Journal of Pain and Symptom Management
Volume 33, Issue 3 , Pages 267-275, March 2007

Ecological Momentary Assessment of Fatigue in Patients Receiving Intensive Cancer Therapy

  • Eileen Danaher Hacker, PhD, APN, AOCN

      Affiliations

    • Corresponding Author InformationAddress reprint requests to: Eileen Danaher Hacker, PhD, University of Illinois at Chicago, 845 S. Damen Avenue (MC 802), Chicago, IL 60612, USA.
    • ,
  • Carol Estwing Ferrans, PhD, RN, FAAN

Department of Medical-Surgical Nursing, University of Illinois at Chicago, College of Nursing, Chicago, Illinois, USA

Accepted 27 August 2006.

Article Outline

Abstract 

The ability to accurately assess the incidence, intensity, and timing of cancer-related fatigue is important for clinicians attempting to manage this symptom and for researchers evaluating interventions to reduce or alleviate fatigue. This methodological report describes our experiences with ecological momentary assessment (EMA) and discusses its applicability for capturing real-time, real-world assessments of fatigue in patients receiving intensive cancer therapy. This methodological report is part of a larger study examining fatigue and physical activity before and after hematopoietic stem cell transplantation (HSCT). A prospective, repeated measures design was used to assess changes in fatigue three days before and three days after intensive cancer therapy and HSCT. A convenience sample (n=20 before HSCT, and n=17 after HSCT) was drawn from two Midwestern academic medical centers. Real-time fatigue was measured with a single-item, global, fatigue intensity scale. Multiple fatigue assessments were conducted throughout each study day. Data were collected electronically, facilitating examination of compliance. Subjects responded to fatigue intensity queries 87% of the time before HSCT and 86% after HSCT. Response rates were not unduly influenced by level of fatigue, time of day, or gender. The study findings demonstrate that it is feasible to use computerized EMA to collect self-report fatigue data in acutely ill oncology patients. Most HSCT patients were able to provide real-time fatigue data even when experiencing multiple side effects from the preparatory regimen. EMA is a novel approach that holds substantial promise for investigating fatigue and other cancer symptoms.

Key Words: Fatigue, ecological momentary assessment, intensive cancer therapy

 

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Introduction 

The ability to accurately assess the incidence, intensity, and timing of cancer-related fatigue is important for clinicians attempting to manage this symptom and for researchers evaluating interventions to reduce or alleviate fatigue. Clinicians and researchers rely on self-report mechanisms to collect information regarding patients' experiences with subjective symptoms, such as cancer-related fatigue. However, retrospective recall of the fatigue experience may be distorted due to cognitive restructuring of memories. Incorporating real-time collection of fatigue data in naturalistic settings may reduce problems associated with retrospective recall of events, summarization of events, and artificial contexts or settings. Ecological momentary assessment (EMA) procedures have been successfully used in the behavioral sciences to study behaviors such as smoking cessation and alcohol use.1, 2, 3 In the oncology literature, however, very few reports of experience with EMA techniques exist.4, 5

The purpose of this methodological report is to describe our experiences with EMA and discuss its applicability for capturing real-time, real-world assessments of fatigue in cancer patients receiving intensive therapy. This methodological report is part of a larger study examining fatigue and physical activity before and after hematopoietic stem cell transplantation (HSCT).6 As far as we know, this is the first study of real-time collection of fatigue data in acutely ill cancer patients. This paper will address the feasibility and applicability issues associated with EMA. To address feasibility issues, response rates were evaluated in terms of 1) overall percentage of completed responses before and after HSCT; 2) effect of fatigue intensity; 3) completion rates per subject; 4) influence of time of day; and 5) gender effects. Applicability issues, such as data acquisition interface, sampling schedule, sampling density, prompting and recording, and data management procedures, are also addressed.

