Abstract
Objectives:
Work ability is a crucial occupational health issue in health care settings where a high physical and psychosocial work capacity is required and a high risk of disabling injuries and illnesses is predictable. This study aims to examine the association between the work ability index (WAI) and individual characterizations, workload, fatigue, and diseases among intensive care units' (ICUs') nurses.
Methods:
The study sample included 214 nurses selected by a random sampling method from a target population consisting of 321 registered nurses working in eight ICUs. Multiple linear regression analysis was used to test the association between WAI scores and each of the independent variables.
Results:
Results of multivariate analysis revealed a strong and negative association between WAI scores and diseases (B=-5.82, 95% CI=-7.16, -4.48, P<0.001). Among the studied individual characterizations, body mass index (BMI) was significantly and inversely associated with WAI scores. A significant and negative association was also found between WAI scores and dimensions of MFI-20, such as general fatigue (B=-0.31, 95% CI=-0.53, -0.09, P=0.005) and physical fatigue (B=-0.44, 95% CI=-0.65, -0.23, P<0.001). From dimensions of workload, frustration (B=-0.04, 95% CI=-0.07, -0.02, P<0.001) and temporary demand (B=-0.04, 95% CI=-0.08, -0.0001, P=0.04) showed a negative and significant association with WAI scores, while performance showed a positive and significant association (B=0.04, 95% CI=0.01, 0.07, P=0.005).
Conclusions:
Based on the study findings, development of health care programs with the aim of setting up a healthy work environment characterized by a well-structured preventive attitude toward controlling diseases, and a well-designed organizational framework toward increasing the level of performance and motivation, reducing the level of fatigue, as well as reducing the workload, is necessary to promote work ability among ICUs' nurses.
Keywords: Intensive Care Units (ICUs), Nurses, Work Ability Index (WAI)
Introduction
Work ability is defined as how physically and mentally able is a worker to cope with his/her mental work and physical demands1). Work ability has been widely assessed by the work ability index (WAI), an instrument developed by the Finnish Institute of Occupational Health (FIOH)2). Studies showed that WAI is a helpful tool in predicting long-term sickness absence3,4), early retirement3,5), and identifying prognostic factors for mortality and work disability6,7).
In recent years, there has been a growing interest in conducting studies on work ability in health care settings. Health care workers (HCWs), especially nurses working in intensive care units (ICUs), often operate in job settings where a high physical and psychosocial work capacity is required and a high risk of disabling injuries and illnesses is predictable.
ICUs' nurses often perform heavy physical activities, such as lifting and transferring patients, working in poor postures, standing for long hours, which can lead to development of a wide range of disabling injuries, such as musculoskeletal disorders (MSDs) 8). Nursing also increases the risk of long-term exposures to biological, chemical, and toxic substances that trigger disabling chronic diseases, such as occupational asthma, allergy, liver diseases, skin dermatitis, and kidney ailments9). Furthermore, from the viewpoint of mental dimension of work, it is inevitable for ICUs' nurses to perform complex tasks in critical situations, such as confronting unpredictable events, decision-making under time pressure, and dealing with aggressive relatives10). Based on the stress-strain concept, these situations impose a high mental load on nurses and brining long-run excessive job strain.
New studies have suggested that fatigue is one of the immediate consequences of job strain, particularly in occupations in which job tasks require both intense physical and mental efforts simultaneously11,12), such as those found in health care settings. Job strain and fatigue have been recognized as important risk factors for impairing job performance and work ability12-14).
Given these conditions along with other factors related to organizational characteristics, such as shift working and long and irregular working hours, have caused nurses to be exposed to a high risk of health attenuating determinants and increasingly affected their work ability.
A previous study on work ability of health care professionals found a lower level of work ability among nurses as compared with other medical staff15). This condition may be more critical among ICUs' nurses who may experience higher levels of workload and health-related stressors. Literature review shows that little information is available on work ability and its associated factors in a particular group of health care staff, such as ICUs' nurses.
