Startseite Medizin Predicting of pain, disability, and sick leave regarding a non-clinical sample among Swedish nurses
Artikel Öffentlich zugänglich

Predicting of pain, disability, and sick leave regarding a non-clinical sample among Swedish nurses

  • Annika Nilsson EMAIL logo , Per Lindberg und Eva Denison
Veröffentlicht/Copyright: 1. Juli 2010
Artikel downloaden (PDF)
Veröffentlichen auch Sie bei De Gruyter Brill
Informationen für Autor*innen Erkunden Sie dieses Fachgebiet
Scandinavian Journal of Pain
Aus der Zeitschrift Scandinavian Journal of Pain Band 1 Heft 3

Abstract

Objective

Health care providers, especially registered nurses (RNs), are a professional group with a high risk of musculoskeletal pain (MSP). This longitudinal study contributes to the literature by describing the prevalence and change in MSP, work-related factors, personal factors, self-reported pain, disability and sick leave (>7 days) among RNs working in a Swedish hospital over a 3-year period. Further, results concerning prediction of pain, disability and sick leave from baseline to a 3-year follow-up are reported.

Method

In 2003, a convenience sample of 278 RNs (97.5% women, mean age 43 years) completed a questionnaire. In 2006, 244 RNs (88% of the original sample) were located, and 200 (82%) of these completed a second questionnaire.

Results

Logistic regression analyses revealed that pain, disability and sick leave at baseline best predicted pain, disability, and sick leave at follow-up. The personal factors self-rated health and sleep quality during the last week predicted pain at follow-up, while age, self-rated health, and considering yourself as optimist or pessimist predicted disability at follow-up, however weakly. None of the work-related factors contributed significantly to the regression solution.

Conclusions

The results support earlier studies showing that a history of pain and disability is predictive of future pain and disability. Attention to individual factors such as personal values may be needed in further research.

Keywords: Registered nurses; Musculoskeletal pain; Work-related factors; Personal factors; Sickness absence; Disability

1 Introduction

The empirical evidence shows a variety of risk factors for developing musculoskeletal pain (MSP), sick leave and disability among the general and working population. In several studies, the risk factors seem to interact with each other ([1,2,35,36,37]). The risk factors are classified differently in various studies, making it difficult to compare the constellations. The majority of the research in the field of MSP and disability is among clinical samples, many of which have been cross-sectional. Prospective studies on high-risk populations, such as subgroups of health care staff, are limited, especially prospective studies among staff not on sick leave.

In general, the main providers of practical patient care are nurses’ aides, who are frequently exposed to different physical work-related factors such as manual handling, heavy lifting, moving or transferring patients [3]. Repeated daily physical work activities, biomechanical strain of the back and manual handling of objects and persons may contribute to gradual development of pain [4,5]. These types of risk factors also concerned registered nurses (RNs) who, in addition to having administrative responsibility, were also responsible for assessing health care needs and provision of medical prescriptions. The RNs is also responsible for assessing and carrying out specific nursing and a degree of coordination responsibilities for other nursing tasks.

Both internationally and in Sweden, MSP have been claimed to be the most prominent work-related problem among RNs [6,7,8,9,10,11]. A review of 80 studies [12] concluded that RNs were among those with the most high-risk occupations with respect to low back pain (LBP). The results showed that the average point prevalence of LBP was approximately 17%, the annual prevalence was 40–50% and the lifetime prevalence was 35–80%. Another study [13] showed that lifetime incidence and point prevalence of LBP were 65% and 30%, respectively, among orthopaedic nurses and 58% and 25%, respectively, among intensive care nurses. A retrospective study [10] among hospital nurses showed that high levels of perceived mental pressure, boring tasks and limited support were identified as risk factors for musculoskeletal complaints. With regard to musculoskeletal complaints, the lower back was the most commonly reported body site (56%). MSP is not normally life threatening, but it can cause unimaginable suffering and disability.

In many cases, persons with MSP experience restrictions in their everyday activities [33]. Work-related injuries have been shown to influence perceptions of injury as well as pain and disability [14]. Relationships with co-workers and management may also influence pain and disability [5,12,15]. One review [16] provided strong evidence that work-related factors such as monotonous work, poor relationships at work, and low perceived ability to work were risk factors for disabling LBP. Both MSP and disability are common diagnoses used when granting sick leave [36,17].

Health-related factors such as previous LBP have been shown to be associated with a higher risk for future sick leave among female nursing aides/assistant nurses not on sick leave. In a 2-year follow-up study [18] on determinants related to health, work and social circumstances were associated with recorded sickness absence among hospital physicians and of female nurses the results showed that all health factors were strongly associated with sickness absence in both groups. In another study [19] the results showed that age, gender, perceived physical workload, poor general health, sciatica, worker’s own perception of his/her ability to return to work, and chronic complaints of LBP were associated with longer sickness absence in workers on sick leave for 2–6 weeks due to MSP.

It is important to define specific subgroups in working populations because the risk factors vary across groups [20]. In Sweden studies [36] has showed, that women working in the public sector (especially in health care and schools) are under-represented in studies of consequences of sick leave for back and neck pain. The relationship between work-related factors and personality characteristics, on one hand, and pain, disability, and sick leave, on the other hand, among women in public health care settings (where RNs constitute a substantial group) has thus not been well studied, especially using longitudinal designs.

The aims of the study were to (a) describe the prevalence of, as well as change over time in, MSP, work-related factors, personal factors, pain, disability and sick leave (>7 days) among RNs working in a Swedish hospital, and (b) predict pain, disability and sick leave at a 3-year follow-up on the basis of work-related factors, personal factors, pain, disability and sick leave at baseline.

2 Method

This was a longitudinal study in which a logistic regression model including work and personal factors at baseline (2003), and the dependent variables pain, disability, and sick leave at the 3-year follow-up (2006) were used.

