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Changes in quality of life in patients with advanced cancer
Journal of Psychosomatic Research 58 (2005) 497 – 504
Changes in quality of life in patients with advanced cancer Evidence of response shift and response restriction Louise Sharpea,T, Phyllis Butow b, Clair Smithb, David McConnellb, Stephen Clarkec a
Clinical Psychology Unit F12, School of Psychology, The University of Sydney, NSW Australia b Medical Psychology Research Unit, The University of Sydney, NSW Australia c Department of Medical Oncology, Royal Prince Alfred Hospital, NSW Australia Received 19 April 2004; accepted 15 February 2005
Abstract Objective: Response shift is a process argued to facilitate adjustment to illness. This study investigated the relationship between response shift and adjustment. Methods: Fifty-six patients with metastatic cancer were interviewed using SEIQoLDW and asked to nominate the five areas of most importance to them. Surviving patients were re-interviewed 3 (n=38) and 6 months (n=28) later. Results: The majority of patients showed evidence of restricted priorities close to the diagnosis of metastatic cancer. Approximately half the sample shifted their
priorities from one area to another over time. Response shift was found to be helpful for those who nominated life domains that were poorly rated, but unhelpful, for those who shifted from a highly rated life domain. Conclusions: These results suggest that response shift is common during adjustment to illness. However, response shift can be helpful or unhelpful depending upon the context. The clinical and theoretical implications of these findings are discussed. D 2005 Elsevier Inc. All rights reserved.
Keywords: Response shift; Cancer; Quality of life; Adjustment
Introduction The preservation of quality of life is a primary goal of interventions in the management of cancer [1], particularly when treatment is given with palliative intent [2,3]. However, as quality of life has been increasingly investigated, a number of research findings that appear to be counterintuitive have emerged. For example, it is well documented that quality of life is not always highly correlated with physical function, and some patients maintain a good quality of life in the presence of considerable physical incapacity [4,5]. Indeed, in some studies, the quality of life of patients with advanced illness has been demonstrated to be equivalent to that of healthy people [6]. It has been demonstrated that lay people, carers and health providers T Corresponding author. Clinical Psychology Unit F12, The University of Sydney NSW 2006 Australia. Tel.: +61 2 9351 4558; fax: +61 2 9351 7328. E-mail address: [email protected] (L. Sharpe). 0022-3999/05/$ – see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2005.02.017
underestimate patients’ quality of life compared with patient ratings [7–9]. Furthermore, patients frequently indicate that they are willing to tolerate unpleasant side effects from treatments even where negligible benefits to survival exist [10]. To explain these paradoxical findings, Sprangers and Schwartz [11] introduced the concept of bresponse shift.Q According to response shift models, all individuals differ in areas of their life that they most value and their expectation of achievements across life domains. Those whose expectations are met within the areas that they deem as most important are those who are argued to report a good quality of life [11]. When an individual experiences a change in circumstance, such as a serious health threat, the areas that seem important to them are likely to be subject to change. Similarly, the level of achievement in a particular area of life is likely to be less than it was previously. Response shift occurs as part of adjustment to illness when persons are able to shift their priorities and expectations in line with their changed circumstances.