Nurses constitute a majority of the HCWs' force in Iran. Although, in recent years, establishment of occupational safety and health services within the Iranian hospital health system has led to a better management of health-related stressors and caused an increased protection of HCWs from exposure to workplace safety and health hazards16), factors related to work ability among nurses still remain widely unknown. This study aims to determine the association between WAI and individual characterizations, workload, fatigue, and diseases among nurses working in ICUs of the hospitals affiliated to Shiraz University of Medical Sciences (SUMS).
Materials and Methods
This is a descriptive and cross-sectional survey design.
Subject and study design
The study subjects included nurses working in ICUs of the hospitals affiliated to SUMS. Based on a random sampling method, 250 individuals were randomly selected from a target population consisting of 321 registered nurses working in 8 ICUs.
The required data were obtained through questionnaire. Data collection was performed during the working hours of the nurses and the head nurses helped the researchers in informing participants about the study purpose and the distribution and collection of questionnaires. A total of 222 questionnaires was returned of which 8 questionnaires were not completed and excluded from the analyses. Therefore, the study was performed among 214 nurses, corresponding to a sample of 85.60% of the population. The mean age of participants was 28.88 (SD=4.10), which ranged from 22 to 39 years. All nurses were asked to provide written consent prior to starting the study.
Measurement of variables
Work Ability
In this study, work ability was measured by the WAI questionnaire2), which is calculated by summing the points of 7 items, including current work ability compared with the lifetime best (0-10 points), subjective work ability with regard to physical and mental demands of work (2-10 points), current number of diagnosed diseases by the physician (1-7 points), subjective estimated work impairment due to diseases (1-6 points), sickness absenteeism during the past year (1-5 points), personal prognosis for work ability 2 years from now (1, 4, or 7 points), and mental resources (1-4 points). The index score ranged from 7 to 49 points and the scores were categorized as poor, moderate, good, and excellent. In the original version, reference limits used to categorize WAI into 4 groups (including poor; 7-27 points, moderate; 28-36 points, good; 37-43 points, and excellent; 44-49 points) were based on the distribution of scores for workers aged from 45 to 58 years. Since the nurses in this study had a mean age of 28.88 years, therefore, to prevent an overestimation of work ability, according to previous studies conducted by Kujala et al. based on the distribution of scores, three cut-off points, including the 15th percentile, median, and 85th percentile, were used among the categories17,18).
The validity and reliability of the Persian version of WAI have been explored in a previous study among Iranian nurses, indicating satisfactory psychometric properties of the questionnaire19).
Workload
The NASA task-load index (NASA-TLX) was used to examine workload. NASA-TLX is one of the most sensitive and applicable instruments to assess subjective workload20,21). This index consists of 6 sub-scales with 21 gradations each. The sub-scales include mental demand, physical demand, temporal demand, performance, effort, and frustration. The first 3 sub-scales are related to demands imposed on an individual and the last 3 ones are related to interaction between an individual with his/her task. A score ranging from 0 to 100 is obtained on each scale. The overall workload (OW) score is calculated based on the weighted average of rating on the 6 sub-scales. To simplify, it has been suggested to eliminate the weighting process all together or weighting the subscales and then analyzing them individually20). Many researchers have eliminated the weighting procedure and instead use the raw test scores22). Therefore, in this study, the mean of the raw test scores (sum of scores of all 6 sub-scales divided by 6, the number of sub-scales) was considered as OW. The face validity and reliability of the Persian version of NASA-TLX among ICUs' nurses was confirmed by Mohammadi et al.23).
Fatigue
Multi-dimensional Fatigue Inventory (MFI) was used to assess fatigue. MFI consists of 20 items grouped in 5 dimensions, including general fatigue, physical fatigue, mental fatigue, reduced activity, and reduced motivation. Each dimension consists of 4 items scored on a 5-point scale. The possible range of the total score for each dimension is 4 to 20; higher scores indicate higher levels of fatigue. MFI has been recently translated and validated into Persian language24).
