk Factor for Trigger Finger –a Longitudinal Cohort Study Over More Than 20 Years

Background and Aim

Trigger finger (TF) or stenosing tenosynovitis has been associated with diabetes mellitus (DM), although today’s knowledge is mostly based on cross-sectional and case-control studies. Thus, the aim of the present population-based cohort study over more than 20 years was to investigate DM as a risk factor for TF.

Methods

Data from Malmö Diet and Cancer Study (MDCS), including 30,446 individuals, were analysed with regards to baseline DM and known or potential confounders. Information regarding TF diagnosis until study end date of Dec 31st, 2018, was retrieved from the Swedish National Patient Register (NPR) using ICD-codes. Survival probability was investigated in Kaplan-Meier plots. Cox proportional hazard regression model was used to evaluate DM as risk factor for TF, adjusted for several confounders and presented as Hazard Ratio (HR) with 95% confidence intervals (CI).

Results

At baseline, 4.6% (1,393/30,357) participants had DM. In total, 3.2% (974/30,357) participants were diagnosed with TF during the study period. Kaplan-Meier plot showed that the probability for incident TF was significantly higher in participants with baseline DM compared with individuals without baseline DM. Adjusted HR for DM as risk factor for TF was 2.0 (95% CI: 1.5-2.6, p&lt;0.001).

Conclusion

This longitudinal study showed that DM is an important risk factor for developing TF. When adjusting for sex, age, BMI, manual work, statin use, smoking and alcohol consumption, DM remained the main risk factor for TF.

Validation of the Revised Strain Index for Predicting Risk of Incident Carpal Tunnel Syndrome in a Prospective Cohort

The Revised Strain Index (RSI), a model that quantifies physical exposure from individual hand/wrist exertions, tasks, and multi-task jobs, was used to quantify exposure for 1372 incident-eligible manufacturing, service and healthcare workers. Workers were followed for an average of 2.5 years (maximum 6 years) and had an average carpal tunnel syndrome (CTS) incidence rate of 4.6 per 100 person-years. Exceeding the a-priori RSI limit of 10.0 showed increased risk of CTS (Hazard Ratio (HR) = 1.45, 95% CI: 1.11-1.91, p = 0.01). There also was a dose-response relationship using proposed low (RSI ≤ 8.5, HR = 1.00), medium (HR = 1.42 (95% CI: 0.96-2.09, p = 0.08)), and high limits (RSI > 15, HR = 1.79 (95% CI: 1.19-2.69, p = 0.01), respectively. RSI as a continuous variable showed CTS risk increased steadily by between 1.9% and 3.3% per unit increase in RSI (p ≤ 0.03). These results suggest that the RSI is a useful tool for surveillance as well as for job intervention/design and continuous improvement processes.Practitioner Summary The Revised Strain Index (RSI) quantifies physical exposure from individual hand/wrist exertions, tasks, and multi-task jobs. Increased cumulative RSI scores (i.e. daily exposure score) are associated with increased risk of carpal tunnel syndrome (CTS). The RSI is potentially useful as a risk surveillance and intervention design tool.

The Coexistence of Carpal Tunnel Syndrome in Workers With Trigger Digit

Background: The objective of this study was to investigate the prevalence of carpal tunnel syndrome (CTS) in workers with trigger digit. There are few cross-sectional studies that assess this relationship. Methods: A baseline examination of 1216 workers from 17 diverse manufacturing facilities was conducted. Worker demographics, medical history, and symptoms of trigger digit were assessed. Age, sex, and body mass index were obtained. Biomechanical factors were individually measured using the Strain Index (SI). Prevalence was assessed with univariate and multivariate logistic regression. Results: Unadjusted prevalence of trigger digit was 12.0%, and among those workers, there was an unadjusted CTS prevalence of 26.7%. The adjusted multivariate model found an odds ratio (OR) of CTS of 1.56 (95% confidence interval [CI], 1.03-2.36) among the workers with trigger digit. The ORs of CTS for SI (OR = 1.53 [95% CI, 1.04-2.23]), age (OR = 1.03 [95% CI, 1.01-1.04]), and current smoking (OR = 1.76 [95% CI, 1.12-2.75]) were also significant. Sex and diabetes were not statistically significant covariates. Conclusion: The prevalence of CTS is higher among workers with trigger digit.