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Hence, response shift is defined as the change in an individual’s internal standards, values or conceptualisations that occur in response to a particular catalyst, such as ill health [11]. That is, when an individual becomes ill, they change their expectations through recalibration, prioritising different goals and/or life domains or completely changing their definition of what is important, to accommodate their change in circumstances. It has been argued that these changes are necessary in the face of deteriorating health to maintain optimism and good quality of life when the person can no longer function at the previous levels. Thus, response shift is a psychological process that allows an individual to maintain acceptable quality of life in the face of deteriorating health. Response shift has proved a popular concept, with numerous papers referring to the phenomena to explain counterintuitive findings [12–14]. In addition, a small number of research papers have developed paradigms to test hypotheses derived from models of response shift, such as the bthentestQ [11]. In this paradigm, patients rate some aspect of quality of life (e.g., total quality of life [15]; fatigue [16]; health utility [17]) before and after treatment. Following treatment, patients also rerate quality of life prior to the intervention. The discrepancy between the pre- and postratings of quality of life is used to determine response shift. Evidence consistently shows that patients overestimate their prior functioning after treatment, when their current functioning has declined. This results in no apparent change in ratings following treatment, despite the patient’s inferred reports of a decline from pretreatment levels. Researchers have assumed that this is evidence that the patients’ expectations have been recalibrated in light of their lowered level of function. However, the thentest paradigm is not without problems. First, response shift can only be inferred from the discrepancy between patients’ ratings [18]. There are other potential explanations, such as recall bias, which could account for the reported findings [19]. Second, even if one assumes that the discrepancies reported in the thentest are response shift, only changes in expectations are assessed. Sprangers and Schwartz [11] argue that response shift occurs not only when expectations change, but also if changes in values or conceptualisations occur. For example, it may be that, prior to a diagnosis of serious illness, a person’s priority is work. They may have valued their work role highly and placed considerable emphasis in their working life. However, when they become ill, work may no longer take priority, with the person valuing family and health more than they had previously. There are considerable anecdotal and qualitative data that support the view that patients reprioritise their life in response to illness. For example, most women report little initial impact of surgery for gynaecological cancer on their sexuality, due to a shift in focus onto survival [20]. To date, however, quantitative research has not addressed whether, when faced with deteriorating health, there is a shift in values or priorities. Furthermore, there has been no research
that has addressed whether response shift is associated with better adjustment. Schwartz and Sprangers [21] proposed various methodologies that would allow response shift in values and conceptualisations to be assessed. One method they described was the repeated use of individualised measures of quality of life over the course of an illness to determine whether the criteria by which patients judge their quality of life changes [26]. Individualised quality of life measures ask patients what they deem important to their overall quality of life, rather than assuming that what is important to a group of patients (or physicians) can be taken to represent quality of life for each individual [22]. One example of an individualised quality of life measure is the SEIQoL-DW [23]. The SEIQoL-DW requires participants to nominate the five most important areas of their life to their quality of life and weight their relative importance. In doing so, the SEIQoL-DW directly assesses the values that underlie judgements that participants make about their quality of life. Although the SEIQoL-DW has been used in other research with terminally ill patients [24], it has not been previously used in the same sample over time. However, repeated administration of the SEIQoL-DW in a group of patients dealing with a particular catalyst would allow a direct test of whether the phenomena of response shift can be observed through changes in conceptualisations and values. The main advantage of this method is that it allows the determination of whether priorities change in the face of ill health, which is not afforded by other paradigms such as the thentest. However, it does not provide an assessment of whether expectations are also changed. Hence, the SEIQoL-DW allows an examination of whether the qualitative, but not the quantitative, aspects of life quality change over time. In the present study, we hypothesized that response shift would be observed among patients who have been diagnosed with terminal cancer and are being treated with palliative intent. We hypothesized that patients would change their priorities over the course of their illness and that response shift would be associated with improvement in overall quality of life.
Methods Participants and procedure Consecutive patients who had been diagnosed with metastatic cancer within the last 3 months and were being treated with palliative intent were recruited into the study from three Medical Oncology Departments in Sydney, Australia. Participants who were unable to speak English, who had significant cognitive impairment or a history of psychosis were excluded. The project was approved by the Human Ethics Committees of participating hospitals and the University of Sydney.
L. Sharpe et al. / Journal of Psychosomatic Research 58 (2005) 497–504
