Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators – A case–control study

Bijar Ghafouri; Bo Rolander; Björn Gerdle; Charlotte Wåhlin

doi:10.1515/sjpain-2023-0142

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Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators – A case–control study

Bijar Ghafouri , Bo Rolander , Björn Gerdle and Charlotte Wåhlin

Published/Copyright: August 28, 2024

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From the journal Scandinavian Journal of Pain Volume 24 Issue 1

Abstract

Objectives

The aim of this study was to investigate a panel of inflammatory biomarkers in plasma from forklift truck operators (FLTOs) and healthy controls, and their relation to neck pain characteristics.

Methods

From employees in a warehouse, 26 FLTOs were recruited and 24 healthy age- and sex-matched controls (CONs) were recruited via advertisement. The inclusion criterion for FLTOs was that they should operate reach decker and/or counterbalanced tilting mast forklift trucks. All participants were asked to answer a questionnaire covering demographic data, pain intensity numeric rating scale (NRS), anatomical spread, psychological distress, and health aspects. Pain sensitivity was measured using a pressure algometer. Blood samples were collected and analyzed for inflammatory proteins in plasma using a panel of 71 cytokines and chemokines. Multivariate data analysis including orthogonal partial least square-discriminant analysis (OPLS-DA) was performed to identify significant biomarkers.

Results

Thirty percent of FLTOs reported NRS > 3 in the neck. Shoulder pain was common in 26% of the FLTOs. Pain and discomfort that most often prevented completion of activities were in the neck (20%), lower back (32%), and hips (27%). The FLTOs reported significantly (p = 0.04) higher levels of anxiety than the CON group and they had significantly lower pressure pain thresholds in the trapezius muscle on both right (p < 0.001) and left sides (p = 0.003). A significant OPLS-DA model could discriminate FLTOs from CON based on nine inflammatory proteins where the expression levels of four proteins were upregulated and five proteins were downregulated in FLTOs compared to CONs. Twenty-nine proteins correlated multivariately with pain intensity.

Conclusions

The profile of self-reported health, pain intensity, sensitivity, and plasma biomarkers can discriminate FLTOs with pain from healthy subjects. A combination of both self-reported and objective biomarker measurements can be useful for better understanding the pathophysiological mechanisms underlying work-related neck and shoulder pain.

Keywords: forklift truck operators; neck/shoulder pain; ergonomics; biomarkers; blood

MSC 2010: Dnr:2013/418–31; Dnr:2016/477–32

1 Introduction

Neck pain is a prevalent problem in the general and working population causing reduced work ability and sick leave [1]. The prevalence of chronic neck and shoulder pain is high in subjects with high exposure to awkward working positions, repetitive movements, and movements with high precision [2,3,4]. Neck pain may be a feature of many disorders that occurs above the shoulder blades [5]. Although several muscles of the shoulder region are affected [6], chronic trapezius myalgia is a very frequent clinical diagnosis in subjects reporting chronic neck/shoulder pain [7]. Although several pathophysiological models have been proposed, the pathogenic processes underlying the translation of exposure to muscle pain risk factors have not been sufficiently elucidated. Patients with pain in the neck and shoulder region often report that their symptoms are aggravated by monotonous and heavy work tasks [8]. Recently, it has been reported that forklift truck operators (FLTOs) have an increased risk of neck pain [9]. They are exposed to repetitive arm work, unnatural neck positions, and extensive neck rotation when driving, loading, and unloading the forklift on shelves at different levels [10]. However, studies measuring neck pain characteristics especially objective markers among a working cohort such as FLTOs are lacking. It has been reported that muscle structure and changes in muscle biochemistry related to inflammatory processes in particular substances related to nociception and pain such as glutamate, serotonin, prostaglandin E2, bradykinin, and IL6 could be of importance [11,12,13]. In a field study during 8 h work, significantly increased levels of glutamate, an important signaling molecule in the development of pathological pain, have been reported in women with chronic work-related trapezius myalgia [14]. Inflammatory biomarkers associated with pain in neck and upper extremities have been reported previously [15,16,17], and in a review by Barbe et al. [18], it was suggested that inflammation may play a role in work-related musculoskeletal disorders. Due to the complexity of the pain system, no unidimensional reliable biomarker for pain has been identified to date. It is more reliable to identify clusters of interacting molecules that together provide an improved understanding of the activated molecular pathways in chronic pain that can be detected in blood. Multivariate data analysis (MVDA) considers the complex intercorrelations between the concentrations of different molecules and is designed to handle data sets with low subject-to-variable ratios and multiple intercorrelated variables [19].

