# Does Job Rotation Lead to Changes in Physical Exposure and Health? A Systematic Review of Studies Reporting Objective Exposure Data

Jennie A Jackson, Svend Erik Mathiassen, Alexis d'Escatha, Marc Fadel

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Jennie A Jackson, Svend Erik Mathiassen, Alexis d'Escatha, Marc Fadel. Does Job Rotation Lead to Changes in Physical Exposure and Health? A Systematic Review of Studies Reporting Objective Exposure Data. IISE Transactions on Occupational Ergonomics and Human Factors, 2025, pp.1-26. 10.1080/24725838.2025.2578449. hal-05425571

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# Does Job Rotation Lead to Changes in Physical Exposure and Health? A Systematic Review of Studies Reporting Objective Exposure Data

Jennie A. Jackson, Svend Erik Mathiassen, Alexis Descatha & Marc Fadel

**To cite this article:** Jennie A. Jackson, Svend Erik Mathiassen, Alexis Descatha & Marc Fadel (11 Dec 2025): Does Job Rotation Lead to Changes in Physical Exposure and Health? A Systematic Review of Studies Reporting Objective Exposure Data, IISE Transactions on Occupational Ergonomics and Human Factors, DOI: 10.1080/24725838.2025.2578449

**To link to this article:** https://doi.org/10.1080/24725838.2025.2578449

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# Does Job Rotation Lead to Changes in Physical Exposure and Health? A Systematic Review of Studies Reporting Objective Exposure Data

Jennie A. Jacksonª ID, Svend Erik Mathiassenª ID, Alexis Descathaᵇ,c,d ID and Marc Fadelᵇ,c ID

ªDepartment of Occupational Health, Psychology and Sports Sciences, University of Gävle, Gävle, Sweden; ᵇUniv Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) – UMR_S 1085, SFR ICAT, Angers, France; ᶜCHU Angers, Occupational Health Unit – Poisoning Control Center – Clinical Data Center – Prevention Federation, Angers, France; ᵈOccupational Medicine, Epidemiology and Prevention, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA

## OCCUPATIONAL APPLICATIONS

Job rotation is one of the most recommended and practiced organizational interventions for injury prevention and health promotion; however, insufficient evidence is currently available to assess the effectiveness of this work organization approach due to limitations in study design and physical exposure assessment metrics used in prior studies. Future studies are required in which research and company/organizational partners collaborate to evaluate job rotation interventions using relevant metrics including assessment of the impact of job rotation on variation across the full duration of the JR cycle and, subsequently, health. Recommendations are proposed to help guide future study designs and exposure assessment metric selection to support the generation of sufficient data for evidence synthesis.

## TECHNICAL ABSTRACT

**Background:** Job rotation (JR) is a common organizational practice expected to improve health by redistributing tasks across workers to increase exposure variation within workers. While evidence for JR effectiveness is currently lacking, previous reviews did not assess changes in objectively measured exposure amplitudes or consider variation. **Purpose:** To systematically review the evidence in studies with objective measurement of physical exposures that JR impacts exposure amplitude and variation and, within the included studies, health. **Methods:** We searched (final search May 2025) three online databases to identify papers, which had (i) studied actual work; (ii) assessed differences between JR- and no-JR worker-groups; and (iii) assessed exposure using observation or technical measurement. Study quality was assessed using the JBI risk of bias tool. Prospero registration number: CRD42023438200. **Results:** Ten studies were included. All studies reported group mean exposures but, as theoretically expected, no consistent effect of JR was found. Most studies reported descriptive data for variation between workers (standard deviation) but did not statistically assess the effect of JR on standard deviation. Two studies assessed the effect of JR on variation within workers but with inconsistent results. Evidence for the effect of JR on health was inconsistent across the nine studies with health outcomes. **Conclusions:** Due to the small number of studies objectively assessing actual workers in real JR initiatives, insufficient evidence is available to assess the impact of JR on physical exposure amplitude or variation and, within these studies, on health. Recommendations are provided to guide design and metric selection in future studies assessing the effectiveness of JR.

## ARTICLE HISTORY

Received 8 January 2025
Accepted 8 October 2025

## KEYWORDS

Variability; task rotation; work organization

# 1. Introduction

Job rotation is a work organization approach whereby a worker is assigned to a set of tasks through which she or he rotates over a set time frame, for example, within a day, between days, or across weeks. While ‘task rotation’ might be a more correct term (Mlekus & Maier, 2021), the term ‘job rotation’ is more common

and will, therefore, be used throughout this article. Combining tasks to create more complex jobs and redistributing tasks to alter the proportions of time workers spend in tasks are related organizational practices that we also include under the umbrella term ‘job rotation’.

Job rotation (JR) is a common initiative in working life (Olesen et al., 2012; Schneider et al., 2005) used to achieve benefits for both individual workers and

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organizations, including improved employee learning, development, attitude at work, psychological and physical health, and organizational performance (Mlekus & Maier, 2021). In the occupational health context, job rotation is used to prevent injury and promote health by decreasing exposure to repetitive and monotonous tasks (Kilbom, 1994) and increasing variation for individual workers (Mathiassen, 2006).

Prior studies assessing the impact of JR have largely focused on group mean exposure amplitudes (levels) as the primary outcomes. In a JR where a set of tasks is redistributed between workers to improve exposure, and the tasks occur to the same extent across the group of workers before and after the implementation of the JR, any changes in the group mean exposure will be due to individual worker factors and not due to the JR. However, the average exposure variation (at the group level) experienced by individual workers is expected to change with the redistribution of tasks (ideally increase), as is the variation between workers (in the opposite direction). Thus, the mechanism of change by which job rotation is expected to improve health is through an increase in exposure variation for individual workers, according to the pathophysiological mechanisms previously reviewed by Visser and Van Dieën (2006). Accordingly, studies evaluating the impact of a JR should include metrics of exposure variation (e.g., variance) within and between workers and not only metrics of the group mean exposure amplitude. A comparison of the utility of assessing exposure amplitude versus variance when evaluating changes related to JR is illustrated in Figure 1. To facilitate consideration of variation, physical exposures must be assessed using suitable methods. In many prior JR studies, exposure has been assessed only by self-report, a method which has been shown to deviate from ‘true’, objectively measured exposures (Barrero et al., 2009; Coenen et al., 2020; Dyrstad et al., 2014; Gupta et al., 2018; Koch et al., 2016; Wick et al., 2016) and which limits the extent to which exposure variation can be considered. One prior review considered the effect of JR on exposure variation (variability) within workers (Rodriguez & Barrero, 2017); however, that review used a much broader definition of JR than is commonly employed. Thus, a review of the effectiveness of JR on objectively measured physical exposures with consideration of exposure amplitude and variation is currently lacking.

An alternative to empirically assessing the effect of JR on exposure is to simulate the effects. Several types of simulation studies have been used to consider the impact of JR (Mlekus & Maier, 2021). Some simulation studies have proposed mathematical algorithms

to simulate organizational strategies according to an optimization criterion but operate with hypothetical scenarios both before and after the JR (Carnahan et al., 2000; Diego-Mas et al., 2009; Mossa et al., 2016; Tharmmaphornphilas & Norman, 2004). Other simulation studies have included empirical task exposure data at baseline (i.e., no JR), but again only simulated the anticipated effect of the hypothetical JRs on exposure outcomes (Barbieri et al., 2015, 2021; Frazer et al., 2003; Leite et al., 2024; Möller et al., 2004; Song et al., 2015). While both types of simulations may be valuable tools to inspire beneficial work organization strategies, simulation studies are unable to verify the actual impact of a JR on exposure implemented in a real work environment, let alone empirically evaluate the impact of the JR on worker health.

The notion that increased variation can positively impact health has been considered extensively in the literature for more than 30 years (e.g., Christmansson et al., 1999; Fredriksson et al., 2001; Jonsson, 1988; Mathiassen & Winkel, 1991; Ólafsdóttir & Rafnsson, 1998; Schneider et al., 2005). Studies in the laboratory have shown that rotation between tasks can lead to short term changes in the structure and variation of individual exposures, and thus that rotation is theoretically likely to be physiologically beneficial (Horton et al., 2012; Howarth et al., 2009; Keir et al., 2011; Luger et al., 2016; Raina & Dickerson, 2009; Wells et al., 2010). However, given the laboratory nature of the studies, assessment of eventual health changes was not possible. On the other hand, many field studies have considered the effects of JR on health without also explicitly considering the effect of JR on exposure variation.

Five reviews over the last decade have synthesized the evidence for a positive effect of JR on health, where ‘health’ has been considered as prevention of fatigue (Luger et al., 2014; Santos et al., 2016) and prevention of musculoskeletal disorders (Leider et al., 2015; Mlekus & Maier, 2021; Padula et al., 2017). All five reviews concluded that the current scientific evidence was, at best, weak; however, none of the reviews also considered the effects of JR on exposure variation. A limitation of these earlier approaches is that it is unknown whether the JRs in the included studies had the intended effect (i.e., an increase in within-worker variation) and thus, it is unclear whether negative findings (e.g., no consistent change in worker health) resulted from a poorly designed JR (no possibility of increased variation), a poorly implemented JR (no achievement of increased variation), or because the JR was not effective in improving health.
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<figure><img src="image_17.png" /><figcaption>**Figure 1.** Four scenarios illustrating the impact of distributing a set of four tasks differently across four workers (with all tasks occurring to the same extent in each scenario) on group mean amplitude, variation between workers (VarBW) and variation within workers (VarWW). The task distributions and effects for each worker are shown in each row, and the group level effects are shown at the bottom of each scenario column. In the No Rotation case, each of the four workers performs a single task throughout the entire workday. Key point 1 – Assessment metrics matter. The group mean is the same for all four scenarios, illustrating that a job rotation (JR) will not impact the group mean if the same tasks occur to the same extent before and after the JR. In contrast, the amount of variance experienced by each individual worker (i.e., VarWW) differs for all workers across the four scenarios, and further, the average within-worker variance (mean VarWW) differs between the No JR, JR#1 and JR#3 scenarios. This illustrates the importance of assessing within-worker variance. Key point 2 – JR design matters. JR#1 and JR#2 are examples of JRs where pairs of tasks are shared between pairs of workers, but the scenarios have a different impact on the resulting variance between and within workers. In JR#1, there is sufficient diversity between the pairs of tasks that the JR leads to an increase in VarWW compared with no JR (both for all individual workers and also for the average VarWW). In contrast, JR#2 does not achieve an increase in the average VarWW compared to the No JR scenario (although it does provide a more homogeneous distribution of VarWW among the workers). These two scenarios illustrate the importance of JR design for increasing variation. Key point 3 – a JR can impact individual workers in different ways. JR#4 illustrates the effects of distributing all tasks equally across all workers. Compared with the No JR scenario, three workers experience increases in VarWW (a positive change) while one worker has a decreased VarWW (negative change). Further, two workers experience a decrease in mean amplitude, while two workers experience an increase in mean amplitude. Thus a JR may differentially impact the health of individual workers. Going a step further, consider the No JR and JR#2 scenarios where all metrics are the same at both the individual and group levels except the individual VarWW values (two workers experience a decrease in JR#2, while two workers experience an increase). Again, this illustrates differential impacts of a JR on individual workers and, further, underlines the need to also consider the spread of individual metrics.</figcaption></figure>

