Original Article
Classical test theory and item response theory/Rasch model to assess differences between patient-reported fatigue using 7-day and 4-week recall periods

https://doi.org/10.1016/j.jclinepi.2008.10.007Get rights and content

Abstract

Objective

This study compared self-reported fatigue between 7-day and 4-week time frames and explored factors that affect patients' responses.

Study Design and Setting

Two hundred and sixteen cancer patients completed either 7-day or 4-week version of the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F). Cochran–Mantel–Haenszel statistics and Cochran–Armitage trend tests were used to assess the association between time frame and item scores. Information function curves at both item and scale levels were depicted to evaluate the precision along the fatigue continuum. Differential item functioning (DIF) was used to examine the stability of the psychometric properties between time frames.

Results

Time frame did not influence patients' item responses. Examination of information function curves at item level did not clearly favor either time frame. At the scale level, the 7-day time frame was slightly more precise overall than the 4-week time frame. No item demonstrated DIF between time frames. Neither gender nor fatigue severity had an impact on above results.

Conclusion

This study suggests 7-day and 4-week time frame are equally appropriate in measuring fatigue, preference might be given to the more informative 7-day time frame. However, substantive considerations regarding the appropriate time frame should outweigh statistical ones.

Introduction

Cancer-related fatigue (CRF) has been defined as overwhelming and sustained exhaustion that decreases capacity for physical and mental work [1], and it is the most common unrelieved symptom experienced by cancer patients and survivors [2], [3], [4], [5]. Numerous measurement tools have been developed to measure patient-reported fatigue: from a 0–10 screening item to a multidimensional fatigue inventory, and from traditional fixed length scales to full-item banks that serve as the foundation for computerized adaptive testing [6], [7], [8], [9], [10], [11]. However, there is still no consensus on which time frame better captures fatigue experienced by patients. For example, the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F [10]) taps fatigue experienced during the past 7 days; the Brief Fatigue Inventory [7] captures it within 24 hours; the Fatigue Symptom Inventory [6] measures fatigue severity in the past 7 days as well as “current” fatigue; and the SF-36 Health Survey [12] and the PedsQL Multidimensional Fatigue Scale [13] have both 7-day and 4-week versions available.

Various factors (e.g., gender, memory, and personality) may have an effect on how patients report their symptom severity [14], [15]. For example, when persons are asked to report their fatigue based on longer periods of time, memory processes and personality disposition are likely to influence their responses. Although little is actually known about the “extraneous” factors that influence CRF, we may extrapolate or infer based on what is known about other symptoms, such as pain or mood. Patient evaluations of pain and mood are known to vary systematically with the length of the recall period [15]. Robinson and Clore [16] argue that different types of memory are accessed when people are recalling relatively recent affect (the last few days) vs. longer-term recall (the last week or month). Episodic memory is operative for short-term recall, whereas semantic memory comes into play with longer-term recall. Because semantic memory is closely aligned with one's beliefs as opposed to actual experience, longer-term recall may be biased. Other factors, such as gender, also influence the accuracy of recall. Additionally, investigators noted that patients tend to endorse fatigue ratings based on selective memory of the worst fatigue experienced during a period and to downplay less severe or fatigue-absent periods (peak-end effect and duration neglect, respectively) [17].

This study aimed to compare fatigue reported based on 7-day and 4-week time frames and explore factors that might affect patients' responses. We compared the impact of a 7-day vs. a 4-week time frame because both are commonly used in fatigue assessments. Results of this study therefore offer insight into the practical importance of differences in ratings by time frame.

Section snippets

Procedures

Two touch-screen computers were dedicated to this study. One was loaded with the FACIT-F [10], using a 7-day time frame. The other was loaded with exactly the same questions, but with a 4-week time frame. These two computers were assigned to two research assistants (RAs) to recruit patients from five Chicago metropolitan clinics. Because both RAs recruited patients from all five clinics, it was expected that each clinic had similar percentage of patients completing 7-day and 4-week versions of

Sample

Two hundred and sixteen patients were recruited, 116 completed the 4-week version and 100 completed the 7-day version of the questionnaires. The Institutional Review Board of each study site approved the study before patients were approached, and all participants provided written informed consent. Sample demographic and clinical information, grouped by the time frame they were assigned, is shown in Table 1. In brief, most of the patients in both groups (4 weeks; 7 days) were female (64%; 63%),

Discussion

At the item and scale levels, self-reported fatigue was not significantly different between 7-day and 4-week time frames. Neither patient gender nor severity of fatigue had an impact on this result. When we examined the amount of information each item provided across the fatigue continuum, inconsistent results for some items were identified. Although some items were equally precise across the fatigue continuum, others (2 of 13) showed considerable differences in precision. The 7-day time frame

Acknowledgments

Funding source: This study was supported by grants from the National Cancer Institute (#CA60068, PI: David Cella) and National Institutes of Health (U01 AR 052177-01; PI: David Cella).

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