Patients diagnosed with cancer may experience various symptoms resulting from the process of the disease and its treatment (Wilkie and Ezenwa, 2012). Cancer-related fatigue (CRF) is one of the most common symptoms reported by patients (Barsevick et al, 2013), regardless of the type of cancer (Brown and Kroenke 2009; Barsevick et al, 2013). In general, CRF is perceived by the individual as a lack of energy, reduced physical or mental ability, general weakness, or becoming easily tired (Jones et al, 2016). CRF is one of the most prevalent symptoms of cancer and its treatment, with its prevalence ranging from 50% to 90% (Campos et al, 2011).
One descriptive study was conducted to examine the prevalence and severity of cancer-related symptoms in a sample of 498 patients diagnosed with cancer in Jordan (Al Qadire and Al Khalaileh, 2016). The results indicated that fatigue was the most prevalent symptom among the study participants, reported by 92.5%. (Al Qadire and Al Khalaileh, 2016). Despite the negative consequences of CRF on the patients and their family members, little information is available regarding the demographic, psychosocial and clinical variables associated with CRF among Jordanian patients.
Prevalence and severity of CRF
Cancer-related fatigue usually persists for a long period after the patient receives curative treatment (Horneber et al, 2012). A survey of 1294 former patients who had been diagnosed with breast, colorectal or prostate cancer in Canada and were cancer free at the time of the study used the Functional Assessment of Cancer Therapy-Fatigue (FACT-F) and the World Health Organization Disability Assessment Schedule (Jones et al, 2016). The results indicated that 29% of the participants had significant levels of fatigue. In addition, those with breast or colorectal cancer had a higher level of fatigue than those with prostate cancer (Jones et al, 2016). Another study was conducted to identify the prevalence of CRF among Norwegian patients who had made a full recovery from cancer (Steen et al, 2017). The sample of 822 patients completed a questionnaire regarding chronic fatigue and demographic and clinical variables. The results indicated that 19% of the participants who had been treated by radical surgery reported chronic fatigue, as did 28% of those treated with chemotherapy (Steen et al, 2017).
CRF has been found to impose a burden on patients before, during and after treatment (Aapro et al, 2017). The results of a descriptive study in one tertiary care centre in Punjab, India, indicated that CRF is experienced by the majority of cancer patients, regardless of their diagnosis, or the type of treatment they received (Banipal et al, 2017).
The prevalence of CRF during cancer treatment seems to be higher than that after the treatment. One study suggested that 95% of oncology patients who were receiving chemotherapy or radiotherapy showed a high level of fatigue (Hofman et al, 2007). This was supported by the results of another study where the prevalence of CRF at the time of cancer diagnosis was 40%; however, this figure increased to 90% among patients being treated with radiotherapy, and was 80% among those being treated with chemotherapy (Koornstra et al, 2014). In the same study, the authors reported that 17-53% of patients experienced CRF post-treatment (Koornstra et al, 2014). In a study conducted in Germany, the prevalence of CRF was 53% at the beginning of treatment for patients aged 20–39 years, regardless of the cancer site (Singer et al, 2011). However, the prevalence rate increased to 55% after hospital discharge (Singer et al, 2011).
Factors associated with CRF
Identification of the psychological, biological and social factors associated with CRF could be helpful in developing specific interventions to support patients at risk of high levels of fatigue (Fabi et al, 2017). Tian et al (2016) noted that there were significant discrepancies in the results of various studies exploring the relationship between sociodemographic variables and fatigue, with some showing no significant relationship at all (Banthia et al, 2009; Goldstein et al, 2012; Spichiger et al, 2012). Furthermore, most studies investigating the factors associated with CRF were conducted in developed countries. However, according to a systematic review, there were various correlates between CRF and patient-related factors, disease-related factors, and treatment-related factors (Colloca et al, 2016). It was suggested that chemotherapy, psychological distress and pain are associated with CRF, and CRF was significantly associated with depression, anxiety (Fabi et al, 2017; Reinertsen et al, 2017) and low haemoglobin (Hb) levels (anaemia) (Obead et al, 2014).
To date, little is known about factors associated with CRF among Jordanian patients. This is one of the most serious obstacles to effective CRF management in the country (Abdalrahim et al, 2014). This justifies the current study, which might contribute to existing knowledge and fill the gap in the literature. Therefore, the main purpose of the current study was to assess the prevalence of CRF and explore its predictors among a group of Jordanian patients.
