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Are Pain and Fatigue in Multiple Myeloma Related to Psychosocial Factors?

A Systematic Review

Wilson Rogers, Luke P. MSc; Rennoldson, Mike DClinPsy

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doi: 10.1097/NCC.0000000000000786
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Multiple myeloma (MM) accounts for around 2% of cancers.1 It is a neoplasm of the plasma cells and particularly prominent symptoms are bone pain from osteolytic lesions and fatigue related to anemia.2 The global incidence of MM is approximately 140 000 people each year, with an increase of 126% from 1990 to 2016.1 Although it is generally regarded as an incurable disease, significant recent developments in effective treatments have led to more effective disease control and increases in median survival for MM patients, when these treatments are available.2 For example, in the United States, 5-year survival increased from around 30% to greater than 50% from 2004 to 2014.3

There have been calls for this improvement in disease control to be matched by improvements in supportive care and a focus on improving the health related quality of life of patients.4 This need is made particularly acute by an increase in available lines of anticancer treatments leading to an accumulation of persistent treatment side effects, notably peripheral neuropathy and fatigue.4 Given these disease and treatment effects, it is not surprising that fatigue and pain, alongside psychological distress, have been found to be among the most significant quality of life concerns for MM patients.5,6 In response to this need, this article reports findings from a systematic search and narrative synthesis of original research into the relationship between psychosocial variables and pain and fatigue in MM.

Within general cancer populations, psychosocial variables have been found to be deeply intertwined with pain and fatigue across the treatment pathway, including to survivorship and end of life.7 This is a 2-way relationship; psychosocial factors are implicated in the maintenance of cancer pain8 and fatigue,9 which in return play a significant role in triggering and maintaining psychological distress tied to cancer.8,10 We define psychosocial variables as processes or phenomena that are psychological in nature, or environmental or social phenomena that intersect with mind and behavior,11 such as social support.

In cancer, the roles of psychosocial factors in pain and fatigue appear to share a number of key features. Higher-level psychological variables such as mood and cognition are thought to both affect, and be affected by, the pathophysiological mechanisms underpinning pain12 and fatigue.13 This means that changes to mood and cognition can impact the intensity of symptom experience. This can happen through the intrusion of the symptom into awareness, the interpretation of the significance of the symptom, and an individual’s behavioral response.12,13 The experience of pain and fatigue can in turn affect cognition, emotion, and behavior in a bottom-up manner.12,13 Evidence for these links comes from a variety of sources. These include studies demonstrating the clustering of symptoms of pain, fatigue, and distress in cancer patients9,10 and, for pain especially, experimental and imaging studies in other populations.12 However, 1 key limitation of research into the psychosocial mechanisms of cancer pain and fatigue to date is its focus on breast cancer,7 which leaves uncertainty about whether these relationships are equally present in other cancer populations.

Further evidence for the role of psychosocial factors in cancer pain and fatigue comes from intervention effectiveness research. Recent meta-analyses have found that psychological interventions for cancer pain14,15 and fatigue16 are effective. Interventions that have been found to be effective vary from cognitive-behavioral interventions in both pain and fatigue, to hypnosis and education interventions in pain.7 However, in the case of cancer pain, it has been documented that psychological interventions are only rarely available.17 This absence of provision contrasts starkly with the significant progress that has been made in expanding the provision of pharmacological treatment for pain in cancer.18 A recent systematic review of the prevalence of cancer pain has found that the expansion of pharmacological treatment alone has not resulted in the hoped-for reduction in the incidence of cancer pain.19

The relationship between psychosocial factors and pain and fatigue in MM patients deserves specific examination. This is because MM presents a unique combination of 4 patterns of disease and treatment effects. First, there are acute effects of the disease causing osteolytic lesions, diffuse bone loss, and anemia and a high risk of fracture around the time of diagnosis (especially of the vertebrae with the accompanying risk of spinal cord compression), although this risk usually reduces considerably after treatment.20 Second, there are acute and persistent effects from treatment toxicity causing fatigue and peripheral neuropathy. Third, there is a high likelihood that (especially older) patients with MM will present with 1 or more comorbid conditions such as cardiovascular, renal, or metabolic diseases21 that may be associated with pain or fatigue. Fourth, the use of opioid analgesia for moderate and severe pain may affect perceived fatigue directly through sedative effects.4 This complex combination presents several important challenges to patients and their care team in interpreting the source of pain and fatigue and responding to this, increasing the risk that patients may fear or feel unable to control these symptoms, and develop symptoms of low mood or anxiety in response.5 We therefore set out to systematically review the research literature on the associations between psychosocial factors and pain and/or fatigue in patients with MM.