Our aim in this article was to investigate, by systematic review of the available literature, the evidence that JR impacts physical exposure amplitude and variation in studies where physical exposures were objectively assessed on workers in an occupational setting both before and after the JR. Our secondary aim in the review was to assess the evidence within the identified studies that JR impacts health.

## 2. Methods

The protocol for this project was registered in PROSPERO: CRD42023438200. Reporting of this systematic review

adheres to the 2020 PRISMA reporting recommendations (Page et al., 2021)—Supplementary material - Table S1.

### 2.1. Search strategy

We conducted a systematic search in three online databases—PubMed, Web of Science, and Google Scholar—to identify papers published in peer-reviewed journals. The search strategy and inclusion criteria were defined according to the PICO (Population, Intervention, Comparison, Outcome) approach (Richardson et al., 1995). A pilot test of the search string was performed, and the string successfully
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retrieved a set of key articles determined a priori by the authors as being central to the review. A librarian assisted in the development of the final search string syntax. The main search work was completed November 2023, and the search was updated in May 2025. Any published papers that were not found in the searches but that the team was aware of were also considered.

The search strategy included three search blocks:

Block 1: Population – workers doing actual work (not laboratory or simulation studies).

Block 2: Intervention – the (re)organization of work using JR or equivalent.

Block 3: Outcome – primary: physical exposure measured using objective methods; secondary: health.

The comparison aspect of PICO (i.e., exposure data for both JR and no-JR work) was addressed during screening, as described in Eligibility Criteria section. To focus on modern sectors and types of work, we limited our search to studies published after 2001. The search string is provided in Figure 2.

Search results were imported to the web-based systematic review tool, Covidence (Veritas Health Innovation, Australia). Duplicate records were removed. During the main search, title and abstract screening, all records were assessed independently by two coauthors according to eligibility criteria outlined in Eligibility Criteria section. Eventual rating disagreements were resolved first in discussion between the pair of coauthors, and, if consensus could not be reached, in discussion by the full group of coauthors. A similar approach was used for full-text record screening, but with a balanced, randomized assignment of articles across pairs of reviewers. All screening was conducted in Covidence. When the search

was updated (May, 2025), the same screening process was used except that the title and abstract screening was performed by a single coauthor.

Following full-text screening, a reference list check was performed for all included records, and for relevant reviews that were identified during the title and abstract screening. Finally, a check was performed in Google Scholar of all papers that had cited any of the included full-text records. For both the reference list and citation checks, a balanced, pair-wise randomization was utilized and conflicts resolved as in early screening stages.

## 2.2. Eligibility criteria

We limited this review to studies written in English, French, or Swedish, and only included original empirical works. We accepted studies with both a classical ‘job rotation’ approach, as well as studies that did not use the term ‘job rotation’ but still investigated a sister organizational practice that redistributed tasks across a group of workers to improve exposures. For example, merging short-cycle tasks into sequences to form longer-cycle tasks is not a traditional type of JR but is an organizational strategy that can be used to increase variation. An illustration of this approach is having line workers assemble a section of a car rather than only assembling a single piece of the car (as could happen in a conventional short-cycle assembly line approach; Engström et al., 1996).

To ensure exposure authenticity, we limited our search to field studies in which JR had occurred on actual workers performing work in their actual work environment. In addition to excluding lab studies, we also excluded simulation studies unless empirical exposure data were provided; however, we did include studies with technical measurement of authentic work-place exposures at baseline that estimated

<figure><img src="image_19.png" /><figcaption>**Figure 2.** Search string syntax used in the Web of Science database. For the main search, the time frame used was 2002–2023; for the updated search, the time frame used was 2022–2025.</figcaption></figure>
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exposure variance for workers in the JR (where no new tasks were included) using baseline data and JR task proportions. We only included studies where physical exposure was objectively assessed by either observation or direct measurement. We limited our study population to working age participants (not children or retirees). Finally, we only included one study if a particular data set was reported in several studies. The results of the hierarchical exclusion during full text screening are shown in Figure 2 in the *Records excluded* box with seven exclusion criteria.

Within the set of included studies considering physical exposures, we further identified studies that also reported health outcomes. We accepted self-reported assessment of health.

## 2.3. Methodological quality assessment

The quality of all included articles was independently assessed by two reviewers, with a balanced randomized assignment across pairs of reviewers. Consensus was reached via discussion. Quality assessment was performed in Covidence according to a modified version of the JBI checklist for prevalence studies (Munn et al., 2020) as detailed in Munn et al. (2015). As a type of calibration, all reviewers rated a test paper (not one of the included articles) and ratings were compared and discussed until all conflicts were resolved. During this step, study-specific decision criteria were added to the quality assessment tool, as shown in the (supplemental materials Table S2).

## 2.4. Data extraction

Data extraction was performed by one coauthor and verified by the lead author. General study data extracted included country, key study design features, investigated exposure contrast, measurements, recruitment method and number of participants. To assess the effectiveness of JR, we extracted the following physical exposure primary outcomes: (i) group mean amplitudes, (ii) variation between workers, and (iii) variation within workers. We extracted group mean exposure values since this was commonly reported in previous reviews. We extracted between-worker variation metrics as they are inherently related to within-subject variation: when one increases, the other will necessarily decrease (and vice versa). Accordingly, between-subject variation metrics may offer insight when within-worker variation metrics are not reported. We extracted within-worker variation as a key metric for assessing JR effectiveness. For studies reporting more than five objectively measured mean exposures,

only variables that showed a significant difference between JR and non-JR groups and variables discussed in the original papers by the authors as being of particular interest are reported. For the secondary outcome, we extracted any health outcomes related to changes in work organization that were reported in the studies. Due to the limited number of studies and data points and the high heterogeneity between exposure variables, study characteristics and JR strategies, no meta-analysis or evaluation of strength of association was performed.

# 3. Results

## 3.1. Characteristics of the included studies

The initial search identified 3444 articles, and after removal of 856 duplicates, 2588 articles remained (Figure 3). After title and abstract screening, 60 articles were retrieved for full-text screening of which six were deemed eligible for inclusion in the review. A search of both the reference lists of all included articles and all articles citing the included articles resulted in the inclusion of one additional article. In the updated search (May, 2025), 580 unique articles were identified, 553 of which were excluded during title and abstract screening. Of the remaining 27 papers, 14 had been identified in the original search and were removed, and 13 were advanced for full text screening. One additional recent article that was known to the authors but that was not identified in the search was added resulting in a total of 14 articles for full text screening of which three were included in the review. Of the 10 included studies, five were observational cross-sectional studies (Arvidsson et al., 2012; Balogh et al., 2016; Bao et al., 2016; Comper et al., 2021; Unge et al., 2007), one was an observational prospective study (Kuijer et al., 2004), one was a pre-post observational study (Rissén et al., 2002), one was a pre-post quasi-experimental (feasibility) study (Vilhelmsen et al., 2025), and two were cluster randomized control studies (CRCT) (Lerche et al., 2024; Lohne et al., 2025; Table 1). Four studies were performed in Sweden, and one study each in: Denmark, Finland, Norway, the Netherlands, the United States, and Brazil. In one study (Bao et al., 2016), JR was considered for a large set of workers employed in different sectors and doing different types or work In all of the other studies, JR was assessed for workers in a single occupation: grocery store workers (Rissén et al., 2002; Balogh et al., 2016), refuse collectors (Kuijer et al., 2004), hospital cleaners (Unge et al., 2007), meat cutters (butchers) at a meat
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<figure><img src="image_22.png" /><figcaption>Figure 3. Prisma flow diagram for systematic review process.</figcaption></figure>

processing plant (Arvidsson et al., 2012), automotive manufacturing workers (Comper et al., 2021), industrial workers (Fritz Lerche et al., 2024), and home care workers (Lohne et al., 2025; Vilhelmsen et al., 2025).

Physical exposure was assessed using technical measurement in eight studies (JR worker sample size range = 5–44; Arvidsson et al., 2012; Bao et al., 2016; Fritz Lerche et al., 2024; Kuijer et al., 2004; Lohne et al., 2025; Rissén et al., 2002; Unge et al., 2007; Vilhelmsen et al., 2025) and using observational assessment in five studies (JR sample size range: 9–710, not stated in 1 study; Bao et al., 2016; Comper et al., 2021; Kuijer et al., 2004; Rissén et al., 2002; Unge et al., 2007); Table 1).

Health outcomes were assessed in nine of the 10 studies (Arvidsson et al., 2012; Balogh et al., 2016; Bao et al., 2016; Comper et al., 2021; Fritz Lerche et al., 2024; Lohne et al., 2025; Rissén et al., 2002; Unge et al., 2007; Vilhelmsen et al., 2025), all by self-report.