Aims of the study
The current study had the following aims:
Methods
Design
The current study used a cross-sectional survey design.
Sample and sample size
The sample comprised people diagnosed with cancer who were inpatients or outpatients at selected hospitals. A convenience sampling technique was used to recruit the study participants; inclusion criteria were adults:
Using G⋆ Power software for multiple regression analysis, a power of 0.95, alpha value of 0.05, a medium effect size, and the number of predictors being 14, the total required sample size was 194. However, because it was thought that some respondents might drop out of the study and others might fail to complete questionnaires, the sample size was increased to 240 (30 were excluded).
Setting
This study was conducted in two hospitals. The first was a government-referral hospital. Patients visit this hospital from all over the country. It provides care for patients with various types of cancer and all forms of treatment are available. The second was a university-affiliated hospital, which has various units and wards providing all types of care for cancer patients including chemotherapy, bone marrow transplants, surgery for brain tumours, and plastic surgery.
Instruments
Two instruments were used to collect data: a demographic data sheet and the Piper Fatigue Scale Revised (PFS-R) (Piper et al, 1998).
Demographic data sheet
The demographic data sheet was developed to collect information about patients' basic sociodemographic variables, including gender, age, education level, marital status and monthly income. It also asked about disease-related data such as type of cancer, type of treatment, exercise pattern, date of diagnosis, length of hospital stay, date of diagnosis with cancer, age at time of diagnosis and stage of cancer.
The Piper Fatigue Scale Revised
The PFS-R is a commonly used measure to assess fatigue among patients with cancer (Piper et al, 1998). The scale covers four dimensions of fatigue: sensory dimension (five items), affective meaning dimension (five items), cognitive-mood dimension (six items), and behavioural-severity or intensity dimension (six items). Each item was rated on an 11-point numerical scale from 0 (meaning not at all) to 10 (meaning a great deal of fatigue) (Borneman et al, 2012). This scale has excellent psychometric properties, with a Cronbach's alpha value of >0.83. In addition, it has well-established discriminant and concurrent validity (Piper et al, 1998). Total mean scores of 1–3 indicate mild fatigue, 3–6 indicate moderate fatigue, and 6–10 indicate severe fatigue (Reis et al, 2012). A further item investigated the duration of fatigue, with four open-ended questions to assess symptoms other than fatigue, measures to relieve fatigue, fatigue duration, perceived causes, and additional descriptions of fatigue. However, these open-ended questions were not included when calculating the total score on the scale (Piper et al, 1998).
Permission to use the Arabic version of PFS-R was obtained. This version had been used previously in Jordan and has excellent psychometric properties, with good content validity and Cronbach's alpha value of 0.95 for the overall measure; and Cronbach's alpha values of 0.92, 0.81, 0.95 and 0.86 for the behavioural, affective, sensory and cognitive subscales respectively (Abu Obead et al, 2014).
Data collection procedure
Ethical approval to conduct the study was obtained from appropriate ethics committees. The researcher (OMA-T) then set up meetings with nurse managers and hospital administrators to arrange the process of data collection. Thereafter, visits were made to the selected hospitals to identify patients who met the inclusion criteria. Potential participants were given a letter containing information regarding the study purpose and its significance and were spoken to individually. Once a participant agreed to take part, a verbal informed consent was obtained by the researcher. Each participant was then asked to complete the demographic data sheet and the PFS-R. All completed questionnaires were collected either personally from the participant by the researcher or were left in the office of the nurse manager for collection by the researcher.
Data analysis
Data were entered and analysed using the Statistical Package for the Social Sciences software (SPSSv22). Descriptive statistics including percentages, frequencies, standard deviation (SD), and means were used to summarise the characteristics. The independent t-test and Kruskal-Wallis tests were also used. Further, the relationships between the mean CRF score and participants' age, age at time of diagnosis, number of children, Hb, and number of hours of sleep were tested using the Pearson Product-Moment Correlation.
