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Liv, Per
Publications (10 of 17) Show all publications
Heiden, M., Mathiassen, S. E., Garza, J., Liv, P. & Wahlström, J. (2016). A comparison of two strategies for building an exposure prediction model. Annals of Occupational Hygiene, 60(1), 74-89
Open this publication in new window or tab >>A comparison of two strategies for building an exposure prediction model
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2016 (English)In: Annals of Occupational Hygiene, ISSN 0003-4878, E-ISSN 1475-3162, Vol. 60, no 1, p. 74-89Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Cluster analysis, non-linear effects, bias, shrinkage, statistical performance
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-17363 (URN)10.1093/annhyg/mev072 (DOI)000369997400007 ()26424806 (PubMedID)2-s2.0-84960400056 (Scopus ID)
Note

Funding Agency: US CDC/NIOSH

Grant Number:   RO1-0H-08781 

Available from: 2014-08-18 Created: 2014-08-18 Last updated: 2019-01-08Bibliographically approved
Mathiassen, S. E. & Liv, P. (2016). Influence of task proportion errors on the effectiveness of task-based job exposure modeling. In: : . Paper presented at Ninth International Conference on the Prevention of Work-Related Musculoskeletal Disorders (PREMUS), June 20-23, 2016, Toronto, Canada.
Open this publication in new window or tab >>Influence of task proportion errors on the effectiveness of task-based job exposure modeling
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Background.Job-based exposure estimation using the occupational mean (JBM) is associated with substantial error. Many studies have therefore estimated job exposures from workers’ tasks, i.e. task-based modeling (TBM), typically by combining individual workers’ task proportions (TP) in the job with a general task exposure matrix. Studies of postures and muscle activity have, however, shown that TBM may be ineffective; one possible reason being that TPs are not correct. The present simulation study investigated the influence of random and systematic TP error on TBM performance.

Methods.We constructed two virtual two-task jobs with task exposure contrasts of 0.2 and 0.8. In both, TPs and task exposures mimicked likely occupational scenarios. We then simulated four cases of TP error: no error, random error, bias, and bias and random error. For each case, we varied the TP error size, and compared the absolute errors of TBM- and JBM-based job exposures for 10,000 virtual workers.

Results.For the low-contrast job, TBM with error-free TPs was, on average, only 6% more efficient than JBM, and the probability of TBM leading to a more correct job exposure than JBM was 56%. TP errors had negligible effects on effectiveness. With error-free TPs in the high-contrast job, TPM was 75% more efficient than JBM, and led to more correct job exposures for 71% of all workers. TP errors decreased TBM performance, down to being 34% better than JBM when both random and systematic errors were “large”; 62% of all individuals being more correctly assessed by TBM.

Discussion.For jobs with limited task exposure contrast, TBM was essentially equivalent to JBM, while TP errors had marginal impact. In high-contrast jobs, TBM was more effec-tive, but was also more sensitive to both random and systematic TP errors. This may feed further discussion of the cost-efficiency of TBM in occupational settings.