Association between Stenosing Tenosynovitis and Dupuytren's Contracture in the Hand

Background:

Both stenosing tenosynovitis and Dupuytren’s contracture are common conditions encountered in hand surgery. Connections between 2 diseases have been suggested in literature. The purpose of this study was to examine whether there’s an association between the 2 processes.

Methods:

A retrospective chart review was performed to include all patients seen by a single surgeon between 2014 and 2017 with the diagnosis of either trigger finger or Dupuytren’s contracture in the same hand. Patients’ demographics, medical history, social and surgical histories are recorded. Univariate and multivariate analysis were conducted.

Results:

A cohort of 238 patients was identified. One hundred ninety-two patients were diagnosed with trigger finger. Eighty-nine patients were diagnosed with Dupuytren’s contracture. Forty-three patients carried both diagnoses. Median age was 61.6 (56–72). Half were male (50.4%) and 66.8% reported current alcohol intake. Other factors include history of former or current tobacco use (52.9%), diabetes (23.9%), and manual labor (31.1%). In the univariate model, trigger finger, sex, and age were significantly associated with the diagnosis of Dupuytren’s contracture, and Dupuytren’s contracture and sex were significantly associated with the trigger finger diagnosis. Diabetes, manual labor, use of alcohol and tobacco were not significant. In the multivariate model, age and trigger finger were significantly associated with Dupuytren’s contracture.

Conclusions:

Significant association between stenosing tenosynovitis and Dupuytren’s contracture was identified in our patient cohort. Patients with stenosing tenosynovitis may be at an increased risk of developing Dupuytren’s contracture or vice versa.

An Upper Extremity Risk Assessment Tool Based on Material Fatigue Failure Theory: The Distal Upper Extremity Tool (DUET)

OBJECTIVE

Musculoskeletal tissues repeatedly loaded in vitro fail in accordance with material fatigue failure theory, and there is evidence to suggest that the same process occurs in vivo. The current paper presents a new upper extremity risk assessment tool, the Distal Upper Extremity Tool (DUET), predicated on material fatigue failure theory.

METHODS

DUET requires an estimate of force exertion level and the number of repetitions performed to derive estimates of damage and probabilities of experiencing a distal upper extremity outcome. Damage accrued over multiple tasks may be summed to estimate the cumulative damage (CD) accrued over a workday. Validation of this tool was performed using five distal upper extremity (DUE) outcomes (involving medical visits and pain) from an existing epidemiological database involving data from six automotive manufacturing plants. Logistic regression was used to assess the association of the log of the DUET CD measure to DUE outcomes.

RESULTS

Results demonstrated that the log of the DUET CD measure was highly associated with all five DUE outcomes in both crude analyses and those adjusted for site, age, gender, and body mass index ( p < .01). A model relating the continuous DUET log CD score to the probability of the DUE outcome Injury + Pain Last Year was developed, which demonstrated a significant dose-response relationship.

CONCLUSIONS

Results suggest that fatigue failure-based risk assessment techniques are highly associated with DUE outcomes and provide support for the notion that an underlying fatigue failure process may be involved in the development of upper extremity musculoskeletal disorders.

Risk assessments using the Strain Index and the TLV for HAL, Part I: Task and multi-task job exposure classifications

BACKGROUND

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value for Hand Activity Level (TLV for HAL) use different constituent variables to quantify task physical exposures. Similarly, time-weighted-average (TWA), Peak, and Typical exposure techniques to quantify physical exposure from multi-task jobs make different assumptions about each task's contribution to the whole job exposure. Thus, task and job physical exposure classifications differ depending upon which model and technique are used for quantification. This study examines exposure classification agreement, disagreement, correlation, and magnitude of classification differences between these models and techniques.

METHODS

Data from 710 multi-task job workers performing 3,647 tasks were analyzed using the SI and TLV for HAL models, as well as with the TWA, Typical and Peak job exposure techniques. Physical exposures were classified as low, medium, and high using each model's recommended, or a priori limits. Exposure classification agreement and disagreement between models (SI, TLV for HAL) and between job exposure techniques (TWA, Typical, Peak) were described and analyzed.

RESULTS

Regardless of technique, the SI classified more tasks as high exposure than the TLV for HAL, and the TLV for HAL classified more tasks as low exposure. The models agreed on 48.5% of task classifications (kappa = 0.28) with 15.5% of disagreement between low and high exposure categories. Between-technique (i.e., TWA, Typical, Peak) agreement ranged from 61-93% (kappa: 0.16-0.92) depending on whether the SI or TLV for HAL was used.