The aim of this study was to analyze inflammatory biomarkers in plasma from FLTOs with and without reported neck and shoulder pain and healthy controls and their correlation with pain characteristics using advanced multivariate data analysis.

2 Materials and methods

2.1 Study design and procedure

This case–control study was performed in 2017 in a Swedish warehouse. FLTOs were recruited among the employees in the warehouse and healthy age- and sex-matched subjects were recruited via advertisement. A total number of 26 FLTOs and 24 controls (CONs) were included in this study. The inclusion criteria for FLTOs were that they should operate reach decker and/or counterbalanced tilting mast trucks. All participants answered a questionnaire covering demographic data, pain intensity, anatomical spreading, psychological distress, and health aspects. Measurement of pain sensitivity was performed, and blood samples were collected from all subjects.

All participants received verbal and written information about the study before informed written consent was obtained and the study was performed in accordance with the Declaration of Helsinki. The study was approved by the Regional Ethical Review Board at the University of Linköping, Sweden (Dnr: 2013/418-31, Dnr: 2016/477-32).

2.2 Self-reported measures

2.2.1 Numeric rating scale (NRS)

Pain intensity in the previous 7 days was reported using a NRS (0 = no pain and 10 = worst possible pain). The subjects were instructed to report an average pain rating over the last 7 days. The Nordic Musculoskeletal Questionnaire [20] was used to identify areas of the body causing musculoskeletal problems. Hence, subjects rated pain using a body map to indicate ten symptom sites: neck, shoulders, upper back, elbows, upper back, low back, wrist/hands, hips/thighs, knees, and ankles/feet and rated average pain during the last 7 days at each site. There were three FLTOs who did not answer questions regarding these ten symptom sites.

The FLTOs rated pain intensity in the neck and the shoulders before and after a working day. High pain was defined as NRS > 4.

2.2.2 Psychological distress

The Hospital Anxiety and Depression Scale (HADS) was used for measuring the psychological distress. The subscales HADS-depression and HADS-anxiety have seven items, scoring range between 0 and 21, in which a lower score indicates a lower possibility of anxiety or depression. HADS is frequently used in clinical practice and research and has good psychometric characteristics [21,22].

2.2.3 Quality of life

The quality-of-life scale (QOLS) is a 16-item instrument that measures the quality of life which includes material and physical well-being, relationships with other people, social, community and civic activities, personal development, and fulfillment [23]. The QOLS is scored by adding up the score on each item to yield a total score for the instrument. Scores can range from 16 to 112.

2.3 Measurements of pressure pain threshold (PPT)

A handheld manual electronic pressure algometer (Somedic, Hörby, Sweden) was used to assess the PPT in kilopascal (kPa) [24]. The pressure was applied at a rate of 30 kPa/s, with a 1-cm diameter probe. All participants were instructed to press a button when they felt the first sensation of pain, not merely pressure. The maximum pressure was set at 600 kPa, at which point the application of pressure ceased. The test sites were located at three points along the upper part of the trapezius muscle, bilaterally, and at three points over the belly of the tibialis anterior muscle, bilaterally. The tibialis anterior muscle was used as control muscle. Each location was tested three times, and the mean value was used.