## 3.2. Quality assessment of studies

Nine of the 10 included articles were rated as having a low risk for bias more than half of the nine items on the assessment scale; however, none of the studies

received low-bias ratings for all items (supplemental material—Table S3). The most common quality issues according to the JBI tool were: unclear description of the sample frame, inadequate sample size related to the aim of this systematic review, unclear description of the study subjects.

## 3.3. Job rotation characteristics

JR was characterized and described very differently across the studies, as shown in Table 1. The least strict characterization of JR used a dichotomous variable (JR/no JR) assigned by researchers based on the number of tasks performed (e.g., more than one task; Bao et al., 2016) or the proportion of workers in a department rotating between a set number of tasks (e.g., 75% of workers performing at least three tasks; Comper et al., 2021). In four studies, tasks were redistributed across workers (Arvidsson et al., 2012; Kuijer et al., 2004; Rissén et al., 2002; Unge et al., 2007), and many workers received new tasks to perform. In the final four studies, the proportion of time workers spent performing each task was changed (Balogh et al., 2016; Lerche et al., 2024; Lohne et al., 2025; Vilhelmsen et al., 2025), with many workers performing the same tasks prior to the JR. In three of these studies (Lerche et al., 2024; Lohne et al., 2025; Vilhelmsen et al.,
<figure><img src="image_23.png" /><figcaption>Table 1. Design and physical exposure assessment methods in the included studies.</figcaption></figure>

<table><thead><tr><th rowspan="2">1<sup>st</sup> Author/<br>Year</th><th rowspan="2">Country/<br>occupation</th><th rowspan="2">Study design</th><th colspan="2">Inclusion/exclusion criteria</th><th rowspan="2">Job rotation contrast<br>(* = job rotation<br>equivalent)</th><th rowspan="2">New<br>tasks in<br>the JR?<br>Y/N</th><th rowspan="2">Measurement<br>method(s)/exposures<br>assessed</th><th rowspan="2">Recruitment<br>method</th><th rowspan="2">Number of<br>participants</th><th rowspan="2">Study period/<br>data sampling<br>strategy</th></tr><tr><th>Inclusion</th><th>Exclusion</th></tr></thead><tbody><tr><td>Arvidsson<br>et al.,<br>2012</td><td>Sweden<br>Meat cutters<br>(pork)</td><td>Observational<br>cross-sectional<br>study</td><td>All male<br>employees at 3<br>meat-processing<br>plants<br>employed for<br>at least three<br>months: total<br>(n = 122);<br>sixth-part<br>system workers<br>(n = 90);<br>split-carcass<br>workers (n =<br>28)</td><td>None given</td><td>Sixth-part system:<br>Manual butchering of a<br>pig carcass that has<br>been split into 6 pieces<br>(3 per half pig; each<br>piece 12–15 kg), using<br>knives. 28 workers<br>debone shoulders, 39<br>workers ham, 10<br>workers middle parts,<br>and 3 workers alternate<br>between parts. Cycle<br>time 40 s to 3 min;<br>daily output per worker<br>162 shoulders, or 170<br>hams, or 680 middle<br>parts</td><td>Y</td><td>Direct<br>measurement<br>Inclinometry: head<br>- upper arm<br>- Goniometry: wrist<br>Surface EMG: trapezius<br>- forearm</td><td>Convenience<br>sample</td><td>Split-carcass:<br>n = 5 sixth-part:<br>n = 10</td><td>Data collected<br>during one<br>regular<br>workday.<br>Mean mst<br>duration<br>sixth-part:<br>234 min<br>(range<br>202–264)</td></tr><tr><td colspan="11"><strong>*Split-Carcass System:</strong><br>Manual butchering of a<br>half pig carcass (40 kg)<br>using knives. All<br>workers handle half<br>carcasses and debone<br>all sections to produce<br>same cuts as in<br>sixth-part system. Cycle<br>time 4–5 min; daily<br>output per worker 96<br>half-carcasses</td></tr></tbody></table>

(Continued)
<figure><img src="image_24.png" /><figcaption>Table 1. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">1<sup>st</sup> Author/ Year</th><th rowspan="2">Country/ occupation</th><th rowspan="2">Study design</th><th colspan="2">Inclusion/exclusion criteria</th><th rowspan="2">Job rotation contrast (* = job rotation equivalent)</th><th rowspan="2">New tasks in the JR? Y/N</th><th rowspan="2">Measurement method(s)/exposures assessed</th><th rowspan="2">Recruitment method</th><th rowspan="2">Number of participants</th><th rowspan="2">Study period/ data sampling strategy</th></tr><tr><th>Inclusion</th><th>Exclusion</th></tr></thead><tbody><tr><td>Balogh et al., 2016</td><td>Sweden Grocery store workers</td><td>Observational cross-sectional study</td><td>All female employees (n = 1600) in 98 grocery stores in Southern Sweden (of 110 possible stores in the concern) working at least 30h/week (n = 989) working a minimum of 80% time with only cashier work, picking, delicatessen or a mix of these three (n = 828)</td><td>Males (since only six met the inclusion criteria)</td><td><strong>Cashier work</strong> (min 80% of working time): ex: scanning items, handling payments, while sitting or standing (n = 185)<br/><strong>Picking work</strong> (min 80% of working time): ex. moving provisions (carrying or pushing a roll cage trolley) from storeroom to shop shelves or freezer (n = 199)<br/><strong>Delicatessen Work</strong> (min 80% of working time): ex. filling refrigerated display, attending to customers buying food at counter (n = 164)<br/><strong>*Mixed Work:</strong> combination of cashier, picking, and delicatessen tasks (mean proportion of time spent per task: 45%, 42%, 13%, respectively) (n = 280)</td><td>N</td><td><strong>Survey</strong><br/>- Time in work tasks</td><td>All employees at the 98 stores were sent the survey. Data from all workers meeting the study criteria were included in the analyses</td><td>n = 786</td><td>Survey mailed home</td></tr><tr><td>Bao et al., 2016</td><td>United States Wide range of jobs ranging from low to very high physical demands</td><td>Observational cross-sectional study</td><td>Data from 3 study cohorts were included</td><td>No exclusion criteria given</td><td>JR was a dichotomous variable assigned by researchers based on number of tasks performed/day:<br/><strong>No Job Rotation:</strong> worker assigned to only 1 task per workday (n = 1124)<br/><strong>*Job Rotation:</strong> worker reports at interview being assigned ≥ 2 tasks per workday; verified by observation. (n = 710)<br/>Ex: office workers, pharmacists, electronic assembly, sawmill and heavy assembly workers</td><td>Unclear</td><td><strong>Direct measurement</strong><br/>Inclinometry:<br/>- head<br/>- upper arms<br/>Goniometry:<br/>- wrist<br/>Surface EMG:<br/>- trapezius<br/>- forearm</td><td>Convenience sample of participants at 8 stores (random selection of 1-5 workers per store).<br/><strong>Cashier and picking work:</strong> n = 17 (split by tasks after data collection)<br/><strong>Delicatessen:</strong> n = 5<br/><strong>Mixed work:</strong> exposures derived from time weighting the mean exposure levels for cashier, picking and delicatessen work (time fractions based on mixed work group mean).<br/>Not detailed in the paper. In prior publications is described that recruitment occurred through worksite visits</td><td>Not reported</td><td>Not reported</td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th rowspan="2">1<sup>st</sup> Author/<br>Year</th><th rowspan="2">Country/<br>occupation</th><th rowspan="2">Study design</th><th colspan="2">Inclusion/exclusion criteria</th><th rowspan="2">Job rotation contrast<br>(* = job rotation<br>equivalent)</th><th rowspan="2">New<br>tasks in<br>the JR?<br>Y/N</th><th rowspan="2">Measurement<br>method(s)/exposures<br>assessed</th><th rowspan="2">Recruitment<br>method</th><th rowspan="2">Number of<br>participants</th><th rowspan="2">Study period/<br>data sampling<br>strategy</th></tr><tr><th>Inclusion</th><th>Exclusion</th></tr></thead><tbody><tr><td rowspan="2">Comper<br>et al.,<br>2021</td><td rowspan="2">Brazil<br>Production of car<br>seats, car<br>interiors, and<br>foam for car<br>seats<br>(automotive<br>manufacturing)</td><td rowspan="2">Observational<br>cross-sectional<br>study</td><td rowspan="2">All blue-collar<br>production<br>employees at<br>one production<br>facility<br>employed at<br>least 3 months<br>were invited<br>(n=276).</td><td rowspan="2">Absent from<br>work during<br>the study</td><td><strong>Low JR adherence</strong><br>(cell<br>production – 5<br>departments): &lt;75% of<br>workers perform a JR,<br>and the rotation is<br>between less than<br>three tasks</td><td rowspan="2">Unclear</td><td rowspan="2"><strong>Observation</strong><br>- Quick Exposure<br>Check (QEC)<br>checklist</td><td rowspan="2">All employees<br>meeting the<br>study criteria<br>were invited<br>to<br>participate</td><td rowspan="2">Not explicitly<br>stated – implied<br>the same as for<br>the survey, i.e.,<br>low JR<br>adherence<br>group n = 42<br>high JR<br>adherence<br>group n = 78<br>All males</td><td rowspan="2">Not reported</td></tr><tr><td><strong>*High JR adherence</strong><br>(serial production – 2<br>departments): ≥75% of<br>workers perform a JR,<br>and the rotation<br>includes three or more<br>tasks and rotation<br>occurs at 2-h intervals.</td></tr><tr><td rowspan="4">Kuijer et al.,<br>2004</td><td rowspan="4">Netherlands<br>Refuse collectors</td><td rowspan="4">Observational<br>prospective study</td><td rowspan="4">Workers from 3<br>refuse<br>management<br>companies who<br>had worked for<br>more than 1<br>year</td><td rowspan="4">No exclusion<br>criteria<br>given</td><td><strong>Only truck driving</strong><br>(100% driving)</td><td rowspan="4">N</td><td rowspan="4"><strong>Observation</strong><br>- Task duration<br><strong>Direct measurement</strong><br>- Heart rate<br>- VO<sub>2max</sub><br>- Total amount of<br>refuse collected<br>per truck</td><td rowspan="4">Convenience<br>sample, 3<br>participants<br>per<br>company</td><td rowspan="4">N = 9<br>(n=8 for VO<sub>2max</sub>)</td><td rowspan="4">Data collection<br>occurred over<br>a 5-week<br>period. Each<br>worker<br>performed<br>work<br>according to<br>the same<br>work scenario<br>on all days<br>within a<br>collection<br>week, and<br>data were<br>collected on<br>the last 3<br>days</td><td rowspan="4">Data collected<br>on 3<br>workdays per<br>work<br>organization<br>scenario per<br>worker<br>(workers did<br>all 4<br>scenarios)<br>– total – 12<br>days per<br>worker</td></tr><tr><td><strong>Only refuse bin<br>collecting</strong><br>(30%<br>driving, 70% bin<br>collecting)</td></tr><tr><td><strong>*Rotation within day:</strong><br>rotation between truck<br>driving and refuse<br>collecting tasks within<br>a day. (60% driving,<br>40% bin collecting)</td></tr><tr><td><strong>*Rotation between<br>days:</strong> alternating days<br>of full day truck driving<br>and full day refuse<br>collecting. (60% driving,<br>40% bin collecting)</td></tr></tbody></table>