Keywords
task-based job, job-based exposure, task-based modelling, TBM performance
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-21896 (URN)
Conference
Ninth International Conference on the Prevention of Work-Related Musculoskeletal Disorders (PREMUS), June 20-23, 2016, Toronto, Canada
Available from: 2016-06-23 Created: 2016-06-23 Last updated: 2018-12-03Bibliographically approved
Jackson, J. A., Mathiassen, S. E. & Liv, P. (2016). Observer performance in estimating upper arm elevation angles under ideal viewing conditions when assisted by posture matching software. Applied Ergonomics, 55, 208-215
Open this publication in new window or tab >>Observer performance in estimating upper arm elevation angles under ideal viewing conditions when assisted by posture matching software
2016 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 55, p. 208-215Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
measurement error, working postures, observation
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-19420 (URN)10.1016/j.apergo.2016.01.012 (DOI)000374074600021 ()26995050 (PubMedID)2-s2.0-84959471678 (Scopus ID)
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2009-1761
Available from: 2015-06-01 Created: 2015-06-01 Last updated: 2020-01-31Bibliographically approved
Jackson, J., Mathiassen, S. E., Wahlström, J., Liv, P. & Forsman, M. (2015). Digging deeper into the assessment of upper arm elevation angles using standard inclinometry [Letter to the editor]. Applied Ergonomics, 51, 102-103
Open this publication in new window or tab >>Digging deeper into the assessment of upper arm elevation angles using standard inclinometry
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2015 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 51, p. 102-103Article in journal, Letter (Other academic) Published
Keywords
Validity, Bias, Posture assessment
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-19344 (URN)10.1016/j.apergo.2015.04.012 (DOI)000358389100012 ()26154209 (PubMedID)2-s2.0-84937416785 (Scopus ID)
Available from: 2015-05-20 Created: 2015-05-20 Last updated: 2020-01-31Bibliographically approved
Jackson, J., Mathiassen, S. E., Wahlström, J., Liv, P. & Forsman, M. (2015). Is what you see what you get? Standard inclinometry of set upper arm elevation angles. Applied Ergonomics, 47, 242-252
Open this publication in new window or tab >>Is what you see what you get? Standard inclinometry of set upper arm elevation angles
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2015 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 47, p. 242-252Article in journal (Refereed) Published
Abstract [en]

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° and 150°. 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 60°.  INC data adjusted using the regression models were superior to un-adjusted data; a subject-specific, two-point calibration based on measurements at 0° and 90° 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.

Keywords
measurement error, observation, working postures
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-16159 (URN)10.1016/j.apergo.2014.08.014 (DOI)000347663600028 ()25479994 (PubMedID)2-s2.0-84919663729 (Scopus ID)
Available from: 2014-01-24 Created: 2014-01-24 Last updated: 2020-01-31Bibliographically approved
Mathiassen, S. E., Liv, P. & Wahlström, J. (2013). Cost-efficient measurement strategies for posture observations based on video recordings. Applied Ergonomics, 44(4), 609-617
Open this publication in new window or tab >>Cost-efficient measurement strategies for posture observations based on video recordings
2013 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 44, no 4, p. 609-617Article in journal (Refereed) Published
Abstract [en]

Assessment of workingpostures by observation is a common practice in ergonomics. The present studyinvestigated whether monetary resources invested in a video-based posture observationstudy should preferably be spent in collecting many video recordings of thework and have them observed once by one observer, or in having multipleobservers rate postures repeatedly from fewer videos. The study addressed thisquestion from a practitioner’s perspective by focusing two plausible scenarios:documenting the mean exposure of one individual, and of a specific occupationalgroup. Using a data set of observed working postures among hairdressers, empiricalvalues of posture variability, observer variability, and costs for recordingand observing one video were entered into equations expressing the total costof data collection and the information (defined as 1/SD) provided by theresulting estimates of two variables: percentage time with the arm elevated<15 degrees and >90 degrees. Sixteen measurement strategies involving 1-4observers repeating their posture ratings 1-4 times were examined for budgetsup to €2000.  For both posture variablesand in both the individual and group scenario, the most cost-efficient strategyat any specific budget was to engage 3-4 observers and/or having observer(s)rate postures multiple times each. Between 17% and 34% less information wasproduced when using the commonly practiced approach of having one observer ratea number of video recordings one time each. We therefore recommend observationalposture assessment to be based on video recordings of work, since this allowsfor multiple observations; and to allocate monetary resources to repeated observationsrather than many video recordings.

