| 1. |
- Carvalho, Raquel N., et al.
(författare)
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Mixtures of chemical pollutants at European legislation safety concentrations : how safe are they?
- 2014
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Ingår i: Toxicological Sciences. - : Oxford University Press (OUP). - 1096-6080 .- 1096-0929. ; 141:1, s. 218-233
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Tidskriftsartikel (refereegranskat)abstract
- The risk posed by complex chemical mixtures in the environment to wildlife and humans is increasingly debated, but has been rarely tested under environmentally relevant scenarios. To address this issue, two mixtures of 14 or 19 substances of concern (pesticides, pharmaceuticals, heavy metals, polyaromatic hydrocarbons, a surfactant, and a plasticizer), each present at its safety limit concentration imposed by the European legislation, were prepared and tested for their toxic effects. The effects of the mixtures were assessed in 35 bioassays, based on 11 organisms representing different trophic levels. A consortium of 16 laboratories was involved in performing the bioassays. The mixtures elicited quantifiable toxic effects on some of the test systems employed, including i) changes in marine microbial composition, ii) microalgae toxicity, iii) immobilization in the crustacean Daphnia magna, iv) fish embryo toxicity, v) impaired frog embryo development, and vi) increased expression on oxidative stress-linked reporter genes. Estrogenic activity close to regulatory safety limit concentrations was uncovered by receptor-binding assays. The results highlight the need of precautionary actions on the assessment of chemical mixtures even in cases where individual toxicants are present at seemingly harmless concentrations.
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| 2. |
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| 3. |
- Heiden, Marina, et al.
(författare)
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A comparison of two strategies for building an exposure prediction model
- 2016
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Ingår i: Annals of Occupational Hygiene. - : Oxford University Press (OUP). - 0003-4878 .- 1475-3162. ; 60:1, s. 74-89
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Tidskriftsartikel (refereegranskat)abstract
- Cost-efficient assessments of job exposures in large populations may be obtained from models in which “true” exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising “true” exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.
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| 4. |
- Hu, Xiao-Lei, et al.
(författare)
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Study protocol for a randomized, controlled, multicentre, pragmatic trial with Rehabkompassen®-a digital structured follow-up tool for facilitating patient-tailored rehabilitation in persons after stroke
- 2023
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Ingår i: Trials. - : BioMed Central (BMC). - 1745-6215. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundStroke is a leading cause of disability among adults worldwide. A timely structured follow-up tool to identify patients' rehabilitation needs and develop patient-tailored rehabilitation regimens to decrease disability is largely lacking in current stroke care. The overall purpose of this study is to evaluate the effectiveness of a novel digital follow-up tool, Rehabkompassen (R), among persons discharged from acute care settings after a stroke.MethodsThis multicentre, parallel, open-label, two-arm pragmatic randomized controlled trial with an allocation ratio of 1:1 will be conducted in Sweden. A total of 1106 adult stroke patients will have follow-up visits in usual care settings at 3 and 12 months after stroke onset. At the 3-month follow-up, participants will have a usual outpatient visit without (control group, n = 553) or with (intervention group, n = 553) the Rehabkompassen (R) tool. All participants will receive the intervention at the 12-month follow-up visit. Feedback from the end-users (patient and health care practitioners) will be collected after the visits. The primary outcomes will be the patients' independence and social participation at the 12-month visits. Secondary outcomes will include end-users' satisfaction, barriers and facilitators for adopting the instrument, other stroke impacts, health-related quality of life and the cost-effectiveness of the instrument, calculated by incremental cost per quality-adjusted life year (QALY).DiscussionThe outcomes of this trial will inform clinical practice and health care policy on the role of the Rehabkompassen (R) digital follow-up tool in the post-acute continuum of care after stroke.Trial registrationClinicalTrials.gov NCT04915027. Registered on 4 June 2021. ISRCTN registry ISRCTN63166587. Registered on 21 August 2023.
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| 6. |
- Jackson, Jennie A., et al.
(författare)
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Is what you see what you get? : Standard inclinometry of set upper arm elevation angles
- 2015
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Ingår i: Applied Ergonomics. - : Elsevier BV. - 0003-6870 .- 1872-9126. ; 47, s. 242-252
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Tidskriftsartikel (refereegranskat)abstract
- Previous research suggests inclinometers (INC) underestimate upper arm elevation. This study was designed to quantify possible bias in occupationally relevant postures, and test whether INC performance could be improved using calibration. Participants were meticulously positioned in set arm flexion and abduction angles between 0 degrees and 150 degrees. Different subject-specific and group-level regression models comprising linear and quadratic components describing the relationship between set and INC-registered elevation were developed using subsets of data, and validated using additional data. INC measured arm elevation showed a downward bias, particularly above 600. INC data adjusted using the regression models were superior to unadjusted data; a subject-specific, two-point calibration based on measurements at 0 and 900 gave results closest to the 'true' set angles. Thus, inclinometer measured arm elevation data required calibration to arrive at 'true' elevation angles. Calibration to a common measurement scale should be considered when comparing arm elevation data collected using different methods.
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| 7. |
- Jackson, Jennie A., et al.
(författare)
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Observer performance in estimating upper arm elevation angles under ideal viewing conditions when assisted by posture matching software
- 2016
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Ingår i: Applied Ergonomics. - : Elsevier BV. - 0003-6870 .- 1872-9126. ; 55, s. 208-215
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Tidskriftsartikel (refereegranskat)abstract
- Selecting a suitable body posture measurement method requires performance indices of candidate tools. Such data are lacking for observational assessments made at a high degree of resolution. The aim of this study was to determine the performance (bias and between- and within-observer variance) of novice observers estimating upper arm elevation postures assisted by posture matching software to the nearest degree from still images taken under ideal conditions. Estimates were minimally biased from true angles: the mean error across observers was less than 2. Variance between observers was minimal. Considerable variance within observers, however, underlined the risk of relying on single observations. Observers were more proficient at estimating 0 and 90 postures, and less proficient at 60. Thus, under ideal visual conditions observers, on average, proved proficient at high resolution posture estimates; further investigation is required to determine how non-optimal image conditions, as would be expected from occupational data, impact proficiency.
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| 8. |
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| 9. |
- Liv, Per, et al.
(författare)
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Accuracy and precision of variance components in occupational posture recordings : a simulation study of different data collection strategies
- 2012
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 12:1, s. 58-58
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Tidskriftsartikel (refereegranskat)abstract
- Background. Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of betweensubjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods. Methods. Minute-by-minute values of average elevation, percentage time above 90degrees and percentage time below 15degrees were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy. Results. Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases. Conclusions. Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty
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