2.1 Procedure

The study was conducted among RNs working in a hospital in a midsize Swedish city. The RNs were recruited from various departments (n = 23; e.g., medical, surgery, obstetrics and gynaecology departments) at the hospital during spring 2003. The hospital director gave permission to perform the study. Nurse administrators at each hospital department were informed about the study and the data collection procedure. RNs were informed about the study during ward meetings and invited to participate. Those who agreed to participate were given a questionnaire with a personal code number. A list including the names and addresses of all RNs who had completed the questionnaire in 2003 was received from the hospital’s chief executive secretary before the 3-year follow-up in 2006 was performed. A questionnaire with the same content was mailed to the participants. Two reminders were sent out: one after 2 weeks (n = 115) and one after 4 weeks (n = 64).

2.2 Measures

Data regarding pain, disability, sick leave, work-related factors, personal factors and demographic and background factors were collected using a self-administered questionnaire standardised evaluation instrument of Linton et al. [21].

Further, a section was added in which RNs who reported pain were asked to answer some questions about their pain. All responders were asked to report what they appreciated at their present jobs and what they viewed as being the most difficult, boring and harmful. The subjects were asked to give a short written description of their daily work tasks. Finally, participants were asked to give their own comments in a free format.

2.2.1 Work-related factors

Five work-related variables were measured using a 0–100 VAS. Three items were taken from Linton et al. [21]. The question “if you take into consideration your work routines, management, salary, promotion possibilities and work mates, how satisfied are you with your job” was divided into two items: satisfaction with work mates and satisfaction with work leaders. The item “Is your work heavy or monotonous?” was modified to “Is your work light or heavy?” Clinical questions included perceived value of the present job and whether work was perceived as calm or stressful.

2.2.2 Personal factors

Questions about personal factors included age, children and marital status. Further, subjects were asked to rate their perceived health (healthy–ill), whether they perceived themselves as optimist or pessimist, and to estimated physical activity on several 0–100 VASs. The item assessing sleep quality during the past week was taken from Linton et al. [21].

2.2.3 Demographic and background items

Two items taken from Linton et al. [21] covered pain-free days and days using medication per week (response format 0–7 days). This section also involved background data on gender, number of years working at the present job, present job situation, number of children, and sick-listing during the past year. Three questions were included at the baseline: present nursing ward, pain (yes/no), pain location (when applicable) and perceived ability to handle pain. The last question was rated on a 0–100 VAS. At the 3-year follow-up, three questions were added: “Have you changed nursing ward or workplace since 2003 and, if so for what reason? Where do you work today?”, and “If you didn’t have a pain at the baseline assessment but have one now, how long have you had it?”

2.2.4 Participants who reported pain

Participants who reported pain were asked to rate the following: perceived correlation between work tasks and pain, perceived limitations caused by pain, perceived ability to handle pain, number of days per week using analgesic medication, and number of pain-free days per week.

2.3 Study population

In 2003, approximately 875 RNs were working in the hospital and being paid on a monthly basis, 794 (91.0%) of them women and 81 (9.0%) of them men. A convenience sample from the entire group was used. Of the 348 RNs asked to participate in the study, 278 (80.0%) completed a questionnaire. The study group consisted of 271 women (97.5%) and seven men (2.5%), and their mean age was 43 years. About half of the participants reported pain, and several reported multiple pain sites.

Two hundred and forty-four RNs (88% of the original sample in 2003) were located in 2006, of whom 200 (82%) returned the second questionnaire. Of those who did not participate (n = 78) 41 RNs worked outside the county council in other disciplines, 1 RN retired from work and 36 RNs declined to participate.

RNs who agreed to participate there were significant differences regarding age (t = 18.66, df = 195, p < 0.001) and years working as a RN (t = 3.46, df = 187, p < 0.001) between baseline and follow-up. No other significant differences were found between the two assessments.

Table 1

Baseline (2003) characteristics of the RNs (n = 278).

Variables n [a] (%) m (SD) Range
Gender        
Female 271 (98)    
Male 7 (2)    
Age 275 (99) 43.0 (9.8) 25-64
Years working as a RN 200 (97) 13.4(11.2) 0.5-40
Marital status        
Spouses 219 (79)    
Single 58 (21)    
Children        
(Yes/no) 215/54 (77) (19)    
Children at home (yes/no) 151/69 (54) (25)    
Employment        
Permanent job 267 (94)    
Nurse substitute 9 (3)    
Working time        
Full time 153 (55)    
Part time 101 (36)    

Table 2

Frequency and/or mean, standard deviation, and range regarding pain problems, sick days, days using medication, restriction in leisure time, pain, disability, and sick leave > 7 days for the RNs (n = 200) at baseline 2003 and at the 3-year follow-up 2006.

Variables n [a] 2003   Range n [a] 2006   Range
    (%) M (SD)     (%) M (SD)  
Pain problems (yes/no) 96/104 (48/52)     104/96 (52/48)    
Neck[b],[*] 29 (15)     41 (21)    
Shoulders[b] 44 (22)     49 (25)    
Upper back[b] 19 (10)     23 (12)    
Lower back[b] 45 (23)     46 (23)    
Other pain sites[b] 41 (21)     51 (26)    
Sick days during past year (yes/no)[***] 116/84 (58/42) 5.4 (13.6) 0–121 108/92 (54/46) 16.4(44.1) 0–354
Pain during past week[b] 92 (46) 3.9 (2.6) 0–7 100 (50) 4.6 (2.3) 0–7
Days using medications[b] 92 (46) 1.2 (1.9) 0–7 100 (50) 0.5 (0.5) 0–7
Restriction in leisure time[b][c] 91 (49) 27.1 (25.1) 0–85 101 (51) 32.6 (26.6) 0–98
Pain (yes/no) 81/118 (41/59)     98/102 (49/51)    
Disability (yes/no) 45/155 (78/23)     59/141 (71/30)    
Sick days (yes/no) >7days[**] 24/176 (1/88)     46/154 (23.77)    

During the 3-year period, 62 of the RNs (31%) changed departments within the hospital. Better salary, new challenges, and more variation in their work tasks were some of the reasons given for moving to other nursing wards. Of these 62 RNs 18 (28%) explicitly mentioned stress and work environment as a reason for the change.

