Multidimensional Fatigue Symptom Inventory-Short Form

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Table of Contents

Psychometric Properties and Evidence
A Summary of Strengths and Weaknesses of the MFSI-SF
Reference List

Psychometric Properties and Evidence

This section aims to not only analyze and critique the psychometric properties of the MFSI based on its use in published research studies, but also to characterize the evidence base supporting its current and future use. A careful analysis of 15 articles, published between 2002 and 2011, was conducted to provide background information and current literature on the MFSI-SF. The analysis demonstrated evidence that this instrument has been used with cancer patients, including breast cancer patients (Banthia et al., 2006; Broeckel et al., 2002; Fagundes et al, 2011), gynecological cancer patients (Liu et al., 2009; Prue et al, 2006), as well as with non-cancer patients.

Non-cancer patients presented with deep vein thrombosis (Lukas et al., 2009; Gunaydin et al, 2009), obstructive sleep apnea (Yue et al., 2009; Lee et al, 2010), orofacial pain (De Leeuw et al, 2005), or as healthy individuals (Lim et al, 2005; Wilkinson et al, 2011) and caregivers (Roepke et al., 2009). A careful analysis of the methodologies used in the selected studies reflected wide-ranging sample sizes selected across both sexes and along a continuum of varying age groups, not mentioning that the study designs used were also varied.

Reliability of the MFSI-SF was assessed in terms of internal consistency and test–retest reliability. With respect to internal consistency, only five of the 15 studies reported Cronbach’s alpha coefficients (Banthia et al., 2006; Broeckel et al., 2002; De Leeuw et al, 2005; Tomfohr et al, 2011; Roepke et al., 2009). The Cronbach’s alpha coefficient for the 30-items ranged from.84 to.86, while the coefficients ranged from.81 to 96 for each individual subscale. This indicates good internal consistency for the MFSI. As for the test–retest reliability of the MFSI-SF, it was reported in any of the identified studies.

Validity of the MFSI-SF was assessed in terms of concurrent, convergent, and divergent validity, as follows:

Concurrent validity

Concurrent validity refers to evidence of the degree to which MFSI-SF subscales are correlated with other published measures of fatigue. Evidence for the concurrent validity of MFSI was reported in the study conducted by Banthia et al (2006), who investigated the correspondence between daily (visual analogue scale) and weekly (MFSI-SF) ratings of fatigue made over a one-month period by survivors of breast cancer. Their findings revealed that each scale captures different information because the VAS is one-dimensional, while MFSI-SF is multidimensional. Furthermore, the VAS was found to provide a quick assessment of general fatigue, with revelations that the visual analogue scale (VAS) ratings shared more variance with the General Fatigue subscale than with the other four subscales of MFSI-SF. Consequently, there exists compelling evidence that the MFSI-SF captures the import and multidimensional aspects of fatigue beyond the quick and general fatigue dimension (Banthia et al. 2006).

Concurrent validity was also reported in studies by Stein et al (1998) and Stein et al (2004). In the former study, concurrent validity was examined by computing correlations between the MFSI-SF, FSI and the SF-36 Vitality Scale score. Results demonstrated moderate to high correlations, indicating that the subscales could measures constructs similar to those measured by FSI and the SF-36 Vitality Scale (Stein et al, 1998). In the latter, concurrent validity was evaluated by computing correlations between MFSI, POMS-F and SF-36 vitality scale. Results revealed moderate to high correlations, suggesting that the MFSI-SF subscales could measure constructs similar to those measured by the POMS-F and the SF-36 (Stein et al, 2004). The study by De Leeuw et al (2005) used several fatigue instruments such as Fatigue Assessment Instrument (FAI), the Profile of Fatigue-Related Symptoms (PFRS), as well as the MFSI-SF, but careful analysis of their measurement tools revealed that these researchers failed to examine the correlation between the stated scales, which could have added valuable information to the body of evidence in regards to concurrent validity of MFSI-SF.

Convergent validity

Concurrent validity refers to the evidence of the degree to which MFSI subscales correlates with measures of conceptually related constructs. Such evidence was reported in most of the identified studies of the MFSI-SF. The following section will provide evidence from the analyzed research studies which support the convergent validity of the MFSI-SF.

Fagundes et al (2011) conducted a study to identify how child maltreatment is associated with quality of life among breast cancer survivors. One of the predictors they looked at was fatigue; and they found a strong correlation between maltreatment and fatigue as measured by childhood trauma questioners. Interestingly, their study also revealed that individuals who experienced more types of abuse or neglect in their formative years experienced more fatigue later in life.

Broeckel et al (2002) examined the characteristics and correlates of sexual functioning in women with breast cancer survivors and in an age-matched comparison group of non-cancer respondents. The results of the study revealed a correlation between poorer sexual functioning and worse fatigue (r = 0.31), and that poor sexual functioning among breast cancer survivors is related to higher levels of fatigue.

