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Effect of a Cognitive Behavioral Intervention on Reducing Symptom Severity During Chemotherapy

From the Department of Family Practice, College of Human Medicine, and College of Nursing, Department of Epidemiology, and Data Coordinating Center, Michigan State University, East Lansing; University of Michigan Cancer Center; Institute of Gerontology, University of Michigan, Ann Arbor; St Joseph Mercy Oakland, Pontiac, MI; Yale University School of Nursing and Cancer Center, New Haven, CT; and The Johns Hopkins University, School of Nursing, Baltimore, MD

Address reprint requests to Charles W. Given, PhD, Department of Family Practice, Michigan State University, B108 Clinical Center; East Lansing, MI 48824; e-mail: givenc{at}msu.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors’ Disclosures of... REFERENCES

 
PURPOSE: To describe a randomized trial of a cognitive behavioral intervention on reducing symptom severity among patients diagnosed with solid tumors and undergoing a first course of chemotherapy and to determine whether the intervention had an additive or interactive effect on symptom severity in the presence of supportive care medications.

PATIENTS AND METHODS: Patients (N = 237) were accrued from comprehensive and community cancer centers, interviewed, and randomly assigned to either the experimental intervention (n = 118) or conventional care (n = 119). A symptom severity index, based on summed severity scores across 15 symptoms, was the primary outcome. Each patient’s site of cancer, stage at diagnosis, chemotherapy protocols, and use of supportive medications were learned from medical records.

RESULTS: Groups were equivalent at baseline, and attrition by characteristics by group was not different. The proportion of patients not receiving chemotherapy at 10 and 20 weeks did not differ by group. At the 10- and 20-week observations, there was a significant interaction between the experimental group and baseline symptom severity. Patients in the experimental group who entered the trial with higher symptom severity reported significantly lower severity at 10 and 20 weeks. Controlling for chemotherapy treatment status at follow-up and supportive care medications did not alter the effect of the experimental intervention.

CONCLUSION: Compared with conventional care alone, the experimental intervention was effective among patients who entered the trial with higher levels of symptom severity. Age, sex, site or stage of cancer, and supportive medications did not modify the effect of this cognitive behavioral intervention on symptom severity.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors’ Disclosures of... REFERENCES

 
Twenty-five years ago, Weisman and Worden [1] and McCorkle and Young [2] demonstrated a relationship between the presence and severity of symptoms reported by cancer patients undergoing chemotherapy and their impact on the quality of life. Subsequently, interventions to assist cancer patients to manage symptoms have focused on either a single cancer site or a single symptom (eg, pain or fatigue) and, as such, are limited as to generalizability [3].

This research tests the efficacy of a behavioral intervention for reducing the severity of multiple symptoms among patients diagnosed with different sites of cancer who are undergoing a first course of chemotherapy. What follows is a review of the literature on the concept and measurement of symptoms, a summary of the more prevalent cancer-related symptoms, a discussion of scoring approaches, and a summary of trials of behavioral interventions directed toward symptom management.

Properties of Symptoms
First, symptoms are temporal; patient reports of the duration of symptoms range from minutes to hours and days, with intervening periods of freedom from any single symptom. Second, symptoms do not correspond closely with particular diseases or with the chemotherapy protocols patients undergo [4,5]. Because of this, measurement of symptoms relies on patient self report and includes frequency and duration (temporal features), severity or intensity, and interference with daily activities [6-11]. Prevalent symptoms among patients undergoing a course of chemotherapy include pain, fatigue, dry mouth, mucositis, dyspnea, constipation, diarrhea, anorexia, insomnia, nausea, vomiting, fever, infection, and dehydration, as well as psychologic symptoms such as depression and anxiety [12-19]. Patients interviewed around the nadir of their chemotherapeutic agents report higher levels of fatigue and other symptoms with a particular treatment [20-22].