Keywords
exposure assessment, research budget, resource consumption
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-12511 (URN)10.1016/j.apergo.2012.12.003 (DOI)000317151100013 ()23333111 (PubMedID)2-s2.0-84875121958 (Scopus ID)
Available from: 2012-08-01 Created: 2012-08-01 Last updated: 2018-03-13Bibliographically approved
Liv, P., Mathiassen, S. E. & Svendsen, S. W. (2012). Accuracy and precision of variance components in occupational posture recordings: a simulation study of different data collection strategies. BMC Medical Research Methodology, 12(1), 58-58
Open this publication in new window or tab >>Accuracy and precision of variance components in occupational posture recordings: a simulation study of different data collection strategies
2012 (English)In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 12, no 1, p. 58-58Article in journal (Refereed) Published
Abstract [en]

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

Keywords
sampling strategies, exposure assessment, ergonomics
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-10693 (URN)10.1186/1471-2288-12-58 (DOI)000305413000001 ()2-s2.0-84862166418 (Scopus ID)
Available from: 2011-10-11 Created: 2011-10-11 Last updated: 2018-03-13Bibliographically approved
Rezagholi, M., Mathiassen, S. E. & Liv, P. (2012). Cost efficiency comparison of four video-based techniques for assessing upper arm postures. Ergonomics, 55(3), 350-360
Open this publication in new window or tab >>Cost efficiency comparison of four video-based techniques for assessing upper arm postures
2012 (English)In: Ergonomics, ISSN 0014-0139, E-ISSN 1366-5847, Vol. 55, no 3, p. 350-360Article in journal (Refereed) Published
Abstract [en]

Many video-based techniques for assessing postures at work have been developed. Choosing the most appropriate technique should be based on an evaluation of different alternatives in terms of their ability to produce posture information at low input costs, i.e. their cost efficiency. This study compared four video-based techniques for assessing upper arm postures, using cost and error data from an investigation on hairdressers. Labour costs associated with the posture assessments from the video recordings were the dominant factor in the cost efficiency comparison. Thus, a work sampling technique associated with relatively large errors appeared, in general, to be the most cost-efficient because it was labour-saving. Measurement bias and other costs than labour cost for posture assessment influenced the ranking and economic evaluation of techniques, as did the applied measurement strategy, i.e. the number of video recordings and the number of repeated assessments of them.

Keywords
Precision, bias, input costs, measurement strategy, model specification
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:hig:diva-9997 (URN)10.1080/00140139.2011.642007 (DOI)000303583400008 ()2-s2.0-84859202677 (Scopus ID)
Available from: 2011-09-01 Created: 2011-09-01 Last updated: 2018-03-13Bibliographically approved
Mathiassen, S. E., Liv, P. & Wahlström, J. (2012). Cost-efficient observation of working postures from video recordings – more videos, more observers or more views per observer?. Work: A journal of Prevention, Assesment and rehabilitation, 41(Suppl. 1), 2302-2306
Open this publication in new window or tab >>Cost-efficient observation of working postures from video recordings – more videos, more observers or more views per observer?
2012 (English)In: Work: A journal of Prevention, Assesment and rehabilitation, ISSN 1051-9815, E-ISSN 1875-9270, Vol. 41, no Suppl. 1, p. 2302-2306Article in journal (Refereed) Published
Abstract [en]

In ergonomics, assessing the working postures of an individual by observation is a very common practice. The present study investigated whether monetary resources devoted to an observational study should preferably be invested in collecting many video recordings of the work, or in having several observers estimate postures from available videos multiple times. On the basis of a data set of observed working postures among hairdressers, necessary information in terms of posture variability, observer variability, and costs for recording and observing videos was entered into equations providing the total cost of data collection and the precision (informative value) of the resulting estimates of two variables: percentages time with the arm elevated 90 degrees. In all 160 data collection strategies, differing with respect to the number of video recordings and the number of repeated observations of each recording, were simulated and compared for cost and precision. For both posture variables, the most cost-efficient strategy for a given budget was to engage 4 observers to look at available video recordings, rather than to have one observer look at more recordings. Since the latter strategy is the more common in ergonomics practice, we recommend reconsidering standard practice in observational posture assessment.