Yue et al (2009) examined the contribution of arousal frequency to fatigue scores. They hypothesized that arousal frequency as well as changes in sleep architecture contribute to the fatigue experienced by patients with obstructive sleep apnea. Results of the study revealed significant correlations between MFSI-SF subscale scores and various arousal indices. More specifically, emotional fatigue scores were associated with total arousal index (r= 0.416), respiratory movement arousal index (r = 0.346), and spontaneous movement arousal index (r=0.378). On the other hand, physical fatigue scores were associated with total arousal index(r = 0.360) and respiratory movement arousal index (r = 0.304).

Lee et al (2010) not only examined the association between psychomotor vigilant tasks (PVT) performance and fatigue, but also if the PVT and fatigue relationship could be influenced by depressive symptoms. The researchers found that the PVT count of lapses was significantly associated with MFSI-SF subscale of physical fatigue (r = 0.324). Additionally, the PVT average response time tended towards a positive correlation with MFSI-SF physical fatigue (r = 0.281).

In their study, De Leeuw et al (2005) investigated the presence and magnitude of fatigue, in addition to determining whether fatigue could be distinguished as a unique clinical symptom in a sample of patients diagnosed with chronic temporomandibular (TMD) joint or masticatory muscle pain. They found correlations between fatigue subscales and somatization, depression, anxiety, pain, general activity level, and sleep. Overall, the fatigue instruments showed moderate to strong correlations with symptoms of somatization, depression, anxiety, and sleep. There were weak to moderate – but significant – correlations between the fatigue instruments and pain. The results further indicated that patients with chronic TMD report significantly severe fatigue than healthy individuals.

Further afield, Gunaydin et al (2009) evaluated the frequency of fatigue, its multidimensional nature and its association with disease- specific variables, depression, and sleep disturbance in patients with Ankylosing Spodylitis (AS). Results indicated that fatigue level correlated with disease-specific parameters, depression, and sleep disturbance.

On their part, Liu et al (2009) hypothesized that women who began treatment with an increased symptom cluster index would also suffer from more symptoms during treatment. They found that women with higher symptom cluster index were correlated with greater fatigue than those with lower symptom cluster index. The sleep disturbance measured by poor sleep quality index (PSQI) and depressive symptoms measured by CES-D, were significantly correlated with total MFSI-SF score.

Lim et al (2005) examined the associations between obesity, depressive symptoms, cytokine levels, and fatigue. Results revealed that the depressive symptoms, as measured by the CES-D, were significantly correlated with the MFSI-SF, and highly correlated with emotional fatigue subscale (r=0.702; P=. 01). Obesity indices, as measured by the percentage of body fat and body mass index, were significantly correlated with the general fatigue subscale (r = 0.3; P????.01) but were not correlated with the other subscales. The researchers also found that when controlling for depressive symptoms and the inflammatory markers, obesity was correlated with physical fatigue symptoms.

Wilkinson et al (2011) assessed the effects of hyperammonaemia on human adults who do not co-present other pathologies. They found that hyperammonaemia correlated with fatigue, where there was an increase in sensation of fatigue associated with the administration of ammonium chloride solution.

Lukas et al. (2009) hypothesized an association between heightened levels of fatigue and psychological distress, as well as decreased QOL in patients with an objectively diagnosed venous thromboembolic event. They found a strong correlation between fatigue and psychological distress. Total distress and total fatigue scores were positively correlated (p<. 0001).

Lastly, Prue et al (2006) explored fatigue experienced in a gynecological cancer population. Results revealed that increased psychological distress level before, during, and after treatment was associated with more fatigue. Additionally, an increase in physical symptom distress level was also shown to be associated with fatigue.

Divergent validity

Divergent validity refers to the degree to which MFSI subscales are either negatively correlated or not correlated with measures of constructs believed to be conceptually distinct from fatigue. Such evidence was reported by two of the selected published research studies (Roepke et al., 2009; Lukas et al., 2009), as well as in the original study by Stein et al (1998).

On their part, Roepke et al. (2009) examined the relationship between personal mastery and multiple dimensions of fatigue. Results revealed levels of mastery, as measured by questioners assessing mastery, correlated negatively with fatigue. Stein et al (1998) results indicated that correlation between MFSI-F and MC-20, a measure of social desirability, was low.

This development, however, was attributed to the large sample size used in the study. In another study, Lukas et al. (2009) found that higher levels of total fatigue were significantly associated with lower levels of physical QOL measured by Physical Health Composite Score (PHCS), while mental QOL was measured by Mental Health Composite Score (MHCS). These findings demonstrated the important relationship between QoL and fatigue.

