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Amsterdam-Zuidoost, Netherlands

Douwes M.,TNO | Boocock M.,Auckland University of Technology | Coenen P.,Research Center on Physical Activity | Coenen P.,VU University Amsterdam | And 2 more authors.
International Journal of Industrial Ergonomics | Year: 2014

The Hand Arm Risk assessment Method (HARM) is a simplified risk assessment method for determining musculoskeletal symptoms to the arm, neck and/or shoulder posed by hand-arm tasks of the upper body. The purpose of this study was to evaluate the predictive validity of HARM using data collected from a three year prospective cohort Study on Musculoskeletal disorders, Absenteeism and Health (SMASH). Structured observations of video recordings using HARM were made of 88 participants performing hand-arm tasks. These video recordings were made at baseline of the SMASH study, whereas self-reported musculoskeletal symptoms were assessed both at baseline and during a three year follow-up. The receiver operating characteristic (ROC) curves showed an area under the curve (AUC) of 0.75 (CI: 0.65-0.86) for symptoms at baseline and 0.70 (CI: 0.58-0.83) for symptoms during follow-up. For a HARM score of 50, the sensitivity and specificity were 64% and 79% at baseline and 53% and 82% during follow up. Participants with a HARM score of 50 or more (considered a high risk) had a significantly higher risk of upper limb symptoms than those with a HARM score of less than 50 at baseline (OR=5.31; 95%CI: 2.10-13.39) and 3 year follow-up (OR=5.11; (95%CI: 1.61-16.27). This study has shown that HARM can be used to assess work tasks of the hands and arms and discriminate between tasks that pose a high or low risk of neck or upper limb symptoms. Relevance to industry: The association of HARM results and self-reported neck and upper limb symptoms was studied. The association found both cross sectionally and at follow up shows that the method can be used in industry to distinguish hazardous hand arm tasks from non-hazardous ones. Thus, HARM can support OSH practitioners and industrial engineers in designing healthy workplaces. © 2013 Elsevier B.V. Source


Boot C.R.L.,VU University Amsterdam | Boot C.R.L.,Research Center on Physical Activity | Boot C.R.L.,University of Toronto | Hogg-Johnson S.,University of Toronto | And 6 more authors.
International Archives of Occupational and Environmental Health | Year: 2014

Purpose: In workers with musculoskeletal injuries, comorbidity is associated with worse return to work (RTW) outcomes. In the context of RTW, it is unclear whether associations between predictors and RTW are similar or different for workers with and without comorbidity. This study aims to investigate differences and similarities between workers with and without comorbidity in 12-month predictors for RTW in workers who are absent from work due to a musculoskeletal injury.Methods: All workers with lost-time claims who were off work at baseline were selected from the Early Claimant Cohort (Canada) (n = 1,566). Follow-up data on RTW were available of 810 workers after 12 months. Predictors included demographic, health-related, and work-related factors. Differences between coefficients of the groups with and without comorbidity were tested.Results: Low household income was a predictor for RTW in workers without comorbidity only. Better mental health was a predictor for RTW in workers with comorbidity only. Higher education, less pain intensity, better general health, less bodily pain, better physical health, and a positive supervisor response were predictors for RTW in the total group.Conclusions: Injured workers with and without comorbidity should be considered as two distinct groups when focusing on mental health or household income. © 2014, Springer-Verlag Berlin Heidelberg. Source


Coenen P.,VU University Amsterdam | Coenen P.,Research Center on Physical Activity | Kingma I.,VU University Amsterdam | Kingma I.,Research Center on Physical Activity | And 9 more authors.
Journal of Biomechanics | Year: 2011