Some researchers have argued that certain symptoms cluster together. If interventions were directed toward a symptom cluster with a particular treatment, then a synergistic effect on reductions in severity might be achieved. Cleeland et al [10] were able to separate 25 symptoms into clusters, using data from 527 outpatients, 30 bone marrow transplantation patients, and a cross-validation sample of 113 outpatients. Pain, fatigue, and insomnia were found to cluster together. Others were unable to create a cluster among these three symptoms [6]. Given et al [23] found that patients who experienced pain and fatigue reported, on average, 6.0 other symptoms (standard deviation [SD], 3.2), whereas patients reporting neither symptom reported only 1.7 (SD, 1.8) other symptoms. Similarly, reductions in pain have been associated with declines in total symptom burden [24]. Finally, patients who received supportive agents (colony-stimulating factors) during their chemotherapy reported lower levels of overall distress [23].

The prevalence, severity, and hypothesized interrelationship among symptoms, in part, could be a function of the assessment tools used. For example, the Memorial Symptom Assessment scale evaluates 32 physical and psychologic symptoms [25], the Symptom Distress Scale [26] examines 12 symptoms, the Rotterdam Symptom checklist [9] evaluates 31 symptoms, and the M.D. Anderson Symptom Inventory uses a 13- and a 19-item scale [10]. All contain some symptoms in common, but each varies in the way symptom severity and impact are assessed, and, regardless of instrument used, reports are likely to vary based on timing of administration with respect to treatment phase or type and site and stage of disease.

Behavioral Interventions
Among cancer patients, behavioral interventions have focused most prominently on reducing emotional distress [27-36], relaxation and coping [37-39], and symptoms such as anticipatory nausea [40-43]. Behavioral interventions for pain management have focused on massage therapy, muscle relaxation, music therapy, and exercise conditioning [44]. Management of fatigue has focused primarily on exercise interventions [45,46]. Trials directed toward multiple symptoms (some with relatively small sample sizes) indicate that patients can participate in strategies that reduce symptom severity [47,48]. Meta-analyses of behavioral trials conclude that cognitive behavioral and psycho-educational interventions are able to achieve effect sizes ranging from 0.17 to 0.56 [31-33]. Building on these findings, a cognitive behavioral intervention was developed and then tested through a randomized trial to determine whether the severity of 15 prevalent symptoms reported by patients with different sites and stages of cancer, all undergoing a first course of chemotherapy, could be reduced when compared with conventional care.

	PATIENTS AND METHODS

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The Sample
Entry criteria were as follows: patient age 21 years, diagnosis of a solid tumor, currently undergoing a first course of chemotherapy, and completion of no more than the first two cycles before the baseline interview. Patients with previous chemotherapy were not eligible, nor were patients who were undergoing radiotherapy at the time of entry. All patients had to have a family caregiver who agreed to participate in the study; both patients and caregivers had to be able to speak and read English, and both had to be cognitively intact. All patients had to be willing to participate in the 10-contact, 20-week intervention as well as telephone interviews at baseline and 10 and 20 weeks.

Nurse recruiters from two comprehensive cancer centers and four community oncology settings were trained according to the study protocol and approached 609 patients who were undergoing a first course of chemotherapy. Two hundred sixty-three patients (43%) agreed to participate and signed consent forms. Among the 346 patients who refused participation, 45% (n = 155) were not interested, 17% (n = 59) had no caregiver, 16% (n = 55) were too overwhelmed by disease and treatment, and 14% (n = 48) indicated that they were too busy; other reasons were personal and formed no pattern. Of the 263 dyads who signed the approved consent forms, 26 did not complete the intake interview because caregivers could not be contacted, patients were too ill, or patients had discontinued their chemotherapy. Thus 237 patients and their family caregivers completed the intake interview and were randomly assigned to either the 10-contact, 20-week experimental intervention or to conventional care. Conventional care was the usual practice for each setting.

Attrition and retention by group at the 10- and 20-week observations are summarized in the flow diagram in Figure 1. Attrition was assessed by comparing patients who were retained and lost at 10 and 20 weeks by assigned group according to their sex, site and stage of cancer, relevant covariates, and symptom severity using ² and t tests. All comparisons were made at the 5% level of significance. Retention at 10 weeks by site or stage of cancer did not differ between patients in the experimental and control groups. At 10 weeks, 86% of female patients and 67% of male patients were retained in both groups, and no differences were observed by age. No differences in retention between the four community and the two comprehensive cancer centers were noted at 10 weeks. Ninety-seven patients (82%) in the experimental group and 94 patients (79%) in the control group were retained for analysis at 10 weeks.

