related musculoskeletal hand pain after work

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Nursing Open. 2023;00:1–7. wileyonlinelibrary.com/journal/nop2

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1 | INTRODUCTION

The work environment at haemodialysis centres is demanding, with high workloads, both physically and mentally, for nurses. The na- ture of haemodialysis, characterized by high dependency (Thomas- Hawkins et al., 2008), has changed over time, but the overall duration of nursing care needed per work shift has not. In recent years, more patients have started performing self- care activities, thereby reducing nurses' workloads, but at the same time, the proportion of older

people with dialysis needs and high care dependency has increased steadily (de Kleijn et al., 2020). The pressure on dialysis centres is expected to rise even further, as the worldwide need for dialysis treatment is steadily increasing (Thurlow et al., 2021). The psychological work environment for haemodialysis nurses is recognized as being stressful and intense (Hayes & Bonner, 2010) and a plethora of studies have demonstrated medium to high levels of burnout (e.g., Hayes et al., 2015; Ling et al., 2020; Moisoglou et al., 2020; Topbaş et al., 2019; Trbojević- Stanković et al., 2015). The patients' need for R E S E A R C H A R T I C L E

Haemodialysis nurses' occupational health and work- related musculoskeletal hand pain after work: A cluster analysis

Eva Westergren | Magnus Lindberg

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

No patient or public contribution Faculty of Health and Occupational Studies Department of Caring Sciences, University of Gävle, Gävle, Sweden Correspondence

Magnus Lindberg, Department of Caring Sciences, University of Gävle.

Kungsbäcksvägen, Gävle, Sweden.

Email: magnus.lindberg@hig.se Funding information

AFA Försäkring, Grant/Award Number:

170075

Abstract

Aim: To identify clusters based on haemodialysis nurses' self- rated work ability, work engagement and self- reported work hours and to compare the identified clusters regarding hand pain after work.

Design: Cross- sectional survey.

Methods: Data based on the Work Ability Index, Utrecht Work Engagement Scale and hand pain severity after work were collected through a web- based survey among 503 haemodialysis nurses working in Sweden and Denmark. A two- step cluster analysis was used to identify homogenous groups of cases within the dataset, followed by comparative analyses of the clusters.

Results: Four distinct clusters were identified, illustrating differing profiles of haemodialysis nurses' work ability, work engagement and working hours. Nurses who worked part- time and reported moderate work ability and average work engagement had significantly higher ratings of hand pain after work.

Conclusions: Haemodialysis nurses are a heterogeneous group as regards work ability, work engagement and self- reported work hours. The four distinct clusters of nurses indicate a need for customized interventions for retaining each subgroup at work.

K E Y W O R D S

haemodialysis nurses, musculoskeletal pain, nursing, work ability, work capacity evaluation, work engagement

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life- long treatment means that haemodialysis nurses often provide care to the same patients for a lengthy period. Regular and frequent interactions with the same patient are ideal for nursing care, but are also associated with high levels of work- related stress (Cousins et al., 2020;Karkar et al., 2015; Vioulac et al., 2016). The main stress- ors are time management, treatment complications/emergencies and technical problems (Karkar et al., 2015; Vioulac et al., 2016), but coping with patient deaths is also associated with high levels of stress (Lee & King, 2014). Further, the materials needed for providing treatments to patients are assumed to contribute to the high prevalence of musculoskeletal complaints among haemodialysis nurses (Westergren & Lindberg, 2022; Westergren et al., 2020). To protect their wellbeing, haemodialysis nurses develop several coping strategies (Cousins et al., 2020).

2 | BACKGROUND

The impact of work- related factors on musculoskeletal complaints in haemodialysis nurses is not yet well understood. In one study, every second haemodialysis nurse reported having hand complaints and about one in ten reported having been absent from work within the preceding 12 months due to hand pain (Westergren et al., 2021). This differs from the prevalence of similar hand pain among other nurses (Heidari et al., 2018; Occhionero et al., 2014), physical therapists (Ezzatvar et al., 2020) and other workers who perform repetitive tasks with hands and arms (Coggon et al., 2019). Musculoskeletal pain is a significant cause of disability and early retirement (Brooks, 2006) and a considerable threat to work ability (Miranda et al., 2010). However, many people continue to work despite musculoskeletal pain, causing their self- rated work ability to decrease (Cochrane et al., 2018; Miranda et al., 2010). Haemodialysis nurses appear to continue to work even when they have musculoskeletal hand pain (Westergren et al., 2021), although increased pain intensity seems to be associated with working fewer hours per week in the general population (Gerdle et al., 2004). Despite the high prevalence of hand pain among haemodialysis nurses, we have not been able to find any study evaluating its association with or impact on work capacity.