CONCLUSIONS

There was disagreement between the SI and TLV for HAL and between the TWA, Typical and Peak techniques. Disagreement creates uncertainty for job design, job analysis, risk assessments, and developing interventions. Task exposure classifications from the SI and TLV for HAL might complement each other. However, TWA, Typical, and Peak job exposure techniques all have limitations. Part II of this article examines whether the observed differences between these models and techniques produce different exposure-response relationships for predicting prevalence of carpal tunnel syndrome.

The Composite Strain Index (COSI) and Cumulative Strain Index (CUSI): methodologies for quantifying biomechanical stressors for complex tasks and job rotation using the Revised Strain Index

The Composite Strain Index (COSI) quantifies biomechanical stressors for complex tasks consisting of exertions at different force levels and/or with different exertion times. The Cumulative Strain Index (CUSI) further integrates biomechanical stressors from different tasks to quantify exposure for the entire work shift. The paper provides methodologies to compute COSI and CUSI along with examples. Complex task simulation produced 169,214 distinct tasks. Use of average, time-weighted average (TWA) and peak force and COSI classified 66.9, 28.2, 100 and 38.9% of tasks as hazardous, respectively. For job rotation the simulation produced 10,920 distinct jobs. TWA COSI, peak task COSI and CUSI classified 36.5, 78.1 and 66.6% jobs as hazardous, respectively. The results suggest that the TWA approach systematically underestimates the biomechanical stressors and peak approach overestimates biomechanical stressors, both at the task and job level. It is believed that the COSI and CUSI partially address these underestimations and overestimations of biomechanical stressors. Practitioner Summary: COSI quantifies exposure when applied hand force and/or duration of that force changes during a task cycle. CUSI integrates physical exposures from job rotation. These should be valuable tools for designing and analysing tasks and job rotation to determine risk of musculoskeletal injuries.

The Revised Strain Index: an improved upper extremity exposure assessment model

The Revised Strain Index (RSI) is a distal upper extremity (DUE) physical exposure assessment model based on: intensity of exertion, frequency of exertion, duration per exertion, hand/wrist posture and duration of task per day. The RSI improves upon the 1995 Strain Index (SI) by using continuous rather than categorical multipliers, and replacing duty cycle with duration per exertion. In a simulation of 13,944 tasks, the RSI and 1995 SI showed good agreement in risk predictions for 1995 SI scores ≤3 (safe) and >13.5 (hazardous). For tasks with 1995 SI scores of >3 and ≤13.5, the two models showed marked disagreement, with the RSI providing much greater discriminations between 'safe' and 'hazardous' tasks for various combinations of force, repetition and duty cycle. We believe the RSI is a substantially improved model that will be useful for DUE task analysis, intervention and design. Practitioner Summary: RSI is a substantial improvement over the 1995 SI. It should be a valuable tool for designing and analysing tasks to determine risk of musculoskeletal injuries. RSI is applicable to a wide variety of tasks including very low force and very high repetition tasks such as keyboard use.

Risk assessment of cheese processing tasks using the Strain Index and OCRA Checklist

The purpose of this study was to conduct and compare two ergonomic risk assessment methods often used in occupational health research and practice: the Strain Index (SI) and Occupational Repetitive Actions (OCRA) Checklist. Seven raters used the SI and OCRA Checklist to assess task-level physical exposures to the upper extremity of workers performing 21 cheese-manufacturing tasks. Of the total task exposures assessed with both methods, nearly half (49.1%) were classified as hazardous using the OCRA Checklist while 60.2% were classified as hazardous using the SI. Although the underlying injury risk characterization constructs of the SI and OCRA Checklist differ, the results indicated that the SI and OCRA Checklist often classified job tasks into similar risk categories. The differences in risk classifications determined by the SI and OCRA Checklist for job tasks were likely related to the definition of variables measured by these assessment methods as well as the complexity of tasks evaluated. By design, the SI is specific to the distal upper extremity while the OCRA Checklist accounts for the entire upper extremity including the shoulder. When conducting risk assessments of industrial work tasks, the choice of analysis tools should be based on the purpose of the assessment and the complexity of task functions. Both the SI and OCRA Checklist yield risk assessment ratings that are similar for cheese processing tasks.