View larger version (40K): [in this window] [in a new window]   Fig 1. Flow of intervention and attrition of patients.

Data Collection
Trained interviewers administered the intake telephone interviews separately to each member of the dyad via a computer-assisted telephone interviewing system. After the interview, each dyad was randomly assigned using a minimization procedure to assure balance between the experimental and control groups according to cancer site. For dyads assigned to the experimental group, nurse interveners established a time during the patient’s next visit to the oncology unit to meet and begin the intervention. All in-person encounters coincided with regularly scheduled clinical visits. Telephone contacts occurred 2 weeks after each in-person contact. All patients were recontacted at 10 weeks (midpoint of the intervention) and at 20 weeks (immediately after conclusion of the intervention) for follow-up interviews.

The Intervention
This intervention was derived from cognitive-behavioral theory that specifies adaptive strategies that patients may undertake to address problems in everyday living [49-51]. This intervention followed the work of Bandura [52,53], who proposed four approaches to developing self-efficacy: mastering skills through practice, observing others as they address problems, persuading oneself that the strategy will work, and convincing oneself that the strategy will reduce aversive symptoms. The goal of this cognitive behavioral intervention was to assist patients to acquire self-management knowledge, skills, and behaviors to address symptom problems. The nurse and patient identified problems, the nurse proposed interventions, and together they evaluated the patient’s abilities to undertake requisite cognitive and behavioral strategies that, if performed successfully, could address the symptom problems [50].

Cognitive behavioral strategies were developed to assist patients to reduce the severity of symptoms, their impact on emotional distress, and physical function [54,55]. Four classes of cognitive behavioral strategies were identified from the literature: self-care management information, problem solving, communication with provider, and counseling and support [54,56-58]. From the four classes of interventions, the nurse, with agreement from the patient, selected specific strategies for patients to implement to manage symptoms, emotional distress, and physical functioning. These strategies were derived from evidence-based standards and guidelines for practice (numerous sets of guidelines and standards exist) [59,60].

Patients who received the experimental intervention were queried at each contact regarding the severity and impact on dimensions of their quality of life of 15 symptoms (alopecia, constipation, cough, diarrhea, fatigue, fever, anorexia, coordination problems, nausea and vomiting, insomnia, dry mouth, pain, mouth sores, inability to concentrate, and shortness of breath), as well as functional limitations and emotional distress. Severity of the symptoms was rated by patients on a 10-point scale ranging from 1 (barely noticeable), to a 10 (worst possible). When severity was rated as 5 or higher, that symptom was transferred to the plan of care for problem resolution. Similarly, if patients reported that a symptom reached a threshold of 3 or higher on a 5-point scale for any one of the quality-of-life dimensions (emotions, relationships with others, sleep, concentration, appetite, usual daily activity, and overall quality of life), then it was transferred to the plan of care.

As an example, a nurse might learn that a patient scored pain severity as 5 points and that it affected the patient’s work and emotions. The nurse might determine what medications were taken and whether pain medications were being taken at appropriate intervals and at the prescribed dosage. If not, the nurse would help the patient set up a schedule and a cueing system as a reminder to take the medications as prescribed. Next, the nurse might encourage the patient to communicate the level of pain to the oncologist. Other strategies for pain management might include use of heat or distraction to complement the administration of medication and monitoring approaches. (For examples of strategies for pain and fatigue, see Table 1).

Quality assurance was completed for all nurses on a monthly basis to ensure that they followed protocol. This included monthly audiotapes with their patients once consent was obtained by each nurse and a review for completeness and quality of all patient encounters based on the computer documentation system. This was completed by a quality assurance coordinator who ensured adherence to the protocol. In addition, monthly reviews with feedback sessions were held with all nurse interveners for all their telephone encounters.

Measures
On the basis of reviews of previous symptom scales and our past research, 15 symptoms were included in an index to assess the impact of the intervention: alopecia, pain, fatigue, nausea and vomiting, insomnia, shortness of breath, diarrhea, coordination problems, anorexia, fever, cough, dry mouth, constipation, mouth sores, and inability to concentrate. At the intake, 10- and 20-week observations patients were queried regarding the presence of each of these symptoms during the previous 2 weeks. Zero indicated that a symptom was not present. If patients acknowledged experiencing a symptom, they were asked to rate its severity on a 10-point scale ranging from 1 (barely noticeable) to 10 (worst severity possible). For analyses, a symptom severity index was created whereby the severity index was a sum, computed for each patient at each observation across severity reports for the 15 symptoms. Potential range of the scores was from zero to 150.