Results
Patients’ characteristics
A total of 240 patients were initially recruited to the study; 30 were excluded because they were not suffering from fatigue at the time of the survey, giving a final sample of 210. The mean age of participants was 52.1 years (SD=12.7), and the mean age at time of diagnosis was 50.7 (SD=12.6). The majority were female (n=136, 65%), married (n=158, 75%), with children (n=149, 71%), not employed (n=190, 90%), admitted to the hospital (n=137, 65%), and having a family income of less than 300 Jordanian dinar (n=149, 71%); see Table 1. About half the participants had a primary level of education (n=101, 48%).
| Variable | Frequency (%) | Mean (SD) | |
|---|---|---|---|
| Age (years) | 52.1 (12.7) | ||
| Age at diagnosis (years) | 50.7 (12.6) | ||
| Sleep hours | 5.9 (2.4) | ||
| Haemoglobin (Hb) (g/dl) | 10.7 (1.7) | ||
| Exercise | Yes |
14 (6.7) |
|
| Gender | Male |
74 (35.2) |
|
| Marital status | Single |
22 (10.5) |
|
| Education | Primary |
101 (48.1) |
|
| Employment | Yes |
20 (9.5) |
|
| Income (Jordanian dinar; 1JOD=£1.08) | 0–300 |
149 (71.0) |
|
| Having children | Yes |
149 (71.0) |
|
| Number of children | 3.1 (2.9) | ||
| Cancer type | Lymphoma |
18 (8.6) |
|
| Stage of cancer | One |
10 (4.8) |
|
| Chemotherapy | Yes |
174 (82.9) |
|
| Radiotherapy | Yes |
55 (26.2) |
|
| Hormonal therapy | Yes |
20 (9.5) |
|
| Surgical therapy | Yes |
153 (72.9) |
|
| Duration of illness | < 1 year |
140 (66.7) |
|
| Type of visit | Outpatient |
73 (34.8) |
|
The mean length of sleep experienced by participants each night was 5.9 hours (SD=2.4) and mean Hb level was 10.7 grams per decilitre (g/dl) (SD=1.7). Only 14 (7%) participants did regular exercise. The majority received chemotherapy (n=174, 83%). However, a large percentage (63%) of the participants did not know the stage of their illness (n=133). Most were diagnosed with cancer less than 1 year previously (n=140, 67%) and over a third of them had breast cancer (n=75, 36%); see Table 1.
Prevalence and severity of CRF
Of the 240 patients approached, 210 (87%) had fatigue at the time of the survey. The total mean fatigue score was 6.2 (SD= 1.7) out of a maximum possible score of 10, indicating a severe level of fatigue. In addition, 167 participants (79%) reported having experienced fatigue for months, and 30 (14.3%) for weeks.
Comparison of CRF based on participants' characteristics
The results of independent samples t tests revealed a statistically significant difference in the mean (M) fatigue score according to employment status (t=−2.23, P=0.020), where the non-employed patients had a higher score (M=6.3, SD=1.7) than those who were employed (M=5.4, SD=5.4). Participants who were treated with radiotherapy had a higher total mean CRF score (M=6.6, SD=1.4) than patients who were not (M=6.02, SD=1.8), (t=2.2, P=0.020); see Table 2. However, participants who underwent surgery had lower mean scores (M=6.0, SD=1.7) than those who did not (M=6.6, SD=1.7), (t=−2.08, P=0.040). Regarding the type of cancer, the results of the Kruskal-Wallis test indicated a statistically significant difference in the mean fatigue score by type. Post-hoc analysis (ie Tukey HSD) indicated that participants with lung cancer (M=7.0, SD=1.4) had a significantly higher mean score than those with breast cancer (M=5.8, SD=1.6, P=0.010). However, no significant differences (P>0.05) in CRF were found with regard to gender, marital status, education levels, income, duration of illness, having children, chemotherapy, hormonal therapy, stage of cancer or exercising (see Table 3).
