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Keywords:

  • cancer;
  • oncology;
  • moderators;
  • symptom management;
  • age;
  • depression

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Objective

The purpose of this study was to test for moderating effects of patient characteristics on self-management interventions developed to address symptoms during cancer treatment. Patient's age, education, and depressive symptomatology were considered as potential moderators.

Methods

A secondary analysis of data of 782 patients from two randomized clinical trials was performed. Both trials enrolled patients with solid tumors undergoing chemotherapy. After completing baseline interviews, patients were randomized to a nurse-delivered intervention versus intervention delivered by a ‘coach’ in trial I and to a nurse-delivered intervention versus an intervention delivered by an automated voice response system in trial II. In each of the two trials, following a six-contact 8-week intervention, patients were interviewed at week 10 to assess the primary outcome of symptom severity.

Results

Although nurse-delivered intervention proved no better than the coach or automated system in lowering symptom severity, important differences in the intervention by age were found in both trials. Patients aged ≤45 years responded better to the coach or automated system, whereas those aged ≥75 years favored the nurse. Education and depressive symptomatology did not modify the intervention effects in either of the two trials. Depressive symptomatology had a significant main effect on symptom severity at week 10 in both trials (p = 0.03 and p < 0.01, respectively). Education was not associated with symptom severity over and above age and depressive symptomatology.

Conclusions

Clinicians need to carefully consider the age of the population when using or testing interventions to manage symptoms among cancer patients. Copyright © 2014 John Wiley & Sons, Ltd.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Targeting interventions to characteristics of specific groups of patients has been supported in research studies and frequently discussed as desirable. Whereas a positive finding of efficacy or effectiveness of an intervention provides evidence base for clinical practice, a negative finding could result from differing effects of the intervention on subgroups of the population. Thus, in addition to the overall comparison of an intervention with usual care or another intervention, it is important to test for moderating effects to identify potential efficacy for subgroups of the population [1]. The examination of moderating effects of patient characteristics on intervention efficacy is fraught with difficulty [2, 3]. Post hoc tests for characteristics, such as age, may lack discrimination because distributions are skewed toward older ages. This is especially true in those with cancer, as the majority is diagnosed at an older age [4, 5]. Further, by design, most trials are powered to detect main effects but underpowered to detect the differential effects of the intervention by patient characteristics. Even when moderating effects are observed, it is unlikely that similarly constructed samples are available for replication. Thus, moderating effects are seldom tested and, as a result, are not incorporated into the inclusion criteria or used as stratification or minimization variables in randomized trials [3]. This paper focuses on two large trials of self-care interventions aiming to manage cancer patients' symptoms during chemotherapy. The purpose of this work was to test for the moderating effects of patients' characteristics in each trial and then to compare the findings from the two trials. The similarity between the two trials provided a unique opportunity to determine whether moderating effects can be replicated.

The nurse-delivered symptom management intervention common to the two trials was developed on the basis of the principles of cognitive behavioral therapy [6, 7]. Nurses helped patients isolate symptom-related problems and taught patients how to assume control for solving these problems and how to use symptom management strategies so that they fit in patient's daily lives [8]. The nurse-delivered intervention, which included both cognitive behavioral and educational elements, was compared with the educational interventions in both trials. The education intervention implemented as the second arm in each of the two trials involved referral of the patients to a written guide that contained information on symptom management strategies. In trial I, this educational intervention was delivered by a non-nurse ‘coach’, and in Trial II, the educational intervention was delivered by an automated telephone system. The two trials enrolled similar samples of patients and incorporated the same self-management intervention delivered by a nurse in one of the arms and the same educational intervention in the other arm (with different modes of delivery in two trials). Patient characteristics tested as potential moderators were selected on the basis of the literature reviewed later and included age, education, and depressive symptomatology.

With few exceptions [9-12], findings indicate that interventions that incorporate cognitive behavioral approaches are equal to, or in some cases, superior to other psychological interventions [13-16]. Combinations of cognitive behavioral and educational strategies have demonstrated effectiveness at reducing symptom severity from side effects of cancer treatment [17, 18].

The literature on moderating effects for symptom management interventions in chronic diseases including cancer has mixed findings. A meta-analysis of insomnia trials indicated that adults who were 55 years and older responded to cognitive behavioral strategies more favorably in terms of sleep efficiency and total sleep time when compared with adults younger than 55 years [19]. Conversely, a study examining cognitive behavioral intervention for symptom management of patients with advanced cancer found that older age reduced the effectiveness of the intervention [17]. Another study found that age did not modify the effect of the behavioral intervention on symptom severity [12]. Evaluations of possible moderating effects of education are limited. Education moderated individuals' responses to attitude measurement [20, 21] and influenced women's responses to genetic counseling and testing for breast/ovarian cancer [22].