MR imaging findings of trigger thumb

Trigger finger (or trigger thumb), also known as sclerosing tenosynovitis, is a common clinical diagnosis that rarely presents for imaging. Because of this selection bias, many radiologists may not be familiar with the process. Furthermore, patients who do present for imaging frequently have misleading examination indications. To our knowledge, magnetic resonance (MR) imaging findings of trigger thumb have not been previously reported in the literature. In this article, we review the entity of trigger thumb, the anatomy involved, and associated imaging findings, which include flexor pollicis longus tendinosis with a distinct nodule, A1 pulley thickening, and tenosynovitis. In addition, in some cases, an abnormal Av pulley is apparent. In the rare cases of trigger finger that present for MR imaging, accurate diagnosis by the radiologist can allow initiation of treatment and avoid further unnecessary workup.

A frequency-duty cycle equation for the ACGIH hand activity level

A new equation for predicting the hand activity level (HAL) used in the American Conference for Government Industrial Hygienists threshold limit value®(TLV®) was based on exertion frequency (F) and percentage duty cycle (D). The TLV® includes a table for estimating HAL from F and D originating from data in Latko et al. (Latko WA, Armstrong TJ, Foulke JA, Herrin GD, Rabourn RA, Ulin SS, Development and evaluation of an observational method for assessing repetition in hand tasks. American Industrial Hygiene Association Journal, 58(4):278-285, 1997) and post hoc adjustments that include extrapolations outside of the data range. Multimedia video task analysis determined D for two additional jobs from Latko's study not in the original data-set, and a new nonlinear regression equation was developed to better fit the data and create a more accurate table. The equation, HAL = 6:56 ln D[F(1:31) /1+3:18 F(1:31), generally matches the TLV® HAL lookup table, and is a substantial improvement over the linear model, particularly for F>1.25 Hz and D>60% jobs. The equation more closely fits the data and applies the TLV® using a continuous function.

A hand speed-duty cycle equation for estimating the ACGIH hand activity level rating

An equation was developed for estimating hand activity level (HAL) directly from tracked root mean square (RMS) hand speed (S) and duty cycle (D). Table lookup, equation or marker-less video tracking can estimate HAL from motion/exertion frequency (F) and D. Since automatically estimating F is sometimes complex, HAL may be more readily assessed using S. Hands from 33 videos originally used for the HAL rating were tracked to estimate S, scaled relative to hand breadth (HB), and single-frame analysis was used to measure D. Since HBs were unknown, a Monte Carlo method was employed for iteratively estimating the regression coefficients from US Army anthropometry survey data. The equation: HAL = 10[e(-15:87+0:02D+2:25 ln S)/(1+e(-15:87+0:02D+2:25 ln S)], R(2) = 0.97, had a residual range ± 0.5 HAL. The S equation superiorly fits the Latko et al. ( 1997 ) data and predicted independently observed HAL values (Harris 2011) better (MSE = 0.16) than the F equation (MSE = 1.28).

The accuracy of conventional 2D video for quantifying upper limb kinematics in repetitive motion occupational tasks

Marker-less 2D video tracking was studied as a practical means to measure upper limb kinematics for ergonomics evaluations. Hand activity level (HAL) can be estimated from speed and duty cycle. Accuracy was measured using a cross-correlation template-matching algorithm for tracking a region of interest on the upper extremities. Ten participants performed a paced load transfer task while varying HAL (2, 4, and 5) and load (2.2 N, 8.9 N and 17.8 N). Speed and acceleration measured from 2D video were compared against ground truth measurements using 3D infrared motion capture. The median absolute difference between 2D video and 3D motion capture was 86.5 mm/s for speed, and 591 mm/s(2) for acceleration, and less than 93 mm/s for speed and 656 mm/s(2) for acceleration when camera pan and tilt were within ± 30 degrees. Single-camera 2D video had sufficient accuracy (< 100 mm/s) for evaluating HAL. Practitioner Summary: This study demonstrated that 2D video tracking had sufficient accuracy to measure HAL for ascertaining the American Conference of Government Industrial Hygienists Threshold Limit Value(®) for repetitive motion when the camera is located within ± 30 degrees off the plane of motion when compared against 3D motion capture for a simulated repetitive motion task.