Work engagement is an established construct to describe positive states related to work and is considered to be an indicator of a healthy workplace. The construct of work engagement has been defined as ‘a positive, fulfilling, work- related state of mind characterized by vigour, dedication and absorption’ (Schaufeli et al., 2002, p. 74). Keyko et al. (2016) have, in a comprehensive systematic review, identified 77 factors influencing nurses' work engagement, which they sorted into six categories. These were: (1) organizational climate (leadership and structural empowerment), (2) job resources (e.g., interpersonal and social relations and organization of work and tasks), (3) professional resources (e.g., professional practice environment and autonomy), (4) personal resources (e.g., psychological resources and skills), (5) job demands (e.g., work pressure, and physical,

mental and emotional demands) and (6) demographic variables (e.g., age and gender). In Europe, employees in health and social care and education generally report more work engagement than workers in other sectors (Hakanen et al., 2019). Work engagement is a popu- lar construct for describing positive states related to work, but few studies have explored work engagement in the high- intensity workplace of haemodialysis nurses. However, two recent studies from China reported average levels of work engagement and that male haemodialysis nurses had slightly higher engagement scores than female nurses (Cao & Chen, 2019, 2021). Work engagement has been put forward as a predictor of maintained work ability among people with chronic pain (Karoly et al., 2013). Therefore, knowledge about the interrelationship between the frequently occurring musculoskeletal hand complaints, work wellbeing and work ability among haemodialysis nurses may increase understanding of how to retain nurses at work. Hypothetically, we imagine that there may be subgroups among the employed nurses based on previously mentioned variables and therefore designed this explorative study. The aim of this study was to identify clusters based on haemodialysis nurses' self- rated work ability, work engagement and self- reported work hours and to compare the identified clusters regarding hand pain after work. The research questions were:

• Are there identifiable subgroups of haemodialysis nurses based on self- rated work ability, work engagement and self- reported work hours? If, so how many?

• Are there differences in hand pain severity after work between the identified subgroups?

• Are there differences in demographic variables between the identified subgroups?

3 | METHODS

3.1 | Design

This was a cross- sectional study.

3.2 | Sampling and participants

We did not perform any a priori power analysis to calculate required sample size because traditional intuitions about statistical power only partially apply to cluster analysis. Dalmaijer et al. (2022) recommend aiming for a minimum sample of 20– 30 per expected subgroup. A con- venience sampling procedure was used. Nurses were recruited from 47 of 97 haemodialysis centres in Sweden and Denmark. The only inclusion criterion used was that the nurses were on duty at the time of data collection. These centres employed a total of 1023 nurses, of whom 545 (53.3%) agreed to participate. Participation was voluntary.

Most of the participating nurses (94.1%) were female and their mean age was 46.8 years (95% confidence interval (CI) 45.9– 47.7).

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3.3 | Procedures and measures

All data were collected using a web- based survey. An offer to participate in the study was sent to the nurses' work e-mail addresses.

To measure haemodialysis nurses' self- rated work engagement, the validated Utrecht Work Engagement Scale 9- item version (UWES- 9) (Schaufeli et al., 2006) was used. The UWES- 9 has been widely adopted in various occupational sectors, including nursing (Tomietto et al., 2019). The UWES- 9 has long been considered a valid and reliable indicator of work engagement. For instance, internal consistencies (Cronbach's alpha) for the total nine- item scale typically range between 0.85 and 0.92 (median = 0.92) across samples from 10 countries (Schaufeli et al., 2006). The items of the UWES- 9 are scored on a 7- point Likert scale from 0 to 6, with 0 representing Never and 6 representing Always. The higher each item is rated, the higher the overall work engagement (Schaufeli et al., 2006). The UWES- 9 was originally designed with three subscales, measuring Vigour, Dedication and Absorption, re- spectively. However, doubts have recently been raised about the factorial validity of this structure (Kulikowski, 2017). A one- factor structure seems to fit the data best (Willmer et al., 2019). Based on norm scores, the UWES- 9 total score can be transformed into five categories of work engagement: Very low, Low, Average, High or Very high.

The Work Ability Index (WAI) has seven components and de- scribes how an employee perceives their work ability by assessing both the mental and physical demands of the workplace and the employee's health condition. The WAI score ranges from 7 to 49 points, with higher scores indicating higher work ability. Work ability is excellent at 44– 49 points, good at 37– 43 points, moderate at 28– 36 points and poor at 7– 27 points. The WAI has been described to have good predictive validity (Lundin et al., 2017), satisfactory internal validity (Nygard et al., 1991) and satisfactory test– retest reliability (De Zwart et al., 2002).