All assessments were conducted in the patient’s home through interview by a trained interviewer (CS). All interviews were conducted on all occasions by the same interviewer. The initial assessment (T1) was conducted within 3 months of diagnosis of metastases. Further assessments were completed 3 (T2) and 6 months later (T3). The participants completed two quality of life measures: the SEIQoL-DW and the Functional Assessment of Cancer Therapy (FACT-G). Eighty-seven eligible patients were identified, and 57 participants volunteered (66%). Data were missing for 1 patient, leaving a sample of 56 participants at T1. Of those eligible patients who declined participation, 14 (47%) refused because they were too ill, 4 stated they had too many other commitments (13%), 3 declined due to language problems (10%), 2 stated they were too old (7%), 2 were cognitively impaired (7%) and 1 reported that the study was too private. Reasons for refusal were not stated for the other two patients. Three and 6 months after the initial interview, participants were contacted again to complete the subsequent assessments. Thirty-eight participants (68%) were still available at T2, and 28 (50%) completed the assessment at T3. Fourteen patients died prior to the second assessment, and the remaining 5 withdrew due to illness. At 6 months, all eight patients who did not take part had died. Analyses were conducted to determine whether there were differences between participants completing one, two or three assessments. Significant differences emerged, such that patients completing fewer assessments were more likely to be male [ F(2,54) =4.584, P =.014] and a shorter estimated survival at study entry as estimated by doctors [ F(2,56) = 5.521, P = .007] and the patients’ themselves [ F(2,56) =5.515, P = .007]. No other differences emerged, although there was a trend for noncompleters to be more poorly educated [ F(2,56) =2.889, P = .064]. Measurements SEIQoL-DW [23] The SEIQoL-DW is a briefer version of the SEIQoL that was developed to assess the quality of life of patients from the individual’s perspective [24]. The SEIQoL-DW is based on a simple apparatus with five interlocking discs that represent the most important domains that a person identifies as contributing to their quality of life. Participants label each disc with an area of their life that they consider important. Participants are then requested to move each disc to represent the relative importance of each domain to their quality of life. Participants then rate themselves on each chosen domain on a scale from the worst to the best possible status (0–100). A single index can be calculated by multiplying the individual’s rating on each domain by its relative importance. In the present study, the domains chosen and the global index are reported. The SEIQoL-DW has been demonstrated to be
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valid, having moderate convergent validity with other weighted measures of quality of life (j=0.44) and moderate reliability (j= 0.51; [26]). Browne et al. [26] have reported that the majority of patients are able to elect five important life domains without further prompting. The majority of research, using the SEIQoL-DW, report similar ease of administration (e.g., Ref. [23]). The only study to find that patients had difficulty nominating important life domains involved patients entering Phase I drug trials for terminal cancer [25]. Although the patients in this trial had some difficulty spontaneously nominating life domains, when given the original instructions and the prompt sheet developed by the original authors [23,24], all but one patient was able to nominate five areas. The same set of instructions was used in the present study, and, if patients were unable to spontaneously nominate five areas, the prompt sheet was also provided to aid them to do so. Functional Assessment for Cancer Therapy (FACT-G; [27]) The FACT-G is a 33-item cancer-specific scale for the assessment of general quality of life. The FACT-G offers five subscales: physical function, functional ability, social functioning, emotional well-being and relationship with doctor. The subscales can be totalled to provide an overall rating of quality of life. Reliability coefficients for the FACT subscales and total FACT score are high, ranging from .65 to .89 for internal consistency and from .82 to .92 for test– retest reliability over a week [27].
Results Demographic characteristics Patients in the present sample were diagnosed with a range of cancers, and all had metastases. Colorectal (28%) and lung cancer (18%) were the most common, with small numbers of patients (n=1–3) diagnosed with other primary cancers (see Table 1). The majority of the sample were receiving chemotherapy (60%), with 11% receiving radiotherapy, 14% receiving no treatment and the remainder receiving a combination of treatments (2%) or some other treatment (5%). Current medications are presented in Table 1. The average age of the participants was 64 years (range: 46 – 82; S.D.=8.6). Forty-nine percent were male and 51% female. Most patients were married (56%) or widowed (23%), with few being divorced (12%) or never married (7%). Most patients were Australian born (68%), with a 23% being of European origin and 1–3% born in Africa, America or Asia. More than half the sample (53%) had fewer than 10 years of education, with 24% completing 12 years and 23% completing tertiary study. Doctors estimated that 49% of the sample had less than a year to live, with 44% estimated to live between 12 and 24 months.
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Table 1 Diagnoses, treatments and medication of patients in the present sample Number of patients (%) Diagnosis Colorectal cancer Lung cancer Copy from the final version of Caregiver Burden Paper
18 (28) 13 (23)
Treatment No treatment Chemotherapy Radiotherapy Combination Other
8 34 6 1 3
(14) (60) (11) (2) (5)
Medication None General Morphine based Antidepressant medication
27 17 12 1
(47) (30) (21) (2)
Quality of life Quality of life scores were analysed to investigate changes over time, using a one-way analysis of variance (ANOVA). No significant differences were observed in FACT-G scores [ F(2,27) =0.42, P =.5]. In contrast, changes over time were observed on SEIQoL-DW [ F(2,27) =3.437, P =.047]. As can be seen from the means (Table 2), these changes indicated small but statistically significant improvements in global ratings of quality of life over 6 months for surviving patients. The percentage of participants nominating particular areas of their life as most important was calculated. Health and family were the two most frequently endorsed areas at each assessment. Health was nominated by 30% of the sample as the most important domain at T1, 35% at T2 and 41% at T3. A similar pattern was observed for family. Twenty-seven percent of the sample nominated family at T1, 30% at T2 and 38% at T3. Smaller proportions nominated other domains, including social, leisure, maintaining independence, psychological functioning, spirituality and work (Table 3). While examining the data on nominated domains, an interesting pattern emerged. Many patients were unable to identify five areas that they believed contributed to their quality of life, even after being given the prompt sheet. Indeed, at T1, only 9% of participants identified five domains, and 54% were unable to identify more than three.
