Pieter Coenen

Bio: Pieter Coenen is an academic researcher from Public Health Research Institute. The author has contributed to research in topics: Medicine & Low back pain. The author has an hindex of 21, co-authored 82 publications receiving 1570 citations. Previous affiliations of Pieter Coenen include VU University Amsterdam & VU University Medical Center.

The effect of lifting during work on low-back pain – a health impact assessment based on a meta-analysis

Abstract: Lifting at work is considered an important risk factor for low back pain (LBP). However, contradictory findings have been reported, partly because frequency, duration and intensity (ie, the weight of the load) of lifting have not been systematically considered. This has hampered developments of threshold values for lifting. The aims of this study were: to assess the effect of lifting during work (quantified in duration, frequency or intensity) on the incidence of LBP and to quantify the impact of these relationships on the occurrence of LBP in occupational populations exposed to lifting. We searched in PubMed and EMBASE.com for longitudinal studies assessing the effect of occupational lifting on LBP incidence. For each study, the exposure-response slope of the association was estimated by loglinear regression analysis. When possible, a meta-analysis on these slopes was conducted. In a health impact assessment, the effects of the pooled exposure-response relationships on LBP incidence was assessed. Eight longitudinal studies were included. Pooled estimates resulted in ORs of 1.11 (1.05 to 1.18) per 10 kg lifted and 1.09 (1.03 to 1.15) per 10 lifts/day. Duration of lifting could not be pooled. Using these ORs, we estimated that lifting loads over 25 kg and lifting at a frequency of over 25 lifts/day will increase the annual incidence of LBP by 4.32% and 3.50%, respectively, compared to the incidence of not being exposed to lifting. Intensity and frequency of lifting significantly predict the occurrence of LBP. Exposure-response relationships show that lifting heavy loads may have a substantial impact on musculoskeletal health of the working population. This information may direct the development of occupational lifting guidelines and workplace design for LBP prevention.

Do highly physically active workers die early? A systematic review with meta-analysis of data from 193 696 participants

Abstract: Objective Recent evidence suggests the existence of a physical activity paradox, with beneficial health outcomes associated with leisure time physical activity, but detrimental health outcomes for those engaging in high level occupational physical activity. This is the first quantitative systematic review of evidence regarding the association between occupational physical activity and all-cause mortality. Design Systematic review with meta-analysis. Data source A literature search was performed in electronic databases PubMed, Embase, CINAHL, PsycINFO and Cochrane. Eligibility criteria for selecting studies We screened for peer reviewed articles from prospective studies assessing the association of occupational physical activity with all-cause mortality. A meta-analysis assessed the association of high (compared with low) level occupational physical activity with all-cause mortality, estimating pooled hazard ratios (HR) (with 95% CI). Results 2490 unique articles were screened and 33 (from 26 studies) were included. Data from 17 studies (with 193 696 participants) were used in a meta-analysis, showing that men with high level occupational physical activity had an 18% increased risk of early mortality compared with those engaging in low level occupational physical activity (HR 1.18, 95% CI 1.05 to 1.34). No such association was observed among women, for whom instead a tendency for an inverse association was found (HR 0.90, 95% CI 0.80 to 1.01). Conclusions The results of this review indicate detrimental health consequences associated with high level occupational physical activity in men, even when adjusting for relevant factors (such as leisure time physical activity). These findings suggest that research and physical activity guidelines may differentiate between occupational and leisure time physical activity.

Cumulative Low Back Load at Work as a Risk Factor of Low Back Pain: A Prospective Cohort Study

Abstract: 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).

Cumulative mechanical low-back load at work is a determinant of low-back pain

Abstract: 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.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

Abstract: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Group-Based Modeling of Development

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