Hand pain intensity at the end of a work day was measured using the Borg CR- 10 scale, which is a 12- point numeric rating scale ranging from 0 (no pain/discomfort), 0.5 (extremely weak) then in integer values to 10 (strong/almost maximal pain). The Borg CR- 10 scale was developed to measure pain, is widely used, and validated (Borg, 1998).

3.4 | Analysis

All descriptive and inferential statistics were calculated in IBM SPSS Statistics for Windows (Version 27.0. Armonk, NY: IBM Corp). A probability level of p < 0.05 (two- tailed) was accepted as statistically significant. Data were initially screened for missing values. Cases with missing data for either the UWES- 9 (n = 2 (0.004%)), the WAI (n = 0) or self- reported working hours per week (n = 40 (0.07%)) were excluded from further analyses. Because of the limited num- bers, we did not apply any imputation method to handle missing data. The final sample resulted in 503 valid responses.

The two- step cluster analysis procedure with a log- likelihood distance measure was then used to identify homogenous groups of cases (clusters) within the dataset. The clustering procedure was based on self- rated work ability, work engagement and self- reported working hours. The two- step approach first pre- clusters cases into small subclasses and then forms final clusters using hierarchical methods. In other words, the method maximizes differences between groups of cases and minimizes differences within groups for the clustering variables. Thus, the two- step method decreases the weaknesses associated with a single clustering approach. It was cho- sen to maximize flexibility in determining the appropriate number of clusters. The silhouette measure of cohesion and separation indi- cated a fair cluster solution. Repeated clustering (three repeats) with cases sorted in a random order demonstrated clear cluster stability.

Comparative analyses (chi- squared tests, one- way analyses of variance or independent samples Kruskal- Wallis tests) were used to validate the cluster solution and to test statistical differences between clusters regarding sample characteristics and levels of hand pain reported. Bonferroni correction for multiple tests was applied.

3.5 | Ethics

The study has been approved by the Regional Ethical Review Board in Sweden. All nurses employed at the involved units received an information letter about the aim of the study, its procedures and pri- vacy policy. All participants participated voluntarily and consent was given by checking a specific box in the web- based survey.

4 | RESULTS

The two- step cluster analysis sorted the nurses into four distinct clusters (Table 1). The nurses in cluster 1 (n = 124) typically worked a little more than full- time, perceived themselves as having excellent work ability and scored high work engagement. Their median hand pain rating after work (0.5; interquartile range (IQR) 4) was below the total sample median (Figure 1).

The nurses in cluster 2 (n = 139) typically had reduced working hours (on average 84% of full- time), rated themselves as having good work ability and scored high work engagement. Their hand pain after work was rated as equal to the total sample (median = 2; IQR = 3).

The nurses in cluster 3 (n = 169) worked full- time and rated their work ability as good, but their work engagement was average. Their hand pain after work was also rated as equal to the total sample (median = 2; IQR = 3).

The nurses in cluster 4 (n = 71) typically worked part- time, perceived themselves as having moderate work ability and scored average work engagement. These nurses rated the highest hand pain after work (median = 3; IQR = 5). There was a statistically significant difference (independent sample Kruskal- Wallis test, H = 17.462 (df 3), p = 0.001) in hand pain after work in cluster 4 compared with clusters 1– 3 (Bonferroni corrected for multiple tests p = 0.001– 0.013).

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There were no differences between the clusters in how long the participants had been working as nurses or in their occupational ten- ure within haemodialysis settings. However, there were differences in gender distribution, with few male nurses in clusters 2 and 4. The nurses in cluster 4 were significantly older than nurses in the other clusters (Table 1).

5 | DISCUSSION

This study aimed to identify clusters based on haemodialysis nurses' self- rated work ability, work engagement and self- reported work hours and to compare the identified clusters regarding hand pain after work. One result was the identification of four subgroups of haemodialysis nurses with differing work capacity profiles. The first subgroup included nurses working more than full- time, with excellent work ability and high work engagement. At the group level, these nurses seemed to be nearly free from musculoskeletal hand complaints, they have a high work capacity and have not yet developed work- related musculoskeletal hand complaints. This is, in a way, an expected finding because there are indications that every second haemodialysis nurse experience such complaints. However, since the prevalence of work- related musculoskeletal hand complaints is high among haemodialysis nurses (Westergren et al., 2021), management should devise preventive strategies that protect this subgroup from developing musculoskeletal hand complaints. One strategy that would reduce the strain on nurses' hands is to increase the number of patients who take active part in the lining and priming of dialysis machines before treatment. Patients are getting more involved (de Kleijn et al., 2020) and for each patient who prepares their own machine, the number of hand twists/turns that a nurse needs to make during machine preparation will decrease. The number of hand twists/turns saved depends on the machine type used (Westergren et al., 2020). Still, every twist counts, as work tasks performed more than ten times a day increase the risk of developing work- related musculoskeletal problems (Serranheira et al., 2015).