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Review of the Literature 

EMA holds substantial promise for assessing fatigue in cancer patients receiving intensive therapy, such as high-dose chemotherapy followed by HSCT. Current studies suggest that these patients experience severe fatigue immediately after the preparatory regimen and HSCT.7, 8, 9 In addition to fatigue, patients also report significant problems with symptoms, such as pain, nausea and vomiting, diarrhea, and appetite loss, as well as changes in physical, cognitive, emotional, and social functioning during this period.8, 10 The ability to rely on self-report data retrieved from memory may be severely limited during this period. Moreover, these patients may not be able or willing to complete questionnaires or participate in interviews, further hampering the researchers' ability to obtain accurate information. Collecting real-time fatigue data, especially if subject burden is minimized, may facilitate timely obtainment of self-report fatigue information in acutely ill subjects. Furthermore, this methodology may also minimize the biases associated with retrospective assessment of symptoms, particularly in patients who may have experienced multiple symptoms simultaneously, which is often the case with intensive cancer therapy.

The Fatigue Guidelines Panel of the National Comprehensive Cancer Network (NCCN) defines cancer-related fatigue as “an unusual, persistent, subjective sense of tiredness related to cancer or cancer treatment that interferes with usual functioning.”11 This highly prevalent and debilitating symptom has the potential to seriously affect patients throughout the cancer trajectory.8, 12 Although a gold standard for defining and measuring cancer-related fatigue does not exist, the current literature supports cancer-related fatigue as a subjective experience that is multidimensional in nature and generally consists of a symptom/sensory dimension, a physiological dimension, and a performance or work reduction dimension.13, 14, 15

Reliance on self-report of fatigue is essential in attempting to describe and treat this symptom. A variety of instruments measuring fatigue can be found in the cancer literature, and most of these instruments rely on the respondent to recall and/or summarize the fatigue experience.16, 17, 18, 19, 20, 21 Table 1 provides examples of instruments that measure fatigue with the time periods they specify for recall. All of the instruments listed rely on recall to some extent, the longest requiring a recall period of two weeks.

Table 1. Self-Report Fatigue Instruments
InstrumentRecall Period
Brief Fatigue Inventory15Past week, past 24hours, current
EORTC QLQ-C30, Fatigue Subscale26Past week
Fatigue Symptom Inventory16Past week, current
Fatigue Assessment Instrument14Two weeks
Functional Assessment of Cancer Therapy – Fatigue Subscale17Past week
Multidimensional Fatigue Inventory19Past 24hours
Schwartz Cancer Fatigue Scale18Past 2–3 days, past week

A number of factors have been identified that may affect the recall process, also referred to as the reconstruction process.22 Events or experiences that are fresh in the mind or unanticipated are more likely to be remembered and tend to disproportionately shape the memory. Events/experiences that are more significant or intense are more easily remembered. Mood may also affect the memory of an experience or event. For instance, people in a negative mood are more likely to retrieve negatively charged information and vice versa. People also may assign meaning to events/experiences after the fact to make the events or experiences more consistent with events/experiences that follow. For instance, a patient may state, “I vaguely remember not feeling quite right, although I don't remember exactly how I felt. Now, I know that I must have been extremely tired after I learned that I was anemic.” As seen in this example, patients may retrospectively assign meaning to a symptom experience that may or may not be accurate. In addition, when asked to summarize and/or aggregate experiences or events, patients may use a number of strategies to respond to the request that go beyond counting and averaging, which also may introduce biases.

All of these factors that affect the memory of the fatigue experience may affect the accuracy of a retrospective assessment. For this reason, alternate methods, such as real-time, real-world fatigue assessment, are intriguing in their promise to increase the accuracy of the assessment by avoiding the pitfalls associated with cognitive restructuring. This is particularly important in patients receiving intensive chemotherapy, who experience a multitude of symptoms simultaneously, and may have difficulty separating the fatigue experience from other symptoms using retrospective recall.

EMA refers to a number of methodological data collection techniques that incorporate repeated real-time measurement of phenomena, such as symptoms, behaviors, or physiological processes, as they occur in naturalistic settings.23 These techniques were developed to address the problems associated with recall biases, summarization processes, and artificial contexts. EMAs are characterized by 1) studying people in their natural environment to enhance ecological validity, 2) conducting assessments of the individual's immediate or near-immediate state to minimize recall biases, and 3) sampling the phenomena under study throughout the course of the day to ensure an adequate representation of the individual's experience. Subjects may provide these data through computerized processes or through a pen-and-paper approach. Computerized EMA offers the added advantage of time stamping data entries, thus permitting documentation of patient compliance.