Depressive symptoms in cancer patients are widely studied across numerous primary and specialty care settings [23]. For most cancer patients, these symptoms are not sufficiently severe to warrant a full clinical diagnosis of depression [24, 25]. Research with cancer patients who do not meet the criteria for a clear diagnosis of clinical depression are summarized as depressive symptoms. Data from several meta-analyses, and our own past trials, suggest that cognitive behavioral and educational strategies can influence cancer-related and treatment-related symptoms as well as depressive symptoms [26-30]. There is strong evidence that chronic diseases increase patient depressive symptoms, but few investigations exist about the possible moderating effects of depressive symptomatology on cancer patients' responses to interventions for symptom management [31]. A Cochrane review [32] and a meta-analysis of six trials [33] demonstrate the effectiveness of both cognitive behavioral and educational interventions on depressive affect in patients with cancer, suggesting possible mediation but not necessarily moderating effects of depressive affect in symptom management in cancer.

In summary, interventions that incorporate cognitive behavioral and/or educational strategies can assist patients with different chronic diseases to manage their symptoms. Some evidence indicates that age, education, and depressive affect may moderate the impact of these interventions on symptom severity; however, none of the existing studies have attempted to replicate moderating effects of these patient characteristics across similarly designed trials. This report fills this gap by presenting the secondary analyses of each of the two completed and similarly designed symptom management trials. The research question of moderating effects of age, education, and depressive symptomatology is answered for each trial, and the findings are compared to assess their replication from one trial to the other.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

In the following, we describe the two trials and summarize the published findings that were based on the analyses of additive effects of trial arms following the intent-to-treat principles [17, 18].

Setting and sample

Enrollment for the original trials occurred at two comprehensive cancer centers, two community cancer oncology programs, and five hospital affiliated community oncology centers. Institutional review board approvals were obtained from each site. Registered nurses from these sites implemented the recruitment protocol. To be eligible for either trial, patients met the following inclusion criteria: 21 years or older, a diagnosis of a solid tumor or non-Hodgkin's lymphoma, undergoing intravenous chemotherapy, able to speak and read English, having a touchtone phone, and without hearing deficits. On the basis of our prior results, following consent and prior to enrollment, each trial used a specific symptom severity criteria that used a scale ranging from 0 (no symptom) to 10 (worst possible) [18]. Patients scoring a severity of 2 on pain and fatigue or a 3 on pain or fatigue and who had a family caregiver entered trial I. Patients scoring a 2 or higher on any symptom entered trial II. All but two patients who completed screening entered either trial I or trial II [34]. The two patients who never reached a 2 in severity on any symptom were sent a letter thanking them for their participation, and no further interviews were conducted. Figure 1 summarizes the flow of patients in both trials.

image

Figure 1. Flow chart showing progression of patients through the trials

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Trial arms

All arms of the trials were delivered entirely by phone with nurse-delivered symptom management intervention implemented as an arm in each trial. In trial I, a six-contact 8-week nurse-delivered self-management intervention was compared with a six-contact 8-week educational intervention delivered by a non-nurse coach. In trial II, a six-contact 8-week nurse delivered self-management intervention was compared with a six-contact 8-week automated voice response (AVR) system. During each of eight telephone contacts spread over 10 weeks (conducted by nurse, coach, or AVR), the severity of 16 symptoms (fatigue, pain, dyspnea, insomnia, anxiety, depression, nausea/vomiting, difficulty remembering, dry mouth, poor appetite, numbness and tingling, diarrhea, cough, constipation, weakness, and alopecia) was rated by patients using the scale from 0 (no symptom) to 10 (worst possible). If a patient rated any symptom at a severity of 4 or higher (threshold) at any contact, they received management strategies [35].