Table 3 The proportion of the sample at each time nominating different life domains as the most important contributor to quality of life Domain
T1 (n=56) [%]
T2 (n=37) [%]
T3 (n=28) [%]
Health Family Social Leisure Independence Psychological Spiritual Work
29 27 7 7 16 7 1 5
35 30 3 0 16 14 0 3
41 38 3 0 0 14 0 3
This pattern was not stable across time (see Fig. 1). At T2, 21% identified five domains, and 42% were unable to identify more than three. By T3, 57% identified five areas, and only 21% identified three or fewer domains. To ensure that the global quality of life ratings derived from the SEIQoL-DW were still valid despite the restriction in the number of nominated areas, Pearson product–moment correlations between the SEIQoL-DW global quality of life score and FACT scores were calculated for T1. The two quality of life scores were highly correlated (r =.72, Pb.001), as was the total SEIQoL-DW score and all subscales of the FACT (r =.57–.67, P b.001), except doctor–patient relationship (r = .26, ns). To determine whether the trend evident in Fig. 1 represented a significant change in the number of areas that participants were able to nominate, a repeated measures ANOVA was conducted for T1, T2 and T3. The changes in the number of areas endorsed over time was significant [ F(2,27) =25.00, P b.001]. Participants nominated more areas of their life that were important to quality of life at each time point (Table 4). To investigate whether the number of areas nominated was associated with quality of life, participants were dichotomised into those able to elicit four or more areas of life (46%) and those unable to do so. An independent t test was performed to assess differences in quality of life scores. The differences between the groups were not significant either for FACT [t(1,56) = 1.534, P =.13] or 60 50 40 30 20 10
Table 2 Means (S.D.) for quality of life ratings at T1, T2 and T3
0
Measure
T1
T2
T3
FACT-G SIEQoLT
84.7 (15.5) 65 (23)
82.1 (15.6) 68 (20)
86.8 (17.3) 74 (20)
T Changes over time are significantly different at the level of P b.05.
1
2 Time 1
3 Time 2
4
5
Time 3
Fig. 1. The proportion of participants able to elicit one to five areas of their life as important to their overall quality of life.
L. Sharpe et al. / Journal of Psychosomatic Research 58 (2005) 497–504 Table 4 Mean difference scores between T1 and T2 quality of life scores for those who made a response shift and those that did not
8
Response shift
2
Present Absent
FACT difference scoreT 15.1 (17.4) 19.4 (21.5)
SEIQoL difference scoreT 6.7 (26) 6.4 (22)
T Difference scores were calculated by subtracting T1 from T2 scores; hence, a positive score is indicative of an improvement in quality of life.
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6 4 0 -2
Low rating
High rating
-4 -6 -8
SEIQoL-DW global scores [t(1,56) = 1.412, P =.17]. Furthermore, the number of areas nominated for the SEIQoLDW was unrelated to either measure (r b.07, P N.6). Response shifts The percentage of participants who changed the most important domain from T1 to T2 was calculated. At T1 and T2, 53% nominated the same area as most important, while the remaining 47% shifted their responses. A similar pattern emerged for changes in nominated area between T2 and T3. That is, of the 28 participants who were re-interviewed, 43% shifted their responses from one area to another, while 57% retained the same area as the most important domain. To determine whether shifting response was associated with improvements in quality of life, an independent samples t test was conducted on changes in quality of life as a function of response shift status. There were no significant differences between those who shifted their response and those who did not on either the SEIQoL-DW [t(1,37) = 1.600, P =.119] or FACT [t(1,37) =0.65, P = .5]. This result was surprising, and we hypothesized that it may be the nature of the response shift, rather than its presence or absence that determines whether it is helpful. That is, if patients shift their focus from an area that is negative to one that is more positive, the response shift will be helpful. However, the reverse would be true of a shift from a positive area to a negative one. Therefore, we dichotomised the sample according to their ratings for the most important domain. Most participants gave high ratings, with half the sample rating this domain N80/100. We conducted a 2 (response shift: present and absent)2 (ratings: high and low) ANOVA, with change in FACT-G score between T1 and T2 as the dependent variable. There was no main effect for response shift [ F(1,37) =1.375, P =.250], nor was there a main effect for rating of the most important domain [ F(1,37) = 0.001, P = .98]. However, the interaction between response shift and rating closely approached significance [ F(3,34) = 3.813, P =.06; Fig. 2]. For those who rated the most important domain as relatively poor and shifted their response, there was an average improvement of 6 (S.D.=15.2) on FACT-G at T2. This is in contrast to a mean deterioration of 11 (S.D.=15.9) on FACT-G for those with poor initial ratings who did not shift their response (ES=1.1). For those who initially gave high ratings and did not change their response, there was no
-10 -12 Response shift
No Response shift
Fig. 2. Change in quality of life scores on FACT-G between T1 and T2, as a function of initial ratings of most important domain (high z80, low b 80) and response shift. Positive scores reflect an improvement and negative scores reflect a deterioration.