2.4 Sample collection

Venous blood samples were collected (FLTOs = 25 and CONs = 24) in two 8 ml ethylenediaminetetraacetic acid tubes before the physical function tests and PPT measurements. One FLTO was not able to provide blood samples. The samples were centrifuged at 1,000 × g for 15 min and the separate layers of plasma from the two blood samples, approximately 5–6 ml in total, were collected into a 12 ml Falcon tube and mixed gently. The plasma was aliquoted in small portions and stored at −86°C until analysis.

2.5 Measurements of biomarkers

A commercially available panel of 71 pro- and anti-inflammatory proteins (cytokines, chemokines, and growth factors) (U-PLEX, Meso Scale Discovery, Maryland, USA) was used for biochemical analyses on plasma (see Table S1). The plasma samples were thawed and analyzed according to the manufacturer’s protocol. The light intensity of all the different proteins examined was converted into concentrations (pg/ml). Data were collected and analyzed using the MESO QUICKPLEX SQ 120 instrument equipped with DISCOVERY WORKBENCH data analysis software.

2.6 Statistical analysis

For the analysis of self-reported data, Mann–Whitney U test was used to compare the FLTOs and CONs, and data were presented as median value (min–max). The statistical analysis was performed using the statistical package IBM SPSS Statistics (version 24.0; IBM Corporation, Route 100 Somers, New York, USA).

MVDA was used to investigate the differences between the concentration of the biomarkers in FLTOs and CONs, and the clinical data using SIMCA-P+ version 17.0 (Sartorius, Umeå, Sweden). MVDA is a recommended method in biomarker research where the number of variables exceeds the number of observations. The procedure to compute multivariate correlation models has been described earlier [25] and was in accordance with the methodology presented by Wheelock [19]. Partial component analysis (PCA) including only biomarkers was performed as the first step to investigate outliers. PCA extracts and displays systematic variation in the data matrix. The presence of any statistically measured outliers was detected using a score plot in combination with Hotelling’s T ² (strong outliers) and distance to model in X-space (moderate outliers). Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to investigate the multivariate correlation between group membership and inflammatory proteins. The following parameters are reported in the evaluation of the OPLS-DA model. Variable influence on projection (VIP) refers to which biomarkers that are most important to group separation. p(corr) is the loading of each X-variable scaled as a correlation coefficient that is comparable between models. Biomarkers with VIP ≥ 1.0 (or VIP_pred if more than one component is identified) and |p(corr)| > 0.3 are considered to be significant. R ² describes the goodness of fit – the fraction of sum of squares of all the variables explained by a principal component and Q ² describes the goodness of prediction – the fraction of the total variation of the variables that can be predicted using principal component cross-validation methods. Q ² should not be greater than R ² and the differences should not be greater than 0.3. coefficient of variation-analysis of variation (CV-ANOVA), which is a SIMCA diagnostic tool for assessing model reliability, was also used and p value ≤0.05 was considered to be a statistically significant model. The correlation between significant biomarkers and pain intensity was analyzed where NRS = 0–3 was defined as low pain, NRS = 4–6 was defined as high pain, and NRS = 7–10 was defined as severe pain.

3 Results

3.1 Self-reported data

The pattern of average pain intensity during the last month in the head, neck, shoulder, arms, hands, back-upper, back-lower, hips, knee, and feet is presented for FLTOs (n = 23) and CONs (n = 24) in Figure 1. In FLTOs, 82% reported musculoskeletal symptoms in at least one body part during the past 7 days. The most prevalent sites for symptoms (NRS > 3) among the FLTOs were the neck (30%), shoulders (26.3%), and lower back (26.3%). Pain and discomfort which frequently limited various activities were in the neck (20%), lower back (31.6%), and hip (26.9%).

Figure 1

Individual pain intensity ratings according to NRS for the different anatomical sites in forklift truck operators (FLTOs) and in healthy controls (CONs).