The study consisted of a systematic search and narrative synthesis of original research into the relationship between psychosocial factors and pain and/or fatigue in patients with MM. A protocol has been published in the International Prospective Register of Systematic Reviews (PROSPERO; reference no. CRD42017077097). The methods accord with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement on reporting systematic reviews.22

Search Strategy and Selection Criteria

The search strategy combined database searching of PubMed, PsycINFO, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Central Register of Controlled Trials and hand searching of reference lists and citations of included papers identified in the database search. The search period included any research entered on the databases up to September 2017. The search strategy consisted of the main components “multiple myeloma,” “psychosocial,” “pain,” or “fatigue” included as appropriate MeSH or subject headings and free-text words in the title and abstract.

The complete search strategy is available from the PROSPERO website (

The inclusion criteria were as follows: adults with a diagnosis of MM (population) and reporting primary data, including a univariate or multivariate analysis of the relationship between a measure of a psychosocial variable (exposure) and a measure of either pain or fatigue in a sample of patients with MM (outcome). Our definition of a psychosocial variable, reported in the Introduction, followed the American Psychological Association dictionary definition. We used a definition of a psychosocial variables, rather than an a priori list of acceptable psychosocial variables. We expected researchers to have examined a very wide range of relevant psychosocial variables. We wished to avoid excluding potentially relevant research by using an a priori list of psychosocial variables. We adopted a broad definition of pain covering both nociceptive and neuropathic pain and, in the case of the latter, did not require measures to report pain separately from other pain syndrome sensations such as numbness. We followed the National Comprehensive Cancer Network definition of fatigue as a “distressing, persistent, subjective sense of physical, emotional and/or cognitive tiredness…that is not proportional to recent activity and interferes with usual functioning.”23 The exclusion criteria were studies not published in English or studies reporting results for mixed populations where findings for adults with MM were not reported separately.

After removal of duplicates, the selection of studies for inclusion was undertaken independently by the 2 authors in 2 stages. First, abstracts and titles of all studies were inspected for potential relevance to undergo full-text review. Second, the full text of each potential study was reviewed for final inclusion in the review. The reviewers compared the above definitions of the terms psychosocial, pain, and fatigue to the variables measured in each study to determine whether studies met the inclusion criteria. For example, both reviewers determined that work satisfied the definition of a psychosocial factor because it is part of a person’s social environment and has psychological effects. We determined that quality of life did not meet our criteria because it is an overarching variable, including physical symptoms such as pain and fatigue. However, subscales of quality of life measures such as social functioning and emotional functioning were determined to meet the definition of a psychosocial variable. Differences between reviewers were resolved by discussion after completion of each stage.

Data Extraction, Risk of Bias Assessment, and Synthesis

Data from each included study was extracted independently by each author using a standardized data extraction form covering the study context, population, sampling strategy, measures and data collection, results, and analysis.

The risk of bias of each study was independently assessed by each author using a modified version of the Cochrane risk of bias tool for nonrandomized studies,24 covering 5 areas of potential bias: (1) selection of participants into the study; (2) measurement of pain/fatigue, (3) measurement of the psychosocial variable, (4) missing data, and (5) reporting bias. The selection of a suitable risk of bias tool was challenging because of the wide range of study designs included within the review, including randomized and nonrandomized intervention studies and cross-sectional observational studies. We therefore adapted the Cochrane tool to enable us to judge the risk of bias in domains where bias might be introduced for our research question. Rather than excluding papers on the basis of these evaluations, evaluations were used to systematically and qualitatively describe the relative strengths and weaknesses of each study’s methodology.

The studies included in the review were too heterogeneous to justify meta-analytic data synthesis. Instead, the extracted findings are presented in narrative synthesis.


Search Results

The results of the search are summarized in the Figure. The initial database search identified 611 potential articles. One additional article was identified through the reference list and citation search. Of these, 129 were duplicates, leaving 483 articles to be screened. On the basis of the title and abstract, 66 were agreed by the reviewers to potentially meet the inclusion criteria, and the full-text versions were reviewed. Of these, 55 were excluded—most commonly because although a study may have reported measuring a psychosocial variable and fatigue and/or pain, no test of association between these variables was carried out and reported (n = 27). The next most common reason for exclusion was that the sample consisted of a mix of cancer types, and results for MM patients were not reported separately (n = 15). Eleven articles were retained for inclusion in the study.