change in quality of life (mean improvement=0.5, S.D.=17.8), with a small deterioration in their quality of life (mean deterioration=4.8, S.D.=16.1) for those who rated the domain highly and then shifted their response (ES = 0.3).
Discussion The present study investigated the extent to which response shift in values and conceptualisations could be identified in a group of patients recently diagnosed with metastatic cancer. Furthermore, we investigated whether response shift was helpful in producing improvements in quality of life. The results support the fact that response shift is common among those recently diagnosed with terminal cancer. Indeed, half of the surviving sample changed the domain that they nominated as most important to their quality of life between T1 and T2. Initial analyses did not confirm that response shift is necessarily associated with positive adjustment. While response shift has often been described as a positive style of adjustment [11], other authors have suggested that response shift may be helpful or unhelpful depending on the context [27]. Intuitively, the degree that an area was rated positively could determine whether shifting focus to or from that area was helpful. Our results support this contention. That is, the interaction between rating their most important area positively versus negatively and response shift closely approached significance. This suggests that when people focus their attention on an area that is positive, this is adaptive and there is no need to shift response. In fact, shifting response from a highly rated area led to a small deterioration in quality of life. However, if a patient focused on an area of life that is less positively rated, then shifting their focus to another area led to an improvement in quality of life. Whereas, leaving the focus in that area was associated with deterioration in quality of life.
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We also found evidence of a particular mechanism associated with response shift, described as goal restriction [28]. That is, a large proportion of the sample were unable to nominate five areas that were important to their quality of life. The restriction of responses was particularly marked at T1. Over time, patients became significantly more able to nominate more domains. This finding was unexpected and has not been reported in the literature. In the original validation papers, Hickey et al. [23] found that all HIV/ AIDS patients nominated five areas that were important to their quality of life. Their sample consisted of 52 HIV positive patients receiving ambulatory care. Hence, their prognosis was better than that of the patients in this study. Differences in the samples may account for the different findings. Indeed, the only study to report patients having difficulty nominating five areas on SEIQoL-DW was in a small sample of patients in Phase I trials [24]. Although prognosis or diagnosis was not reported, it is likely that the sample were similar to patients in this study. In the study of Campbell and Whyte [25], only 1 of their 15 patients identified five areas spontaneously. However, with the use of the prompts, all but one of their respondents eventually identified five areas. In the present study, this was not the case. Despite the use of standard prompts, only 9% of patients were able to identify five areas important to their quality of life. One explanation that Campbell and Whyte [25] gave for the difficulty that their patients had in generating domains was that the patients’ disease may have restricted their focus in life. This explanation is similar to bscaling back goalsQ described by Carver and Scheier [28] as one process associated with response shift. They argued that when a goal becomes no longer viable, an individual scales back the goal to make it more achievable and avoid feeling as though they have failed [28]. Alternately, some goals are shifted from one domain to another to focus on areas where success is more likely. Our data provide evidence for both these processes. That is, near diagnosis of metastases (i.e., within 3 months), patients in our sample restricted their focus on a small number of life areas that were of most importance. Some patients maintained the same narrow focus across time, while others identified additional domains that became important over time. Response shift is described as a normal part of adjustment, which allows a person to adapt to their changing circumstances. Our results provide direct evidence that supports the contention that response shifts (and restriction) are common among individuals with metastatic cancer. We found that shifting one’s focus from one area to another can be helpful if the former area is not progressing well. However, response shift was not found to be universally helpful. For example, a person who becomes ill and focuses on their health will fare well if health improves. However, if their focus shifts from improving health to another area (e.g., conflict ridden family relationships), our results suggest that the shifted position may contribute to
