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Objectives Most studies of carpal tunnel syndrome (CTS) incidence and prevalence among workers have been limited by small sample sizes or restricted to a small subset of jobs. We established a common CTS case definition and then pooled CTS prevalence and incidence data across six prospective studies of musculoskeletal outcomes to measure CTS frequency and allow better studies of etiology. Methods Six research groups collected prospective data at >50 workplaces including symptoms characteristic of CTS and electrodiagnostic studies (EDS) of the median and ulnar nerves across the dominant wrist. While study designs and the timing of data collection varied across groups, we were able to create a common CTS case definition incorporating both symptoms and EDS results from data that were collected in all studies. Results At the time of enrollment, 7.8% of 4321 subjects met our case definition and were considered prevalent cases of CTS. During 8833 person-years of follow-up, an additional 204 subjects met the CTS case definition for an overall incidence rate of 2.3 CTS cases per 100 person-years. Conclusions Both prevalent and incident CTS were common in data pooled across multiple studies and sites. The large number of incident cases in this prospective study provides adequate power for future exposure— response analyses to identify work- and non-work-related risk factors for CTS. The prospective nature allows determination of the temporal relations necessary for causal inference.

Background Between 2001 and 2010, five research groups conducted coordinated prospective studies of carpal tunnel syndrome (CTS) incidence among US workers from various industries and collected detailed subject-level exposure information with follow-up of symptoms, electrophysiological measures and job changes. Objective This analysis examined the associations between workplace biomechanical factors and incidence of dominant-hand CTS, adjusting for personal risk factors. Methods 2474 participants, without CTS or possible polyneuropathy at enrolment, were followed up to 6.5 years (5102 person-years). Individual workplace exposure measures of the dominant hand were collected for each task and included force, repetition, duty cycle and posture. Task exposures were combined across the workweek using time-weighted averaging to estimate job-level exposures. CTS case-criteria were based on symptoms and results of electrophysiological testing. HRs were estimated using Cox proportional hazard models. Results After adjustment for covariates, analyst (HR=2.17; 95% CI 1.38 to 3.43) and worker (HR=2.08; 95% CI 1.31 to 3.39) estimated peak hand force, forceful repetition rate (HR=1.84; 95% CI 1.19 to 2.86) and per cent time spent (eg, duty cycle) in forceful hand exertions (HR=2.05; 95% CI 1.34 to 3.15) were associated with increased risk of incident CTS. Associations were not observed between total hand repetition rate, per cent duration of all hand exertions, or wrist posture and incident CTS. Conclusions In this prospective multicentre study of production and service workers, measures of exposure to forceful hand exertion were associated with incident CTS after controlling for important covariates. These findings may influence the design of workplace safety programmes for preventing work-related CTS.

BackgroundAlthough recent studies have identified important risk factors associated with incident carpal tunnel syndrome (CTS), risk factors associated with its severity have not been well explored.ObjectiveTo examine the associations between personal, workplace psychosocial and biomechanical factors and incident work disability among workers with CTS.MethodsBetween 2001 and 2010 five research groups conducted coordinated prospective studies of CTS and related work disability among US workers from various industries. Workers with prevalent or incident CTS (N=372) were followed for up to 6.4 years. Incident work disability was measured as: (1) change in work pace or work quality, (2) lost time or (3) job change following the development of CTS. Psychosocial factors were assessed by questionnaire. Biomechanical exposures were assessed by observation and measurements and included force, repetition, duty cycle and posture. HRs were estimated using Cox models.ResultsDisability incidence rates per 100 person-years were 33.2 for changes in work pace or quality, 16.3 for lost time and 20.0 for job change. There was a near doubling of risk for job change among those in the upper tertile of the Hand Activity Level Scale (HR 2.17; 95% CI 1.17 to 4.01), total repetition rate (HR 1.75; 95% CI 1.02 to 3.02), % time spent in all hand exertions (HR 2.20; 95% CI 1.21 to 4.01) and a sixfold increase for high job strain. Sensitivity analyses indicated attenuation due to inclusion of the prevalent CTS cases.ConclusionPersonal, biomechanical and psychosocial job factors predicted CTS-related disability. Results suggest that prevention of severe disability requires a reduction of both biomechanical and organisational work stressors.