The second subgroup (cluster 2) of haemodialysis nurses had quite similar ratings on work ability and work engagement as cluster 1, but typically had fewer working hours per week and almost all were female. We can only speculate as to why these nurses worked part- time, since we did not request such information in the survey.

Part- time work has long been common among working women in the Nordic countries, although the proportion of part- time work has a declining trend (Lanninger Wennemo & Sundström, 2014).

One far- fetched explanation but still clearly connected to previous research (Gerdle et al., 2004) could be that these nurses actively reduced their working hours because they, at the group level, experi- enced mild musculoskeletal hand pain that could affect functioning.

However, the reduced working hours can also be explained by many other factors, which makes it difficult to give any recommendations to management. Individually tailored preventive measures might be needed to sustain nurses' work capacity. One of these measures should focus on retaining the levels of work engagement (Keyko TABLE 1 Sample characteristics and clustering variables overall and by cluster, n= 503 Demographic variablesTotal sample,n = 503Cluster 1, n= 124Cluster 2, n= 139Cluster 3, n = 169Cluster 4, n= 71

Test value p valueDf= 3 Gender, female (%)95.291.999.392.898.6X2 = 11.7760.008 Age, years, mean (SD), 95% CI46.8 (10.7), 45.9– 47.846.2 (10.3), 44.4– 48.145.4 (10.0), 43.7– 47.146.8 (11.0), 45.1– 48.450.7 (11.9), 47.8– 53.6F = 3.8520.010 Nursing experience, years mean (SD), 95% CI19.6 (10.8), 18.6– 20.518.9 (10.5), 17.0– 20.819.3 (10.6), 17.5– 21.719.0 (11.0), 17.3– 20.722.4 (11.1), 19.7– 25.1F = 1.8570.136 Haemodialysis experience, years mean (SD), 95% CI11.5 (9.2), 10.7– 12.410.9 (9.2), 9.2– 12.512.4 (9.2), 10.8– 13.910.8 (9.2), 9.2– 12.212.8 (9.7), 10.4– 15.2F = 1.3400.261 Clustering variables Working hours per week, mean (SD), 95% CI34.6 (4.1), 34.2– 35.037.6 (1.1), 37.4– 37.831.3 (2.1), 30.1– 31.737.2 (1.5), 37.0– 37.429.3 (4.7), 28.2– 30.5F = 353.456<0.001a Work ability index (WAI), mean (SD), 95% CI41.2 (5.3), 40.7– 41.645.4 (2.4), 45.0– 45.943.0 (3.3), 42.5– 43.639.9 (3.7), 39.3– 40.432.8 (4.9), 31.6– 34.0F = 203.157<0.001a Work engagement (UWES- 9), mean (SD), 95% CI4.4 (0.9), 4.4– 4.55.2 (0.4), 5.1– 5.24.8 (0.6), 4.7– 4.83.9 (0.8), 3.8– 4.03.7 (1.1), 3.5– 4.0F = 98.852<0.001a Abbreviations: CI, confidence interval; df, degrees of freedom; SD, standard deviation; UWES- 9, Utrecht Work Engagement Scale 9- item version; WAI, Work Ability Index. aA significant difference between clusters on the clustering variables is expected and validates the cluster solution.

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et al., 2016) so that the nurses stay healthy and flourish in their workplace.

In the third subgroup, the haemodialysis nurses worked full- time and had good work ability, but had lower work engagement than the other subgroups. Since haemodialysis nurses who rate lower levels of work engagement often rate the likelihood of leaving their job as greater (Cao & Chen, 2021), the organization and the responsible nurse manager should do their best to enhance these nurses' work engagement to retain them at work. Many initiatives to promote work engagement among haemodialysis nurses are possible. Keyko et al. (2016) state that influencing factors are present at several levels, from the broader organizational climate to the specific job and professional and personal resources. For instance, they state that first- line managers who directly interact with nurses are in a pivotal position to influence operational resources, which can positively affect nurses' work engagement (Keyko et al., 2016). In this context, it is particularly significant that managers focus on nurses who remain at work despite chronic pain as these individuals often have an adap- tive pattern that could contribute to premature job turnover (Karoly et al., 2013).