For symptoms at 4 or higher, the coach in trial I and the AVR in trial II referred the patient to a section of a written Symptom Management Toolkit (SMT), which has been proven effective in several trials [8, 18, 36] to assist in managing the symptoms. This printed SMT, which was developed and refined through previous studies, was written at the sixth grade level and contained evidence-based self-care strategies specific to each symptom [31, 37]. Each symptom was presented in an identical format of frequently asked questions: what the symptom is, how people describe it, the causes of the symptom including medications, and a set of strategies presented in short points for managing the symptom. At contacts 2–6, the coach or the AVR asked the patient if they read the section of the SMT, and if so, how successful it was for managing symptoms reported at the last contact. Patients who did not read assigned sections of the SMT, or who rated it unsuccessful, were encouraged to read the section again and continue to follow the strategies. Thus, the content of the educational interventions delivered by the coach and AVR was identical; only mode of delivery differed. In contrast to these two targeted approaches of referral to the SMT, the nurse arms of the trials offered more specific approaches to symptom management based on patients' responses, in addition to the referral to SMT. Nurses worked in partnership with the patients on prioritizing which symptoms experienced by patients should be addressed. Then, nurses used a drop-down list of strategies (specific and relevant to each symptom) on their computer screen to select strategies for the management of selected symptoms. The list of strategies that could be delivered by nurses was broader than the list in the SMT and could include nurses' clinical judgment. Symptom management strategies delivered by nurses were organized into four domains: teaching (adherence to medications, prioritizing, and limiting daily tasks), prescribing (diet, exercise, and lifestyle changes), counseling and support (coping strategies and reframing), and communicating with healthcare providers (how to report problems, prepare for appointments, and ask for help). According to the nurse intervention protocol, up to four strategies could be delivered for each symptom at each phone contact. At contacts 2–6, nurses asked patients if they had tried the previously suggested interventions, and if so, how successful it was for managing the symptom. Interventions not tried or unsuccessful were replaced with new ones, and a suggestion was made to continue the successful interventions.

Measures

Age, sex (male or female), site of cancer (breast, prostate, lung, colon, or Hodgkin's Lymphoma), stage of cancer (I–IV), and comorbidities were obtained from the patients' medical records. On the basis of the distribution of age and its nonlinear relationship with symptom severity, age was grouped into five categories: 45 years or younger, 46–55 years, 56–65 years, 66–75 years, and older than 75 years. Patients' educational levels were collapsed into three categories: high school or less, more than high school to completed college, and graduate or professional education. The Center for Epidemiologic Studies – Depression (CESD) [38], a 20-item reliable valid instrument with responses assessed on a 4-point scale (0–3), a range of 0–60, was used to assess depressive symptomatology during intake. Although not designed for clinical diagnosis because of relatively low specificity, the score of 16 is an established highly sensitive screening cutoff for clinical depression [38, 39], and for analysis, the CESD variable was dichotomized as less than 16 versus 16 or higher. The Symptom Experience Inventory, developed in past studies by Given et al. [35, 40, 41] (internal consistency reliability of 0.79), was used to assess symptom severity during screening, baseline interview, the six intervention contacts, and 10-week interview. The severity of each symptom was rated from 0 (no symptom) to 10 (worst possible), and severity scores were summed across the 16 symptoms to create an index of severity ranging from 0 to 160 [17]. The symptom list from the interviews differed slightly from the list from the six intervention contacts. During the interviews, nausea and vomiting were separated into two items, and a single item of depression asked during the intervention contacts was replaced with the CESD for a more detailed assessment of depressive symptoms.

Previous results from the two trials

In both trials I and II, no differences in summed symptom severity were found between the trial arms in the intent-to-treat analyses [18, 42]. All four intervention arms had significant improvements in symptom severity over baseline [43]. Per-protocol analyses revealed differences in patient subgroups and success with the management of specific symptoms. First, nurses were more successful than the AVR in retaining lung cancer patients and managing their symptoms [18]. When compared with patients in the nurse arm of trial II, patients in the AVR arm had a better response to the management of anxiety, depression, poor appetite, cough, and fatigue. In trial II, nurses were more successful than the AVR in managing cancer pain [36]. These findings are from intent-to-treat and per-protocol analyses that included the main effect of trial arm variable within each trial but no interaction terms. This paper extends the completed primary analyses to include tests of moderating effects of the patient characteristics based on the significance of the interactions of trial arm variable with patient characteristics. Although both trials were powered to detect main effects of the moderate size, neither trial was formally powered to detect these interactions. We draw upon the similarity of the design of the two trials to assess if any evidence of moderating effects in one trial is replicated in the other one.

Data analyses

Because separate randomization procedures were carried out for each trial, the analyses of data from each trial were performed separately and the results compared. Descriptive statistics for the demographic, outcome, and potential moderator variables were obtained. The baseline differences between the groups in each of the trials were evaluated using chi-square and t-tests. Attrition analyses were conducted to examine the baseline characteristics of patients who dropped out between baseline and week 10 and were compared by trial arm according to the potential moderators.