Because some cancers are sex-specific (eg, breast cancer), plausible combinations of sex and site of cancer categories were developed, which included male and female lung, male and female colon, male and female other cancer sites, and female breast cancers. Appropriate dummy variables were then created to assess the effect of combinations of sex and cancer site variables in the multivariate models. This approach was used to avoid empty cells for certain sex–cancer site combinations.

Sites of cancer were collected through an audit of medical records and included breast, lung, colon, and a number of other cancer sites that included pancreatic and ovarian (Table 2). Cancer stage was classified according to the tumor-node-metastasis system staging criteria of the American Joint Committee on Cancer [61]. This system classifies tumors on a scale of 0 to IV. For this study, stage of disease was collapsed into early (in situ, or zero, and stages I and II) and late (stages III and IV). The distributions of all these variables at baseline are presented in Table 2.

Data Analysis
The primary outcome was the 15-item symptom severity index measured at intake and at 10 and 20 weeks. Using symptom severity index measures from our pilot studies, sample size calculations were performed considering baseline symptom severity, which resulted in effect sizes of approximately 0.35. To detect a similar effect size between the two groups after 10 and 20 weeks, with at least 80% power at 5% level of significance, we targeted 122 patients per study arm [62].

Tests for equivalencies at baseline, according to group assignment, were completed through two-sample t tests for continuous variables and ² tests for categoric variables, with no between-group differences at the 5% level of significance. Tests for differences in site and stage of cancer, severity, number of symptoms, total numbers of supportive medications, and between community and comprehensive sites revealed no differences in symptom severity at baseline at the 5% level of significance.

The primary hypothesis sought to compare the experimental and control groups according to their scores at 10 and 20 weeks on the symptom severity index after adjusting for important baseline characteristics. Because of differences in the number of observations after 10 and 20 weeks owing to death or withdrawal, a repeated measures analysis of variance design was not used. Instead, a GLM was used to test separate group effects at 10 and 20 weeks. By testing for group effects at both 10 and 20 weeks, we could determine whether and when the intervention might affect symptom severity. The following regression model was used for the primary outcome: symptom severity at 10 and 20 weeks = symptom severity at baseline + study group + covariates (age, sex and cancer site, stage of disease) + study group interaction with severity index at baseline

The essential parameter tested in the model is associated with the study group variable, but in the case of interactions, the group effect was dependent on another covariate. In such instances, we report the final model to describe the interactive effect of the groups with covariates [62-64].

Finally, two models were tested: one examining the additive effects of supportive medications and a second exploring possible interactions between group and numbers of supportive care medications on symptom severity at the 20-week observation.

	RESULTS

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Forty percent of the sample was composed of female breast cancer patients, followed by 34% with lung cancer. Two thirds of the patients had cancers at stage III or greater at the time of entry onto the study. More than 75% of the participants were female, with three quarters of the patients coming from comprehensive cancer centers (Table 2).

Table 4 lists the means and SDs for symptom severity and number of symptoms for patients surviving through the 10- and 20-week observations.

In the initial models, baseline symptom severity—along with age, sex by sites of cancer, stage of disease, and group by baseline symptom severity—were entered to explain symptom severity at the 10- and 20-week observations. Age, stage, and sex by site of cancer had no effect on symptom severity at either the 10- or 20-week observations and, therefore, were removed from the final models. Tables 5 and 6 present the parameter estimates, SEs, t values, and significance for baseline severity, group, and the interaction of group and baseline severity on symptom severity at 10 and 20 weeks, respectively. Baseline severity affected severity at both 10 and 20 weeks. There was no main effect for group, but there was a strong statistically significant effect for the interaction of group and baseline severity on symptom severity at 10 and 20 weeks. Figures 2 and 3 show the interactive effects of group and baseline symptom severity on severity at 10 and 20 weeks, respectively. The equations for deriving each figure are included as footnotes. Patients who entered the study with higher symptom severity and who received the experimental intervention reported lower symptom severity at both the 10- and 20-week observations than their counterparts who received conventional care alone.