Diseases
Information on the prevalence and type of diseases was obtained based on the nurses' responses to item 3 of the WAI questionnaire, number of diagnosed diseases by the physician. The third item of the WAI questionnaire consists of a detailed list of diseases as follows:
・Trauma/injury from accident
・Musculoskeletal diseases (e.g., chronic disorders of the musculoskeletal system)
・Cardiovascular disease (e.g., hypertension, coronary heart disease, and myocardial infarction)
・Respiratory disease (e.g., chronic bronchitis and sinusitis, bronchial asthma, and emphysema)
・Mental disorder (e.g., slight/severe mental diseases, such as depression, tension, anxiety, and insomnia)
・Neurological and sensory diseases (e.g., problems or injuries related to hearing, visual diseases, neurological diseases, such as stroke, neuralgia, and migraine)
・Digestive disease (e.g., gall bladder stones, liver or pancreatic disease, and gastric or duodenal ulcer)
・Genitourinary disease (e.g., urinary tract infection, kidney disease, and genital disease)
・Skin diseases (e.g., allergic rash/eczema and other skin diseases)
・Tumors (malignant or benign)
・Endocrine and Metabolic diseases (e.g., obesity, diabetes, goiter, or other thyroid diseases)
・Blood diseases (e.g., anemia and other blood disorders)
・Birth defects
Statistical Analysis
Data was analyzed using SPSS software (version 21). The main study variables included both quantitative (BMI, WAI, general fatigue, mental fatigue, reduced activity, reduced motivation, mental demand, physical demand, temporal demand, performance, effort, frustration, and OW) and qualitative measures (gender, education, marital status, physical exercise, type of diseases, work experience, and smoking).
Univariate analysis was used as the primary analysis strategy to determine the unadjusted association between the study variables and WAI. Multivariate linear regression was used to measure the adjusted association between the study variables and WAI. All variables (MFI-20, NASA-TLX, and diseases) were entered into the 3 different models adjusted by BMI, age, and job experience. The modeling procedure was started after collinearity between independent variables was measured using the variance inflation factor index (VIF). The cut-off point of VIF was set at 10.
Results
The demographic characteristic of participants and their associations with the mean WAI score are shown in Table 1. Participants were predominantly females (80.37%, P=0.49) younger than 30 years (60.28%, P=0.02), with a bachelor's degree (91.59%, P=0.93), were non-smokers (98.60%, P=0.52), and had a mean job experience of 6.11 (SD=4.68) years.
Table 1.
Individual characterizations of participants in the study and their association with mean WAI score
| Characterizations | N (%) | B** | CI | P-value | |
|---|---|---|---|---|---|
|
* Selected as reference group **Un standard regression coefficient |
|||||
| Age | 30<* | 129 (60.28) | - | - | - |
| 30-39 | 85 (39.72) | –1.59 | –2.96, –0.22 | 0.02 | |
| BMI | 25<* | 150 (70.09) | - | - | - |
| 25-30 | 61 (28.50) | –2.17 | –3.65, –0.69 | 0.004 | |
| 30> | 3 ( 1.40) | –4.15 | –9.81, 1.51 | 0.15 | |
| Education | Diploma* | 13 ( 6.07) | - | - | - |
| B.Sc. | 196 (91.59) | –0.12 | –2.96, 2.72 | 0.93 | |
| M.Sc. | 5 ( 2.34) | –0.12 | –5.35, 5.10 | 0.96 | |
| Gender | Male* | 42 (19.63) | - | - | - |
| Female | 172 (80.37) | 0.59 | –2.29, –1.11 | 0.49 | |
| Marital status | Single* | 99 (46.26) | - | - | - |
| Marriage | 115 (53.74) | –0.73 | –2.08, –0.62 | 0.28 | |
| Physical Exercise | No* | 162 (75.70) | - | - | - |
| Yes | 52 (24.30) | 0.05 | –1.52, 1.63 | 0.94 | |
| Smoking | No* | 211 (98.60) | - | - | - |
| Yes | 3 ( 1.40) | 1.88 | –3.87, 7.64 | 0.52 | |
| Job Experience | <5* | 120 (56.07) | - | - | - |
| >5 | 94 (43.93) | –1.65 | –2.99, –0.30 | 0.01 | |
The mean WAI score was 39.80 (SD=5; range 24-49) and based on the distribution of the scores, the 15th percentile was 35 points, median was 40 points, and 85th percentile was 45 points. Therefore, 35 points was selected as the cut-off point between poor and moderate work ability, 39 points as that between moderate and good work ability, and 44 points as that between good and excellent work ability, and WAI scores between 7-35, 36-39, 40-44, and 45-49 points were considered to be in the poor, moderate, good, and excellent categories, respectively. After the above-mentioned categorization, 17.8% of the studied population was in the poor, 25.7% in the moderate, 37.4% in the good, and 19.2% in the excellent category.