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Background Between 2001 and 2010, six research groups conducted coordinated multiyear, prospective studies of carpal tunnel syndrome (CTS) incidence in US workers from various industries and collected detailed subject-level exposure information with follow-up symptom, physical examination, electrophysiological measures and job changes. Objective This analysis of the pooled cohort examined the incidence of dominant-hand CTS in relation to demographic characteristics and estimated associations with occupational psychosocial factors and years worked, adjusting for confounding by personal risk factors. Methods 3515 participants, without baseline CTS, were followed-up to 7 years. Case criteria included symptoms and an electrodiagnostic study consistent with CTS. Adjusted HRs were estimated in Cox proportional hazard models. Workplace biomechanical factors were collected but not evaluated in this analysis. Results Women were at elevated risk for CTS (HR=1.30; 95% CI 0.98 to 1.72), and the incidence of CTS increased linearly with both age and body mass index (BMI) over most of the observed range. High job strain increased risk (HR=1.86; 95% CI 1.11 to 3.14), and social support was protective (HR=0.54; 95% CI 0.31 to 0.95). There was an inverse relationship with years worked among recent hires with the highest incidence in the first 3.5 years of work (HR=3.08; 95% CI 1.55 to 6.12). Conclusions Personal factors associated with an increased risk of developing CTS were BMI, age and being a woman. Workplace risk factors were high job strain, while social support was protective. The inverse relationship between CTS incidence and years worked among recent hires suggests the presence of a healthy worker survivor effect in the cohort.

Heavy load carrying has been associated with musculoskeletal discomfort (MSD) and disability. However, there is a lack of research investigating this association in resource-constrained settings where heavy load carrying by women is common. We assessed the impact of heavy load carrying on musculoskeletal pain and disability among women in Shinyanga Region, Tanzania, in an exploratory cross-sectional study. Eligible participants were a convenience sample of women, at least 18 years of age, who passed a study recruitment site carrying a load. We collected information on load-carrying practices, including frequency and time spent carrying water, wood, agricultural products, coal, sand, or rocks, and measured the weight of the load carried at the time. Outcomes included self-reported MSDs, defined as experiencing pain lasting >3 days in the neck, head, back, knees, feet and/or ankles within the last 1 year, and related disability. Using multivariable logistic regression we assessed for associations between load carrying exposures and MSDs and disability. Results showed a high prevalence of MSDs across the body regions assessed and evidence to suggest a relationship of back pain and related disability with several measures of load-carrying, including duration, frequency, and weight. Multivariable analyses revealed associations of increased load carrying exposures with low back pain (LBP) and related disability, including statistically significant increases in odds of LBP with increasing weight, total duration of load carrying/week and cumulative loads/week. Findings indicate a substantial burden of MSDs and disability in this population of women who carry heavy loads daily. The extent of discomfort and disability increased with increasing exposure to various load-carrying measures, especially for LBP. Larger epidemiologic studies that definitively assess relationships of load carrying with MSDs and disability are warranted.

Virtual and augmented reality (VR, AR) systems present 3D images that users can interact with using controllers or gestures. The design of the user input process is crucial and determines the interactive efficiency, comfort, and adoption. Gesture-based input provides a device-free interaction that may improve safety and creativity compared to using a hand controller while allowing the hands to perform other tasks. Microgestures with small finger and hand motions may have an advantage over the larger forearm and upper arm gestures by reducing distraction, reducing fatigue, and increasing privacy during the interaction. The design of microgestures should consider user experience, ergonomic principles, and interface design to optimize productivity and comfort while minimizing errors. Forty VR/AR or smart device users evaluated a set of 33 microgestures, designed by ergonomists, and linked them to 20 common AR/VR commands based on usability, comfort, and preference. Based primarily on preference, a set of microgestures linked to specific commands is proposed for VR or AR systems. The proposed microgesture set will likely minimize fatigue and optimize usability. Furthermore, the methodology presented for selecting microgestures and assigning them to commands can be applied to the design of other gesture sets.