2.3.1 Non-responders

There were no significant differences between responders and non-responders (n = 78) to the follow-up (2006) questionnaires regarding age, the number of days the person had been on sick leave during the year, years working as an RN, children, marital status or pain problems at the time of the baseline assessment in 2003 (Table 1).

2.4 Data handling and statistical analyses

All analyses were performed using the Statistical Package for Social Sciences (SPSS, version 14.0). Before the statistical analyses were performed, three new categorical variables were created based on the original items of the 2003 and 2006 questionnaires. (1) The number of days per week rated as free from pain was transformed to categorical value “pain” (yes/no). (2) “Disability” was originally self-reported as perceived limitations caused by pain symptoms in leisure time, on a 0–100 VAS where a cut-off point was set at 20 [22]. Subjects who reported a value lower than 20 were not considered as disabled (“no”), while those scoring 21–100 were judged as disabled (“yes”). (3) “Sick leave” was originally assessed using a single question concerning annual total time being sicklisted. Subjects who had been sick-listed for more than 7 days were labelled “yes”, while RNs reporting fewer days were labelled “no”. The cut-off point is based on the general insurance system in Sweden, where if you are ill more than 7 days, you are normally expected to produce a medical certificate from the doctor in order to continue receiving sickness benefits. Wilcoxon signed rank test and dependent t-test were used to analyse differences between baseline and the 3-year follow-up.

Prediction of pain, disability and sick leave at the 3-year follow-up based on work-related factors (value of present job, satisfaction with work mates, satisfaction with work leaders, light–heavy work, and calm–stressful work), personal factors (age, civil status, children, self-rated health, whether they perceived themselves as optimist or pessimist, sleep quality during the past week and value of physical exercise, and pain, disability and sick leave at baseline were analysed using binary logistic regression. Separate analyses were performed for each group of predictors. Prior to the logistic regression analyses, Spearman’s rho correlation coefficient (r) was used to study bivariate relationships between the outcome and predictor variables as well as among the predictor variables. Multicollinearity (values of r > 0.9) [22] was not present in the data. Missing data (<10%) [38] were not substituted. Thus, the sample size varies across the different analyses. The results are presented as odds ratios with 95% confidence intervals. The level of significance was set at 5% for all statistical tests.

2.5 Ethical considerations

The study was approved by the Ethics Committee at the Medical Faculty, Uppsala University (reference number 02-314). RNs who were asked to participate in the study were informed that their participation was voluntary and that confidentiality would be assured.

Table 3

Mean (m), standard deviation (SD), and range forwork-related factors and personal factors at baseline 2003 and at the 3-year follow-up 2006 (n = 200).

Variables 2003   Range 2006   Range
  n[a] m (SD)   n[a] m (SD)  
Work–related variables            
Correlation: symptom–work–task[b],[c] 91 46.7 (27.1) 0–98 102 44.1 (32.1) 0–100
Value of present job[d] 195 75.5 (20.0) 6–100 196 78.0(16.4) 9–100
Satisfaction with workmates[d] 195 86.1 (18.3) 6–100 198 84.3 (15.2) 16–100
Satisfaction with work leaders[d] 193 71.2(24.2) 2–100 198 64.1[***] (24.0) 3–100
Work: light–heavyc 193 56.4 (24.2) 3–100 198 57.2 (25.2) 3–100
Work: calm–stressful[c] 195 67.0(20.1) 5–100 198 68.8(18.9) 8–98
Personal factors Ability to handle pain[b],[c] 92 21.7 (20.2) 0–92 100 19.0(16.6) 0–75
Healthy–ill[c] 198 14.2(15.0) 0–90 197 16.2(15.5) 0–82
Optimist–pessimist[c] 196 18.5(17.9) 0–80 197 18.9(18.5) 0–88
Sleep quality during the past week[c] 198 28.6 (25.9) 0–100 197 29.8 (26.0) 0–99
Value of physical exercise[d] 198 68.7 (25.1) 1–100 194 74.3[***] (22.5) 2–98

3 Results

3.1 Group characteristics and differences

Table 2 shows descriptive statistics and differences in pain problems, pain sites, sick days and restriction in leisure time among the 200 RNs responding at baseline and follow-up. Ninety-six subjects reported pain problems at baseline and 104 at the 3-year follow-up. The most commonly reported pain sites at baseline were lower back and shoulder pain. “Other pain sites” and shoulder pain were the most commonly reported at the 3-year follow-up. Problems with neck pain increased significantly at the 3-year follow-up (z = −2.12, p = 0.034) as compared to baseline. The mean number of sick days (t = 3.40, df = 199, p < 0.001) and sick days >7 (z = −2.89, p = 0.004) during the past year were significantly higher at the 3-year follow-up compared to baseline.

Table 3 shows work-related factors and personal factors for the 200 RNs responding at baseline and follow-up. For the work-related factors, satisfaction with work leaders decreased significantly (t = −3.50, df = 188, p < 0.001) at the 3-year follow-up as compared to baseline. Perceived value of physical exercise increased significantly (t = 3.56, df = 191, p < 0.001) at the 3-year follow-up as compared to baseline.

Table 4

Spearman rank correlations (rs) between the outcome variables pain, disability, and sick leave (1–3) at follow-up 2006 and predictors related to work (4–8) and personal factors (9–15) at baseline 2003.