To determine if age, education, or depressive affect moderated the impact of the interventions on symptom severity, the criteria established by Baron and Kenny [2] and Kraemer et al. [3] were followed. Age, education, and depressive symptomatology were evaluated at baseline to determine if they had a main effect on symptom severity at week 10 and if there was a significant interaction between each potential moderator variable and intervention arm variable. Least squares (LS) means, also known as adjusted means of symptom severity, were calculated according to the interaction terms. Further, the moderating effect of age in both trials was tested in the presence of depressive symptoms and education variables as main effects and in the interaction with intervention arm to evaluate if the effect of age persisted after adjusting for the level of education and depressive symptomatology. P-values in tables are reported without adjustments for multiple comparisons. When LS means for multiple age groups were compared by intervention arm, conclusions about significance were made using Bonferroni correction. For example, when five age groups were tested, a significance was indicated by p < 0.01 instead of p < 0.05 to control the overall probability of type I error. All analyses were performed using sas version 9.3 (SAS Institute, Cary, NC, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Table 1 summarizes the socio-demographic information and sites and stages of cancer for each arm of the two trials. Table 2 provides the means and standard deviations of symptom severity and CESD scores at intake. Baseline equivalence was achieved between the arms for the two trials. When baseline scores were compared for cases lost versus those retained at week 10 with respect to age, education, depressive symptomatology, and symptom severity, no differences were found between arms within trials. Thus, any moderating effects are unlikely to be confounded by differential attrition between arms. Figure 1 displays the flow of patients through the two trials. At intake, all patients were undergoing chemotherapy, as this was one of the inclusion criteria. At week 10, in trial I, 51% of the patients in the nurse arm and 58% in the coach arm remained in treatment (p = 0.38 for arm difference). The corresponding figures for trial II were 58% for the nurse arm and 59% for AVR (p = 0.88). Thus, symptom severity at week 10 did not differ by treatment status (ongoing versus completed or stopped) in either trial. After adjusting for symptom severity at baseline for each trial, there was no significant difference for symptom severity at week 10, and there were no main effects for age or education in either trial. Depressive symptoms at baseline were significantly associated with symptom severity at week 10 in both trials (p = 0.03 and p < 0.01, respectively; Table 3). When testing for moderating effects, age by group interactions were not significant for trial I but were significant for trial II (Table 3), because of the smaller sample size of trial I.

Table 1. Descriptive statistics by trial arm for the two trials
 Trial ITrial II
NurseCoachNurseAVR
N = 115N = 119N = 218N = 219
N (%)N (%)N (%)N (%)
Age (years)
≤4512 (10)16 (13)40 (18)33 (15)
46–5532 (28)30 (25)56 (26)68 (31)
56–6538 (33)41 (34)72 (33)69 (32)
66–7527 (23)20 (17)36 (17)27 (12)
>756 (5)12 (10)14 (6)21 (10)
Sex
Male47 (41)47 (39)57 (26)53 (24)
Female68 (59)72 (61)161 (74)166 (76)
Race
Caucasian99 (88)110 (92)186 (86)184 (86)
African American10 (9)7 (6)27 (12)22 (10)
Others4 (3)2 (2)4 (2)8 (4)
Education
≤High School38 (33)45 (38)71 (33)67 (31)
>High School to ≤college56 (49)55 (46)113 (52)108 (49)
Graduate/professional21 (18)19 (16)34 (16)44 (20)
Marital status
Never married7 (6)13 (11)23 (11)26 (12)
Married/living together94 (82)85 (72)138 (63)131 (59)
Divorced/separated/widowed14 (12)20 (17)57 (26)62 (29)
Employed
Yes31 (27)30 (25)67 (31)74 (34)
No84 (73)89 (75)151 (69)145 (66)
Cancer site
Breast27 (23)30 (25)90 (41)87 (40)
Colon10 (9)8 (7)30 (14)32 (15)
Lung34 (30)35 (29)37 (17)34 (16)
Genitourinary13 (11)10 (8)13 (6)15 (7)
Gastrointestinal including pancreas6 (6)16 (13)16 (7)16 (7)
Other25 (22)20 (17)32 (15)35 (16)
Cancer stage
Early8 (7)13 (11)31 (14)44 (20)
Late105 (93)104 (89)185 (86)175 (80)
Metastasis
Yes75 (65)69 (58)128 (59)112 (51)
No40 (35)50 (42)90 (41)107 (49)
Table 2. Descriptive statistics for symptom severity and Center for Epidemiologic Studies – Depression (CESD) scores at intake and week 10 by trial arm for the two trials
 Trial ITrial II
NurseCoachNurseAVR
IntakeWeek 10IntakeWeek 10IntakeWeek 10IntakeWeek 10
N = 115N = 89N = 119N = 85N = 218N = 183N = 219N = 177
Symptom severity mean (SD)39.9 (21.0)21.3 (17.9)39.2 (22.9)21.0 (16.6)32.5 (20.9)20.9 (19.1)36.1 (22.8)20.9 (17.9)
CESD mean (SD)12.9 (6.3)9.7 (5.6)14.1 (7.5)10.4 (6.7)12.3 (7.7)10.0 (7.3)12.9 (8.0)9.5 (6.5)
Table 3. Summary of the model relating symptom severity at week 10 to severity at baseline, trial arm, and patient characteristics
 Trial ITrial II
SourcedfType III SSMean SquareF-valuePr > FType III SSMean squareF-valuePr > F
Symptom severity at baseline15197519720.62<0.01194511945183.50<0.01
Trial arm135350.140.7126260.110.74
CESD1128412845.090.03190919098.19<0.01
Age category410892721.080.375211300.560.69
Age category × trial arm411692921.160.3336589143.93<0.01
 