| Variable | Category | N | Mean fatigue score | SD | t | P value |
|---|---|---|---|---|---|---|
| Gender | Male |
74 |
6.4 |
1.7 |
1.579 | 0.116 |
| Employment | Yes |
20 |
5.4 |
1.8 |
−2.23 | 0.027* |
| Have children | Yes |
149 |
6.3 |
1.7 |
1.265 |
0.207 |
| Chemotherapy | Yes |
174 |
6.2 |
1.7 |
0.437 | 0.662 |
| Radiotherapy | Yes |
55 |
6.6 |
1.4 |
2.17 | 0.017* |
| Hormonal therapy | Yes |
20 |
5.7 |
1.5 |
−1.19 | 0.236 |
| Surgical therapy | Yes |
153 |
6.0 |
1.7 |
−2.08 | 0.039* |
| Exercise | Yes |
14 |
5.5 |
2.0 |
−1.50 | 0.135 |
P value <0.05
| Variable | Category | N | Mean fatigue score | SD | t | P value |
|---|---|---|---|---|---|---|
| Marital status | Married |
158 |
6.0 |
1.9 |
0.625 | 0.536 |
| Education | Primary |
101 |
6.4 |
1.7 |
1.127 | 0.339 |
| Income (JD) | 0–300 |
149 |
6.2 |
1.7 |
0.285 | 0.753 |
| Duration of illness | Less than 1 year |
140 |
6.2 |
1.8 |
0.208 | 0.812 |
| Cancer type | Lymphoma |
18 |
6.0 |
1.7 |
3.27 | 0.010* |
| Stage of cancer | One |
10 |
5.6 |
1.8 |
0.96 | 0.428 |
P value <0.05
The relationships between the mean fatigue score and participants' age, age at time of diagnosis, number of children, Hb, and number of hours of sleep were tested using Pearson Product-Moment Correlation. There were no significant relationships between the mean fatigue score and age (r=−0.037, P=0.6), age at time of diagnosis (r=−0.029, P=0.680), or number of children (r=0.006, P=0.930). However, there were significant negative relationships with Hb (r=−0.254, P<0.001), and number of hours of sleep (r=−0.161, P=0.020).
Predictors of CRF among Jordanian patients with cancer
A multiple linear regression analysis was used to examine the predictors of CRF. All variables that were significantly associated with CRF were entered into the regression model: employment, having surgery, radiotherapy treatment, Hb, length of stay, having lung cancer, and number of hours of sleep. Table 4 presents the results of the multiple-regression analysis. The overall regression model was significant (F (4, 205)=10.93, P<0.001). The model explained 18% of the variance in CRF (R2=0.18). The results show that being unemployed, with longer hospitalisation, low Hb, and having lung cancer seem to predict higher levels of CRF.
Discussion
In the current study, about 80% of the participants had experienced fatigue for several months. Additionally, participants rated their fatigue as moderate to severe. This outcome is consistent with the findings of another study that measured the severity of CRF among 362 patients undergoing chemotherapy in the Republic of Ireland using the Piper Fatigue Scale-Revised, which found that participants had a moderate level of fatigue (the mean total fatigue score was 4.9 (SD= 2.2) (O'Regan and Hegarty, 2017). The results of the current study were also consistent with those of previous studies that reported moderate levels of CRF among patients who received various types of treatment such as chemotherapy, radiotherapy, and surgery (Galiano-Castillo et al, 2014; Wang et al, 2014; Wang and Woodruff, 2015). Thus, nurses should assess patients with cancer on a regular basis to improve the chance of fatigue being detected and treated. Without this approach, patients will continue to experience what can be a devastating symptom.
In this study, unemployed patients reported higher fatigue levels than employed patients. This could be due to the fact that there is a negative relationship between CRF levels and the ability of the patient to work; patients who suffer from fatigue might lose their job and become unemployed (Hofman et al, 2007; Behringer et al, 2016). A previous study found that 75% of patients reported that CRF had a negative influence on their work (Curt et al, 2000). CRF could also reduce the number of hours a person could work, increase the frequency of taking sick leave, cause disability and even cause loss of a job due to an inability to return to work (Behringer et al, 2016).
The results of the current study indicated that participants who received radiotherapy had higher CRF levels than patients who did not. This outcome was consistent with a previous study (Jereczek-Fossa et al, 2002), which reported that fatigue became cumulative during the course of radiotherapy among 80% of patients, and this fatigue could become chronic in 30% of cancer patients receiving radiotherapy. However, the mechanism underlying CRF among patients receiving radiotherapy is poorly understood and could be associated with genetic factors (Du et al, 2018).
On the other hand, participants who underwent surgery had lower CRF levels than patients who did not. This could be related to removal of the tumour or other fatigue-inducing factors (Miller et al, 2016). This is supported by a previous study conducted in Norway (Steen et al, 2017), which indicated that about 81% of cancer patients who underwent surgery did not have chronic fatigue. Further, patients diagnosed with lung cancer had significantly higher CRF scores than those diagnosed with breast cancer (Steen et al, 2017). To date, lung cancer has been considered the most common type of terminal cancer worldwide (Carnio et al, 2016). The mortality statistics indicate that only 11% of individuals with lung cancer live more than 5 years after diagnosis (Torre et al, 2015). Furthermore, even many years of after lung cancer diagnosis, patients reported that relief from fatigue was rarely experienced (Sanders et al, 2010). Another source of fatigue in lung cancer patients is associated with different comorbidities and the decline in lung function (Carnio et al, 2016). All of these factors could contribute to high levels of CRF among patients with lung cancer compared with those who have breast cancer.