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Methods 

Design 

The study received institutional review board approval from two academic medical centers. A prospective, repeated measures design was used to assess changes in fatigue, physical activity, health status perceptions, and quality of life in patients receiving an HSCT. This report will focus on the assessment of real-time fatigue intensity for three days before HSCT and three days after, for a total of six days. Data before HSCT were collected when patients were medically stable and not receiving any type of therapy. Data after transplantation were expected to coincide with the period of profound neutropenia and the greatest impact of acute side effects from the intensive therapy. Data from this second time period provided information regarding types of problems experienced after the intensive therapy and stem cell rescue, and the ability and willingness of patients to provide real-time fatigue data when experiencing the full impact of the intensive therapy. Subjects were informed that the real-time fatigue data were collected for research purposes only and would not influence their care.

Sample 

Adult patients who met the eligibility criteria and elected to undergo a HSCT at two Midwestern academic medical centers were invited to participate. Written informed consent was obtained from all subjects. Twenty subjects participated in data collection activities before transplantation; of these, 17 also participated after the transplant. The remaining three subjects did not go on to receive a transplant. The mean age of the sample was 48.7 years (range, 23–64 years). Males and females were fairly well represented: 45% and 55%, respectively. The sample consisted primarily of African Americans (40%) and Caucasians (35%), with other minorities accounting for the remainder. Sixty percent of the sample was married. In terms of education level, 60% had at least taken some college courses, and 40% of the sample had a high-school education or less. Fifty-five percent reported incomes below $40,000. The subjects were receiving HSCT for a variety of hematologic malignancies. Ten patients received an autologous HSCT and seven received an allogeneic HSCT.

Instruments 

Real-time fatigue was measured with a single-item, global, fatigue intensity scale using computerized EMA (real-time assessment). Subjects entered their fatigue rating using the subjective event marker on an Actiwatch-Score® (Mini Mitter Company, Inc., Bend, OR), which is worn like a wristwatch and signals subjects to complete the rating. The Actiwatch-Score® is an accelerometer used to measure physical activity, and the subjective event marker is located on its face. Subjects rated their fatigue intensity on a scale from 1 (no fatigue) to 10 (worst fatigue). Subjects were asked to complete the fatigue intensity rating three times during the course of the day (10:00 am, 2:00 pm, and 6:00 pm). These times were chosen to avoid conflicts with mealtimes. The self-report data were then stored in the onboard memory of the Actiwatch-Score®. As patients entered the fatigue intensity ratings into the Actiwatch-Score®, the data were time-stamped to allow for examination of compliance.

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Results 

Overall Response Rates for Real-Time Fatigue Assessment 

Overall response rates were calculated for the entire sample for the three days before HSCT and three days after HSCT. The total number of possible responses before HSCT was 180 (20 patients×3 possible responses per day×3 days). The total number of possible responses after the transplant was 153 (17 patients×3 possible responses×3 days). The overall response rate was 87% (159 responses/180 possible responses before HSCT) and 86% (131 responses/153 possible responses) after the transplant. In addition, overall response rates were calculated by day to examine the pattern of responses (Figure 1). The overall response rates for the three days before HSCT were 95%, 88%, and 82%, and after HSCT were 80%, 90%, and 86%. Although there was some variations between days before and after HSCT, there was no large drop-off in responses even during the acutely ill phase after HSCT.

Response Rates by Fatigue Intensity 

The NCCN recommends classifying fatigue intensity ratings as mild (rating of 1–3), moderate (rating of 4–6), or severe.7, 8, 9, 10, 11 Following these guidelines, most patients in this study rated their fatigue intensity as mild before transplant (Day 1=75%, Day 2=83%, Day 3=88%). After the transplant, most patients rated their fatigue intensity as moderate to severe (Day 5=90%, Day 6=77%, and Day 7=80%).