The results of univariate analysis revealed statistically and inversely significant associations between age (B=-1.59, 95% CI: -2.96, -0.22, P=0.02), BMI (B=-2.17, 95% CI: -3.65, -0.69, P=0.004), and job experience (B=-1.65, 95% CI: -2.99, -0.30, P=0.01) with WAI scores, while other individual characterizations, such as gender, marital status, education, physical exercise, and smoking, did not show a significant association (Table 1).
The mean of WAI scores was not significantly different among different hospitals (P > 0.05). However, the mean of workload and age was significantly different among different hospitals (P<0.05).
In this study, more than one quarter of the participants reported a chronic disease diagnosed by a physician. Musculoskeletal problems, digestive disease, and skin disease accounted for the most prevalent diseases (Table 2). Based on the results from Table 2, 57.14% of the participants with trauma were at the poor level of WAI and 14.29% were at the moderate level. Those with musculoskeletal disease (67.76%), mental disorder (62.50%), digestive disease (52.94%), skin disease (60%), and genitourinary disease (87.50%) were at the poor level of WAI. Furthermore, those with a history of trauma (71.43%), musculoskeletal disease (20.00%), and digestive disease (10.59%) were more than 30 years. According to WAI scores, it was observed that the mean WAI score of nurses with diseases diagnosed by a physician was lower than those without diseases, a poor work ability level (WAI=35.42) as compared with a good work ability level (WAI=41.47).
Table 2.
WAI scores according to presence and type of diagnosed diseases by physician for mean and WAI categories (n=214)
| Age groups | WAI categories | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| <30 N (%) | 30-39 N (%) | Total N (%) | WAI X (SD) | Poor N (%) | Moderate N (%) | Good N (%) | Excellent N (%) | |||
| Diseases | Yes | 24 (11.21) | 35 (16.35) | 59 (27.57) | 35.42 (4.77) | 26 (12.14) | 22 (10.28) | 9 (4.20) | 2 (0.93) | |
| No | 105 (49.06) | 50 (23.36) | 155 (72.42) | 41.47 (4.01) | 12 (5.60) | 33 (15.42) | 71 (33.17) | 39 (18.22) | ||
| Type of disease | ||||||||||
| Trauma | Yes | 2 (28.57) | 5 (71.43) | 7 (3.3) | 34.28 (7.15) | 4 (57.14) | 1 (14.29) | 1 (14.29) | 1 (14.29) | |
| No | 127 (61.35) | 80 (38.65) | - | 34 (16.43) | 54 (26.09) | 79 (38.16) | 40 (19.32) | |||
| Musculoskeletal disease | Yes | 11 (39.29) | 17 (60.71) | 28 (13.1) | 33.46 (4.84) | 19 (67.76) | 6 (21.43) | 3 (10.71) | 0 | |
| No | 118 (91.47) | 68 (36.56) | - | 19 (10.22) | 49 (26.34) | 77 (41.40) | 41 (22.04) | |||
| Cardiovascular disease | Yes | 4 (66.67) | 2 (33.33) | 6 (2.8) | 35.50 (3.27) | 4 (66.67) | 2 (33.33) | 0 | 0 | |
| No | 125 (60.10) | 83 (39.90) | - | 34 (16.35) | 53 (25.48) | 80 (38.46) | 41 (19.71) | |||
| Respiratory disease | Yes | 2 (28.57) | 5 (71.43) | 7 (3.3) | 30.85 (3.28) | 7 (100.00) | 0 | 0 | 0 | |
| No | 127 (61.35) | 80 (38.65) | - | 31 (14.98) | 55 (26.57) | 80 (38.65) | 41 (19.81) | |||
| Mental disorder | Yes | 1 (12.50) | 7 (87.50) | 8 (3.7) | 31.37 (4.98) | 5 (62.50) | 3 (37.50) | 0 | 0 | |
| No | 128 (62.14) | 78 (37.86) | - | 33 (16.02) | 52 (25.24) | 80 (38.83) | 41 (19.90) | |||