As shown in Figure 2, the number of subjects who rated high pain (NRS > 4) in the neck and shoulder after a working day increased from five to nine afterward. There were significant differences in pain intensity between the FLTOs and CON groups (Table 1). The FLTOs reported significantly higher anxiety levels (p < 0.05) than the CON group (Table 1).

Figure 2

Pain intensity in the neck and the shoulders for the 7 last days, together with before and after a working day in FLTOs. Pain intensity ratings were dichotomized into two groups. The gray color represents pain intensity NRS 0–3 and the black color represents NRS 4–10.

Table 1

Self-reported data for forklift operators (FLTOs) and healthy controls (CONs)

Variables	FLTO (n = 23)	Healthy (n = 24)	p-value
Age	42 (21–67)	37 (23–56)	0.240
NRS – head	3 (0–10)	0 (0)	<0.001
NRS – neck	5 (0–10)	0 (0–1)	<0.001
NRS – shoulder	6 (0–10)	0 (0–5)	<0.001
NRS – arms	5 (0–10)	0 (0)	<0.001
NRS – hands	3.5 (0–10)	0 (0–3)	<0.001
NRS – upper back	4 (0–10)	0 (0–8)	<0.001
NRS – low back	6 (0–10)	0 (0–7)	<0.001
NRS – hips	2 (0–10)	0 (0)	<0.001
NRS – knee	3 (0–10)	0 (0–2)	<0.001
NRS – feet	3 (0–10)	0 (0–1)	<0.001
HAD – depression	2 (0–18)	2 (0–10)	0.921
HAD – anxiety	4 (0–20)	2 (0–8)	0.041
PCS	7 (0–44)	7 (0–15)	0.923

Abbreviations: NRS, pain intensity measured using a numeric rating scale; PCS, pain catastrophizing.

Values are presented in median (minimum-maximum). The pain rating (NRS) is average pain over the last 7 days at each site.

The statistically significant p-values are marked in bold.

3.2 Pain sensitivity

The mean value for PPTs was significantly lower in the trapezius muscle (both right [p < 0.001] and left [p = 0.003] sides) in the FLTOs compared to CONs: trapezius right: FLTOs = 352 ± 142 kPa vs CON = 531 ± 107; trapezius left: FLTOs = 401 ± 148 kPa vs CON = 525 ± 115 kPa. There were significantly (p = 0.03) lower PPT in the tibialis muscle left (460 ± 157 kPa) in FLTOs compared to CON (567 ± 74 kPa). In the right tibialis muscle, the FLTOs had lower PPT (483 ± 179 kPa) than CON (564 ± 68 kPa) but were not statistically significant (p = 0.60).

3.3 Biochemical analysis

Multivariate data analysis was performed to identify differences in the levels of inflammatory proteins in plasma from FLTOs and CONs. A significant OPLS-DA model (two components, R ² = 0.83, Q ² = 0.45, CV-ANOVA p = 5.75 × 10⁻⁵) could discriminate FLTOs from CONs based on nine inflammatory proteins (Figure 3 and Table 2). Levels of M-CSF, IL-16, MIP-1α, and IL-17A/F were higher in FLTOs and IL-9, MIP1B/CCL4, IL-17A, and Eotaxin were lower compared to CON (Figure 4).

Figure 3

Score plot of the OPLS-DA model (two components, R ² = 0.83, Q ² = 0.45 CV-ANOVA p = 5.75 × 10⁻⁵) discriminating FLTOs from CON based on inflammatory proteins; the most important proteins are shown in Table 2. The colors refer to reported pain intensity (NRS) in the neck from 0 (blue) to 10 (red) as shown in the color gradient staple on the right.