The frequency of response rates at each level of intensity was examined to determine whether subjects would continue to enter responses even when experiencing debilitating fatigue. As can be seen in Table 2, the percentage of completed responses was approximately the same before and after transplantation (87% vs. 86%), even though fatigue was significantly greater after transplantation.

Table 2. Frequency of Fatigue Intensity Ratings Responses
Before HSCT (n=180 responses) Freq (%)After HSCT (n=153 responses) Freq (%)
Severe fatigue
102 (1%)9 (6%)
94 (2%)13 (8%)
81 (0.5%)12 (8%)
72 (1%)12 (8%)

Moderate fatigue
68 (4%)20 (13%)
57 (4%)17 (11%)
411 (6%)14 (9%)

Mild fatigue
327 (15%)16 (10%)
236 (20%)7 (5%)
159 (33%)11 (7%)

Missing23 (13%)22 (14%)

The data in Table 2 were also examined to determine whether there were ceiling and floor effects. One concern that often arises when conducted assessments using a 1 to 10 rating scale is whether patients will use the entire scale or will only select responses at one or both ends of the scale and ignore the middle response choices. The data from this study showed that subjects used the entire 1 to 10 continuum when rating their fatigue intensity. Before HSCT, most of the subjects rated their fatigue as a 1, 2, or 3, indicating mild fatigue, and after HSCT, most rated their fatigue as 4–10, indicating moderate to severe fatigue.

Real-Time Fatigue Response Rates by Individual Subjects 

Because multiple fatigue assessments are completed each day by every subject, and not all subjects complete every assessment, it is necessary to determine the minimum number of responses permitted for retaining the subject in the final data set. Subjects that complete only a limited number of real-time fatigue assessments may not provide an adequate representation of their fatigue intensity. For this study, response rates were considered acceptable if the subject completed seven out of the nine assessments (3 assessments per day×3 days) for each time period (before and after HSCT). Eighty-five percent of subjects (17/20 subjects) completed at least seven out of nine responses before HSCT, and 77% of subjects (13/17 subjects) met this criterion after HSCT. Of the four patients who had excessive missing data in the immediate post-transplant period, three were incapable of providing valid responses for some or all of the days. Two were critically ill and eventually hospitalized in the medical intensive care unit and the third was intermittently confused.

Response Rates by Time of Day 

Response rates by time of day were calculated to determine whether specific times were associated with more missing data (e.g., when visitors are most likely to be present). Before hospitalization for the intensive chemotherapy and HSCT, subjects completed 90% of the real-time fatigue assessments at 10:00 am, 83% at 2:00 pm, and 92% at 6:00 pm. Figure 2 presents the data for each day before HSCT, which shows a clear pattern. Subjects reported their fatigue most consistently at 10:00 am and least consistently at 2:00 pm. After HSCT, while acutely ill, subjects completed 82% of the real-time assessments at 10:00 am, 94% at 2:00 pm, and 82% at 6:00 pm. Figure 3 presents the data for each day after HSCT and shows no consistent pattern regarding missing data.

Response Rates by Gender 

Response rates by gender were calculated to assess whether women were more likely than men to provide data. Nine men participated in research activities before intensive chemotherapy and HSCT, and two of these did not receive HSCT and so did not participate post-transplantation. The men completed 84% of the fatigue assessments before HSCT and 94% of the fatigue assessments after HSCT.

Eleven women participated in research activities before intensive chemotherapy and HSCT, and one of these did not receive HSCT and so did not participate post-transplantation. The women completed 90% of the fatigue assessments before HSCT and 81% of the fatigue assessments after HSCT.

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Discussion 

Patients receiving HSCT frequently experience significant side effects after intensive chemotherapy and HSCT. Because of the increased incidence and severity of side effects, subject burden and patient attrition become important issues to consider when designing a study that relies on self-report information. In this study, the data collection processes were intentionally designed to maximize patient compliance and minimize subject burden. These processes included collecting real-time fatigue data using a one-item, global, fatigue intensity rating scale, using a computerized EMA approach to collect and store the fatigue data, and collecting the other self-report data via scheduled interviews instead of relying on self-administered questionnaires. Real-time response rates of 87% before HSCT and 86% after HSCT illustrate that it is possible to collect this needed information when patients are acutely ill. The high response rates after HSCT are particularly salient given the difficulty associated with engaging acutely ill patients in research that requires frequent self-report of symptoms.