This study aimed to develop, compare and validate two versions of a video analysis method for assessment of low back moments during occupational lifting tasks since for epidemiological studies and ergonomic practice relatively cheap and easily applicable methods to assess low back loads are needed. Ten healthy subjects participated in a protocol comprising 12 lifting conditions. Low back moments were assessed using two variants of a video analysis method and a lab-based reference method. Repeated measures ANOVAs showed no overall differences in peak moments between the two versions of the video analysis method and the reference method. However, two conditions showed a minor overestimation of one of the video analysis method moments. Standard deviations were considerable suggesting that errors in the video analysis were random. Furthermore, there was a small underestimation of dynamic components and overestimation of the static components of the moments. Intraclass correlations coefficients for peak moments showed high correspondence (>0.85) of the video analyses with the reference method. It is concluded that, when a sufficient number of measurements can be taken, the video analysis method for assessment of low back loads during lifting tasks provides valid estimates of low back moments in ergonomic practice and epidemiological studies for lifts up to a moderate level of asymmetry. © 2011 Elsevier Ltd. Source


Coenen P.,VU University Amsterdam | Coenen P.,Research Center on Physical Activity | Kingma I.,VU University Amsterdam | Kingma I.,Research Center on Physical Activity | And 6 more authors.
Occupational and Environmental Medicine | Year: 2014

Objectives: Reported associations of physical exposures during work (eg, lifting, trunk flexion or rotation) and low-back pain (LBP) are rather inconsistent. Mechanical back loads (eg, moments on the low back) as a result of exposure to abovementioned risk factors have been suggested to be important as such loads provide a more direct relationship with tissue failure and thus LBP. Since information on the effect of such load metrics with LBP is lacking yet, we aimed to assess this effect in a prospective study. Methods: Of 1131 workers, categorised into 19 groups, LBP was prospectively assessed over 3 years. Video and hand force recordings of 4-5 workers per group (93 in total) were used to estimate mechanical low-back loads ( peak load and three cumulative load metrics, ie, linear weighted load, squared weighted load and load weighted to the tenth power) during manual materials handling (MMH) tasks using a video analysis method. These data were combined with static mechanical load estimates based on structured observation of non-MMH tasks. Associations of mechanical loads and LBP were tested using generalised estimating equations. Results: Significant effects on LBP were found for cumulative low-back moments (linear and squared weighted; both p<0.01 and ORs of 3.01 and 3.50, respectively) but not for peak and cumulative moments weighted to the tenth power. Conclusions: Results of this first prospective study on the effect of mechanical low-back load on LBP support a LBP aetiology model of cumulative loads, potentially due to accumulation of microdamage or fatigue. Therefore, prevention of LBP should focus on reducing cumulative low-back loads, especially in highly exposed occupational groups, for example, by reducing handling of heavy loads and working in awkward body postures. Source


Coenen P.,VU University Amsterdam | Coenen P.,Research Center on Physical Activity | Kingma I.,VU University Amsterdam | Kingma I.,Research Center on Physical Activity | And 6 more authors.
Journal of Occupational Rehabilitation | Year: 2013

Purpose Much research has been performed on physical exposures during work (e.g. lifting, trunk flexion or body vibrations) as risk factors for low back pain (LBP), however results are inconsistent. Information on the effect of doses (e.g. spinal force or low back moments) on LBP may be more reliable but is lacking yet. The aim of the present study was to investigate the prospective relationship of cumulative low back loads (CLBL) with LBP and to compare the association of this mechanical load measure to exposure measures used previously. Methods The current study was part of the Study on Musculoskeletal disorders, Absenteeism and Health (SMASH) study in which 1,745 workers completed questionnaires. Physical load at the workplace was assessed by video-observations and force measurements. These measures were used to calculate CLBL. Furthermore, a 3-year follow-up was conducted to assess the occurrence of LBP. Logistic regressions were performed to assess associations of CLBL and physical risk factors established earlier (i.e. lifting and working in a flexed posture) with LBP. Furthermore, CLBL and the risk factors combined were assessed as predictors in logistic regression analyses to assess the association with LBP. Results Results showed that CLBL is a significant risk factor for LBP (OR: 2.06 (1.32-3.20)). Furthermore, CLBL had a more consistent association with LBP than two of the three risk factors reported earlier. Conclusions From these results it can be concluded that CLBL is a risk factor for the occurrence of LBP, having a more consistent association with LBP compared to most risk factors reported earlier. © 2012 The Author(s). Source

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