 F(11, 151) = 4.02, p < 0.0001F(11,348) = 16.42, p < 0.0001

Examining the adjusted means for symptom severity by age at week 10 revealed a remarkably similar pattern with the differences in the age categories for the trials (Table 4). Among patients 45 years or younger, the self-care strategies delivered by the AVR were more successful than the intervention delivered by the nurse in lowering symptom severity at the 10-week assessment (p < 0.01). The comparison of the ‘coach’ versus nurse arm in trial I showed a similar relationship between LS means, but the difference did not reach statistical significance. LS means of symptom severity in trial I were 29.28 for the nurse arm and 19.16 for the coach arm and, in trial II, 30.39 for the nurse arm and 15.63 for the AVR arm at week 10. The pattern of LS means reverses for patients 75 years and older compared with younger patients in both trials. In trial I, the LS means of symptom severity at week 10 were 18.04 for the nurse arm and 34.67 for the coach arm and, in trial II, 19.84 for the nurse arm and 26.35 for the AVR arm. Even though the magnitude of the differences between LS means by arm was similar for the two trials, the differences in sample sizes (Table 1) resulted in the LS means being statistically significant for trial II but not for trial I. In the middle-aged categories, 46–55, 55–65, and 66–75 years, both arms in each trial appear to be equally successful.

Table 4. Least square means symptom severity at week 10, by age category and trial arm, adjusted for baseline severity and depressive affect
 Least square means from final model
 Trial ITrial II
Age groupsNurseCoachp-valueNurseAVRp-value
NMeanNMeanNMeanNMean
≤451229.281619.160.153230.392715.63<0.01
46–552919.672816.860.575220.525720.340.98
56–653519.833722.830.496122.515721.500.72
66–752425.172024.710.932720.721826.780.19
>75418.041234.670.191119.841826.350.27

Tests for the moderating effects of education and depressive symptoms by arm revealed no associations with symptom severity at the 10-week end point. Education had neither a main nor moderating effect, and depressive symptoms had a main effect on symptom severity at week 10 but did not moderate the effects of the interventions. The LS means by age and arm listed in Table 3 did not change after adjusting for education and depressive symptoms and are not presented. Thus, the moderating effect of age on patient response to the trial arms is not influenced by education or depressive symptoms, and the observed effects for age are not artifacts of educational attainment.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Findings from these two trials contribute important information about differential effects of the interventions and the need to select the best performing interventions for specific patient subgroups. A carefully constructed intervention delivered by specially trained nurses was compared against an interpersonal coach and an AVR with referral to a SMT. When data from all patients were analyzed according to the intent-to-treat principle, an elaborate nurse-delivered symptom management intervention fared no better than streamlined approaches, where patients were directed to follow specific written directions on their symptoms that were assessed above a severity threshold. When several patient characteristics were examined in relation to the intervention effect, only age showed evidence of modifying the effects of interventions. In the assessment of moderating effects of age, several limitations deserve notice. First, in both trials, the numbers of patients in the youngest and oldest age groups were relatively small. Second, we obtained these findings using age as a categorical variable. After evaluating alternatives, we used categories that best fit the data because the effect of age was nonlinear. Finally, our sample had a large proportion of women and relatively low proportion of minorities, reflective of the population of patients treated at the participating oncology clinics. Despite these limitations, the core strength of our argument regarding the moderating effects of age relies on the fact that the patterns of differences according to age were the same across separate but very similarly constructed trials, each designed to address a common endpoint.