The feasibility of conducting real-time fatigue assessments was evaluated by several parameters, including overall percentage of completed responses before and after HSCT, effect of fatigue intensity, completion rates per subject, influence of time of day, and gender effects. Given the lack of information in the cancer literature, it was important to note if any of these factors influenced the frequency of subject responses. In the present study, the results indicate that response rates were not unduly influenced by current fatigue level, time of day, or gender. The finding of generally acceptable response rates helps to establish the feasibility of conducting real-time fatigue assessments in acutely ill subjects. It should be noted, however, the sample size was small and further evaluation is required.

One trend identified in the data requires further comment. There was a drop-off in overall response rates from 95% to 82% in the three days before HSCT, even though all of the daily response rates were considered acceptable (>80%). Several explanations may account for this trend. First, it is possible that the uniqueness of the real-time fatigue methodology fades over time, resulting in reduced response rates. This explanation seems unlikely, as a drop-off in response rates in the three days after HSCT did not occur. Second, responding to multiple fatigue intensity assessments in one day may not be viewed as important to patients who are only mildly fatigued. This explanation is supported by the lack of a trend after HSCT. After HSCT, patients may have found it more important to report fatigue over the course of three days because they were experiencing moderate to severe fatigue. Third, patients were told that the fatigue data were being collected for research purposes only and these data would not influence their care. If this had been a clinical situation and not a research study, the fatigue response rates may have been higher if patients knew that their reports would be used to manage the fatigue. In any case, querying patients in future studies for reasons why they choose to respond or not respond would provide helpful information for further analysis of trends in response rates.

The success of real-time fatigue assessments is influenced by a number of research design issues. Researchers need to consider the data acquisition interface when selecting a device to use with EMA methodology. Factors to consider include the user friendliness of the data collection instrument or device, simplicity of the design, and thoughtful uses of response interfaces. In general, assessments should be brief and completed within a minute or two. Frequent, long assessments may irritate subjects and reduce subject compliance.

In the current study, the Actiwatch-Score® was chosen to collect EMA data because the device is small (about the size of a wristwatch) and may be considered less intrusive by subjects accustomed to wearing wrist watches, thereby enhancing subject compliance. Using the subjective event marker of the Actiwatch-Score®, however, limited the fatigue assessment to a single-item, global measure of fatigue. In this study, the benefits of a brief assessment to capture real-time fatigue intensity ratings outweighed the disadvantages. The selection of a 1 to 10 fatigue intensity rating scale was based on ease of use in acutely ill patients and ability to capture repeated measurements of fatigue throughout the day without placing undue burden on the subjects. Although numeric intensity ratings do not provide information regarding the multidimensional nature of fatigue, this type of scale has been suggested for use as a fatigue-screening device in clinical situations.5, 24 Furthermore, this type of rating is more likely to be implemented in clinical practice, thus facilitating understanding of research findings by clinicians and facilitating translation of research findings into clinical practice.

Determining the sampling scheduling for collecting real-time fatigue data also impacts the design of the study and type of information that can be obtained. For instance, researchers need to consider the sampling schedule for collecting real-time fatigue data. Questions, such as “When should data be collected to obtain an adequate representation of fatigue?”, must be asked. In the present study, the sampling schedule was devised to collect real-time fatigue data during two distinct time periods: when patients were medically stable and in their natural home environment (before intensive cancer therapy and HSCT) and when patients were acutely ill (after the intensive cancer therapy and HSCT and hospitalized). This sampling schedule of three days before HSCT and three days after HSCT allowed the feasibility of methodology to be assessed during these two distinct periods when the health status of people with cancer was known to be different. As expected, most of the subjects in the present study reported mild fatigue before HSCT and moderate to severe fatigue after HSCT, indicating that the methodology is adequate for capturing expected differences in fatigue intensity before and after HSCT.