Variables Pain Disability Sick leave
1. Pain –    
2. Disability 0.64[**] _  
3. Sick leave>7 days 0.10 0.20[*] _
4. Value of present job 0.01 –0.07 –0.15[*]
5. Satisfaction of workmates 0.05 –0.04 –0.18[*]
6. Satisfaction of work leaders 0.02 –0.01 –0.10
7.Work: light–heavy 0.03 –0.07 0.01
8.Work: calm–stressful 0.02 –0.07 –0.00
9. Age 0.10 0.24[**] 0.00
10. Spouses/single 0.10 –0.01 0.03
11.Children 0.16[*] 0.09 –0.01
12.1 am: healthy–ill 0.23[**] 0.27[**] 0.16[*]
13. I am: optimist–pessimist 0.09 0.00 –0.03
14. Sleep quality during the past week 0.24[**] 0.22[**] 0.05
15. Value of physical exercise 0.01 –0.06 –0.11

  1. n = 200 for variables 1, 2,3,9 and 10; n =198 for variables 12,14 and 15; n = 196 for variable 13; n =195 for variables 4,5, 8 and 11; n = 193 for variables 6 and 7.

RNs reported what they experienced as being most difficult, boring and harmful at their present jobs. Several reported both physical work factors and stress as most harmful. Thirty-nine RNs explicitly pointed out physical work factors (e.g., heavy lifts) at baseline, while 74 pointed out stress. At the 3-year follow-up, the corresponding figures were 33 and 63, respectively.

Table 5

Beta coefficient (B), odds ratio (OR), and confidence interval (CI) for the prediction of pain, disability and sick leave at follow-up by work-related and personal factors and pain, disability and sick leave at baseline 2003 (n = 200).

Predictors Pain Disability Sick leave
B OR (95%CI) B OR (95%CI) B OR (95%CI)
Work–related factors   n =191     n = 191     n = 191
Value of present job –0.03 1.00 (0.98,1.02)   –0.01 0.99 (0.97, 1.01)   –0.01 0.99 (0.97, 1.01)
Satisfaction of workmates 0.00 1.00 (0.98,1.02)   0.00 1.00 (0.98, 1.02)   –0.03 0.97 (0.95, 1.00)
Satisfaction of work leaders 0.00 1.00 (0.99, 1.02)   0.00 1.00 (0.99, 1.02)   0.01 1.01 (0.99,1.03)
Work: light–heavy –0.00 1.00 (0.98, 1.01)   –0.01 0.99 (0.98, 1.01)   0.01 1.01 (0.99,1.03)
Work: calm–stressful 0.00 1.00 (0.99,1.03)   0.00 1.00 (0.98,1.03)   0.01 1.01 (0.98,1.03)
Personal factors   n =192     n =192     n = 192
Age 0.01 1.00 (0.98,1.05)   0.07 1.07 (1.02, 1.12)   –0.00 1.00 (0.96, 1.04)
Spouses/single 0.29 1.33 (0.57, 3.11)   –0.12 0.80 (0.35, 2.26)   0.10 1.10 (0.42, 2.88)
Children 0.51 1.66 (0.65, 4.23)   –0.34 0.71 (0.24, 2.16)   –0.10 0.94 (0.33, 2.65)
Healthy–ill 0.03 1.04 (1.01,1.06)   0.05 1.06 (1.02, 1.09)   0.04 1.04 (1.01, 1.06)
Optimist–pessimist –0.00 1.00 (0.98, 1.02)   –0.03 0.96 (0.95, 1.00)   –0.02 0.99 (0.96, 1.01)
Sleep quality the past week 0.01 1.01 (1.00,1.03)   0.02 1.02(1.00, 1.03)   –0.01 1.00 (0.98, 1.01)
Value of physical exercise 0.00 1.00 (0.99, 1.01)   –0.00 1.00 (0.99, 1.02)   –0.01 0.99 (0.98, 1.01)
    n =199     n =199     n = 199
Pain at baseline 1.88 6.56 (2.97,14.48)   1.17 3.23 (1.42, 7.32)   –0.22 0.81 (0.31, 2.08)
Disability at baseline 0.50 1.65 (0.61, 4.44)   1.17 3.21 (1.33, 7.70)   1.30 3.67 (1.34, 0.05)
Sick leave at baseline –0.10 0.91 (0.34, 2.41)   1.13 3.08 (1.17, 8.13)   0.63 1.88 (0.73, 4.88)

  1. Bold figures, significant values; pain, healthy–ill and sleep quality during the past week: p < 0.05, pain at baseline: p < 0.001; disability, sick leave at baseline: p < 0.05, age and optimist–pessimist, pain at baseline, disability at baseline: p < 0.01, healthy–ill: p < 0.001; Sick leave, disability at baseline: p < 0.05.

3.2 Prediction of pain, disability and sick leave

Logistic regression analyses were performed with pain, disability and sick leave as outcomes at the 3-year follow-up (2006) and work-related factors, personal factors, and pain, disability and sick leave as predictor variables at baseline (2003). The results of the regression analyses in table five give coefficients, odds ratios and confidence intervals (CI) for each predictor variable in the models.

3.3 Bivariate correlations—outcome variables and predictor variables

Table 4 shows Spearman’s correlations test (rs) of the outcome variables pain, disability and sick leave at the 3-year follow-up and predictor variables at baseline. There was a significant correlation between pain and disability (rs = 0.64, n = 200, p < 0.01). Correlations between the independents variables were weak to moderate, the range of significant correlations being rs = −0.15 to rs = 0.64.

3.4 Pain

The prediction of pain based on work-related factors was analysed in a total of 191 cases (n = 99 with no pain, n = 92 with pain). The full model was not significant (chi-square = 0.27, df = 5, p = 0.998). The prediction of pain based on personal factors was analysed in a total of 192 cases (n = 99 with no pain, n = 93 with pain). The full model significantly predicted pain (chi-square = 25.18, df = 7, p < 0.001). Self-rated health and sleep quality during the past week significantly predicted pain as single items in the model. For healthy, the OR was 1.04, which means that the odds of being in the pain group increased by 4% with a one-unit increase in this variable. For sleep quality during the past week, the OR was 1.01, thus reflecting an increase of only 1%. The prediction of pain based on pain, disability and sick leave at baseline was analysed in a total of 199 cases (n = 101 with no pain, n = 98 with pain). The full model significantly predicted pain (chi-square = 47.56, df = 3, p < 0.001). Pain at baseline predicted pain at follow-up as a single item in the model. The OR was 6.56, which means that being in the pain group at baseline increased the odds of being in the pain group at follow-up by 6.56 times (see Table 5).