The results of this analysis and critique demonstrate the use of MFSI-SF not only in cancer patients but also in non-cancer patients. With respect to the specific psychometric properties of the MFSI-SF, it has been noted that data are limited with respect to the measure’s test–retest reliability, which is therefore suggestive of the need for more research regarding this aspect of the MFSI-SF. In conclusion, the analysis from the selected published research studies generates evidence of the usefulness of MFSI-SF and strongly supports its use in future studies. Pragmatic Considerations

There are several aspects that need to evaluated and systematically critiqued in respect of pragmatic considerations. First, it is important to underline that MFSI-SF is offered for free. Another consideration is the completion time, which makes the measurement non-stressful to participants. As mentioned earlier, the MFSI-SF can be used as a substitute to the MFSI when the time factor and fatigue of respondents are decisive in allowing participation.

The format of this measurement provides fodder for the third pragmatic consideration. It is indeed true that the measurement is formatted on a piece of paper, and includes concise, clear instructions which indicate the recall time and the period it is concerned with (last 7 days). Lastly, it is indeed true that permission is needed to use this measurement from Paul B. Jacobsen, PhD Psychosocial Oncology Program at Moffitt Cancer Center and Research Institute based in Florida (Stein et al 1998; Stein et al 2004).

A Summary of Strengths and Weaknesses of the MFSI-SF

The MFSI-SF is a measurement used to measure fatigue, particularly to quantify the multiple dimensions of fatigue. This section aims to outline some of the strengths and weaknesses of the instrument.

Among the strengths, researchers and practitioners often portends that the MFSI is not only a brief and comprehensive measure, but it has the capacity to provide information on the overall level of fatigue and the extent to which an individual is experiencing fatigue in each of the five domains. Another advantage is the short time taken to complete the assessment using MFSI-SF, which makes it a non-stressful and user-friendly measurement. It is user-friendly by virtue of the fact that it utilizes a simple 5- point Likert-scale response format. Additionally, it is not disease-specific, and hence is applicable to measure fatigue in many different situations. It is also useful for measuring a diverse population due to its lack of reference to a specific illness and diagnosis (Stein et al, 1998).

Still on the strengths, it has been found that MFSI-SF does not assume the presence of fatigue, thereby increasing its clinical utility and research appeal. The non-specific nature of the MFSI- SF allows for its use in acute settings, whereas other commonly used fatigue scales/questionnaires are suited to more chronic measurements. More importantly, the MFSI-SF is useful for measuring perceptions of fatigue because it captures the multidimensional nature of fatigue. Finally, the MFSI-SF deals with different aspects of fatigue, which provides for a more detailed profiling of fatigue.

Among the weaknesses, it is known that the theoretical basis for this measurement is unspecified. It is of fundamental importance that the development of measurements be well grounded on substantive theories related to the phenomena under investigation. Theories are a wonderful aid to clarity and help in setting the boundaries for the phenomena, so the developer does not unintentionally address other unintended domains (Waltz et al, 2010).

Another identified weakness is that the measure is yet to be used on healthy populations to evaluate their relationship between fatigue and other variables. However, Bardwell et al. (2006) has attempted to use the measure to investigate the interaction of ethnicity and social class, highlighting the importance of considering SES when studying ethnic differences in fatigue. The results of their study revealed that social class is important for understanding fatigue in African Americans but not in Caucasians.

Other weaknesses are related to item generation and redundancy of items in each subscale which refers to the same idea but are phrased differentially. A case in point is the general fatigue subscale for the item 14 and 28 (I feel fatigued and I feel tired), which can lead to artificially inflating reliability results. Another example is the physical fatigue subscale for item 15 and 20 “I have trouble paying attention and I am unable to concentrate”, or for item 1 and 27 “I have trouble remembering things and I’m forgetful”.

Additionally, in regards to item generation, there seems to be some dependency between the general fatigue subscale, which includes item 10, 12, 17, 14, 18, 28 and the other four subscales. Second, some phrases are worded in a way that may not be translated to other languages, while others are perceived as not making sense, such as the 10^th item “I feel pooped”. When the 10^th item in the MFSI-SF is translated from Chinese to English, it states “I have difficulty talking with others”, and from Spanish to English, it states “I feel exhausted”. The same applies to the 17th item “I feel run down” and the 18^th item “I feel sluggish”. Lastly, after analyzing the evidence, the MFSI-SF seems to be a multidimensional measure which is very broad; that is, it could be capturing other constructs that correlate with fatigue. An example would be the emotional fatigue subscale, more specifically with item 13 and 21 “I feel sad and I feel depressed.” These items seem to resemble depression more than fatigue. Please refer to Appendix A for the MFSI-SF items.

Reference List

Banthia, R., Malcarne, V. L., Roesch, S. C., Ko, C. M., Greenbergs, H. L., Varni, J. W., & Sadler, G. R. (2006). Correspondence between daily and weekly fatigue reports in breast cancer survivors. Journal of Behavioral Medicine, 29(3), 269-279.