(Continued)

<figure><img src="image_25.png" /></figure>
<table><caption>Table 1. Continued.</caption><thead><tr><th rowspan="2">1st Author/ Year</th><th rowspan="2">Country/ occupation</th><th rowspan="2">Study design</th><th colspan="2">Inclusion/exclusion criteria</th><th rowspan="2">Job rotation contrast (* = job rotation equivalent)</th><th rowspan="2">New tasks in the JR? Y/N</th><th rowspan="2">Measurement method(s)/exposures assessed</th><th rowspan="2">Recruitment method</th><th rowspan="2">Number of participants</th><th rowspan="2">Study period/ data sampling strategy</th></tr><tr><th>Inclusion</th><th>Exclusion</th></tr></thead><tbody><tr><td>Lerche et al., 2024</td><td>Denmark<br>Industrial workers primarily performing manual work</td><td>Cluster randomized controlled trial</td><td>Workers employed for ≥ 20 h/week</td><td>Only working night shifts, Away from work during intervention<br>Pregnant</td><td><strong>No Job rotation: usual work (handling containers at assembly lines by automated robots, using trucks or forklifts)</strong><br><strong>*Job rotation: workers re-organised their tasks to work toward (i) a composition of 60% sitting, 30% standing, and 10% active work; and (2) alternating between the physical behaviors at least once per hour</strong></td><td>Y</td><td><strong>Direct measurement</strong><br>Accelerometer:<br>- Physical behaviors (sitting, standing, active)</td><td>All workers were invited at two convenience production sites with a total of 28 teams</td><td>No JR n = 36<br>JR n = 33</td><td>Data collected for 5 consecutive workdays at baseline and follow-up</td></tr><tr><td>Lohne et al., 2025</td><td>Norway<br>Home-care workers (nurses and assistants)</td><td>Cluster randomized controlled trial</td><td>Workers primarily caring for clients, and working ≥ 50% of full-time</td><td>Pregnancy, fever on day of measure, allergy to fixation tape, physical incapacities hindering normal home care work</td><td><strong>No Job Rotation: usual work (assisting clients with bathing, dressing, household tasks and health-related tasks)</strong><br><strong>*Job Rotation: re-distribution of "high" and "low" demands clients (based on the clients' Activities of Daily Living (ADL) score)</strong></td><td>N</td><td><strong>Direct measurement</strong><br>Accelerometer:<br>- Physical behaviors (sitting, standing, active)<br>Upper arm posture Forward bending</td><td>All workers at 12 home care units in a municipality were invited</td><td>No JR n = 44<br>JR n = 39</td><td>Data collected at baseline and follow-up<br>Data collection for up to 7 consecutive workdays at both occasions</td></tr></tbody></table>

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /><figcaption>Table 1. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">1<sup>st</sup> Author/<br>Year</th><th rowspan="2">Country/<br>occupation</th><th rowspan="2">Study design</th><th colspan="2">Inclusion/exclusion criteria</th><th rowspan="2">Job rotation contrast<br>(* = job rotation<br>equivalent)</th><th rowspan="2">New<br>tasks in<br>the JR?<br>Y/N</th><th rowspan="2">Measurement<br>method(s)/exposures<br>assessed</th><th rowspan="2">Recruitment<br>method</th><th rowspan="2">Number of<br>participants</th><th rowspan="2">Study period/<br>data sampling<br>strategy</th></tr><tr><th>Inclusion</th><th>Exclusion</th></tr></thead><tbody><tr><td>Rissén<br>et al.,<br>2002</td><td>Sweden<br>Grocery store<br>workers</td><td>Pre-post<br>observational<br>study</td><td>Female cashiers<br>who had<br>participated in<br>a prior<br>study(reference<br>group) and<br>were still<br>employed at<br>the time of<br>follow-up<br>measurement</td><td>Absent during<br>data<br>collection due<br>to<br>sick-maternity-<br>or<br>study- leave</td><td><strong>No Job rotation:</strong> cashier<br>work only (from a<br>previous study)<br>including<br>scanning items and<br>handling payments<br><br><strong>*Job rotation:</strong> workers<br>performed 40% cashier<br>work and 60% work in<br>other departments<br>including produce<br>(vegetables and fruits,<br>dairy), household<br>goods and clothing</td><td>Y</td><td><strong>Observation</strong></td><td>N = 31</td><td>Continuous<br>posture<br>observation<br>on-site during<br>departmental<br>work</td></tr><tr><td>Unge et al.,<br>2007</td><td>Sweden<br>Hospital cleaners</td><td>Observational<br>cross-sectional<br>study</td><td>All female cleaners<br>from 2<br>hospitals in<br>southern<br>Sweden (n =<br>151 TO group,<br>n= 117 EO<br>group)</td><td>Absent during<br>data<br>collection due<br>to maternity-<br>or study- or<br>sick-leave</td><td><strong>Direct<br>measurement</strong><br>- Heart rate<br>- Blood pressure</td><td><strong>Direct<br>measurement</strong><br>72 workers<br>from four<br>different<br>supermarkets<br>who had<br>participated<br>in the prior<br>study were<br>invited</td><td>Baseline n = 72<br>Follow-up n = 31<br>(27 had JR)</td><td>All<br>measurements<br>performed<br>during a<br>2-hour<br>window on 1<br>working day;<br>Baseline and<br>follow-up<br>measurements<br>occurred at<br>the same<br>time of the<br>day (± 1h)<br>Blood pressure<br>and Heart<br>Rate were<br>assessed after<br>1h of work<br>and at the<br>end of the 2h<br>work period</td><td>Continuous<br>2-hour<br>collection<br>Continuous<br>posture<br>observation<br>on-site during<br>departmental<br>work</td></tr><tr><td>Vilhelmsen<br>et al.,<br>2025</td><td>Denmark<br>Home health care<br>workers</td><td>Pre-/post.test<br>quasi-experimental<br>feasibility study</td><td>Not explicitly<br>stated</td><td>Not explicitly<br>stated</td><td><strong>Surface EMG:</strong><br>- Bilateral upper<br>trapezius<br><br><strong>Observation</strong></td><td><strong>Direct<br>measurement</strong><br>Accelerometer:<br>- 8 complementary<br>Physical behaviors</td><td>N = 18</td><td>Continuous<br>2-hour<br>collection<br>Continuous<br>posture<br>observation<br>on-site during<br>departmental<br>work</td><td>N = 15 before the JR<br>N = 14 after the JR<br>Data collected<br>before and<br>after the JR<br>Data collection<br>for up to 5<br>consecutive<br>workdays at<br>both<br>occasions</td></tr></tbody></table>

mst: measurement; EMG: electromyography.
<figure><img src="image_28.png" /></figure>

2025), the aim of the JR was not to increase variation for the individual worker, but rather to obtain a more equal distribution of exposure across workers and thus to decrease between-worker variability. In all studies, JR involved rotation between tasks within a day; one study additionally considered the effects of a between-day rotation (Kuijer et al., 2004).

The extent to which the tasks comprising the different work organizational strategies were described also varied greatly between studies. In general, insufficient information was provided to assess whether exposures differed between tasks (i.e., the task diversity; Mathiassen, 2006) or the amount of time spent performing each task. The least specific study included a wide range of jobs in different sectors and gave no information about the individual tasks comprising the JRs (Bao et al., 2016).

In some studies, JR was characterized by the relative time spent working in different departments in the two studies of grocery workers (e.g., Balogh et al., 2016; Rissén et al., 2002), with examples given of typical tasks in each department, and similarly for time spent in each of the two tasks for refuse workers (Kuijer et al., 2004). The most detailed task descriptions were provided in the meat cutter study (Arvidsson et al., 2012) and included estimated cycle times, loads handled and total daily outputs. While these data offer some insight into the extent of diversity between JR tasks, they fall short of permitting calculation of the potential effects of the JR on variance within workers and therefore whether the JR has the potential to fulfill the proposed mechanism of change (i.e., increased within-worker variance)—see Figure 1, JR #1 vs JR #2.