3.5 Disability

The prediction of disability based on work-related factors was analysed in a total of 191 cases (n = 134 with no disability, n = 57 with disability). The full model was not significant (chisquare = 3.75, df = 5, p = 0.586). The prediction of disability based on personal factors was analysed in a total of 192 cases (n = 135 with no disability, n = 57 with disability). The full model significantly predicted disability (chi-square = 41.91, df = 7, p < 0.001). Age, selfrated health and considering yourself as optimist or pessimist significantly predicted disability at follow-up as single items in the model. For age the OR was 1.07, which means that an increase in age by 1 year is associated with a 7% increase in the odds of being in the disabled group. For healthy-self-rated health, the OR was 1.06, which means that the odds of being in the disabled group increased by 6%. For optimist–pessimist, the OR was 0.96, which means that the odds of being in the disabled group decreased by 4% with a oneunit increase in this variable. The prediction of disability based on pain, disability and sick leave at baseline was analysed in a total of 199 cases (n = 140 with no disability, n = 59 with disability). The full model significantly predicted disability (chi-square = 40.58, df = 3, p < 0.001). All three factors significantly predicted disability in the model. For pain at baseline the OR was 3.23, which means that being in the pain group at baseline increased the odds of being in the disabled group by 3.23 times. For disability at baseline the OR was 3.21, and for sick leave at baseline the OR was 3.08 (see Table 5).

3.6 Sick leave

The prediction of sick leave based on work-related factors was analysed in a total of 191 cases (n = 147 with sick days < 7 days, n = 44 with sick days > 7 days). The full model was not significant (chi-square = 8.24, df = 5, p = 0.143). The prediction of disability based on personal factors was analysed in a total of 192 cases (n = 149 with sick days < 7 days, n = 43 with sick days > 7 days). The full model was not significant (chi-square = 10.52, df = 7, p = 0.161). The prediction of sick leave based on pain, disability and sick leave at baseline was analysed in a total of 199 cases (n = 154 with sick days < 7 days, n = 45 with sick days > 7 days). The full model significantly predicted sick leave (chi-square = 10.98, df = 3, p = 0.012). Disability at baseline significantly predicted sick leave as a single item in the model. The OR was 3.67, which means that being in the sick-leave group at baseline increased the odds of being in the sick-leave group at follow-up by 3.67 times (see Table 5).

4 Discussion

The main findings in the present study were that personal and outcomes factors such as pain, disability and sick leave at baseline (2003) predicted pain, disability and sick leave at follow-up (2006). Pain at baseline predicted pain and disability at follow-up. Pain, disability, and sick leave at baseline predicted disability at follow-up, while disability at baseline predicted sick leave at follow-up. None of the work-related factors showed any predictive value for pain, disability, and sick leave at follow-up.

Previous studies have shown that back pain is a predictor of back-related pain and disability among nurses [5], and that among workers with LBP, individuals with high pain intensity or disabling LBP are more likely to have MSP [23]. Pain, especially persistent pain, has been shown to be associated with increased incidence of other symptoms (e.g., depression, anxiety and other somatic symptoms), limitations and negative consequences in daily life, including both work and leisure time [36]. Greater self-reported pain and functional disability at baseline has been shown to predict disability in patients with back and mixed injuries [24]. Thus, our findings support the results of previous studies. We found that sick leave at baseline predicted disability at follow-up and disability at baseline predicted sick leave at follow-up. One explanation could be that sick leave may create new problems, such as increased pain and inactivity [25,36].

Personal factors at baseline (2003) were shown to predict pain, disability and sick leave at follow-up (2006), although the odds ratios were low. Two personal factors – self-rated health and sleep quality during the past week – predicted pain at follow-up. Other studies [26,27,28] have shown that sleep problems are more common among health care personnel on rotating work shifts, which was the case for several of the RNs in the present study. The personal factors age and self-rated health significantly predicted disability, but in these cases the odds ratios were low. Studies [36] have shown that the prevalence of persistent pain generally increases with increasing age. A systematic review [20] of predictors of chronic disability in injured workers showed that older workers have poorer outcomes, such as greater pain and functional disability. In the present study, the personal factor considering yourself as optimist or pessimist decreased the odds of being in the disabled group at follow-up. This means that RNs who perceived themselves as being more optimistic reported less disability than did RNs who perceived themselves as being more pessimistic. Studies [29,30] have shown that optimism is associated with less depression, greater well-being and health benefits in different populations.

Our finding that the work-related factors, as a group, did not predict pain, disability and sick leave at follow-up is at odds with previous research. Previous studies [12,31,34] have shown that work-related and personal factors play an important role in the development of pain, disability and sick leave. One explanation for our results, owing to the fact that we used a non-clinical sample, may be that persons with pain, disability and high levels of sick leave prior the follow-up in 2006 changed wards, moving to departments with lower-exposure jobs (i.e., less heavy lifting and manual handling of patients).

Some limitations of the present study deserve mentioning: Firstly, regarding whether the results may be generalized to all RNs, this study was limited to one county in Sweden. However, the present RNs would seem to be typical in terms of their pain complaints and sick leave. It must also be noted that 97.5% of the RNs were women, thus no comparison with respect to gender was possible. The sampling differences across studies must be taken into consideration before discussing the results. Physicians, nurses’ aides, and nurse assistants have been included in some previous studies, which can make it difficult to compare the results in the present study with previous results. Secondly, the use of visual analogue scales for the measurement of several of the variables may introduce bias because of well-known problems with this type of scale, e.g., requirement of intellectual capacity. We judge that the participants in our study were capable of using these scales appropriately. In general, and in spite of potential shortcomings, the visual analogue scale has been shown to have acceptable psychometric properties [32]

Thirdly, the use of a cut off at 20 points to categorize participants as disabled was arbitrary, although we judged that a cut-off value at 20 points would represent some limitation. Thus, with another cut off we might have obtained different results.