Keywords
resource consumption, mean exposure, risk assessment, posture observation, efficiency
National Category
Occupational Health and Environmental Health
Identifiers
urn:nbn:se:hig:diva-11553 (URN)10.3233/wor-2012-0456-2302 (DOI)000306361802071 ()2-s2.0-84859858198 (Scopus ID)
Available from: 2012-02-23 Created: 2012-02-23 Last updated: 2018-03-13Bibliographically approved
Liv, P. (2012). Efficient strategies for collecting posture data using observation and direct measurement. (Doctoral dissertation). Umeå: Umeå universitet
Open this publication in new window or tab >>Efficient strategies for collecting posture data using observation and direct measurement
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Effektiva strategier för insamling av data om arbetsställningar genom observation och direkt mätning
Abstract [en]

Relationships between occupational physical exposures and risks of contracting musculoskeletal disorders are still not well understood; exposure-response relationships are scarce in the musculoskeletal epidemiology literature, and many epidemiological studies, including intervention studies, fail to reach conclusive results. Insufficient exposure assessment has been pointed out as a possible explanation for this deficiency. One important aspect of assessing exposure is the selected measurement strategy; this includes issues related to the necessary number of data required to give sufficient information, and to allocation of measurement efforts, both over time and between subjects in order to achieve precise and accurate exposure estimates. These issues have been discussed mainly in the occupational hygiene literature considering chemical exposures, while the corresponding literature on biomechanical exposure is sparse. The overall aim of the present thesis was to increase knowledge on the relationship between data collection design and the resulting precision and accuracy of biomechanical exposure assessments, represented in this thesis by upper arm postures during work, data which have been shown to be relevant to disorder risk.

Four papers are included in the thesis. In papers I and II, non-parametric bootstrapping was used to investigate the statistical efficiency of different strategies for distributing upper arm elevation measurements between and within working days into different numbers of measurement periods of differing durations. Paper I compared the different measurement strategies with respect to the eventual precision of estimated mean exposure level. The results showed that it was more efficient to use a higher number of shorter measurement periods spread across a working day than to use a smaller number for longer uninterrupted measurement periods, in particular if the total sample covered only a small part of the working day. Paper II evaluated sampling strategies for the purpose of determining posture variance components with respect to the accuracy and precision of the eventual variance component estimators. The paper showed that variance component estimators may be both biased and imprecise when based on sampling from small parts of working days, and that errors were larger with continuous sampling periods. The results suggest that larger posture samples than are conventionally used in ergonomics research and practice may be needed to achieve trustworthy estimates of variance components.

Papers III and IV focused on method development. Paper III examined procedures for estimating statistical power when testing for a group difference in postures assessed by observation. Power determination was based either on a traditional analytical power analysis or on parametric bootstrapping, both of which accounted for methodological variance introduced by the observers to the exposure data. The study showed that repeated observations of the same video recordings may be an efficient way of increasing the power in an observation-based study, and that observations can be distributed between several observers without loss in power, provided that all observers contribute data to both of the compared groups, and that the statistical analysis model acknowledges observer variability. Paper IV discussed calibration of an inferior exposure assessment method against a superior “golden standard” method, with a particular emphasis on calibration of observed posture data against postures determined by inclinometry. The paper developed equations for bias correction of results obtained using the inferior instrument through calibration, as well as for determining the additional uncertainty of the eventual exposure value introduced through calibration.

In conclusion, the results of the present thesis emphasize the importance of carefully selecting a measurement strategy on the basis of statistically well informed decisions. It is common in the literature that postural exposure is assessed from one continuous measurement collected over only a small part of a working day. In paper I, this was shown to be highly inefficient compared to spreading out the corresponding sample time across the entire working day, and the inefficiency was also obvious when assessing variance components, as shown in paper II. The thesis also shows how a well thought-out strategy for observation-based exposure assessment can reduce the effects of measurement error, both for random methodological variance (paper III) and systematic observation errors (bias) (paper IV).

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2012. p. 47
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1519
Keywords
Exposure assessment, arm elevation, exposure variability, variance components, random effects model, precision, bias, sample size, sample allocation, calibration, bootstrapping
National Category
Occupational Health and Environmental Health
Research subject
Occupational and Environmental Medicine
Identifiers
urn:nbn:se:hig:diva-18695 (URN)978-91-7459-469-0 (ISBN)
Public defence
2012-10-02, NA320, Umeå Universitet, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-03-13Bibliographically approved

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