Only four of the studies studied specified that no new tasks were introduced between the reference (no-JR) and JR groups, namely the Balogh et al. grocery store study (Balogh et al., 2016), the refuse collector study (Kuijer et al., 2004), and the two studies in which the proportion of time workers spent working with high- and low-demand clients was adjusted (Lohne et al., 2025; Vilhelmsen et al., 2025). The remaining six studies stated (to varying extents) that new tasks were present in the JR group compared with the no-JR reference (Arvidsson et al., 2012; Bao et al., 2016; Comper et al., 2021; Lerche et al., 2024; Rissén et al., 2002; Unge et al., 2007; Table 1). Specifically in the meat-cutter study (Arvidsson et al., 2012), JR workers were given the additional task of splitting half pig carcasses into six pieces before deboning each of the pieces, while no-JR workers only performed the deboning tasks of the six pieces. In the Rissén et al. (2002) grocery worker study, the

no-JR reference group comprised only cashier workers, while the JR workers performed 40% cashier work and 60% work in other departments. In the hospital cleaner study (Unge et al., 2007), the no-JR group performed only cleaning tasks, while the JR group performed cleaning, planning, client contact and other tasks. In the Lerche et al. (2024) study, the JR permitted redesign of individual tasks, although the extent to which this occurred or to which exposures changed was not described in the study. Finally, In the Bao et al. (2016) and Comper et al. (2021) studies, JR was defined based on the number of tasks performed by workers, with JR workers performing more tasks than no-JR workers.

## 3.4. Effects of job rotation

### 3.4.1. Exposure: amplitude

All studies assessed the effect of JR on mean exposure (Table 2). Statistical differences in group mean exposure amplitudes were reported between JR and no-JR workers for several parameters. Positive effects of JR on exposures were reported for some of the exposure variables in four of the 10 studies. Specifically, workers in JR were shown to have: lower elevation angles (Arvidsson et al., 2012; Unge et al., 2007), joint velocities (Arvidsson et al., 2012; Unge et al., 2007), muscle activity amplitudes (Arvidsson et al., 2012; Rissén et al., 2002), self-reported workloads (Unge et al., 2007), diastolic blood pressure (Rissén et al., 2002), and more time in muscle rest (Rissén et al., 2002). In addition, JR workers were shown to work at a lower proportion of heart rate reserve (Unge et al., 2007). In contrast, negative effects of JR on exposure were reported for some of the reported exposure variables in three of the 10 studies. Specifically, workers in JR were shown to have: decreased time at rest (Arvidsson et al., 2012), higher levels of biomechanical stressors (Bao et al., 2016), and worse (or similar) QEC exposure ratings (Comper et al., 2021). However, for all nine studies that considered statistical differences in group mean exposure amplitudes (all studies except Balogh et al., 2016), for most of the variables studies, differences between JR and no-JR workers in group mean exposure amplitudes were not significant.

In a job rotation where a fixed set of tasks is redistributed and all tasks occur to the same extent before and after the job rotation, the group mean exposure is not expected to change. Thus, any systematic change (increase or decrease) in the group mean may be indicative that new tasks have been introduced to the JR, or that the existing tasks were altered. All studies that reported a change in mean exposure amplitudes
<figure><img src="image_29.png" /><figcaption>Table 2. Physical exposure variables assessed and job rotation related findings in the included studies.</figcaption></figure>

<table><thead><tr><th rowspan="2">Author/year</th><th colspan="2">Physical exposure variables assessed</th><th colspan="2">Variation between subjects</th><th colspan="2">Variation within subject</th><th colspan="2">Covariates</th><th colspan="3">Results of job rotation</th></tr><tr><th>Mean exposure</th><th>Mean deviation across subjects</th><th>Mean deviation for each exposure variable</th><th>Standard deviation for each exposure variable</th><th>Mean within-minute and between-minute standard deviations for right upper arm elevation angles</th><th>None</th><th>On mean exposure</th><th>Lower elevation angles, joint velocities, muscle activity amplitudes and time at rest with JR (*split-carcass) compared with sixth-part system:</th><th>On variation between subjects</th><th>Group SDs presented but no statistical tests of group differences</th><th>Significantly higher within-minute SD for right upper arm elevation angles in JR (*split-carcass) than in sixth-part system (56deg vs 47deg), but no significant difference in between-minute SD (8deg vs 11deg)</th></tr></thead><tbody><tr><td>Arvidsson et al., 2012</td><td>From interview:<br/>- Number work cycles performed daily<br/>From Inclinometry & goniometry:<br/>- Select percentiles for head, back, upper arm and wrist postures<br/>- 50th percentile for head, upper arm and wrist velocity<br/>- Time &lt; 1deg/s for wrist velocity<br/>From EMG:<br/>- % time at rest<br/>- 50th and 90th percentile for trapezius and forearm extensor muscle activity (MVE normalised)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Balogh et al., 2016</td><td>From survey:<br/>- Time spent in each work task per week<br/>From inclinometry:<br/>- Select percentiles for head, upper arm and wrist postures<br/>- Proportion of time &gt;90deg for right upper arm elevation<br/>- 50th percentile for wrist, head and upper arm velocity<br/>- Time &lt; 1deg/s for wrist velocity<br/>- 10th, 50th and 90th percentile, and time &lt; 0.5%MVE for forearm extensor and trapezius EMG amplitude</td><td>Standard deviation across subjects for each exposure variable</td><td>Standard deviation across subjects for each exposure variable</td><td>Standard deviation for each exposure variable</td><td>Mean within-minute (WMV) and between-minute (BMV) standard deviations, and between/ within-minute SD ratio (BWR) for head inclination and right upper arm elevation</td><td>Age, employment time, BMI, smoking, job exercise, job content questionnaire</td><td>Mean within-minute (WMV) and between-minute (BMV) standard deviations, and between/ within-minute SD ratio (BWR) for head inclination and right upper arm elevation</td><td>Age, employment time, BMI, smoking, job exercise, job content questionnaire</td><td>Mean exposures presented but no statistical tests of group differences</td><td>Group SDs presented but no statistical tests of group differences</td><td>WMV, MBV and BWRs presented but no statistical tests of group differences.</td></tr></tbody></table>

(Continued)
<figure><img src="image_30.png" /><figcaption>Table 2. Continued.</figcaption></figure>

<table><thead><tr><th rowspan="2">Author/year</th><th rowspan="2">Mean exposure</th><th colspan="3">Physical exposure variables assessed</th><th colspan="3">Results of job rotation</th></tr><tr><th>Variation between subjects</th><th>Variation within subject</th><th>Covariates</th><th>On mean exposure</th><th>On variation between subjects</th><th>On variation within subjects</th></tr></thead><tbody><tr><td>Bao 2016</td><td>From interview &amp; self-report:<br/>- Tasks performed<br/>From observation:<br/>- Tasks performed, force (analyst estimated hand loads), repetition, duration of exertions and hand/wrist postures<br/>From observation and self-report:<br/>- Peak force, hand activity level (HAL), efforts/min, duty cycle, hand/wrist posture, and speed of work for each individual task in the job, effort/min, duty cycle, strain index (SI)</td><td>None</td><td>None</td><td>Working overtime, second jobs, work pace, general health, depression, physical and mental exhaustion, psychosocial factors</td><td>Workers in JR group had higher biomechanical stressors (strain index (TWA 4.0 vs 1.5), TLV for hand activity level (TWA 0.42 vs 0.33)), and higher exposure amplitudes for hand activity level (TWA 4.5 vs 4.0) effort/ min (TWA 5.8 vs 1.9), and duty cycle (TWA 20.9 vs 9.4)</td><td>None</td><td>None</td></tr><tr><td>Comper et al., 2021</td><td>From observation:<br/>- QEC assessed postures, movements, force/ load for the back, shoulder/arm, wrist/ hand, and neck<br/>- task duration</td><td>SD between subjects of QEC metrics<br/>From Survey: perceived exposure</td><td>None</td><td>Age, time on job, marital status, education status</td><td>In general, workers with *high JR adherence had worse or similar QEC exposures than workers with low JR adherence. Significant differences found for cervical spine, shoulder-arm, rhythm and stress</td><td>Group SDs presented but no statistical tests of group differences</td><td>None</td></tr><tr><td>Kujjer et al., 2004</td><td>From observation:<br/>- Task analysis using TRAC-system<br/>- Work demands metrics, including working duration, time in different tasks, amount of refuse collected<br/>From direct measurement:<br/>- Physical workload (heart rate (beats/min)<br/>- estimated percentage of the maximum oxygen uptake (%VO2max) during work</td><td>Standard error of means indicated in graphs (all exposure metrics)</td><td>None</td><td>None</td><td>JR workers had lower % VO2max per workday than collecting only workers</td><td>Group-level standard errors were presented in the figures, and SD could be calculated [SD = SEM*sqrt(sample size)] but no statistical tests of group differences were performed</td><td>None</td></tr><tr><td>Lerche et al., 2024</td><td>From direct measurement:<br/>- Work time spent sitting, standing and active, expressed in terms of an index measuring the Aitchison distance to a 60/30/10 composition<br/>- Work time spent in short (≤30 min) and long (>30 min) uninterrupted bouts, irrespective of behavior, expressed in terms of an ilr coordinate</td><td>SD between subjects for each measured variable</td><td>None</td><td>None</td><td>No significant effect of JR on Aitchison index values or time spent in short or long uninterrupted bouts of sitting or standing.</td><td>Group SDs presented but no statistical tests of group differences</td><td>None</td></tr><tr><td>Lohne et al., 2025</td><td>From direct measurement:<br/>- time spent at work sedentary, standing, and active<br/>- Work time spent with elevated arms, expressed as the composition of sedentary time, time upright with arms elevated ≤60°, and time upright with arms elevated >60°<br/>- Work time spent in forward bending, expressed as the composition of sedentary time, time upright with back bended ≤30°, and time upright with back bended >30°</td><td>Compositional biplot illustrating variance between subjects</td><td>None</td><td>None</td><td>No significant effect of the JR intervention on the composition of physical behaviors, time with arms elevated, or time in forward bending</td><td>No significant effect of JR on the variability between subjects in time spent in the three physical behaviors, nor on variability in arm elevation and trunk forward bending</td><td>None</td></tr></tbody></table>
<figure><img src="image_31.png" /></figure>