The strength of the present study was its longitudinal design, which made it possible to predict pain, disability and sick leave over time.

In the present study, MSP was a common condition among the RNs, especially pain in the shoulders, lower back and “other pain sites” (e.g., leg, hands, ankle). These results are in line with other studies [10,12] showing that MSP is common among nurses worldwide. The major results of the present study support earlier findings regarding the ability of pain, disability, and sick leave to predict outcomes related to the same factors longitudinally [20,36]. The present mixed findings on work-related and personal factors indicate the need for further research in this area. Attention to individual factors, such as personal values related to daily life, may be needed in prospective studies of persons working in the health care sector who are at risk of developing pain and disability.

DOI of refers to article: 10.1016/j.sjpain.2010.05.037.

Department of Caring Science and Sociology, University of Gävle, Gävle, SE-801 76, Sweden. Tel.: +46 26 64 82 82; fax: +46 26 64 82 35

Acknowledgements

The present study was supported by grants from the Department of Caring Science and Sociology, University of Gävle. We will also thank all the registered nurses who participated in the study.

  1. Conflict of interest

    The authors have no conflict of interest in relation to this study.

References

[1] Turk DC, Okifuji A. Evaluating the role of physical, operant, cognitive, and affective factors in the pain behaviours of chronic pain patients. Behav Modif 1997;21:259–80.Suche in Google Scholar

[2] Dysvik E, Natvig GK, Eikeland OJ, Lindstrom TC. Coping with chronic pain. Int J Nurs Stud 2005;42:297–305.Suche in Google Scholar

[3] Hornsten AH, Sandstrom H, Lundman B. Personal understandings of illness among people with type 2 diabetes. J Adv Nurs 2004;47:174–82.Suche in Google Scholar

[4] Yip Y. A study of work stress, patient handling activities and the risk of low back pain among nurses in Hong Kong. J Adv Nurs 2001;36:794–804.Suche in Google Scholar

[5] Eriksen W, Bruusgaard D, Knardahl S. Work factors as predictors of intense or disabling low back pain; a prospective study of nurses’ aides. Occup Environ Med 2004;61:398–404.Suche in Google Scholar

[6] Lagerstrom M, Hansson T, Hagberg M. Work-related low-back problems in nursing. Scand J Work Environ Health 1998;24:449–64.Suche in Google Scholar

[7] Bond FW, Bunce D. Job control mediates change in a work reorganization intervention for stress reduction. J Occup Health Psychol 2001;6:290–302.Suche in Google Scholar

[8] Alexopoulos EC, Burdorf A, Kalokerinou A. Risk factors for musculoskeletal disorders among nursing personnel in Greek hospitals. Int Arch Occup Environ Health 2003;76:289–94.Suche in Google Scholar

[9] Smith DR, Kondo N, Tanaka E, Tanaka H, Hirasawa K, Yamagata Z. Musculoskeletal disorders among hospital nurses in rural Japan. Rural Remote Health 2003;3:241.Suche in Google Scholar

[10] Smith DR, Wei N, Kang L, Wang RS. Musculoskeletal disorders among professional nurses in mainland China. J Prof Nurs 2004;20:390–5.Suche in Google Scholar

[11] Fochsen G, Josephson M, Hagberg M, Toomingas A, Lagerstrom M. Predictors of leaving nursing care: a longitudinal study among Swedish nursing personnel. Occup Environ Med 2006;63:198–201.Suche in Google Scholar

[12] Hignett S. Work-related back pain in nurses. J Adv Nurs 1996;23:1238–46.Suche in Google Scholar

[13] Vieira ER, Kumar S, Coury HJ, Narayan Y. Low back problems and possible improvements in nursing jobs. J Adv Nurs 2006;55:79–89.Suche in Google Scholar

[14] Nicholas MK. In: Linton S, editor. Reducing disability in injured workers: the importance of collaborative management. Pain research and clinical management. Vol. 12: New avenues for the prevention of chronic musculoskeletal pain and disability, 2. Amsterdam: Elsevier; 2002. p. 306.Suche in Google Scholar

[15] Linton S. New research provides new avenues for prevention: an overview of the book. In: Linton S, editor. Pain research and clinical management, vol. 12. Amsterdam: Elsevier; 2002. p. 1–3.Suche in Google Scholar

[16] Linton S. Occupational psychological factors increase the risk for back pain: a systematic review. J Occup Rehabil 2001;11:53–66.Suche in Google Scholar

[17] Kivimaki MJ, Ferrie E, Hagberg J, Head J, Westerlund H, Vahtera J, Alexanderson K. Diagnosis-specific sick leave as a risk marker for disability pension in a Swedish population. J Epidemiol Community Health 2007;61:915–20.Suche in Google Scholar

[18] Kivimaki MR, Sutinen R, Elovainio M, Vahtera J, Rasanen K, Toyry S, Ferrie JE, Firth-Cozens J. Sickness absence in hospital physicians: 2 year follow up study on determinants. Occup Environ Med 2001;58:361–6.Suche in Google Scholar

[19] Lotters F, Burdorf A. Prognostic factors for duration of sickness absence due to musculoskeletal disorders. Clin J Pain 2006;22:212–21.Suche in Google Scholar

[20] Turner JA, Franklin G, Turk DC. Predictors of chronic disability in injured workers: a systematic literature synthesis. Am J Ind Med 2000;38:707–22.Suche in Google Scholar

[21] Linton S, Keefe F, Lefebvre JC. The behavior management and prevention of chronic pain. In: Caddy GR, Byrne DG, editors. Behavirol medicine: international perspectives. Norwood, NJ: Ablex Publishing; 1992.Suche in Google Scholar