PRISMA flow diagram.

Study Characteristics

Table 1 gives an overview of the included studies.25–35 The sample size of studies ranged from 32 to 1071 participants (mean, 240). The studies were located in Europe and the United States—there was a notable absence of literature from Asia. Studies included participants from across the disease trajectory. Four studies25,28,31,32 had mixed samples (combined n = 1587). There were 3 studies27,33,34 of newly diagnosed patients, but 2 of these studies appeared to report data from the same sample33,34 (combined unique n = 400). Two studies were of patients with stable disease (combined n = 164). One study35 was of patients with refractory disease (n = 202), and 1 study29 did not report the disease stage of participants (n = 79). Only 1 of the studies explicitly set out a primary aim to study the relationship between a psychosocial variable and fatigue or pain.32 All of the remaining included studies had a different primary focus, which perhaps accounts for the reporting weaknesses noted in the risk of bias assessment reported later. Of the 11 studies, 7 were cross-sectional survey designs, 2 were randomized controlled trials of interventions (although both of these studies only extracted information relevant to this review from their baseline measurement), 1 study was a repeated-measures design validating a new measure (again extracting only baseline information of relevance to this review), and 1 study was a pre-post design evaluating an intervention.

Table 1
Table 1:
Overview of Study Characteristics

Measurement of Pain and Fatigue

All studies used validated measures for pain and fatigue. Four different measures were used for fatigue and 7 for pain—the most common being the European Organization for Research and Treatment of Cancer Quality of Life of Cancer Patients 30 (EORTC QLQ-C30) symptom subscales for both pain and fatigue. The measures ranged from specialist scales such as the Functional Assessment of Cancer Therapy–Fatigue for fatigue and the Brief Pain Inventory for pain, to single-item measures such as pain and fatigue questions in the 12-Item Short Form Health Survey quality of life measure. No study included measures of pain that distinguished between neuropathy and other types of pain.

Psychosocial Variables

The included psychosocial variables, and the measures used, were highly heterogeneous across the sample. To enable comparison, we grouped similar psychosocial variables together. In this process, we identified 15 different areas, such as global distress, sleep, and religiosity. Twelve of these areas featured in studies reporting associations with fatigue (Table 2) and 14 featured in studies reporting associations with pain (Table 3). These psychosocial variables were measured by 14 different measures. The most common psychosocial areas in the sample were global distress and depression, each included in 4 studies. As with pain and fatigue, the most common measure was the quality of life measure EORTC QLQ-C30. Although studies varied in which psychosocial subscales were reported from this measure, psychosocial subscales from quality of life measures such as this formed the largest group of variables measured in the included studies. Given this heterogeneity, and the low number of studies of the same variables, we decided not to attempt any meta-analysis, providing instead a narrative synthesis of the results.

Table 2
Table 2:
Summary Associations Between Psychosocial Variables and Fatiguea
Table 3
Table 3:
Summary of Associations Between Psychosocial Variables and Paina

Relationship Between Fatigue and Psychosocial Factors

An overview of the relationship between psychosocial factors and fatigue can be found in Table 2. Findings from the included studies are summarized by different areas of psychosocial variable. Overall, fatigue was found to be related to a range of psychosocial factors. The strongest evidence was found for a relationship between fatigue and global distress and anxiety. The evidence for relationships with other psychosocial variables was either weak or contradictory. Eight studies reported results of tests of association between a measure of fatigue and a psychosocial variable. To help interpret the findings in Table 2, we use Cohen’s36 widely used descriptions of effect size. For example, a medium-sized effect of a correlation of 0.46 between fatigue and global distress would indicate just over 20% shared variance; thus, a change in the level of global distress can be expected to be accompanied by a notable proportionate change in fatigue. Cohen suggests cutoffs of 0.1 for a low effect, 0.3 for a medium effect, and 0.5 for a large effect, meaning the evidence suggests a medium to large effect in the relationship between fatigue and global distress. The evidence for a more specific relationship between fatigue and depression was mixed, with 2 studies reporting a range of mostly medium-sized associations but 1 finding no significant relationship. Surprisingly, the 2 studies that reported associations between sleep and fatigue found only small or nonsignificant relationships. The results suggest that fatigue has a relationship with a wide range of psychosocial variables. However, most psychosocial variables were addressed by only 1 study, and replications of these are needed.