<table><tbody><tr><td>Risséno et al.,<br>2002</td><td>From direct measurements: Systolic/diastolic<br>blood pressure<br>Heart rate (beats/min)<br>Percentiles of upper trapezius muscle activity<br>at work as %RVE; percent time in muscular<br>rest (individually determined threshold)</td><td>SD between<br>subjects for all<br>direct<br>measurements</td><td>None</td><td>None</td><td>Lower DBP with JR than without<br>(82.3 vs 79.8mmHg), no<br>statistical difference in SBP and<br>heart rate<br>Lower muscle activity with JR<br>(50th percentile left side<br>118%RVE vs 63%RVE, right<br>side 92%RVE vs 81%RVE), only<br>left side significant.<br>No difference in number of<br>customers or weight handled<br>in JR (27/h, 305 kg/h) and<br>no-JR (24/h, 350 kg/h).</td><td>Group SDs presented but<br>no statistical tests of<br>group differences</td><td>None</td></tr><tr><td>From observation:</td><td>Workload during cashier work<br>- Time spent in 7 'risk' postures during<br>departmental work</td><td>None</td><td>None</td><td>None</td><td>Cleaning covered 83 and 94% of<br>the working time for the *EO<br>and TO groups. The *EO group<br>spent more time in the task<br>"attending to equipment",<br>while no difference was found<br>for "ambulation". The TO group<br>had no time in 'meetings'<br>Steps/hour and time in sitting/<br>squatting did not sig. differ<br>between groups (trend that<br>*EO had higher step count and<br>time sitting)<br>The *EO group worked at a lower<br>HRR than the TO group (23%<br>vs 32%)</td><td>Group SDs presented but<br>no statistical tests of<br>group differences</td><td>None</td></tr><tr><td>Unge et al.,<br>2007</td><td>From observations:<br>- work tasks performed and duration,<br>including breaks<br>From direct measurements:<br>- # of steps/min<br>- Time in sitting and standing<br>- Heart rate ratio<br>- 10th, 50th and 90th percentile posture for<br>head, upper arm and wrist<br>- 10th, 50th and 90th velocity percentiles of<br>the head, upper arm and wrist<br>- %time &lt;1deg/s for wrist velocity<br>- 10th, 50th and 90th percentile, and time<br>&lt; 0.5%MVE for upper trapezius EMG</td><td>None for time in<br>tasks, SD<br>between<br>subjects for all<br>other metrics</td><td>None</td><td>None</td><td>*EO group had lower head and<br>upper arm flexion/elevation<br>angles and velocities and<br>lower wrist velocities and time<br>&lt;1deg/s than TO<br>*EO group had a higher<br>proportion time at muscular<br>rest (6.3 vs 2.7% of time) but<br>no difference found in EMG<br>percentiles between groups.<br>*EO had lower self-reported<br>physical workloads than TO<br>group</td><td>Group SDs presented but<br>no statistical tests of<br>differences between<br>baseline and follow-up</td><td>None</td></tr><tr><td>Vilhelmsen et al.,<br>2025</td><td>From direct measurement:<br>- time spent sitting, standing still, standing<br>with movement, walking slow, walking<br>fast, stair climbing, running and cycling.<br>Further categorized into sedentary<br>behavior (lying and sitting). LPA (standing,<br>move and walking slow), and MVPA<br>(walking fast, stair climbing, running and<br>cycling).</td><td>SD between<br>subjects</td><td>None</td><td>None</td><td>No significant change in physical<br>behaviors between baseline<br>(no JR) and follow-up (JR).</td><td>Group SDs presented but<br>no statistical tests of<br>differences between<br>baseline and follow-up</td><td>None</td></tr></tbody></table>
<figure><img src="image_32.png" /></figure>

for JR workers also introduced new tasks (Arvidsson et al., 2012; Bao et al., 2016; Rissén et al., 2002; Unge et al., 2007) or were unclear regarding whether new tasks were present (Comper et al., 2021).

### 3.4.2. Exposure: variation between workers

None of the nine studies reporting standard deviations (SDs) between workers assessed whether SDs were significantly different between JR and no-JR workers. Further, none of the studies isolated the true between-subject variance by separating the between- and within-subject components, as has been previously recommended and performed (Jackson et al., 2009; Liv et al., 2011; Loomis & Kromhout, 2004; Mathiassen et al., 2003, 2010).

### 3.4.3. Exposure: variation within workers

Two studies considered within-subject variation, one of which (Arvidsson et al., 2012) found a significant difference related to JR: slaughterhouse workers with JR had a larger within-minute variability (WMV) in terms of the average 5th-95th percentile range than no-JR workers. However, the same study also found no difference in between-minute variability (BMV) between worker groups (Arvidsson et al., 2012).

### 3.4.4. Health

Five of the nine studies presenting data on health suggested positive effects of JR (Table 3), including: lower prevalence of MSK complaints in the elbows and hands (Arvidsson et al., 2012; Unge et al., 2007) and neck/shoulder (Balogh et al., 2016; Rissén et al., 2002; Unge et al., 2007), lower MSK disorder diagnosis prevalence in the elbows and hands (Arvidsson et al., 2012; Balogh et al., 2016) and neck/shoulder (Balogh et al., 2016; Unge et al., 2007), and lower physical fatigue (Vilhelmsen et al., 2025). Two studies found negative effects of JR on health, including higher MSK symptom prevalence rates (Comper et al., 2021), and higher rates of pain in the right shoulder (Rissén et al., 2002). No effect was found of JR on self-rated general health (Bao et al., 2016) or in any of the other health related variables considered in the other studies (e.g., Arvidsson et al., 2012; Balogh et al., 2016; Bao et al., 2016; Comper et al., 2021; Lerche et al., 2024; Lohne et al., 2025).

## 4. Discussion

This systematic review investigated the body of literature to assess the evidence for the impact of JR on physical exposure (amplitude and variation) in actual

workers where physical exposures were objectively measured and, in the included studies, to assess the impact of JR on health. Ten studies were included. All studies reported group-level average exposure amplitudes, but (as theoretically expected) no consistent pattern emerged for the effect of JR: four studies found JR led to lower exposure amplitudes in some of the studied variables, three studies found JR led to higher exposure amplitudes in some of the studied variables, and all studies found no effect of JR on exposure for at least some of the exposure variables studied. A wide range of exposure variables were considered across the studies (Table 1), for example, segment posture angles, muscle activity amplitudes, and time in physical behavior categories (sit-stand-active), which complicated synthesis and comparison across studies. Eight of the studies reported standard deviations of individual mean exposures, a measure of variation between workers, but none of the studies statistically assessed the effect of JR on the SDs. Two studies reported metrics for variation within workers, with inconclusive evidence. We, therefore, conclude there is insufficient evidence to evaluate the impact of JR on physical exposure. Regarding the secondary aim, nine of the ten included studies evaluated the impact of JR on health and results were inconsistent across studies.

### 4.1. Exposure—amplitude

While we do not advocate for the use of group mean exposure amplitudes when evaluating the effects of a JR (the limitations of this metric are illustrated in Figure 1), we reported mean exposures in the current review for comparison with prior studies. The effect of JR on physical exposure amplitude has previously been considered in three reviews that also considered the impact of JR on health-related outcomes (Leider et al., 2015; Luger et al., 2014; Padula et al., 2017). Similar to the current review, two of these prior reviews reported inconsistent evidence for the effects of JR on group exposure amplitude (Leider et al., 2015; Luger et al., 2014); the third review concluded there was no positive effect of JR on physical exposure (Padula et al., 2017). In all three prior reviews, studies with self-reported assessment of exposure amplitude were included, which is in contrast to the current review in which only studies using objective measures were included.

Our more stringent objective assessment criteria was used since self-reported exposure assessments have been shown to deviate from ‘true’, objectively measured exposures (Barrero et al., 2009; Coenen
<figure><img src="image_33.png" /><figcaption>Table 3. Study design, outcome variables and job rotation related findings for health outcomes in the included studies.</figcaption></figure>

<table><thead><tr><th>Author/year</th><th>Health assessment method</th><th>Recruitment method</th><th>Number of participants</th><th>Outcome variables assessed</th><th>Covariates</th><th>Results of job rotation on health</th></tr></thead><tbody><tr><td>Arvidsson et al., 2012</td><td>Interview - health factors - physical examination - health factors</td><td>All workers meeting the study criteria were invited</td><td>n = 118 (97% RR)</td><td>From interview: - 7-day and 12-month prevalence of MSK complaints in the neck, shoulder, elbow and wrist/hand (Nordic questionnaire)<br>From physical examination: - diagnosis of 21 MSK diseases of the neck, shoulder, elbow and wrist/hand (19 described in Nordander et al., 2019 + cervicalgia & Quervain's)</td><td>None</td><td>Workers in the *split-carcass system had lower prevalences of elbow and hand complaints and diagnoses than sixth-part system workers</td></tr><tr><td>Balogh et al., 2016</td><td>Survey - Health and psychosocial factors</td><td>All employees at the 98 stores were sent the survey. Data from all workers meeting the study criteria were included in the analyses</td><td>n = 786 (answered health and psychosocial factors questions)</td><td>From survey: Prevalence and odds ratios for self-reported neck/shoulder and elbow/hand complaints (Nordic questionnaire - last 7 days) for each work organization group<br>From physical examination: Examination of neck, and upper extremities (Ohlsson et al. 1994) resulting in diagnosis for 19 MSK disorders (Nordander et al., 2019)</td><td>Age, employment time, BMI, smoking, exercise, job content questionnaire</td><td>Workers in *mixed work had lower prevalence rates of neck/ shoulder complaints in the previous 7 days (44% and 46%, respectively) than cashier and delicatessen workers (55% and 56%, respectively).<br>*Mixed work workers (reference group) had lower neck/ shoulder complaint odds ratios than cashier (1.5, 95% CI 1.0-2.2) and delicatessen workers (1.6, 95% CI 1.1-2.3)</td></tr><tr><td>Bao et al., 2016</td><td>Survey - Health factors</td><td>Not detailed in the paper. In prior publications is described that recruitment occurred through worksite visits</td><td>N = 1834 JR n = 710 no JR n = 1124</td><td>From survey: General health rating ("How would you describe your general health compared to other of your own age?") in 1-4 Likert scale</td><td>Working overtime, second jobs, work pace, general health, depression, physical and mental exhaustion, psychosocial factors</td><td>No sig difference in self-reported general health between *job rotation (reference group) and no job rotation groups (OR 1.03, 95% CI 0.86-1.22)</td></tr><tr><td>Comper et al., 2021</td><td>Survey - Health factors</td><td>All employees meeting the study criteria were invited to participate.</td><td>Total survey respondents: N = 120-All were male (no females were working in the sectors studied)</td><td>From survey: Prevalence (7-day and 12-month) of symptoms in different body parts; self-reported (Nordic Questionnaire)</td><td>Age, time on job, marital status, education status</td><td>No significant difference between *high and low JR adherence groups in MSK symptom prevalence (32.1% vs 26.2%, respectively) or perceived occupational exposure (71.4 ± 45.7 vs 56.4 ± 46.8)</td></tr><tr><td>Kujjer et al., 2004</td><td>Survey: - Low back pain rating - Energy - Tiredness</td><td>All workers at two production sites (total 28 teams) were invited</td><td>No JR n = 36 JR n = 33</td><td>From survey: - Low back pain rating (0-10) - Energy (0-10)</td><td>None</td><td>None</td></tr></tbody></table>