[22] Tait RC, Chibnall JT, Krause S. The Pain Disability Index: psychometric properties. Pain 1990;40:171–82.Suche in Google Scholar

[23] Jzelenberg IW, Burdorf A. Impact of musculoskeletal co-morbidity of neck and upper extremities on healthcare utilisation and sickness absence for low back pain. Occup Environ Med 2004;61:806–10.Suche in Google Scholar

[24] Cannella DT, Lobel M, Glass P, Lokshina I, Graham JE. Factors associated with depressed mood in chronic pain patients: the role of intrapersonal coping resources. J Pain 2007;8:256–62.Suche in Google Scholar

[25] Ockander M, Timpka T. A female lay perspective on the establishment of longterm sickness absence. Int J Soc Welfare 2001;10:74–9.Suche in Google Scholar

[26] Poissonnet CM, Veron M. Health effects of work schedules in healthcare professions. J Clin Nurs 2000;9:13–23.Suche in Google Scholar

[27] Edell-Gustafsson UM. Sleep quality and responses to insufficient sleep in women on different work shifts. J Clin Nurs 2002;11:280–7.Suche in Google Scholar

[28] Trinkoff AM, Lipscomb JA, Geiger-Brown J, Brady B. Musculoskeletal problems of the neck, shoulder, and back and functional consequences in nurses. Am J Ind Med 2002;41:170–8.Suche in Google Scholar

[29] Mahat G. Perceived stressors and coping strategies among individuals with rheumatoid arthritis. J Adv Nurs 1997;25:1144–50.Suche in Google Scholar

[30] Wong WS, Fielding R. Quality of life and pain in Chinese lung cancer patients: is optimism a moderator or mediator? Qual Life Res Int J Qual Life Asp Treat Care Rehabil 2007;16:53–63.Suche in Google Scholar

[31] Bourbonnais R, Comeau M, Vezina M, Dion G. Job strain, psychological distress, and burnout in nurses. Am J Ind Med 1998;34:20–8.Suche in Google Scholar

[32] Jensen MP, Karoly P. Self-report scales and procedures for assessing pain in adults. In: Turk DC, Melzack R, editors. Handbook of pain assessment. 2nd ed. USA: The Guilford; 2001. p. 15–34.Suche in Google Scholar

[33] Breivik H. International Association for the Study of Pains: update on WHO-IASP activities. J Pain Symptom Manage 2000;24:97–101.Suche in Google Scholar

[34] Lu W, Gwee KA. Functional bowel disorders in rotating shift nurses may be related to sleep disturbances. Eur J Gastroenterol Hepatol 2006;18:623–7.Suche in Google Scholar

[35] Swedish Council on Technology in Health Care. Ont i ryggen och ont i nacken (Back and neck pain). Stockholm: Swedish Council on Technology Assessment in Health Care; 2000.Suche in Google Scholar

[36] Swedish Council on Technology in Health Care. Sjukskrivning, orsaker konsekvenser och praxis (Sickness absence, causes, consequences and practices). Stockholm: Swedish Council on Technology Assessment in Health Care; 2003.Suche in Google Scholar

[37] Swedish Council on Technology in Health Care. Metoder för behandling av långvarig smärta (Methods for long term pain treatment). Stockholm: Swedish Council on Technology Assessment in Health Care; 2006.Suche in Google Scholar

[38] Twisk J, de Vente W. Attrition in longitudinal studies. How to deal with missing data. J Clin Epidemiol 2002;55:329–37.Suche in Google Scholar
Received: 2010-01-03
Revised: 2010-04-21
Accepted: 2010-05-06
Published Online: 2010-07-01
Published in Print: 2010-07-01

© 2010 Scandinavian Association for the Study of Pain

Artikel in diesem Heft

  1. Editorial comment
  2. Functional brain imaging of acute postoperative pain
  3. Clinical pain research
  4. Brain activation due to postoperative pain from the right hand measured with regional cerebral blood flow using positron emission tomography
  5. Editorial comment
  6. Long-term low-dose transdermal buprenorphine therapy for chronic noncancer pain
  7. Original experimental
  8. A 6-months, randomised, placebo-controlled evaluation of efficacy and tolerability of a low-dose 7-day buprenorphine transdermal patch in osteoarthritis patients naïve to potent opioids
  9. Editorial comment
  10. Repeated nociceptive stimulation for detecting drug effects
  11. Original experimental
  12. The effects of gabapentin in human experimental pain models
  13. Editorial comment
  14. Whether the weather influences pain: High prevalence of chronic pain in Iceland and Norway: Common genes? Or lack of sunshine and vitamin D?
  15. Observational studies
  16. A population based study of the prevalence of pain in Iceland
  17. Editorial comment
  18. Swedish nurses are prone to chronic shoulder and back pain because of miserable working conditions and poor leadership?
  19. Observational studies
  20. Predicting of pain, disability, and sick leave regarding a non-clinical sample among Swedish nurses
  21. Abstracts
  22. Trigeminal neuralgia or odontogenic pain
  23. Abstracts
  24. What’s wrong with animal models of pain?
  25. Abstracts
  26. Immunotherapy for neuroblastoma elicits a complement dependent whole body allodynia
  27. Abstracts
  28. Pain mechanisms in animal models of rheumatoid arthritis
  29. Abstracts
  30. Translating basic research to pharmacological treatment of neuropathic pain
  31. Abstracts
  32. Free Poster Presentations: Forearm heat pain does not inhibit electrically induced tibialis anterior muscle pain
  33. Abstracts
  34. Offset analgesia evoked by non-contact thermal stimulator
  35. Abstracts
  36. Inhibition of FAAH reverses spinal LTP
  37. Abstracts
  38. Hyperexcitable C-nociceptors in human paroxysmal pain
  39. Abstracts
  40. Pain sensitivity and experimentally induced sensitization in red haired women
  41. Abstracts
  42. How are opioids used in Norway? Persistent use, utilization of depot formulation and age profile in non-palliation patients
  43. Abstracts
  44. To which extent does incident and persistent use of weak opioids predict problematic opioid use?
  45. Abstracts
  46. Is transdermal buprenorphine for chronic non-malignant pain used long term without co-medication with other potentially addictive drugs?
  47. Abstracts
  48. Prostaglandin E2 production in synovial tissue and acute postoperative pain after knee arthroscopy
  49. Abstracts
  50. Girl presenting with oesophageal spasm pain after fundoplication
  51. Abstracts
  52. Epidemiology of persistent postoperative pain: Association of persistent pain and sensory abnormalities
  53. Abstracts
  54. Cost–benefit of a 13-week multidiciplinary rehabilitation course for chronic non-malignant pain patients
  55. Abstracts
  56. A novel and effective treatment modality for medically unexplained pain
  57. Abstracts
  58. Somatocognitive therapy in the management of chronic gynaecological pain. A review of current approach and historical background
  59. Abstract
  60. The Manual Intervention Trial (MINT)—The effect of various combinations of naprapathic manual therapy. The study protocol of a randomized controlled trial
  61. Abstracts
  62. The experience of chronic pain, loss and grief
  63. Abstracts
  64. Effect of buprenorphine and fentanyl in experimental induced superficial, deep and hyperalgesic pain
  65. Abstract
  66. Pretreatment with opioids enhances afferent induced long-term potentiation in the rat dorsal horn
  67. Abstracts
  68. Pigs in pain—Porcine behavioural responses towards mechanical nociceptive stimulation directed at the hind legs
  69. Abstracts
  70. A human experimental bone pain model
https://doi.org/10.1016/j.sjpain.2010.05.029
Schlagwörter für diesen Artikel
Registered nurses; Musculoskeletal pain; Work-related factors; Personal factors; Sickness absence; Disability