Another issue related to sampling scheduling is sampling density. Episodes that are brief and infrequent require a higher density of sampling to capture the phenomena under study. Conversely, episodes that are longer require a lower density of sampling. Sampling density in patients receiving intensive cancer therapy and HSCT must be balanced between sampling fatigue enough to obtain a true representation and not over-sampling to avoid placing undue burden on subjects. This study sampled fatigue intensity ratings three times a day based on the belief that fatigue lasts for longer periods of time (hours) and it is not brief and fleeting (i.e., here one moment, gone the next). The times for sampling fatigue intensity (10:00 am, 2:00 pm, and 6:00 pm) were chosen so that the assessments would not interfere with mealtimes, thought to further enhance subject compliance. The results from the current study suggest that patients receiving intensive cancer therapy and HSCT are able to complete three fatigue assessments per day without being overburdened and they are willing to provide these data during different times of the day.

It is possible that particularly fatiguing parts of the day were missed in the current study because of the fixed time assessments. In an attempt to capture these data, patients were asked to enter fatigue ratings directly into the wrist actigraph anytime during the course of the day that they felt particularly fatigued. Building this option directly into the study helped to minimize the possibility that highly fatiguing times during the course of the day were missed. Few patients, however, took advantage of this option, suggesting that three assessments during the course of the day may be enough to capture the fatigue experience, particularly after HSCT.

Prompting and recording methods for real-time fatigue assessments may influence subject compliance. Using appropriate methods to prompt patients and record data should be carefully planned. Previous studies have indicated that patient compliance is enhanced when using an electronic record compared to a paper diary.25 In the current study, patients entered fatigue intensity ratings directly into the subjective event marker on the Actiwatch-Score,® which is a novel approach for collecting fatigue data electronically. The Actiwatch-Score® offers the added advantage of time stamping data entries, thus permitting examination of patient compliance. The alarm on the device served as a prompter by sounding at prefixed times during the course of the day to remind patients to complete the data. These strategies were implemented to reduce subject burden and enhance subject compliance. The satisfactory response rates established in the current study suggest that using the subjective event marker and the alarm on the Actiwatch-Score® are successful methodological strategies for assessing real-time fatigue intensity.

Training procedures should be considered when deciding on real-time collection of data. The simpler the training procedures, the more likely the subject will comply. In this study, the training procedures were very simple. The research assistant explained how to enter the rating into the Actiwatch-Score® and handed out an instruction sheet. The one-day lead time gave the subjects an opportunity to become accustomed to the alarm and to entering the rating into the Actiwatch-Score®. These data were not used in the data analysis. No other procedures were implemented to enhance compliance rates illustrating the simplicity of the methodology.

Finally, data management procedures should be decided in the early stages of planning the study. Decisions, such as replacement of missing data and acceptance of late responses, should be considered early. EMA methodology has the potential to generate large amounts of data, particularly if frequent assessments are conducted. Because this EMA portion of this study was primarily descriptive in nature, missing data were not replaced so that real-time fatigue response rates could be evaluated.

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Conclusion 

This study demonstrates the feasibility of collecting real-time fatigue data in patients with cancer. Computerized real-time collection of fatigue data minimized biases associated with patient recall and allowed documentation of patient compliance. Most stem cell transplant patients were able to provide real-time fatigue data even when experiencing multiple side effects from the preparatory regimen. EMA is a novel approach that holds substantial promise for investigating fatigue and other cancer symptoms.

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Acknowledgments 

The author thanks Kevin Grandfield for editorial assistance.

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References 

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 This study was supported by the Oncology Nursing Society Foundation (supported by Ortho Biotech Products); American Cancer Society IRG-99-224; and the Center for Research on Cardiovascular and Respiratory Health (CRCRH), College of Nursing, University of Illinois at Chicago. The CRCRH is supported by the National Institute of Nursing Research, National Institutes of Health, grant # P20 NR07812.

PII: S0885-3924(06)00685-3

doi:10.1016/j.jpainsymman.2006.08.007

Journal of Pain and Symptom Management
Volume 33, Issue 3 , Pages 267-275, March 2007

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