The nurse-delivered intervention required an average of 54 min per contact, whereas the AVR required only 20 min, and the associated costs per contact were around $60 for the nurse arm and $17 for the AVR arm [43]. However, time and costs are not the only considerations in developing a symptom management intervention. If patients do not accept the intervention, then it is of little value. Further, certain subgroups of the patient population may benefit from a more extensive intervention, whereas other subgroups would do just as well with a simpler intervention. Answering the question about moderating effect is key to selecting the best interventions that tailor to patient characteristics and ultimately to the development of more effective therapeutic approaches [1].

As Kraemer and colleagues have argued, moderating effects can be disguised [3]. A difficulty with moderating effects is that formal hypotheses are seldom presented and trials are rarely powered to test for moderating effects. As such, arguments to explain the moderating effects are largely post hoc and seldom flow from the conceptual framework upon which the trial is based. Although no main effects were observed for the intervention arm, the difference existed in the patient's age in trial II, and a similar pattern toward significance was observed in trial I. Younger patients responded to educational approaches delivered by the AVR or a coach and older patients to nurse-delivered symptom management strategies. New strategies were presented in response to new symptoms that rose above threshold at successive contacts. Our evidence reveals that younger patients appeared able to identify specific strategies from the SMT and to incorporate them into their daily lives resulting in improved symptom management. The oldest group of patients may, based on some evidence, possess less attention control and adaptability [44, 45]. Therefore, they may value interactions with nurses who assist them to select and tailor interventions that address their symptoms in a manner that conforms to their daily schedules. The oldest patients in the coach or AVR system, who were referred to the written guide, appeared less able to extract information and apply it to their personal situation to manage symptoms. In contrast, the youngest patients responded more favorably to classic written material that guided them to self-management approaches they could read and implement.

Further supporting the similarity of the effects of age in the two trials is the fact that the nurse, coach, and AVR arms were tested on very similar samples of cancer patients undergoing chemotherapy. The significant age by trial arm interaction in trial II and similar patterns in the adjusted means by trial arm and age in both trials strongly support the argument that older patients prefer the intervention utilizing interpersonal interactions with a nurse as compared with interactions with the AVR or a coach who refer them to an SMT. This difference may be based on the tailoring and support that nurses provide to older patients. In contrast, the younger patients may prefer approaches that allow them to read and act on information in their own styles. They may not want to invest the time or engage in extended conversations with nurses. They appear equally responsive to both the coach and the AVR because these arms drove the symptom severity lower than in the nurse arm. This suggests that their responsiveness is not due to a greater comfort with technology (there was no technology in the coach arm), but because of the relative simplicity of the intervention where symptoms were assessed, symptoms above threshold were specified, and information was provided to the patients to draw upon as needed to manage symptoms.

We saw no differences in the numbers of symptoms reported by the oldest and youngest age groups, so it is unlikely that the educational approaches worked better for persons with fewer symptoms to manage. Further, even though trial I required that patients report pain and/or fatigue at a specified severity and have a participating family caregiver in order to enroll, we found no differences in average number of symptoms between the two trials. Fatigue was the most prevalent symptom among all arms and pain while a bit higher in trial I did not produce unique effects. Participation of a family caregiver with the patient in trial I but not in trial II may have impacted the outcomes. However, given the similar findings, we believe such impacts are modest. The written strategies presented in the SMT were carefully measured so that reading and comprehension were at a sixth grade level. We believe this may have helped reduce the effect of educational level on use of the SMT and allowed us to identify the moderating effects of age and not of education.

Finally, we found that higher levels of patients' depressive symptoms at intake resulted in poorer symptom management at week 10, regardless of the intervention arm. Thus, given a standard dose of the intervention in either arm, patients with CESD scores of 16 or higher responded less favorably than those with scores below 16. This appears consistent with reports by Given and colleagues [31], who indicated that patients with worse depressive symptoms required more contacts in order to respond to symptom management interventions.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

This research is supported by Grant CA79280 Family Home Care for Cancer: A Community Based Model and Grant CA030724 Automated Telephone Intervention for Symptom Management from the National Cancer Institute and in affiliation with the Walther Cancer Institute, Indianapolis, Indiana.