View larger version (25K): [in this window] [in a new window]   Fig 2. Plot of symptom severity index at 10 weeks versus baseline severity by group.

View larger version (24K): [in this window] [in a new window]   Fig 3. Plot of symptom severity index at 20 weeks versus baseline severity by group.

The potentially confounding effects of supportive medications on symptom severity at 20 weeks were evaluated (Table 7). Baseline severity, along with number of supportive medications, group, and the interaction between the number of supportive care medications and group, was entered to explain symptom severity at 20 weeks. Because group by number of supportive care medications did not reach significance, we concluded that there was no significant interaction effect between the numbers of supportive care medications and the behavioral intervention in reducing reported symptom severity at 20 weeks.

	DISCUSSION

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The results of this randomized trial indicate that for patients undergoing a first course of chemotherapy, a 10-contact, 20-week, nurse-administered cognitive-behavioral intervention focusing on symptom management resulted in significantly lower levels of symptom severity for patients who entered the trial with higher severity scores at 10 and 20 weeks compared with patients receiving conventional care alone. In this assessment of symptom severity at 10 and 20 weeks, baseline symptom severity was treated as a continuous covariate. The coefficient for baseline severity and the coefficient for the interaction of baseline severity and group indicated that as baseline severity increases, the effect of the intervention increases. The adjusted means comparing symptom severity scores for the experimental and control group at 10 and 20 weeks for increasing baseline severity scores summarizes these relationships (Table 8). No attempt was made to establish a clinical threshold at which the intervention would have a clinical effect. However, given the greater effect of the intervention among patients with higher baseline severity, future analyses should focus on where clinically important thresholds may occur. The effect of the intervention with increasing baseline severity was unaffected by the numbers of supportive medications administered to patients.

Further, although patients presented with different constellations of symptoms, this behavioral intervention, which followed a problem-solving approach targeted toward those symptoms that reached threshold, was able to engage patients in specific intervention strategies designed to reduce total symptom burden. Finally, when this behavioral intervention was tested against the receipt of supportive care medications, those with higher severity at 20 weeks received more medications, but no evidence was found to indicate that supportive medications confounded the effects of the intervention. This is one of the few trials that has contrasted the impact of a behavioral intervention with supportive care medications to better understand how these two approaches might work independently or might complement one another.

Several limitations of this trial should be noted. First, the accrual rate was below 50%; little is known about those who refused participation except that they had a solid tumor and were within two cycles of beginning an initial course of chemotherapy. Second, although no statistical differences were observed at the 20-week observation, greater attrition from the experimental group suggested that some patients might have viewed the intervention as an additional burden. Further, patients who attrited from the experiment tended to report more severe symptoms. Although we found no evidence that supportive care medications influenced the impact of the behavioral intervention, this comparison must be considered crude. We have no indication of whether medications were taken as prescribed, whether the dosage was appropriate, or whether the severity of the condition that prompted the prescription was correctly recorded.

This 10-contact, 20-week cognitive behavioral intervention demonstrated significant impact on patient symptom severity after only four contacts (10 weeks), and this difference was sustained over 20 weeks. The behavioral intervention was integrated into the ongoing treatment plans of patients and family caregivers by assessing patients during their chemotherapy clinic visits and interspersing additional contacts via telephone. Finally, this trial demonstrated that the cognitive behavioral intervention had a significant interaction with higher symptom severity at baseline and that this effect was sustained over and beyond the effects of supportive medications prescribed for patients. Finally, from a clinical perspective, it is important to identify interventions that can be directed toward patients’ high symptom burden. This intervention may be such a solution.

	Authors’ Disclosures of Potential Conflicts of Interest

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The authors indicated no potential conflicts of interest.

	Acknowledgment

 
We thank Danielle DeVoss, PhD, Assistant Professor of American Thought and Language, Michigan State University, for her editorial contributions.

	NOTES

 
Supported by National Cancer Institute grant RO1 CA79280; Family Home Care for Cancer: A Community-Based Model, in affiliation with the Walther Cancer Institute, Indianapolis, IN; and National Institute of Aging grant P30 AG08808.

Authors’ disclosures of potential conflicts of interest are found at the end of this article.

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Submitted January 31, 2003; accepted November 24, 2003.

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