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 Impact of COVID-19 and Pandemic Mitigation Measures on Persons With Sensory Impairment.

PURPOSE: To assess the impact of the COVID-19 pandemic and associated mitigation measures on persons with sensory impairments (SI), including visual impairments (VI) and hearing impairments (HI). DESIGN: Cross-sectional survey. METHODS: Adults with VI (best-corrected visual acuity <20/60 in the better-seeing eye), HI (International Classification of Diseases, Tenth Revision, codes), and age- and sex-matched controls (n = 375) were recruited from the University of Michigan. The 34-item Coronavirus Disability Survey was administered. Both χ2 tests and logistic regression were used to compare survey responses between groups. RESULTS: All groups reported high levels of disruption of daily life, with 80% reporting "a fair amount" or "a lot" of disruption (VI: 76%, HI: 83%, CT: 82%, P = .33). Participants with VI had greater difficulty with day-to-day activities and were more likely to cite the following reasons: caregiver was worried about COVID-19 (odds ratio [OR]VI = 7.2, 95% CI = 3.5-14.4, P < .001) and decreased availability of public transportation (ORVI = 5.0, 95% CI = 1.5-15.6, P = .006). Participants with VI, but not HI, showed a trend toward increased difficulty accessing medical care (ORVI = 2.0, 95% CI = 0.99-4.0, P = .052) and began relying more on others for day-to-day assistance (ORVI = 3.1, 95% CI = 1.6-5.7, P < .001). Overall, 30% reported difficulty obtaining trusted information about the pandemic. Those with VI reported more difficulty seeing or hearing trusted information (ORVI = 6.1, 95% CI = 1.6-22.1, P = .006). Employed participants with HI were more likely to report a reduction in wages (ORHI = 2.5, 95% CI = 1.2-5.3, P = .02). CONCLUSIONS: Individuals with VI have experienced increased disruption and challenges in daily activities related to the pandemic. People with SI may benefit from targeted policy approaches to the current pandemic and future stressors. Minimal differences in some survey measures may be due to the large impact of the pandemic on the population as a whole. The SARS-CoV-2 (COVID-19) pandemic and public health mitigation measures have had an exceedingly large impact around the globe. As of the time of writing, more than 114 million global cases (28 million US) had been diagnosed, and there had been more than 2.5 million fatalities attributed to COVID-19 (517,000 US).1,2.

Predictors of upper extremity discomfort: a longitudinal study of industrial and clerical workers.

Upper extremity discomfort associated with work activity is common with a prevalence of over 50% in many settings. This study followed a cohort of 501 active workers for an average of 5.4 years. Cases were defined as workers who were asymptomatic or had a low discomfort score of 1 or 2 at baseline testing and went on to report a discomfort score of 4 or above on a 10-point visual analog scale. This change is considered clinically significant. Controls had a low baseline discomfort score and continued to have a low discomfort rating throughout the study. The risk factors found to have the highest predictive value for identifying a person who is likely to develop a significant upper extremity discomfort rating included age over 40, a BMI over 28, a complaint of baseline discomfort, the severity of the baseline discomfort rating and a job that had a high hand activity level (based upon hand repetition and force). The risk profile identified both ergonomic and personal health factors as risks and both factors may be amenable to prevention strategies.

A longitudinal study of industrial and clerical workers: predictors of upper extremity tendonitis.

Upper extremity tendonitis (UET) associated with work activity is common but the true incidence and risk factors can best be determined by a prospective cohort study. This study followed a cohort of 501 active workers for an average of 5.4 years. Incident cases were defined as workers who were asymptomatic at baseline testing and had no prior history of UET and went on to be diagnosed with an UET during the follow-up period or at the follow-up evaluation. The incident cases were compared to the subset of the cohort who also had no history of an UET and did not develop tendonitis during the study. The cumulative incidence in this cohort was 24.3% or 4.5% annually. The factors found to have the highest predictive value for identifying a person who is likely to develop an UET in the near future included age over 40, a BMI over 30, a complaint at baseline of a shoulder or neck discomfort, a history of CTS and a job with a higher shoulder posture rating. The risk profile identifies both ergonomic and personal health factors as risks and both categories of factors may be amenable to prevention strategies.

A longitudinal study of industrial and clerical workers: incidence of carpal tunnel syndrome and assessment of risk factors.

This study followed workers over an extended period of time to identify factors which may influence the onset of Carpal Tunnel Syndrome (CTS). The purpose was to evaluate incidence of CTS and to create a predictive model of factors that play a role in the development of CTS. This prospective study followed 432 industrial and clerical workers over 5.4 years. Incident cases were defined as workers who had no prior history of CTS at baseline testing and were diagnosed with CTS during the follow-up period or at the follow-up screening. On the basis of logistic regression, significant predictors for CTS include baseline median-ulnar peak latency difference, a history of wrist/hand/finger tendonitis, a history of numbness, tingling, burning, and/or pain in the hand, and work above the action level of the peak force and hand activity level threshold limit value. This longitudinal study supports findings from previous cross-sectional studies identifying both work related ergonomic stressors and physical factors as independent risk factors for CTS.

A cross-sectional assessment of the ACGIH TLV for hand activity level.

The ACGIH Worldwide Threshold Limit Value (TLV) for hand activity "considers average hand activity level or "HAL" and peak hand force." We report cross-sectional data that assess the validity of the TLV with respect to symptoms and selected upper extremity musculoskeletal disorders among workers. The prevalence of symptoms and specific disorders were examined among 908 workers from 7 different job sites in relation to the TLV. Worker exposures were categorized as above the TLV, above the TLV Action Limit but below the TLV, or below the TLV Action Limit. Symptoms in the distal upper extremities did not vary by TLV category. Tendonitis in the wrist/hands/fingers did not vary by TLV category, but elbow/forearm tendonitis was significantly associated with TLV category. All measures of carpal tunnel syndrome were associated with TLV category. In all instances, prevalence of symptoms and specific disorders were substantial in jobs that were below the TLV action limit, suggesting that even at "acceptable" levels of hand activity, many workers will still experience symptoms and/or upper extremity musculoskeletal disorders, which may be important in the rehabilitation and return to work of injured workers. Future analyses need to examine the incidence of symptoms and upper extremity musculoskeletal disorders prospectively among workers in relation to the TLV for hand activity.

Case studies of ergonomic interventions in automotive parts distribution operations.

Ergonomic job analysis, intervention design, and intervention implementation are essential components of an ergonomics program designed to reduce worker exposure to risk factors associated with musculoskeletal disorders. As part of a 4-year study to reduce overexertion injuries in the service parts division of a major automaker, intervention case studies were identified that could be used at multiple facilities across the division. Interventions were developed and implemented. The three case studies include 1) self-elevating powered vehicle for transporting parts throughout the facility and for reaching to high bin locations; 2) lift and tilt pallet jacks for packing small parts into large bin-like containers; and 3) single-level telescoping conveyor used for delivering hand-held totes for subsequent sorting operations. Several analysis methods were used to assess worker exposure before and after intervention implementation (biomechanical analysis, posture analysis, worker interviews, and activity analysis). Following implementation, a decrease in exposure to risk factors for musculoskeletal disorders was documented. Worker interviews revealed acceptance and agreement that risk factors associated with the targeted tasks were reduced. Each case study includes a description of the implementation hurdles and can serve as both primary and secondary prevention of musculoskeletal disorders. Future work should document worker health and/or symptom changes as well as changes in risk factor exposure.