Conflict of interest

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

The authors have declared that there is no conflict of interest.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References
  • 1
    Emsley R, Dunn G, White IR. Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Stat Methods Med Res 2010;19(3):237270.
  • 2
    Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51(6):11731182.
  • 3
    Kraemer HC, Frank E, Kupfer DJ. Moderators of treatment outcomes: clinical, research, and policy importance. JAMA 2006;296(10):12861289.
  • 4
    Dale W, Mohile SG, Eldadah BA, et al. Biological, clinical, and psychosocial correlates at the interface of cancer and aging research. J Natl Cancer Inst 2012;104(8):581589.
  • 5
    Dillin A, Gottschling DE, Nystrom T. The good and the bad of being connected: the integrons of aging. Curr Opin Cell Biol 2014;26C:107112.
  • 6
    Sorocco KH, Lauderdale S. Cognitive Behavior Therapy with Older Adults: Innovations Across Care Settings, Davis D (ed.), Springer Publishing Company: New York, 2011.
  • 7
    Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin Psychol Rev 2006;26(1):1731.
  • 8
    Given CW, Sikorskii A, Tamkus D, et al. Managing symptoms among patients with breast cancer during chemotherapy: results of a two-arm behavioral trial. J Clin Oncol 2008;26(36):58555862.
  • 9
    Denis C, Lavie E, Fatseas M, Auriacombe M. Psychotherapeutic interventions for cannabis abuse and/or dependence in outpatient settings. Cochrane Database Syst Rev 2006;(3):CD005336.
  • 10
    Brown P, Clark MM, Atherton P, et al. Will improvement in quality of life (QOL) impact fatigue in patients receiving radiation therapy for advanced cancer? Am J Clin Oncol 2006;29(1):5258.
  • 11
    Gould RL, Coulson MC, Howard RJ. Cognitive behavioral therapy for depression in older people: a meta-analysis and meta-regression of randomized controlled trials. J Am Geriatr Soc 2012;60(10):18171830.
  • 12
    Doorenbos A, Given B, Given C, Verbitsky N. Physical functioning: effect of behavioral intervention for symptoms among individuals with cancer. Nurs Res 2006;55(3):161171.
  • 13
    Arnedt JT, Cuddihy L, Swanson LM, et al. Randomized controlled trial of telephone-delivered cognitive behavioral therapy for chronic insomnia. Sleep 2013;36(3):353362.
  • 14
    Farronato NS, Dursteler-Macfarland KM, Wiesbeck GA, Petitjean SA. A systematic review comparing cognitive-behavioral therapy and contingency management for cocaine dependence. J Addict Dis 2013;32(3):274287.
  • 15
    Hamdan-Mansour AM, Constantino RE, Farrell M, et al. Evaluating the mental health of Jordanian women in relationships with intimate partner abuse. Issues Ment Health Nurs 2011;32(10):614623.
  • 16
    Knight SJ, Scheinberg A, Harvey AR. Interventions in pediatric chronic fatigue syndrome/myalgic encephalomyelitis: a systematic review. J Adolesc Health 2013;53(2):154165.
  • 17
    Sherwood P, Given BA, Given CW, et al. A cognitive behavioral intervention for symptom management in patients with advanced cancer. Oncol Nurs Forum 2005;32(6):11901198.
  • 18
    Sikorskii A, Given CW, Given B, et al. Symptom management for cancer patients: a trial comparing two multimodal interventions. J Pain Symptom Manag 2007;34(3):253264.
  • 19
    Irwin MR, Cole JC, Nicassio PM. Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychol 2006;25(1):314.
  • 20
    Rammstedt B, Rammsayer TH. Self-estimated intelligence: gender differences, relationship to psychometric intelligence and moderating effects of level of education. Eur Psychol 2002;7(4):275284.
  • 21
    Narayan S, Krosnick J. Education moderates some response effects in attitude measurement. Public Opin Q 1996;60(1):5888.
  • 22
    Joseph G, Beattie MS, Lee R, et al. Pre-counseling education for low literacy women at risk of Hereditary Breast and Ovarian Cancer (HBOC): patient experiences using the Cancer Risk Education Intervention Tool (CREdIT). J Genet Couns 2010;19(5):447462.
  • 23
    Addington-Hall J. The legacy of cancer on depression and anxiety. Lancet Oncol 2013;14(8):675676.