Relationship Between Pain and Psychosocial Factors

An overview of the relationship between psychosocial factors and pain can be found in Table 3. These relationships show similarities with the patterns already observed in the research on psychosocial factors and fatigue. The strongest evidence was found for a relationship between pain and global distress, anxiety, depression, and cognitive functioning. Ten studies reported results of tests of association between a measure of pain and a psychosocial variable. The evidence was strongest for a relationship between anxiety and pain, with 5 studies reporting significant small to medium-sized associations and no nonsignificant findings. The evidence for a relationship with global distress, depression, and cognitive functioning was also stronger than for other variables, each with 3 studies reporting mostly medium effect sizes and no nonsignificant findings was mixed with 2 studies reporting a range of low to medium-sized associations.

Study Quality

Overall, most studies were rated as having a low risk of bias on most criteria. A summary of the risk of bias assessment for each study can be found in Table 4. Three studies26,28,29 appeared at higher risk of bias largely because of their use of measurement instruments with less well established validity and reliability. Across the sample, the greatest risk of bias arose from inadequate reporting. Issues with reporting included incomplete reporting of test statistics, incomplete reporting of data from measures, or errors in reporting. One paper35 had clearly mistakenly reported the direction of the associations between Functional Assessment of Chronic Illness Therapy Fatigue results and the defined psychosocial variables. The reported associations between this measure and the defined psychosocial variables shared the same valence as the EORTC QLQ-C30, despite the authors indicating in their method section that these values should be inversely related. Consequently, these associations were omitted from this review. We contacted the authors to check these data but received no response.

Table 4
Table 4:
Assessment of Risk of Bias


This review has found consistent evidence of associations between psychosocial variables—notably, global distress and depression, and pain and fatigue in patients with MM across the disease trajectory. Furthermore, there was good evidence of a relationship between pain and anxiety and some evidence of a relationship between pain and subjective cognitive functioning. The included studies most commonly estimated these relationships to be in the medium range of effect sizes.36 The review also reports evidence of potential relationships with a much wider range of psychosocial variables, but the number of studies reporting these is too few to support even tentative conclusions. The cross-sectional nature of most included data means, we cannot be sure about the direction of causation between psychosocial variables and pain and fatigue in MM. That noted, leading models of these phenomena in cancer suggest a complex inter-relationship rather than a straightforward linear one.7,13

This review was thorough and robust, with key processes preregistered and fulfilling the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. The principle weakness of this review is its dependence on published data from studies located in western Europe and the United States. We expect that there is a great deal of data on the relationship between psychosocial variables and pain and fatigue in people with MM that is unpublished. This is because many studies of MM patients use quality of life measures that include subscales measuring fatigue, pain, and psychosocial variable. However, they address other research questions and do not report analysis of interrelationships between these variables. This assumption reduces our confidence in the findings of the review and their transferability to other parts of the world.

Nevertheless, the review has important implications for the future care of people with MM and research. For practice, we have reasonable evidence that supportive care assessments and interventions focused on fatigue and pain would benefit from considering psychosocial variables. These include global distress, depression, anxiety and perceived cognitive functioning. Nurses should educate patients about the relationship between these psychosocial variables and pain and fatigue. They should consider referring patients with persistent pain and fatigue to psychological services specifically designed to help patients cope with these symptoms. Pain, fatigue, and psychosocial variables appear to covary in people with MM, and improvements or deterioration in one is likely to have knock-on effects on the other. Interventions to alleviate psychological suffering may, at the very least, reduce the wider impact of pain and fatigue.

In the light of this review, future research into psychosocial variables and pain and/or fatigue in MM should have 3 strategies. First, it needs to be longitudinal to uncover the likely complex causal relationships between these phenomena. Second, it needs to be multivariate, seeking to account for confounding variables such as analgesia. Finally, research should use theoretically informed measures of fatigue and pain that aim to disentangle psychological components of pain from bottom up physical factors. For example, how does a person’s degree of catastrophic interpretation of pain in MM, where pain may be benign or an indicator of fracture and perhaps disease progression, affect their experience of pain? In doing so, this research will support the development of more targeted and personalized psychosocial interventions for people with MM.


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Fatigue; Multiple myeloma; Pain; Psycho-oncology

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