Bardwell, W. A., Burke, S. C., Thomas, K. M. S., Carter, C., Weingart, K., & Dimsdale, J. E. (2006). Fatigue Varies by Social Class in African Americans but not Caucasian Americans. International Journal of Behavioral Medicine, 13(3), 252-258.

Broeckel, J. A., Thors, C. L., Jacobsen, P. B., Small, M., & Cox, C. E. (2002). Sexual functioning in long-term breast cancer survivors treated with adjuvant chemotherapy. Breast Cancer Research and Treatment, 75, 241-248.

De Leeuw, R., Studts, J. L., & Carlson, C. R. (2005). Fatigue and fatigue-related symptoms in an orofacial pain population. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Edontology, 99, 168-174.

DeVellis, R. F. (2011). Scale development: Theory and applications. London: SAGE.

Fagundes, C. P., Lindgren, M. E., Shapiro, C. L., & Kiecolt-Glaser, J. K. (2011). Child maltreatment and breast cancer survivors: Social support makes a difference for quality of life, fatigue and cancer stress. European Journal of Cancer (Oxford, England : 1990), doi:10.1016/j.ejca.2011.06.022

Gunaydin, R., Goksel Karatepe, A., Cesmeli, N., & Kaya, T. (2009). Fatigue in patients with ankylosing spondylitis: Relationships with disease-specific variables, depression, and sleep disturbance. Clinical Rheumatology, 28(9), 1045-1051. doi:10.1007/s10067-009-1204-1

Lee, I. S., Bardwell, W. A., Ancoli-Israel, S., & Dimsdale, J. E. (2010). Number of lapses during the psychomotor vigilance task as an objective measure of fatigue. Journal of Clinical Sleep Medicine, 6(2), 163-168.

Lim, W., Hong, S, Nelesen, R, & Dimsdale, J. E. (2005). The association of obesity, cytokine levels, and depressive symptoms with diverse measures of fatigue in healthy subjects. Archives of Internal Medicine, 165, 910-915.

Liu, L., Fiorentino, L., Natarajan, L., Parker, B. A., Mills, P. J., Sadler, G. R…Ancoli-Israel, S. (2009). Pre-treatment symptom cluster in breast cancer patients is associated with worse sleep, fatigue and depression during chemotherapy. Psycho-Oncology, 18(2), 187-194. doi:10.1002/pon.1412

Lukas, P. S., Krummenacher, R., Biasiutti, F. D., Begre, S., Znoj, H., & von Kanel, R. (2009). Association of fatigue and psychological distress with quality of life in patients with a previous venous thromboembolic event. Thrombosis and Haemostasis, 102(6), 1219-1226. doi:10.1160/TH09-05-0316

Pien, L., Chu, H., Chen, W., Chang, Y., Liao, Y., Chen, C., & Chou, K. (2011). Reliability and validity of a Chinese version of the multidimensional fatigue symptom inventory-short form (MFSI-SF-C). Journal of Clinical Nursing, 20(15), 2224-2232.

Prue, G., Rankin, J., Cramp, F., Allen, J., & Gracey, J. (2006). Fatigue in gynecological cancer patients: A pilot study. Supportive Care in Cancer : Official Journal of the Multinational Association of Supportive Care in Cancer, 14(1), 78-83.

Roepke, S. K., Mausbach, B. T., von Kanel, R., Ancoli-Israel, S., Harmell, A. L., Dimsdale, J. E… Grant, I. (2009). The moderating role of personal mastery on the relationship between caregiving status and multiple dimensions of fatigue. International Journal of Geriatric Psychiatry, 24(12), 1453-1462.

Stein, K. D., Martin, S. C., Hann, D. M., & Jacobsen, P. B. (1998). A multidimensional measure of fatigue for use with cancer patients. Cancer Practice, 6, 3.

Stein, K. D., Jacobsen, P. B., Blanchard, C. M., Thors, C. T. (2004). Further validation of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Journal of Pain and Symptom Management, 27, 14-23.

Waltz, C., Strickland, O., & Lenz, E. (2010). Measurement in Nursing and Health Research (pp. 3-25; 91-144; 145-162; 393-398). New York, NY: Springer Publishing Company.

Wilkinson, D. J., Smeeton, N. J., Castle, P. C., & Watt, P. W. (2011). Absence of neuropsychological impairment in hyperammonaemia in healthy young adults; possible synergism in development of hepatic encephalopathy (HE) symptoms? Metabolic Brain Disease, 26(3), 203-212.

Yue, H. J., Bardwell, W., Ancoli-Israel, S., Loredo, J. S., & Dimsdale, J. E. (2009). Arousal frequency is associated with increased fatigue in obstructive sleep apnea. Sleep & Breathing = Schlaf & Atmung, 13(4), 331-339.

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