| Neurological and sensory diseases | Yes | 1 (14.29) | 6 (85.71) | 7 (3.3) | 33.57 (3.45) | 4 (57.14) | 3 (42.86) | 0 | 0 | |
| No | 128 (61.84) | 79 (38.16) | - | 34 (16.43) | 52 (25.12) | 80 (38.65) | 41 (19.81) | |||
| Digestive disease | Yes | 8 (47.06) | 9 (52.94) | 17 (7.9) | 33.82 (5.25) | 9 (52.94) | 5 (29.41) | 3 (17.65) | 0 | |
| No | 121 (61.42) | 76 (38.58) | - | 29 (14.72) | 50 (25.38) | 77 (39.09) | 41 (20.81) | |||
| Genitourinary disease | Yes | 2 (25.00) | 6 (75.00) | 8 (3.7) | 31.87 (5.27) | 7 (87.50) | 0 | 1 (12.50) | 0 | |
| No | 127 (61.65) | 79 (38.35) | - | 31 (15.05) | 55 (26.70) | 79 (38.35) | 41 (19.90) | |||
| Skin disease | Yes | 7 (46.67) | 8 (53.33) | 15 (7) | 34.00 (5.15) | 9 (60.00) | 5 (33.33) | 0 | 1 (6.67) | |
| No | 122 (61.31) | 77 (38.69) | - | 29 (14.57) | 50 (25.13) | 80 (40.20) | 40 (20.10) | |||
| Endocrine and Metabolic | Yes | 2 (22.22) | 7 (77.78) | 9 (4.2) | 34.66 (5.93) | 4 (44.44) | 4 (44.44) | 0 | 1 (0.46) | |
| No | 127 (61.95) | 78 (38.05) | - | 34 (15.59) | 51 (24.88) | 80 (39.02) | 40 (19.51) | |||
| Tumor | Yes | 0 | 1 (100) | 1 (0.46) | 40.00 (-) | 0 | 0 | 1 (100) | 0 | |
| No | 129 (60.56) | 84 (39.44) | - | 38 (17.84) | 55 (25.82) | 79 (37.09) | 41 (19.25) | |||
With regard to the results from Table 3, the mean of general fatigue was (X=10.92, SD=2.63, P<0.001) and that of reduced activity was (X=11.81, SD=3.39, P=0.007). In addition, the mean of physical demand was (X=82.92, SD=21.32, P=0.98) and that of frustration was (X=57.37, SD=30.72, P<0.001).
Table 3.
Unadjusted association between study variables and WAI
| Dimensions | Min | Max | Mean (SD) | B | CI | P-value |
|---|---|---|---|---|---|---|
| MFI-20 | ||||||
| General fatigue | 4 | 20 | 10.92 (2.63) | –0.71 | –0.91, –0.51 | <0.001 |
| Physical fatigue | 4 | 20 | 11.56 (2.78) | –0.76 | –0.93, –0.59 | <0.001 |
| Mental fatigue | 4 | 20 | 11.22 (2.57) | –0.54 | –0.73, –0.35 | <0.001 |
| Reduced activity | 4 | 20 | 11.81 (3.39) | –0.22 | –0.38, –0.05 | 0.007 |
| Reduced motivation | 4 | 20 | 11.11 (2.62) | –0.60 | –0.79, –0.40 | <0.001 |
| NASA-TLX | ||||||
| Mental demand | 0 | 100 | 72.95 (25.41) | 0.03 | 0.004, 0.05 | 0.02 |
| Physical demand | 5 | 100 | 82.92 (21.32) | –0.0003 | –0.03, 0.03 | 0.98 |
| Temporal demand | 5 | 100 | 83.61 (17.51) | –0.03 | –0.07, 0.003 | 0.07 |
| Performance | 0 | 100 | 77.86 (20.66) | 0.05 | 0.02, 0.08 | 0.001 |
| Effort | 10 | 100 | 84.07 (16.90) | 0.0006 | –0.03, 0.04 | 0.97 |
| Frustration | 0 | 100 | 57.37 (30.72) | –0.05 | –0.07, –0.03 | <0.001 |
| Overall workload (OW) | 43.33 | 100 | 76.46 (12.17) | –0.02 | –0.08, 0.03 | 0.36 |
Based on the results of univariate analysis (Table 3), significant and inverse associations were found between general fatigue (B=-0.71, 95% CI: -0.91, -0.51, P<0.001), physical fatigue (B=-0.76, 95% CI: -0.93, -0.59, P<0.001), mental fatigue (B=-0.54, 95% CI: -0.73, -0.35, P<0.001), reduced activity (B=-0.22, 95% CI: -0.38, -0.05, P=0.007), reduced motivation (B=-0.60, 95% CI: -0.79, -0.40, P<0.001), and frustration (B=-0.05, 95% CI: -0.07, -0.03, P<0.001) with WAI. However, physical demand, temporal demand, effort, and OW did not show significant associations with WAI (P > 0.05 for all).