Objective This paper aimed to quantify exposure-response relationships between the American Conference of Governmental Industrial Hygienists' (ACGIH) threshold limit value (TLV) for hand-activity level (HAL) and incidence of carpal tunnel syndrome (CTS). Methods Manufacturing and service workers previously studied by six research institutions had their data combined and re-analyzed. CTS cases were defined by symptoms and abnormal nerve conduction. Hazard ratios (HR) were calculated using proportional hazards regression after adjusting for age, gender, body mass index, and CTS predisposing conditions. Results The longitudinal study comprised 2751 incident-eligible workers, followed prospectively for up to 6.4 years and contributing 6243 person-years of data. Associations were found between CTS and TLV for HAL both as a continuous variable [HR 1.32 per unit, 95% confidence interval (95% CI) 1.11-1.57] and when categorized using the ACGIH action limit (AL) and TLV. Those between the AL and TLV and above the TLV had HR of 1.7 (95% CI 1.2-2.5) and 1.5 (95% CI 1.0-2.1), respectively. As independent variables (in the same adjusted model) the HR for peak force (PF) and HAL were 1.14 per unit (95% CI 1.05-1.25), and 1.04 per unit (95% CI 0.93-1.15), respectively. Conclusion Those with exposures above the AL were at increased risk of CTS, but there was no further increase in risk for workers above the TLV. This suggests that the current AL may not be sufficiently protective of workers. Combinations of PF and HAL are useful for predicting risk of CTS.

Many workers who engage in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSDs). Quantifying the physical demands of a job is important for identifying high risk jobs and is a legal requirement in the United States for hiring and return to work following injury. Currently, most physical demand analyses (PDAs) are performed by experts using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can be problematic, particularly when identifying restrictions during the return-to-work process. Further, when a worker does return-to-work on modified duty, there is no way to track compliance to work restrictions conflating the effectiveness of the work restrictions versus adherence to them. To address this, we applied a deep learning model to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities. Overall, a 95% accuracy was reached for predicting isolated occupational physical activities. However, when applied to more complex tasks that combined occupational physical activities (OPAs), accuracy varied widely (0–95%). More work is needed to accurately predict OPAs when combined into simulated work tasks.

This paper aimed to quantify exposure-response relationships between the American Conference of Governmental Industrial Hygienists' (ACGIH) threshold limit value (TLV) for hand-activity level (HAL) and incidence of carpal tunnel syndrome (CTS). Manufacturing and service workers previously studied by six research institutions had their data combined and re-analyzed. CTS cases were defined by symptoms and abnormal nerve conduction. Hazard ratios (HR) were calculated using proportional hazards regression after adjusting for age, gender, body mass index, and CTS predisposing conditions. The longitudinal study comprised 2751 incident-eligible workers, followed prospectively for up to 6.4 years and contributing 6243 person-years of data. Associations were found between CTS and TLV for HAL both as a continuous variable [HR 1.32 per unit, 95% confidence interval (95% CI) 1.11-1.57] and when categorized using the ACGIH action limit (AL) and TLV. Those between the AL and TLV and above the TLV had HR of 1.7 (95% CI 1.2-2.5) and 1.5 (95% CI 1.0-2.1), respectively. As independent variables (in the same adjusted model) the HR for peak force (PF) and HAL were 1.14 per unit (95% CI 1.05-1.25), and 1.04 per unit (95% CI 0.93-1.15), respectively. Those with exposures above the AL were at increased risk of CTS, but there was no further increase in risk for workers above the TLV. This suggests that the current AL may not be sufficiently protective of workers. Combinations of PF and HAL are useful for predicting risk of CTS.