Fatigue is among the most frequent complaints reported by patients with chronic illnesses [1–4] and has far-ranging, often debilitating consequences on their wellbeing and physical, emotional and social functioning [5]. Although there is no consensus definition of fatigue, it is often described as ‘a persistent, overwhelming sense of tiredness, weakness or exhaustion resulting in a decreased capacity for physical and⁄or mental work [6]. Fatigue is a subjective experience and is commonly assessed by means of patient-reported outcome measures (PROMs). PROMs are widely used today in evaluating the effects of illness and treatment on symptoms, functioning, and other outcomes from the patient’s perspective [7].

Currently, there are some 40 generic and disease-specific PROMs for assessing fatigue in use today [8]. Most of these fatigue measures have been evaluated regarding various aspects of validity and reliability. Although these are important psychometric properties reflecting the quality of the measure, they are of little value in interpreting the meaning of scores derived from that measure [9]. Nonetheless, interpretation of scores, in particular changes in scores, is of critical concern in trials evaluating effects of treatments aimed at reducing fatigue, as well as in routine clinical practice in monitoring and managing fatigue in individual patients. In clinical trials, it has long been recognized that conventional statistical significance testing provides information regarding the probability that an effect exists, not about the meaningfulness of the size of the effect [10]. In clinical practice, difficulties in evaluating and interpreting changes in PROM scores often impinge on their usefulness in informing clinical decision-making [11].

The interpretation of changes in PROM scores may be enhanced by estimates of the so-called minimal important difference (MID). MID was originally defined over 25 years ago as “the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient’s management” [12]. During the past decades considerable research attention has been directed towards deriving MIDs for PROMS. In this pursuit a variety of methods have been developed and applied, but no clear consensus exists regarding which method or methods are most suitable.

To date, two main methods have been applied, namely anchor-based approaches and distribution-based approaches. Descriptions of these methods are beyond the scope of this paper and are summarized in detail elsewhere [13]. Briefly, anchor-based approaches use various external criteria (patient-reported, physician-reported, or clinical anchors) to interpret whether a particular magnitude of change is important. For example, a common anchor-based method involves the use of global rating scales (GRS) where MIDs are derived by comparing patients’ self-ratings of change (e.g., “much worse”—“much better”) to change in PROM scores. The MID is often defined as lying within the range of “slightly worse/better” on the GRS [9]. Distribution-based approaches rely on the statistical characteristics of the distribution of scores in the sample, in which the magnitude of change is generally expressed as a function of the standard deviation (SD) of scores alone or in combination with the reliability of the PROM (standard error of the measurement (SEM)) [14]. Various SD and SEM cut-off values have been proposed for estimating MIDs, including ½ or 1/3 SD and 1–2 SEM. Another commonly applied method is the use of effect sizes (ES) or standardized response means (SRM), where change scores are divided by the SD at baseline or the SD of change, respectively. The MID is often defined as change values lying within the range of 0.2-0.5. A disadvantage to distribution-based approaches is that they do not address the clinical importance of the change. Recent recommendations have proposed that as a first-line method multiple anchor-based approaches should be used, which, supported by distribution-based methods, may be triangulated to a single MID value or smaller range of values [14–17].

Although appealing for its simplicity, the idea of a single, universal MID value for any particular PROM remains elusive for a number of reasons. Firstly, different MID estimation approaches have been shown to yield highly disparate MIDs and hence triangulation (combining different methods to estimate a MID) may be problematic [18]. Secondly, MIDs have also been shown to differ by population and context [14]. For example, MIDs vary by diagnostic group, characteristics of the study sample, e.g., demographics and baseline levels; disease severity; treatment; choice of anchors [18, 19] as well as if MIDs gauge improvement versus deterioration [20]. This variability suggests the need to understand how a particular MID value was determined in order to judge its appropriateness for use in research for interpreting change and/or computing sample sizes, or in clinical practice for monitoring fatigue in specific patient groups [21].

The purpose of this paper is to compile published MIDs for fatigue PROMs, spanning diagnostic/patient groups and estimation methods, and to provide information relevant for appraising their appropriateness for use in specific clinical trials and in monitoring fatigue in defined patient groups in routine clinical practice.