Multivariate analysis of the data was performed using linear regression with WAI scores (Table 4). Among the studied individual characterizations, BMI was significantly and inversely associated with WAI scores. On the other hand, age and job experience showed no significant association with WAI scores. Some dimensions of MFI-20, such as general fatigue (B=-0.31, 95% CI=-0.53, -0.09, P=0.005) and physical fatigue (B=-0.44, 95% CI=-0.65, -0.23, P<0.001), showed negative and significant associations with WAI scores. The association between NASA-TLX subscales and WAI scores are also shown in Table 4. In this study, due to collinearity between OW and NASA-TLX subscales, OW was not entered into the final model. Hence, frustration (B=-0.04, 95% CI=-0.07, -0.02, P<0.001) and temporary demand (B=-0.04, 95% CI=-0.08, -0.0001, P=0.04) showed negative and significant associations with WAI scores, while performance showed a positive and significant association (B=0.04, 95%CI=0.01, 0.07, p=0.005).
Table 4.
Multiple linear regression results for WAI scores by diseases and different dimensions of workload and fatigue
| Variable | *B | CI | P-value | |
|---|---|---|---|---|
| * Adjusted by BMI, age, and job experience | ||||
| Disease | –5.82 | –7.16, 4.48 | <0.001 | |
| BMI | 25< | - | - | - |
| 25-30 | –0.84 | –2.16, 0.47 | 0.20 | |
| 30> | –3.29 | –8.22, 1.64 | 0.19 | |
| Age | 30< | - | - | - |
| 30-39 | 0.38 | –1.24, 2.01 | 0.63 | |
| Job experience | <5 | - | - | - |
| >5 | –0.65 | –2.25, 0.94 | 0.42 | |
| MFI-20 | ||||
| BMI | 25< | - | - | - |
| 25-30 | –1.20 | –2.48, 0.06 | 0.06 | |
| 30> | –2.35 | –7.16, 2.44 | 0.33 | |
| Age | 30< | - | - | - |
| 30-39 | –0.53 | –2.11, 1.03 | 0.50 | |
| Job experience | <5 | - | - | - |
| >5 | –0.64 | –2.21, 0.91 | 0.41 | |
| General fatigue | –0.31 | –0.53, –0.09 | 0.005 | |
| Physical fatigue | –0.44 | –0.65, –0.23 | <0.001 | |
| Mental fatigue | –0.12 | –0.33, 0.08 | 0.23 | |
| Reduced activity | 0.01 | –0.13, 0.16 | 0.82 | |
| Reduced motivation | –0.20 | –0.42, 0.01 | 0.07 | |
| NASA-TLX | ||||
| BMI | 25< | - | - | - |
| 25-30 | –1.54 | –2.95, –0.14 | 0.03 | |
| 30> | –2.95 | –8.29, 2.39 | 0.27 | |
| Age | 30< | - | - | - |
| 30-39 | –0.18 | –1.91, 1.54 | 0.83 | |
| Job experience | <5 | - | - | - |
| >5 | –1.20 | –2.91, 0.50 | 0.16 | |
| Mental demand | 0.02 | –0.003, 0.04 | 0.08 | |
| Physical demand | 0.01 | –0.02, 0.04 | 0.46 | |
| Temporal demand | –0.04 | –0.08, –0.0001 | 0.04 | |
| Performance | 0.04 | 0.01, 0.07 | 0.005 | |
| Effort | 0.01 | –0.02, 0.05 | 0.54 | |
| Frustration | –0.04 | –0.07, –0.02 | <0.001 | |
Discussion
This cross-sectional study examined the association between a wide range of demographic and clinical characteristics with work ability among ICUs' nurses. In this study, a low mean WAI score was found among ICUs' nurses, especially among those with a history of diseases. WAI was influenced by individual characterizations, diseases, fatigue, and workload.