The fourth subgroup consisted mostly of female haemodialysis nurses with relatively few working hours per week, who had moderate work ability and average work engagement. These nurses were also significantly older, but with many years left to retirement, and had significantly higher hand pain ratings at the end of a work day than nurses in the other subgroups. As exposure to bor- ing tasks, job stress, insufficient support and time pressure are associated with the prevalence of musculoskeletal hand complaints among nurses (Zare et al., 2021), promoting positive states related to work is as at least as important as reducing strain on joints causing pain. It seems that management needs to take a holistic approach in proactive measures, to prevent further development of musculoskeletal hand pain in this group of nurses. Otherwise, there is a risk that these nurses will develop disabilities causing early retirement (Brooks, 2006) or that they leave their profession prematurely (Cao & Chen, 2021). The fact that these nurses might

have reduced their working hours due to hand pain intensity is a warning sign that should be considered; working fewer hours is a strategy used in the general population when experiencing current or chronic pain (Gerdle et al., 2004).

To meet the future need for dialysis treatment (Thurlow et al., 2021) and assure high- quality patient care, the nursing work- force in haemodialysis centres must be maintained. Our study identified a vulnerable subgroup of haemodialysis nurses that ex- hibits reduced work capacity and prevalent hand complaints, factors known to be related to absenteeism from work and early retirement (Brooks, 2006; Cao & Chen, 2021; Cochrane et al., 2018). There are indications that work- related factors contribute to haemodialysis nurses' musculoskeletal hand complaints [Redacted] and future research needs to explore the root causes of work- related hand pain among these nurses.

5.1 | Limitations

Due to this study's cross- sectional design and the self- report method for gathering data, certain potential biases should be considered. One primary limitation of the cross- sectional study design is that there is generally no evidence of causal mechanisms among the studied variables. Hence, we can only draw firm conclusions about differences between the clusters, not about the causes of the hand pain reported. A known weakness with the two- step cluster analysis is that the final solution might depend on the order of the cases in the dataset. For methodological rigour, the analysis should be repeated with the cases in a different order (Norusis, 2012), which we did.

6 | CONCLUSION

This study illustrates that haemodialysis nurses are a heterogeneous group as regards work ability, work engagement and self- reported clusters 1– 4, p = 0.001, clusters 2– 4,

p = 0.013, clusters 3– 4, p = 0.001, other comparisons non- significant.

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work hours. The four distinct clusters of nurses, based on the afore- mentioned variables, indicate a need for customized interventions for retaining each subgroup at work.

7 | IMPLICATIONS FOR NURSING

MANAGEMENT

Management should focus on strategies to prevent disability from work- related musculoskeletal hand pain and efforts to improve the ergonomics of haemodialysis- related nursing tasks that cause mechani- cal loads to the hands. Management should also allocate resources specifically addressing the needs of older nurses who experience both musculoskeletal hand pain and reduced work capacity.

AUTHOR CONTRIBUTIONS

Eva Westergren and Magnus Lindberg designed and Magnus Lindberg directed the project; Eva Westergren collected the data;

Magnus Lindberg analysed the data; Magnus Lindberg wrote the article. Both authors discussed the results and Eva Westergren com- mented on the manuscript.

ACKNO WLE DGE MENTS

The authors would like to thank Mette Spliid Ludvigsen for adminis- tering the data collection in Denmark. Mette is Associate professor at the Department of Clinical Medicine - Randers Regional Hospital, Aarhus University, Aarhus, Denmark and Professor at the Faculty of Nursing and Health Sciences, Nord University, Norway.

FUNDING INFORMATION

This study was financially supported by research grants from AFA Insurance under grant [AFA reg. no. 170075] and the University of Gävle, Sweden.

CONFLIC T OF INTEREST STATEMENT

No conflict of interest has been declared by the authors.

DATA AVAIL ABILIT Y STATEMENT

Research data are not shared due to ethical restrictions.

ETHICAL APPROVAL

The study protocol was approved by the Regional Ethics Review Board in Uppsala, Sweden (Registration number 2017/229) and reported to the Danish Data Protection Agency (ID- number 1- 16- 02- 806- 17). For the Danish data, no ethical approval was needed as the study does not involve human biological material (ID- number 1- 10- 72- 168- 17). All participants were informed about the aim of the survey and participated voluntarily.

ORCID

Magnus Lindberg https://orcid.org/0000-0003-1289-9896

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