(Continued)
<figure><img src="image_34.png" /><figcaption>Table 3. Continued.</figcaption></figure>

<table><thead><tr><th>Author/year</th><th>Health assessment method</th><th>Recruitment method</th><th>Number of participants</th><th>Outcome variables assessed</th><th>Covariates</th><th>Results of job rotation on health</th></tr></thead><tbody><tr><td>Lohne et al., 2025</td><td>Survey:<br/>- Fatigue<br/>- General Health<br/>- Sickness absence<br/>- Musculoskeletal pain</td><td>All workers at 12 home care units in a municipality were invited</td><td>No JR n = 44<br/>JR n = 39</td><td>From survey:<br/>- Ratings of pain in the low back and shoulder/neck (0 to 10)<br/>- Fatigue rating (0 to 10)</td><td>None</td><td>No significant effect of the JR intervention on any of the health-related outcomes</td></tr><tr><td>Rissén et al., 2002</td><td>Diary:<br/>- pain</td><td>72 workers from four different supermarkets who had participated in a prior study were invited</td><td>Baseline n = 72<br/>Follow-up n = 31 (27 had JR)</td><td>From self-report:<br/>Pain in the neck and shoulders (7-day ratings and pain drawings) after JR implementation</td><td>None</td><td>With JR – significantly more participants reported less pain or similar pain than reported more pain<br/>With JR - no change in pain intensity, 6-month pain prevalence, or number of participants with pain<br/>With JR - increase in proportion of workers with right shoulder pain (45% vs 32%)</td></tr><tr><td>Unge 2007</td><td>Interview:<br/>- Subjective ratings of MSK during the latest 7 days<br/>Physical examination:<br/>- pain, numbness, movement limitations</td><td>All participants who satisfied study criteria were invited to participate</td><td>N = 114<br/>participants in TO group<br/>N = 104 in EO (extended organization') group for the questionnaire and the physical examination</td><td>From interview:<br/>MSK complaints last 7 days (Nordic questionnaire)<br/>From physical examination:<br/>Prevalence of 20 neck/shoulder/arm MSK diagnoses</td><td>None</td><td>*EO had lower 7-day prevalence of neck/shoulder and elbow/hand (49% and 41%, respectively) complaints than TO (64% and 51%, respectively)<br/>*EO had lower frequency of MSK diagnoses than TO including tension neck syndrome (28% vs 35%) and overused hand syndrome (1% vs 10%)</td></tr><tr><td>Vilhelmsen 2025</td><td>Survey:<br/>- physical fatigue</td><td>All workers in one health care unit in a Danish Municipality were invited</td><td>N = 15 before the JR<br/>N = 14 after the JR</td><td>Physical fatigue (measured on a 0–100 Likert scale)</td><td>None</td><td>Significant reduction of physical fatigue with JR intervention (mean difference of 17 (scale 0–100) [CI =3.67–30.70])</td></tr></tbody></table>

<footnote>*Denotes JR work organization system.</footnote>
<figure><img src="image_35.png" /></figure>

et al., 2020; Dyrstad et al., 2014; Gupta et al., 2018; Koch et al., 2016; Wick et al., 2016) and, further, limit the extent to which exposure variation (a key outcome in our review) can be considered. Nonetheless, none of the reviews to date (including the current review), have found evidence for a positive effect of JR on physical exposure amplitudes. This was, however, expected. As outlined in the introduction and illustrated in Figure 1, no systematic effect of JR on group mean exposure amplitudes is expected in a JR which redistributes the same set of tasks at the same overall proportions across a group of workers. Minor changes in group mean exposures may occur related uniquely to individual worker characteristics, for example, anthropometry, capacity or working style, or because of random variability. However, consideration of exposure amplitude can be useful if evaluating the effect of JR at the level of the individual workers. Even if the group mean exposure is not expected to change in a JR, exposure amplitudes may increase for some workers, while other workers may experience a decrease (Figure 1, Key Point 3). Consideration of changes in exposure amplitude for individual workers may, therefore, be warranted as the change in exposure may subsequently impact the health of the individual workers (e.g., workers with higher exposures may experience worse health).

Finally, a common challenge to evidence synthesis in both the current and the prior reviews is the broad range of physical exposure metrics that have been used to date in assessing the effectiveness of JR. This issue stems both from the use of different types of measurements as well as from different metrics derived from similar types of measurements. For example, in the six studies included in the present review in which accelerometers were used to objectively measure exposure, the reported variables included postures and velocities of individual body segments for the head and upper arm (Arvidsson et al., 2012; Balogh et al., 2016; Unge et al., 2007), as well as consideration of a range of variables related to physical behavior (e.g., Sit-stand-active).

## 4.2. Exposure—variation

The only known prior review assessing the effects of JR on exposure variation reported “substantial” evidence supporting a relationship between job rotation strategies and increased muscle activity variability (in particular for upper extremity muscles; Rodriguez & Barrero, 2017). We were unable to corroborate that conclusion. However, this prior review approached

variation and job rotation more broadly than the current study and included variation metrics that do not reflect changes in within-worker variance, for example, standard deviation between workers, and median EMG frequency (Rodriguez & Barrero, 2017). Further, many of the studies included in that review were laboratory experiments and/or studies simulating hypothetical JR scenarios.

In the current review, most (9 of 10) studies reported the standard deviation (SD) between individual mean exposure variables which is a measure of the total between-subjects variability. However, SD does not reflect the true between-subjects variance, since it also includes within-subjects contributions. None of the studies isolated the true between-subject variance by separating the between- and within-subject components of variance (Jackson et al., 2009; Liv et al., 2011; Loomis & Kromhout, 2004; Mathiassen et al., 2003, 2010). Within-worker variation was considered in two of the 10 studies. Both studies assessed the mean percentile range, which has been shown to be associated with risk of bias and poor precision in shorter sampling periods (Mathiassen et al., 2012), as was the case in both studies. None of the included studies reported more conventional metrics of variability, for example, cycle-to-cycle variance (Möller et al., 2004; Srinivasan et al., 2015; Srinivasan & Mathiassen, 2012) or minute-to-minute variance of mean exposures (Liv et al., 2012; Richter et al., 2009).

The only evidence in the current review supporting JR as an organizational approach to increase variation within workers was the significantly higher within-minute SD in right upper arm elevation angles reported by Arvidsson et al. (2012) for JR workers in a meat processing plant. However, JR workers in that study performed an additional task compared with no-JR workers, which may have influenced the results. In any case, our consideration of the effects of JR on variation (both between- and within-workers) was limited by the lack of adequate data.

## 4.3. Health

The effect of JR on health (without consideration of variation) has been widely studied, and five reviews in the last decade have concluded the evidence was, at best, weak for an effect of JR (Comper et al., 2017; Leider et al., 2015; Luger et al., 2014; Mlekus & Maier, 2021; Santos et al., 2016). ‘Health’ in the reviews was considered as prevention of fatigue (Luger et al., 2014; Santos et al., 2016) and prevention of musculoskeletal disorders (Leider et al., 2015; Mlekus & Maier, 2021;
<figure><img src="image_36.png" /></figure>

Padula et al., 2017). However, since none of the reviews also considered the effects of JR on exposure variation, it is unclear whether negative findings regarding JR effects resulted from a poorly designed or poorly implemented JR (both of which can lead to a lack of change in variation). One review was found which considered the effects of JR on both variation and health outcomes (Rodriguez & Barrero, 2017). Similar to the current review, they concluded the evidence was “uncertain” as to whether increased variability resulting from JR was beneficial to the workers.

While we strongly advocate for the use of the average within-worker variance in assessing the effectiveness of JR, we also acknowledge that this metric alone will not capture all relevant aspects of a JR. Consideration of the spread of the within-worker variance and mean exposure values of the individual workers can help understand differential health impacts of a JR across individual workers (cf. Figure 1, Key point 3). Further, elucidating the relationship between JR and health (and understanding how to create an optimal JR for promoting health) will ultimately require consideration of both the types of tasks in the JR (i.e., the amplitude and variation of both physical and mental exposures) as well as how the tasks are combined (Mathiassen et al., 2014; Mixter et al., 2019; Rashedi & Nussbaum, 2016) (cf. Figure 1, JR#1 vs JR#2). Further, consideration of the way in which tasks are scheduled is also likely important to the physiologic and psychologic outcome of a JR. For example, task order (i.e., sequence of exposure; Dickhout et al., 2018; Yoon et al., 2016), and frequency of rotation between tasks (i.e., the temporal structure of shifts between tasks; Mixter et al., 2021) in a JR have been shown (mainly in laboratory or simulation studies) to impact health related outcomes. Neither of these aspects are captured in the assessment of variance between and within workers, nor were they considered by any of the studies included in the current review.