Table 2

Most important proteins (VIP_pred > 1.0) for the discrimination between FLTOs and health controls (CON)

Proteins	VIP_pred	p(corr)	p-value
M-CSF	2.51	0.59	<0.001
IL-16	2.46	0.58	<0.001
MIP1α/CCL3	1.81	0.43	0.020
IL-9	1.78	−0.42	0.034
MIP1B/CCL4	1.70	−0.40	0.058
IL-17A/F	1.68	0.40	0.010
IL-17A	1.57	−0.37	0.119
Eotaxin	1.46	−0.35	0.479
Eotaxin-3	1.42	0.34	0.001

The sign of p(corr) represents increased () and decreased (−) levels of the proteins in FLTOs. Thus, the levels of M-CSF, IL-16, MIP-1α, and IL-17A/F were higher in FLTOs compared to CON. The p-values in the right column represent statistical significance (p < 0.05) in the concentrations of the biomarkers between FLTOs and CONs using univariate nonparametric data analysis Mann–Whitney.

Figure 4

Concentrations of most important proteins (i.e., variable of importance [VIP] > 1) in forklift operators (FLTOs) and in healthy controls (CONs).

3.4 Multivariate correlation analysis

A significant OPLS (two components, R ² = 0.89, Q ² = 0.56, CV-ANOVA p = 0.005) was achieved when investigating pain intensity of the shoulders as a dependent variable (Y variable) and all inflammatory proteins as X variables (Figure 5). The size of the circles refers to NRS in the shoulder and the color refers to NRS in the neck. The bigger circle indicates higher pain in the shoulder and the red color indicates severe pain in the neck. Subjects with high pain in the shoulder and neck are clustering on the right side of the ellipse. There were 27 proteins (VIP_pred > 1.0, |p(corr)| > 0.4) that multivariately correlated to pain intensity (NRS) and they are shown in Table 3. The mean concentration of the sex most important proteins (VIP_pred > 1.50) in subjects with low pain (NRS 0–3), moderate pain (NRS 4–6), and severe pain (NRS 7–10) are shown in Figure 6 indicating a low concentration of inflammatory biomarkers in FLTOs with low pain.

Figure 5

OPLS (orthogonal partial least) model showing multivariate correlation of pain in the shoulder (NRS-shoulders as y-variable) to inflammatory biomarkers in FLTOs. The size of the circles refers to NRS in the shoulder and the color refers to NRS in the neck. The bigger circle indicates higher pain in the shoulder and the red color indicates severe pain in the neck. Subjects with high pain in the shoulder and neck are clustering on the right side of the ellipse.

Table 3

Most important proteins (VIP_pred > 1.0 and p(corr) > 0.40) that multivariately correlated with pain intensity in the forklift operators

Proteins	VIP_pred	p(corr)
IL-17C	1.93	0.73
MCP-3	1.72	0.65
IL-22	1.69	0.64
IL-23	1.63	0.61
IL-17B	1.55	0.58
IL-9	1.51	0.57
VEGF-A	1.50	0.56
IL-21	1.47	−0.55
IL-33	1.46	0.55
IL-1β	1.42	0.53
IL-12p70	1.41	0.53
IL-17F	1.40	0.53
IL-13	1.39	0.52
I-309	1.35	0.51
IL-29/IFN-L1	1.32	−0.49
IL-3	1.31	0.49
IL-17A/F	1.30	0.49
IL-8	1.29	0.49
TRAIL	1.26	0.47
FLT3L	1.25	−0.47
IL-7	1.25	0.47
IL-12/IL-23p40	1.23	−0.46
IFN-β	1.19	0.45
IL-17D	1.13	0.43
IL-17E/IL-25	1.11	0.42
IL-2	1.10	0.41
IL-31	1.07	0.40

The sign of p(corr) represents (−) negative and () positive association of proteins to pain intensity.

Figure 6

The distribution of the most important proteins correlated with pain intensity in shoulders in FLTOs. The FLTOs are grouped according to their pain intensity, i.e., low pain (NRS 0–3), moderate pain (NRS 4–6), and severe pain (NRS 7–10). NRS, numeric rating scale.