A systematic literature review where three databases (Scopus, CINAHL and Cochrane) were searched from January 2000 to April 2015 to identify studies with calculated MIDs in fatigue scales, subscales and single item measures. The searches were limited to English language (search string: “minimal clinical important difference*” OR “minimal important difference*” OR “minimal clinically important difference*” OR “minimally important difference*” OR “clinical important improvement*” OR “clinically important improvement*” OR “minimal important clinical difference*” OR “minimally important clinical difference*” OR ”responder definition”) AND Fatigue). The search was augmented with screening of article reference lists. All expressions including “difference/change/improvement” or equivalent, “important” as well as “minimal” or “clinical”, or “responder definition” were defined as MIDs. To facilitate the reading all minimally important changes are called MIDs in this paper.

Selection of articles

Inclusion criteria were reporting MIDs in text and/or tables for a fatigue scale, subscale or single item measurement of fatigue. Exclusion criteria were: reported MID was not derived directly in the study; insufficient information supplied about the study sample, study design and/or method for determining the MID; study sample < 18 years, not separate reporting of MIDs for a fatigue subscale and conference abstracts. Exclusion on title/abstract and on full-text levels were done independently by two researchers (ÅN and AD), see Fig. 1.

This systematic review identified 41 studies reporting MIDs for 28 fatigue PROMs or subscales measuring fatigue, yielding a total of 60 studies or substudies estimating MID. It is important to note that there are many more fatigue PROMs available today than the 28 reported here. For example, a critical review of fatigue PROMs from 2009 [8] identified 39 such PROMs; however, only 11 of these overlapped with PROMs in our review. This suggests that there are roughly 56 or more fatigue PROMs currently represented in the literature. Considering the importance attributed to MIDs for interpreting the meaningfulness of change in PROM scores [9, 63], it is somewhat surprising that MIDs are available for only about half of all published fatigue PROMs. Moreover, few PROMs had MIDs that were determined in more than two studies and diagnostic groups, and more than half of the PROMs had MIDs that were derived in only one diagnostic group. Important exceptions were the SF-36 Vitality scale (>8 diagnostic groups/8 studies; the FACIT-Fatigue scale (4 diagnoses/6 studies); the EORTC QLQ-C30 Fatigue subscale (6 cancer diagnoses/6 studies); and the VAS-100 Fatigue (6 diagnoses/6 studies). Considering that these scales are some of the most widely used and oldest PROMs in use today it is unsurprising that greater research attention has focused on determining MIDs for these scales; however, it is noteworthy that so few separate studies reported MIDs for commonly used generic fatigue PROMs, such as the MFI, FSS, FIS and FAS.

Previous research has highlighted considerable variability in MID values as a function of estimation method, population and context [14, 18, 19], suggesting the importance of considering such factors when appraising the appropriateness of published MIDs for use in clinical research and practice. In line with this, substantial variation was observed in MID values for individual fatigue PROMs in this review. For example, MIDs for the SF-36 Fatigue scale ranged from as low as 4.2 to as high as 20.0 points (0–100 point scale) in studies varying in methodologies, anchors, diagnostic groups and direction of change assessed. Similarly, MIDs for the VAS-100 Fatigue scale ranged from 1.4 to 17. MIDs for the cancer-specific EORTC QLQ-C30 fatigue scale also varied between 1.8 and 24.5 points (0–100 scale) and those for the FACIT-Fatigue scale ranged between 6 and 16 (converted to percent), see Table 3. This wide variation in MIDs for individual fatigue scales suggests the importance of understanding how any particular MID was derived and of applying this knowledge when appraising its appropriateness for interpreting changes in fatigue scores.

MID estimation methods varied considerably in the identified studies and substudies. However, in accordance with recent recommendations regarding methods for MID estimation [14], nearly all studies applied an anchor-based approach, where at least one anchor was used. Patient global change ratings were by far the most common anchor, but even clinician-reported and clinical anchors were implemented. Where more than one anchor was applied either a range of values was generally reported or, as recommended [14, 63, 64], values were often triangulated to a single or smaller range of MIDs. Distribution-based methods were used in about a third of the studies and only in conjunction with anchor-based approaches. A few studies used a Delphi method (Table 3).