The findings of this study showed a lower mean WAI score for ICUs' nurses when compared with values reported from other studies for general hospitals' nurses. In this study, because the studied population included young adults, the mean age of 28.88 (SD=4.10) ranged from 22 to 39 years, WAI categorization was conducted by three cut-off points (15th percentile, median, and 85th percentile), based on the classification suggested by Kujala et al. for young employees17,18). The mean WAI score for ICUs' nurses in this study was in the moderate category (39.80 points; SD=5; range 24-49), which, in comparison with the findings of previous studies conducted among general hospitals' nurses, especially considering the mean age of the studied samples, seems to be at an unsatisfactory level of WAI. In a recent study12) conducted among 272 nurses, the mean WAI score was 38.1 points (SD=5.7) for nursing technicians and assistants with a mean age of 41.7 years (SD=9.3; range 23-65 years) who were more than 10 years older than the nurses in this study, and it was in the good category. Likewise, the mean WAI score was 38.6 (SD=6.2) among 1,194 Brazilian nurses and nursing aides with a mean age of 40.3 (SD=13.1) years25), and it was 38.3 (SD=6.1) among 1212 Croatian nurses with a mean age of 42 (range 32-47) years26), and both were in the good category of WAI.
The distribution of WAI scores revealed that 17.8% of nurses were in the poor (7-35 points) and 25.7% in the moderate (36-39 points) work ability category (in total, 43.5% of nurses were in poor to moderate WAI category). In line with this finding, the distribution of poor and moderate WAI scores among a sample including 236 Iranian nurses was 11% and 36.9%, respectively (in total, 47.9% of nurses were in poor to moderate WAI category)19). However, the results of the study conducted by NEXT27), the most extensive international research on the collection of WAI data among 22,355 registered nurses from 10 European countries, showed that the distribution of poor (7-27 points) and moderate (28-36 points) WAI scores was 3.5% and 19.5%, respectively (in total, 23% nurses were in poor to moderate WAI category), almost half of those obtained in this study. However, when the distribution for each country was considered, only in Poland, similar to the results of this study, 42% of the nurses showed poor to moderate WAI scores. With respect to the obtained mean WAI score and high proportion of its poor to moderate categories, a high risk of disability and early retirement among the studied nurses can be predictable in the near future. Hence, planning and implementation of appropriate intervention measures to help improve work ability are recommended.
The results of univariate analysis revealed statistically and inversely significant associations between age, job experience, and BMI and WAI scores. Age and job experience were also explored in previous studies28-30) as significant determinants of WAI. It has been reported that aging is associated with a reduction in functional capacity, work ability, and employability31). In longitudinal surveys, BMI showed a U-shaped association with mortality and low WAI scores32). Overweight and obesity are associated with increased risks of a wide range of diseases, such as cardiovascular diseases, mental disorders, and musculoskeletal problems, and their final outcome is disability and early retirement32).
The findings of this study revealed a strong and negative association between WAI scores and diseases. Chronic diseases have been identified to be one of the major determinants for disability and work-related absence33,34). In a recent study, Alavinia and Burdorf among 11,462 participants of the Survey of Health, Ageing and Retirement in Europe (SHARE) found that, independent from self-perceived poor health, chronic diseases were strongly related to the risk of unemployment and labor force exit35). In this study, more than one quarter of the participants reported a chronic disease, of which musculoskeletal problems, digestive diseases, and skin diseases accounted for the most prevalent diseases. These findings are in agreement with the findings of the study conducted by NEXT27).