4 Discussion

This study identified a group of inflammatory biomarkers in plasma that were altered in FLTOs with reported neck pain compared to CONs. Further, we found concentrations of several of the biomarkers that multivariately correlated to pain in shoulders.

In FLTOs, 82% reported musculoskeletal symptoms in at least one body part during the past 7 days. FLTOs reported problems mainly in the neck, shoulders, and lower back. These results indicate that FTLOs exhibit signs of chronic neck pain and are in agreement with the previous study by our research group where the FLTO cohort was studied using questionnaires and inclinometer with video recording [10]. The load of the work differed depending on the type of forklift truck that was used. There were repeated working positions with longer moments with a strongly rotated head or strong extension of the neck. In addition, there were longer periods during which there were no opportunities for support to relieve the arms. In this study, we found significantly higher pain sensitivity (low PPT) in trapezius muscle in FLTOs compared to CONs; this might be a consequence of the load of the work on the neck and arms. Reduced PPT in occupationally active subjects with symptoms of upper extremity musculoskeletal disorders [26] has been reported to be a relevant measurement in working populations suffering from musculoskeletal disorders [27,28,29,30]. Unfavorable working posture and excessive repetition are known physical factors that can lead to work-related musculoskeletal disorders (MSDs) [31]; therefore, future intervention studies aimed to decrease the risk of MSDs among FLTOs are warranted.

The need for objective biomarkers for the improvement of diagnostic tools and understanding mechanisms behind work-related neck and shoulder pain is desirable [13]. Genetic variants associated with neck and shoulder pain have been reported recently from the UK biobank cohort [32]. The role of inflammation in developing pain in subjects with work-related MSD has been highlighted [18]. As a result, this study measured a panel of 71 inflammatory proteins in plasma to capture the biological mechanism ongoing in FLTOs with neck and shoulder pain. We found that the concentrations of M-CSF, IL-16, MIP-1α/CCL3, IL-17A/F, and Eotaxin-3/CCL26 were increased in plasma from FLTOs compared to healthy subjects. M-CSF (macrophage colony-stimulating factor 1) is a cytokine that promotes the release of proinflammatory chemokines such as MIP-1α/CCL3 and Eotaxin-3/CCL26, and thereby, those proteins together play an important role in innate immunity and in inflammatory processes. IL-16 is a lymphocyte chemoattractant protein with a broad spectrum of both pro- and anti-inflammatory biological activities. Increased levels of IL-16 have been reported in patients with rheumatoid arthritis (RA) showing a potential role as a mediator of the inflammatory process [33]. IL-17A/F belongs to the IL-17 family (A–F), which is involved in driving inflammatory response [34]. IL-17 can directly induce chronic pain by binding to the receptor IL-17R or indirectly induce chronic pain by regulating infiltrating immune cells and pain mediator production [35]. It has been reported that IL-17A acts as a pain mediator in inflammation-evoked mechanical hyperalgesia [36]. Increased levels of IL-17A have been reported in patients with RA and animal models of neuropathic, low back, and cancer pain [37,38,39,40]. In patients with lumbar disc herniation, an increased level of IL-17 was positively correlated with pain intensity preoperatively [41]. Interestingly in this study, the levels of two other family members, IL-17C and IL-17B, showed a positive correlation with reported pain intensity where FTLOs who reported pain 7–10 had increased concentrations of IL-17C and IL-17B. The role of the immune system in chronic pain has been highlighted previously [42]. Elevated levels of MIP-1α/CCL3 in subjects with chronic neck pain have been reported [17] suggesting that a systemic inflammatory response is present in subjects with chronic neck pain. The presence of widespread inflammation in work-related MSDs has been reported by measuring pro-inflammatory cytokines in a rat model [43].