### 4.4. Limitations in study design of the included studies

The studies included in the present review had several common methodological issues in both job rotation design and study design which limited the extent to which the effectiveness of the JR could be assessed.

Regarding JR design, the studies generally lacked clear descriptions of the types and durations of tasks involved in both the JR and no-JR groups. Second,

none of the studies considered whether JR tasks were sufficiently diverse that a re-distribution across workers could lead to the desired change. A prerequisite for a successful JR (i.e., a JR leading to increased variation within workers) is that the tasks included in the JR must be sufficiently diverse and occur to a sufficient extent that rotation between tasks can lead to a change in the average within-worker exposure variation in the set of workers in the rotation (Mathiassen, 2006). The lack of consideration of task diversity was evident despite previous research suggesting insufficient task diversity accounted for the lack of exposure changes following JR in dentists (Finsen et al., 1998) and office workers (Fernström & Åborg, 1999). A third JR design issue present in most (6 of 10) studies was the introduction of new tasks in the JR. If new tasks are introduced, it is not possible to determine whether eventual changes in exposure are due to the new tasks or due to the rotation.

Regarding study design, in the four included studies which did not introduce new tasks (Balogh et al., 2016; Kuijer et al., 2004; Lohne et al., 2025; Vilhelmsen et al., 2025), the comparison no-JR group often lacked the complete set of tasks included in the JR. In the Kuijer et al. (2004) paper, comparison was made between the JR group and workers performing each of the component tasks, but not across the entire set of workers and tasks. Thus, similar to the studies in which new tasks were introduced in the JR, changes in exposure amplitude could not be uniquely attributed to the JR. In the Balogh et al. (2016) paper, no statistical comparisons could be made between the JR mixed work group exposures and the objectively assessed exposures for all the comprising tasks since the JR exposures were simulated from the exposures in the comprising tasks. A second study design issue present in several of the studies was that workers in the allegedly ‘no-JR’ group(s) also had some extent (likely lower) of JR in their work, and this may have minimized the differences between the JR and no-JR group. For example, in the no-JR group in the Comper et al. (2021) study, up to 25% of workers may have rotated as often as JR group workers, and up to 100% of workers may have rotated between two tasks. A third study design issue was that none of the included studies considered variation across the full time frame of the rotation schedule, which can span several days or even weeks, and thus did not capture the full extent of variation in the JR. Finally, and as discussed in detail above, the studies included in the review did not sufficiently assess or report metrics of exposure
<figure><img src="image_37.png" /></figure>

variation even though the purpose of the JR was to
change variation, either within or between workers.

4.5. Strengths and limitations of our methods

In the current review, we included publications in
English, Swedish, and French, but important publica-
tions may exist in other languages. The aim of the
current review deviated from the registered protocol
aim as we realized that the number of studies in
which within-worker variance had been assessed was
insufficient, and accordingly, we took a broader per-
spective and included studies which also assessed
exposure amplitude, and also considered evidence for
the impact of JR on health within the set of included
studies. The search was limited to three scientific
databases plus citation and reference list checks, and
thus it is again possible that relevant studies were
missed. In assessing risk of bias, we used (a revised
version of) the JBI tool and thus the quality of all
studies was judged based on the standards for epide-
miological studies and not observational case studies,
even if the latter may be the best design for assessing
the effects of JR (Burdorf, 2023). In rating our studies
according to epidemiological standards, we may have
underestimated the quality of the included studies.
We only assessed the evidence for the effect of JR on
physical exposures and not on psychosocial work envi-
ronment factors, even though physical and mental
factors are related.

Strengths of the current review include the com-
prehensive screening and consideration of exposure
variables in our data extraction. To avoid missing
studies which examined relevant work organization
strategies but did not explicitly identify as job rotation
studies, we relied on screening and not on search
terms to determine whether studies examined JR. We
extracted both variables that the study authors explic-
itly identified as being key to assessing the effects of
JR as well as variables that we deemed interesting
even if the variables were not included in the original
article's assessment of JR effectiveness (e.g., SD as a
metric of between-worker variability). We only
included studies that used observation or direct mea-
surement, excluding studies relying solely on
self-report. Finally, our JR definition required rotation
between productive tasks and did not permit the
insertion of nonproductive work or rest breaks, as
has been permitted in some prior reviews (e.g., Luger
et al., 2014; Rodriguez & Barrero, 2017). We believe
this more production-oriented JR definition will lead
to data being more attractive for future workplace
collaborations and JR studies.

4.6. Implications of the results for practice, policy and future research

Given the lack of current evidence (as discussed
above), we offer a list of recommendations to help
guide future studies in the hopes of ultimately gen-
erating sufficient data to adequately address the ques-
tion of whether JR is, indeed, an effective organizational
tool to increase physical exposure variation within
workers and subsequently positively impact health.
Further, we have also included recommendations to
guide JR design. While evaluating the design of the
JRs in the reviewed papers was not central to the aims
of the current review, while performing the review
we identified JR design limitations that impacted the
possibility to assess the effects of the JRs.

The recommendations were primarily based on the
results of the present review but also with consider-
ation of highly related knowledge accumulated in the
research team over the last decade, for example, in
the following intervention studies (Jackson et al., 2023,
2025; Lerche et al., 2024; Lohne et al., 2025). The
recommendations are intended to guide studies that
will assess the impact of JR on physical exposure and
health outcomes and were not created to guide studies
assessing other possible beneficial outcomes of JR, for
example, better worker motivation, team building, and/
or efficiency (Mlekus & Maier, 2021). In assessing the
effect of JR on exposure, (i.e., the primary objective
in this paper), we identify types of data that we deem
critical but that were largely missing in the included
studies, as well as identifying challenges we had in
attempting to synthesize data. While a full wish list
for future study design and exposure assessment might
typically include intervention randomization, we
acknowledge the intrinsic difficulties of randomized
controlled trials in workplace research (Schelvis et al.,
2015) and suggest theory-driven observational studies
can provide necessary insights into organizational-level
JR interventions. Finally, we acknowledge that it may
not be possible to include all the suggestions listed
below, however, incorporating any of the suggestions
will improve future study designs.

4.6.1. Recommendations for future JR study
design elements

* Evaluate the JR in a real occupational setting on real workers performing real work.

* If researchers are involved in the JR design: establish collaboration between researchers and key company stakeholders and use a participatory/co-creation approach.
<figure><img src="image_38.png" /></figure>

* Use a pre-post measurement design across the set of workers performing all tasks included in the JR.

* Clearly describe the JR intervention including specific tasks and task durations in the manuscript. The JR should only include tasks that are present in the reference or pre-JR group.

* Measure over the full extent of the JR schedule (e.g., multiple days or weeks may be required).

* Measure workers during both no-JR and JR work organization schedules to directly assess the impact of the job rotation on exposure variation rather than simulating the effects of a hypothetical job rotation

* Consider the company-wide impact of the JR and any possible side-effects on workers outside the JR.

* Evaluate the intervention using a theory-driven evaluation (e.g., logic model, framework, outcome hierarchy, realist evaluation, or Theory of Change) that considers at a minimum: (i) how the JR intervention is expected to lead to changes in the measured outcomes, and (ii) the extent to which the JR intervention was implemented as intended (ex. assessment of reach and adoption).

* If evaluating the effect of JR on health, have a follow-up period that is realistic with respect to the expected changes and assess health following existing guidelines for intervention studies addressing changes in health.

### 4.6.2. Recommendations for assessing exposure in future JR studies

* When selecting exposure metrics, consider whether previously reported metrics are relevant and can be used: contributions to the body of evidence for JR effectiveness that can be compared across studies are necessary.

* Use best practices for assessing exposures, including optimal measurement strategies (e.g., (Mathiassen et al., 2023, 2024; Trask et al., 2014). When possible, direct measurement of exposures is preferred, however observation and self-report may also be appropriate for certain exposures.

* Assess mean exposure and within-worker variance for each task of relevance to the JR, the latter for example by using a variance components analysis (Jackson et al., 2009; Liv et al., 2011; Loomis & Kromhout, 2004; Mathiassen et al., 2003, 2010).

* Assess within-worker variance across the combination of tasks included in the worker's full JR cycle, for example by using the within-worker variance algorithm proposed by Mathiassen (2006). Also consider the dispersion of the individual values of within-worker variance to avoid assigning excessively high or low levels of variance to individual workers (cf. Figure 1—key point 3).

* Compare within-worker variance estimates between JR strategies, for example, by using a Job Variance Ratio (JVR; Barbieri et al., 2015).

## 5. Conclusions

In conclusion, there is currently insufficient evidence available to assess the effectiveness of job rotation on changing objectively assessed physical exposure and, subsequently, health. The current review followed a historical tradition and considered the effects of JR on group mean exposure amplitude. However, given the mechanism of change by which JR is expected to impact health (i.e., through increased within-worker variation) and, given that the average mean amplitude of the group is not expected to change as a result of a JR, we argue that assessing exposure variation (in particular, within-worker variance) is necessary. We propose a set of recommendations that can guide future research studies to ultimately generate sufficient evidence to address the effectiveness of job rotation. These future data are necessary to help companies and organizations in make evidence-based decisions guiding future organizational initiatives to promote worker health and, thus, organizational resilience.

## Acknowledgements

Co-authors are part of the EU Green Alliance of European Universities.

## Author contributions

CRediT: **Jennie A. Jackson**: Conceptualization, Data curation, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing; **Svend Erik Mathiassen**: Conceptualization, Methodology, Writing – review & editing; **Alexis Descatha**: Conceptualization, Methodology, Writing – review & editing; **Marc Fadel**: Conceptualization, Data curation, Methodology, Software, Writing – review & editing.

## Conflict of Interest

The authors declare no conflicts of interest.
<figure><img src="image_39.png" /></figure>

ORCID

Jennie A. Jackson https://orcid.org/0000-0003-2939-0236

Svend Erik Mathiassen https://orcid.org/0000-0003-1443-6211

Alexis Descatha https://orcid.org/0000-0001-6028-3186

Marc Fadel https://orcid.org/0000-0002-1554-0021

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