Tuomi et al. have introduced musculoskeletal problems as a disease with a negative effect on work ability36). Literature shows that working in ICUs impose a high degree of biomechanical and ergonomic risks that can lead to the development of chronic MSDs/discomforts8). The findings of the studies on MSDs in health care settings revealed that MSD symptoms seemed to be experienced much more by ICU nurses8,37). The prevalence of musculoskeletal problems among a sample of 201 Turkish ICU nurses38) was 19.9% (n=40), which is higher than those reported in this study with a sample size consisting of 214 nurses. The reasons for higher prevalence of MSDs among nurses in ICUs as well as surgical wards have been explained by Kee and Seo37) to be activities related to patient handling and transferring, such as moving, lifting, and repositioning of patients.
According to the results, work ability was influenced by general fatigue (B=-0.31, 95% CI=-0.53, -0.09, P=0.005) and physical fatigue (B=-0.44, 95% CI=-0.65, -0.23, P<0.001). This is consistent with the findings of a recent study conducted by Vasconcelos et al.12), which reported that perceived fatigue was associated with inadequate work ability among Brazilian nurses. Fatigue is a gradual and cumulative process that reflects a decrement in vigilance, motivation, and ability of individuals to perform a particular task39). Furthermore, fatigue has been associated with a variety of both physical illness and mental health measures40), which may affect work ability.
According to the study findings (Table 4), from the NASA-TLX sub-scales, temporary demand (B=-0.04, 95% CI=-0.08, -0.0001, P=0.04) and frustration (B=-0.04, 95% CI=-0.07, -0.02, P<0.001) showed an inverse and significant association with WAI scores, while performance showed a direct and significant association (B=0.04, 95% CI=0.01, 0.07, P=0.005). Temporary demand and frustration have been identified as significant risk factors in the occurrence of MSDs41) accounting for the main cause of permanent work disability (PWD)42). Tuomi et al. reported that the occurrence of poor work ability may be manifold when a combination of the presence of a disease, high work load, and high level of stress symptoms are to be considered36). They concluded that there are several workload factors, such as heavy physical work, hot and cold work environments, lack of freedom, few possibilities to develop, and role conflicts at work, which predict poor work ability or disability. Considering the above mentioned findings from Tuomi et al. and the results obtained from this study on the associations found between WAI scores and diseases, fatigue, and workload, it is suggested to develop a healthy work environment characterized by a well-structured preventive attitude toward controlling diseases and a well-designed organizational framework toward increasing the level of performance and motivation, reducing the level of fatigue, as well as reducing the workload in all hospitals equipped with ICUs.
Study limitations
The cross-sectional study design, nature of subjective collected data, small number of sample size, and unequal distribution of male and female nurses should be considered when using the findings of this study.
Conclusion
There is a lack of studies conducted on WAI among ICUs' nurses. With respect to the sensitivity and importance of the work in ICUs, it is necessary for nurses working in such units to have work ability and work capacity corresponding to their job demands. The results of this study showed that ICUs' nurses were to have a lower mean WAI score when compared with values reported for general hospitals' nurses. Given a moderate level of mean WAI score and a high proportion of its poor to moderate categories, it is recommended to develop appropriate intervention measures toward improving nurses' work ability. High prevalence of musculoskeletal problems underlines a crucial need for identifying and reducing ergonomic risk factors as well as redesigning jobs in such units.
Based on the findings of this study, individual characterizations, disease, fatigue, and workload were the most important factors associated with work ability. Hence, development of health care programs with the aim of setting up a healthy work environment characterized by a well-structured preventive attitude toward promotion of health and controlling diseases and a well-designed organizational framework toward increasing the level of performance and motivation, reducing the level of fatigue, as well as reducing the workload in all hospitals equipped with ICUs is necessary to promote nurses' work ability.
Acknowledgments: This study was extracted from a study proposal written by Mr. Akbar Rostamabadi, which was approved by the Student Research Committee of Shiraz University of Medical Sciences (No. 10642). The authors would hereby like to thank the head nurses for their kind cooperation in informing participants about the study purpose and the distribution and collection of questionnaires. The authors would also like to acknowledge the nurses who kindly participated in this study.
Conflicts of interest: The authors declare no conflicts of interest.
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