The results of the current study revealed that participants who took regular exercise had lower CRF scores than those who did not exercise regularly. However, this difference was not statistically significant. Previous research suggested that physical exercise is associated with improvement in fatigue among cancer patients (Medysky et al, 2017; Oberoi et al, 2018). This outcome was also supported by a meta-analysis showing the benefits of physical exercise in improving several dimensions of CRF (Van Vulpen et al, 2016), including in patients with an advanced stage of cancer (Tian et al, 2016). The authors of the present study concluded that the number of respondents (14) taking regular exercise was too small to be statistically significant. In the same context, the current study showed no significant difference in CRF levels between patients who received chemotherapy and those who did not. This is not consistent with the previous literature because it is well-known that chemotherapy is positively associated with CRF (Obead et al, 2014). Again, the researcher suggests that the small number of respondents who did not receive chemotherapy might have caused a reduction in the statistical power, resulting in non-significant outcomes.
Although previous research indicated that advanced stages of cancer were significantly and positively associated with CRF (Tian et al, 2016; Fabi et al, 2017), the current study did not find a significant relationship. This outcome could be because most respondents did not know the stage of their illness. Nevertheless, it was found that respondents with stage four cancer had the highest scores on PFS-R, although this outcome was not statistically significant. In the current study, Hb levels were significantly and negatively correlated with CRF. This outcome is consistent with the previous literature, indicating that anaemia is associated with fatigue among cancer patients (Obead et al, 2014; Saligan et al, 2015).
It was found that the number of hours of sleep was significantly associated with CRF, in agreement with previous research (Tian et al, 2016). Inability to sleep is not only associated with fatigue but also with depression and pain experienced by patients with cancer (Loh et al, 2018). Sleep disturbance is considered one of the most important contributing factors to CRF (Campos et al, 2011). Current interventions to manage CRF tend to focus on helping patients to have adequate sleep (Berger et al, 2015). It has been reported that CRF accompanied by inadequate sleep might contribute to poor quality of life among patients with cancer (Ancoli-Israel et al, 2014). Therefore, there is a need to promote sleep quality and adequacy among cancer patients.
Finally, the regression model confirmed that being unemployed, with longer hospitalisation, low Hb, and having lung cancer seemed to predict higher levels of CRF. This result is in accord with previous studies (Van Vulpen et al, 2016; Medysky et al, 2017; Du et al, 2018; Loh et al, 2018), and it is imperative that healthcare providers and nurses in particular take these factors into consideration in clinical practice. Hence, those patients need focused assessment and early intervention to reduce and treat their fatigue to enable them to continue their treatment journey and reduce unpleasant symptoms.
Healthcare institutions and providers should adopt one of the number of guidelines and recommendation that are available and updated regularly, such as the Canadian Practice Guideline for Screening, Assessment, and Management of CRF (Howell et al, 2015) or the National Comprehensive Cancer Network guidelines for clinical practice (Berger et al, 2010).
Limitations of the study
The current study has limitations that need to be considered when interpreting the findings. First, the participants were recruited using a convenience sampling technique. This could limit the possibility of generalisation. Another limitation is the use of a cross-sectional design, which eliminates the possibility of identifying changes in CRF over time and establishing causality. In addition, the CRF levels were measured on a self-reported questionnaire, which could have influenced the accuracy of the results. Further, the quantitative research design used in this study reduced the ability to conduct an in-depth investigation about the experience of CRF. Finally, including a heterogeneous group of participants in terms of the type of their cancer might also limit the generalisability of the study findings.
Conclusion
This study demonstrated that fatigue was prevalent among patients with cancer, and that they experienced moderate to severe CRF. Several variables and factors associated with CRF were identified. In response to these results, nurses should pay more attention to CRF, which needs to be assessed on a regular basis and to be managed with the available pharmacological and non-pharmacological interventions. Implementing the available guidelines to treating CRF is crucial to reduce patients' suffering and improve their quality of life.