In the studies using several anchors to determine MID values, global MID ranges varied within single studies from as little as two points (percent scores), in relation to the FACIT-Fatigue scale using patient-based anchors [29], to about 20 points for the TOI-F [28] using patient, clinician and clinical anchors. Interestingly, two studies reporting MIDs for the SF-36 Vitality scale, using the same diagnostic group (RA) but different anchors, yielded two distinct ranges of MIDs. In the study by Kosinski et al. [35], using patient and physician global assessments as anchors, MIDs ranged from 4.9–11.1, whereas a range of 11.0–20.0 was reported by Ward et al. [56] using the HAQ, CES-D and the SF-36 health transition item. Neither of these studies triangulated the range of values to a single MID or smaller range of values and hence these wide ranges of MIDs are arguably of questionable practical value for interpreting change in fatigue in RA patients as measured with the SF-36 Vitality.

Triangulation was used in 17 substudies, of which 10 used more than two anchors. This method has been recommended for consolidating MIDs derived from different methods to a single or small range of MID values [14]. However, it has been criticized [19] since it may in practice involve the need to converge widely disparate MIDs derived using different estimation methods and diverse anchors, which often represent very different stakeholder perspectives. An example of a MID triangulated from a wide range of MIDs is the TOI-F [28] where a MID range of 4.4–24.6 (percent scores) was triangulated to 5.0. Where MID ranges are smaller, the value and applicability of the triangulated MID may be more immediately apparent. For example, Schünemann et al. [52] reported a MID range for the CRQ of 6.7–8.5 (percent scores), derived from patient anchors, a systematic review and distribution-based methods, which was triangulated to a MID of 6.7.

A second factor known to influence variation in MID values is the patient population in which the MID is determined. Variation by diagnostic group is exemplified by comparing MIDS from two studies, each using the same estimation method (7-step global rating scale) and study design (cross-sectional) but different diagnostic groups [33, 46]. One of the studies [33] determined MIDs for seven different fatigue PROMs in patients with SLE and the other [46] did the same in patients with RA. Comparison of the global MIDs for the SLE and RA patients, shown in Table 3, shows consistently smaller MIDs for SLE versus RA across all seven PROMs. It is noteworthy that most PROMs had MIDs that were determined in only one patient population and the relevance of these MIDs for use in other patient groups thus remains unclear.

A third factor influencing variation in MID values is the context within which the MID is determined. Context issues concern, for example, characteristics of the patient population, e.g., such as baseline state [65], disease severity [66], and direction of change [13, 20], as well as study design and intervention. For example, patients with baseline scores indicating more severe fatigue may value magnitudes of change in fatigue differently than those with less severe fatigue. Corroborating previous research finding, MIDs for improvement differed from those for deterioration in all identified studies. MIDs tended to be larger for deterioration than improvement, except in the EORTC QLQ-30 and VAS Fatigue item. MIDs for improvement were consistently smaller than global MIDs.

A strength of this study is that reported MIDs for fatigue scales or subscales were systematically compiled and described. Assessment for inclusion or exclusion and data extraction from included studies was done independently by two authors (ÅN and AD). A limitation is that the search period was restricted to studies from 2000 onwards and search strings for the many variations on MID was also limited and therefore some studies reporting MIDs for fatigue scales may not have been captured in the literature searches. Another limitation is that the description of the study designs and results had to be summarized and simplified in tables and information could be lost. Therefore, when evaluating MIDs the original study/studies should be consulted.

MIDs vary substantially by estimation method, patient population and context both across and within fatigue PROMs. In light of this variation, published MIDs should be applied judiciously, after carefully considering their applicability to characteristics of the study in question. The information provided in this paper may serve to aid researchers and clinicians in making informed decisions regarding the appropriateness of published MIDs for their particular study and patients.

ES, effect size; GRS, global rating scale; ITP, immune thrombocytopenic purpura; MCID, minimal clinical important difference; MCII, minimal clinically important improvement; MDD, major depressive disorder; MID, minimal important difference; MS, multiple sclerosis; PRO, patient reported outcome; PROM, patient reported outcome measure; PROMIS, patient-reported outcomes measurement information system; RA, rheumatoid arthritis; SD, standard deviation; SEM, standard error of measurement; SLE, systemic lupus erythematosus; QoL, quality of life.