  • 24
    Krebber AM, Buffart LM, Kleijn G, et al. Prevalence of depression in cancer patients: a meta-analysis of diagnostic interviews and self-report instruments. Psycho-Oncology 2014;23(2):121130.
  • 25
    Singer S, Szalai C, Briest S, et al. Co-morbid mental health conditions in cancer patients at working age – prevalence, risk profiles, and care uptake. Psycho-Oncology 2013;22(10):22912297.
  • 26
    Barsevick A, Beck SL, Dudley WN, et al. Efficacy of an intervention for fatigue and sleep disturbance during cancer chemotherapy. J Pain Symptom Manage 2010;40(2):200216.
  • 27
    Brothers BM, Yang HC, Strunk DR, Andersen BL. Cancer patients with major depressive disorder: testing a biobehavioral/cognitive behavior intervention. J Consult Clin Psychol 2011;79(2):253260.
  • 28
    Carpenter JS, Wu J, Burns DS, Yu M. Perceived control and hot flashes in treatment-seeking breast cancer survivors and menopausal women. Cancer Nurs 2012;35(3):195202.
  • 29
    Fors EA, Bertheussen GF, Thune I, et al. Psychosocial interventions as part of breast cancer rehabilitation programs? Results from a systematic review. Psycho-Oncology 2011;20(9):909918.
  • 30
    Hart SL, Hoyt MA, Diefenbach M, et al. Meta-analysis of efficacy of interventions for elevated depressive symptoms in adults diagnosed with cancer. J Natl Cancer Inst 2012;104(13):9901004.
  • 31
    Given C, Given B, Rahbar M, et al. Does a symptom management intervention affect depression among cancer patients: results from a clinical trial. Psycho-Oncology 2004;13(11):818830.
  • 32
    Edwards AG, Hulbert-Williams N, Neal RD. Psychological interventions for women with metastatic breast cancer. Cochrane Database Syst Rev 2008;(3):CD004253.
  • 33
    Akechi T, Okuyama T, Onishi J, Morita T, Furukawa TA. Psychotherapy for depression among incurable cancer patients. Cochrane Database Syst Rev 2008;(2):CD005537.
  • 34
    Jeon S, Given CW, Sikorskii A, Given B. The utility of screening in the design of trials for symptom management in cancer. J Pain Symptom Manag 2009;38(4):606614.
  • 35
    Given B, Given C, Sikorskii A, et al. Establishing mild, moderate, and severe scores for cancer related symptoms: how consisten and clinically meaningful are interference based severity cut points? J Pain Symptom Manage 2008;35(2):126135.
  • 36
    Sikorskii A, Given CW, You M, Jeon S, Given BA. Response analysis for multiple symptoms revealed differences between arms of a symptom management trial. J Clin Epidemiol 2009;62(7):716724.
  • 37
    Given C, Given B, Rahbar M, et al. Effect of a cognitive behavioral intervention on reducing symptom severity during chemotherapy. J Clin Oncol 2004;22(3):507516.
  • 38
    Radloff LS. The CES-D scale: a self report depression scale for research in the general population. Appl Psychol Meas 1977;1(3):385401.
  • 39
    Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging 1997;12:277287.
  • 40
    Decker V, Spoelstra S, Miezo E, et al. A pilot study of an automated voice response system and nursing intervention to monitor adherence to oral chemotherapy agents. Cancer Nurs 2009;32(6):E20E29.
  • 41
    Spoelstra SL, Given BA, Given CW, et al. An intervention to improve adherence and management of symptoms for patients prescribed oral chemotherapy agents: an exploratory study. Cancer Nurs 2013;36(1):1828.
  • 42
    Given B, Given CW, Sikorskii A, et al. Analyzing symptom management trials: the value of both intention-to-treat and per-protocol approaches. Oncol Nurs Forum 2009;36(6):E293E302.
  • 43
    Given CW, Bradley C, You M, Sikorskii A, Given B. Costs of novel symptom management interventions and their impact on hospitalizations. J Pain Symptom Manag 2010;39(4):663672.
  • 44
    Cashdollar N, Fukuda K, Bocklage A, et al. Prolonged disengagement from attentional capture in normal aging. Psychol Aging 2013;28(1):7786.
  • 45
    McVay JC, Meier ME, Touron DR, Kane MJ. Aging ebbs the flow of thought: adult age differences in mind wandering, executive control, and self-evaluation. Acta Psychol (Amst) 2013;142(1):136147.