Artikel in diesem Heft

  1. Editorial comment
  2. Functional brain imaging of acute postoperative pain
  3. Clinical pain research
  4. Brain activation due to postoperative pain from the right hand measured with regional cerebral blood flow using positron emission tomography
  5. Editorial comment
  6. Long-term low-dose transdermal buprenorphine therapy for chronic noncancer pain
  7. Original experimental
  8. A 6-months, randomised, placebo-controlled evaluation of efficacy and tolerability of a low-dose 7-day buprenorphine transdermal patch in osteoarthritis patients naïve to potent opioids
  9. Editorial comment
  10. Repeated nociceptive stimulation for detecting drug effects
  11. Original experimental
  12. The effects of gabapentin in human experimental pain models
  13. Editorial comment
  14. Whether the weather influences pain: High prevalence of chronic pain in Iceland and Norway: Common genes? Or lack of sunshine and vitamin D?
  15. Observational studies
  16. A population based study of the prevalence of pain in Iceland
  17. Editorial comment
  18. Swedish nurses are prone to chronic shoulder and back pain because of miserable working conditions and poor leadership?
  19. Observational studies
  20. Predicting of pain, disability, and sick leave regarding a non-clinical sample among Swedish nurses
  21. Abstracts
  22. Trigeminal neuralgia or odontogenic pain
  23. Abstracts
  24. What’s wrong with animal models of pain?
  25. Abstracts
  26. Immunotherapy for neuroblastoma elicits a complement dependent whole body allodynia
  27. Abstracts
  28. Pain mechanisms in animal models of rheumatoid arthritis
  29. Abstracts
  30. Translating basic research to pharmacological treatment of neuropathic pain
  31. Abstracts
  32. Free Poster Presentations: Forearm heat pain does not inhibit electrically induced tibialis anterior muscle pain
  33. Abstracts
  34. Offset analgesia evoked by non-contact thermal stimulator
  35. Abstracts
  36. Inhibition of FAAH reverses spinal LTP
  37. Abstracts
  38. Hyperexcitable C-nociceptors in human paroxysmal pain
  39. Abstracts
  40. Pain sensitivity and experimentally induced sensitization in red haired women
  41. Abstracts
  42. How are opioids used in Norway? Persistent use, utilization of depot formulation and age profile in non-palliation patients
  43. Abstracts
  44. To which extent does incident and persistent use of weak opioids predict problematic opioid use?
  45. Abstracts
  46. Is transdermal buprenorphine for chronic non-malignant pain used long term without co-medication with other potentially addictive drugs?
  47. Abstracts
  48. Prostaglandin E2 production in synovial tissue and acute postoperative pain after knee arthroscopy
  49. Abstracts
  50. Girl presenting with oesophageal spasm pain after fundoplication
  51. Abstracts
  52. Epidemiology of persistent postoperative pain: Association of persistent pain and sensory abnormalities
  53. Abstracts
  54. Cost–benefit of a 13-week multidiciplinary rehabilitation course for chronic non-malignant pain patients
  55. Abstracts
  56. A novel and effective treatment modality for medically unexplained pain
  57. Abstracts
  58. Somatocognitive therapy in the management of chronic gynaecological pain. A review of current approach and historical background
  59. Abstract
  60. The Manual Intervention Trial (MINT)—The effect of various combinations of naprapathic manual therapy. The study protocol of a randomized controlled trial
  61. Abstracts
  62. The experience of chronic pain, loss and grief
  63. Abstracts
  64. Effect of buprenorphine and fentanyl in experimental induced superficial, deep and hyperalgesic pain
  65. Abstract
  66. Pretreatment with opioids enhances afferent induced long-term potentiation in the rat dorsal horn
  67. Abstracts
  68. Pigs in pain—Porcine behavioural responses towards mechanical nociceptive stimulation directed at the hind legs
  69. Abstracts
  70. A human experimental bone pain model
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 29.12.2025 von https://www.degruyterbrill.com/document/doi/10.1016/j.sjpain.2010.05.029/html
Button zum nach oben scrollen