The concentrations of several of the inflammatory proteins correlated with self-reported pain intensity in FLTOs where a trend from low to high concentration mirrored the reported pain intensity from low to severe pain. Recently, Dong et al. reported that musculoskeletal pain among workers with posture load was associated with an increase in inflammatory cytokines [44]. Elevated levels of several chemokines have been reported in patients with whiplash injuries suggesting a local tissue injury can be detected as a systemic up-regulation of chemokine [45]. The FLTOs showed significantly higher pain sensitivity and had increased levels of inflammatory biomarkers. All these findings are consistent and support the previously reported role of inflammatory cytokines and chemokines in the modulation and regulation of chronic neck pain and work-related MSDs [46,47,48]. The increased concentrations of the inflammatory markers may induce sensitization of the peripheral nociceptors [49] that cause increased pain sensitivity and thereby the subjects report higher pain intensity. The aim of the inflammatory process is to remove the initiating stimulus that causes the injury in the tissue, but when the body is affected by the unfavorable posture, awkward work positions, and prolonged static force as for the FLTOs, and is exposed to repeated bouts of damage, it may affect and hinder the healing process and subjects develop chronic work-related muscle pain because of the presence of a continuously low-grade inflammation [50]. In a rat model, Barr et al. [43] showed that a localized injury caused by exposure to a highly repetitive forelimb-intensive task induces a systemic inflammatory response. This finding in the animal study can be translated to humans showing that the FLTOs’ exposure to awkward work positions has caused tissue injury that can be measured as inflammation in the blood.

5 Conclusion

The profile of self-reported health, pain intensity, sensitivity, and inflammatory proteins in the blood can discriminate FLTOs with pain from CONs. This study contributes to the literature by providing the first report on the use of self-reported physiological measures and objective measurements of inflammatory biomarkers to understand the pathophysiological mechanisms underlying work-related neck and shoulder pain in the working population.

† Deceased.

# Previous presentation of study data at scientific meetings SASP, Rigshospitalet, Copenhagen, 14-10-2022, Werner, Mads U. “Abstract for the special issue” Scandinavian Journal of Pain, vol. 22, no. 4, 2022, pp. 639–663. https://doi.org/10.1515/sjpain-2022-2002.

tel: +46-13282664

Research ethics: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as amended in 2013) and has been approved by the authors’ Institutional Review Board of the University of Linköping, Sweden (Dnr: 2013/418-31; Dnr: 2016/477-32).
Informed consent: Informed consent has been obtained from all individuals included in this study.
Author contributions: All authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas, took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Competing interests: Authors state no conflict of interest.
Research funding: This study was funded by the Medical Research Council of southeastern Sweden (FORSS) and ALF Grants, Region Ostergotland.
Data availability: The datasets generated and/or analysed in this study are not publicly available as the Ethical Review Board has not approved the public availability of these data.
Supplementary Material: This article contains supplementary material (followed by the link to the article online).
Artificial intelligence/Machine learning tools: Not applicable.

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Received: 2023-12-05

Revised: 2024-08-01

Accepted: 2024-08-09

Published Online: 2024-08-28

This work is licensed under the Creative Commons Attribution 4.0 International License.

Supplementary material

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https://doi.org/10.1515/sjpain-2023-0142

Keywords for this article

forklift truck operators; neck/shoulder pain; ergonomics; biomarkers; blood

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BY 4.0

Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators – A case–control study

Article

Abstract

Objectives

Methods

Results

Conclusions

1 Introduction

2 Materials and methods

2.1 Study design and procedure

2.2 Self-reported measures

2.2.1 Numeric rating scale (NRS)

2.2.2 Psychological distress

2.2.3 Quality of life

2.3 Measurements of pressure pain threshold (PPT)

2.4 Sample collection

2.5 Measurements of biomarkers

2.6 Statistical analysis

3 Results

3.1 Self-reported data

3.2 Pain sensitivity

3.3 Biochemical analysis

3.4 Multivariate correlation analysis

4 Discussion

5 Conclusion

References

Supplementary Material

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