deteriorating quality of life despite improving health. Whether response restrictions are helpful or not also depends on the context in which is not clear from the present data. Among the limitations of the study is the fact that we did not assess psychological functioning. However, there was no relationship between the number of areas nominated in the SEIQoL-DW and emotional well-being or other aspects of quality of life. Another limitation is the sample size, particularly given attrition over time and in subgroup analyses (e.g., response shifters vs. nonshifters). To minimise the problems of power, we focused the analyses on changes between T1 and T2, but the fact remains that some effects approached but did not reach significance. This confirms that the current study is somewhat underpowered. High rates of attrition among terminally ill patients are inevitable, but limit the relationships between variables that can be investigated, particularly complex, multiply determined relationships, such as response shift. Nonetheless, the effect size for change in quality of life for response shifts where the initial nominated area was rated poorly was large (ES=1.1) although considerably smaller for those initially rating their nominated area as positive (ES=0.3). This suggests that in larger samples, response shifts were likely to be found to be significantly associated with differences in quality of life, particularly for those who initially nominate an area where quality of life is rated as being poorer. Second, although the sample included a consecutive sample of patients with different cancers, the degree to which the phenomena identified are generalisable to patients with localised cancer or other illnesses is unknown. This is particularly so because more men than women were unavailable for follow-up. This may be due to the different types of cancer that are overrepresented among men, such that their mortality rate is higher and they were either unavailable for subsequent assessments due to death or ill health. Nonetheless, this means that in subsequent assessments, women were overrepresented. The level of education among participants was also relatively low, with more than half the sample completing less than 10 years of education. Results may be different for more highly educated participants. Another issue is that the patients in this study were initially assessed within 3 months of the diagnosis of metastases, however, it is possible that some patients had already experienced a response shift that was not captured in the time frame available for the research. Furthermore, it may be that previous response shifts were associated with the initial diagnosis of cancer, rather than the diagnosis of metastases. Future research is needed to determine the timeframe on making response shifts and/or restrictions and how common these processes are in other illnesses or earlier stages of cancer. Despite these limitations, the present study offers original data that have important methodological, clinical and theoretical implications. One advantage of the SEIQoL-DW is that bIf the respondent nominates fewer than five cues,
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the system allows for a corresponding number of segments to be manipulatedQ [25]. One problem with standardised quality of life assessment is that participants cannot indicate the importance of different areas to their quality of life [1,22]. The fact that so many patients did not report valuing a large number of domains suggests that this characteristic of the SEIQoL-DW is advantageous. Furthermore, the fact that the SEIQoL-DW score remained highly correlated with FACT-G suggests that it is appropriate for patients (if they choose) to restrict the number of domains that they nominate without compromising the validity of the assessment. At a theoretical level, this study is the first to examine response shifts in values and conceptualisations in adjustment to metastatic cancer. The strengths of the study include a consecutive sample of patients with different cancers, all of whom had recently received the diagnosis of metastatic cancer and its prospective nature. The present study is the first to demonstrate direct evidence of a shift over time in the values that are important to determining patients’ quality of life. Moreover, our results suggest that this process can be helpful to the quality of life of the patient when the shift is away from an area that is not being successfully negotiated. Furthermore, the present study reveals evidence of what we have termed response restriction—whereby participants reconceptualise their quality of life with reference to a few very important domains. While response restriction was common in this sample of patients, the degree to which it was helpful to adjustment is unclear and should be the focus of future research. The present results have clinical implications for how health care workers can facilitate adjustment to illness in patients with advanced cancer. Our results suggest that trying to encourage patients to focus on areas of their life that are positive and set goals in areas that are highly valued and progressing well would be helpful. However, clinicians need to be aware that shifting priorities only appears to be helpful if the current area that is prioritised is not progressing well. Hence, if patients are invested in an area that that they value and feel is positive, patients can be encouraged to continue to prioritise that area. On the other hand, according to our results, if the area is not progressing well, helping patients to make shifts in their values should facilitate better adjustment. Similarly, on the basis of the current findings, there does not appear to be any reason to discourage patients from focusing on only one or two important aspects of their life, especially if patients view these areas positively.
Acknowledgments This study was supported by a grant from the NSW Cancer Council. We would like to express our sincere thanks to the patients, for the generous time that they gave to take part in this study.
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References [1] Browne JP, McGee H, O’Boyle CA. Conceptual approaches to the assessment of quality of life. Psychol Health 1997;12:737 – 51. [2] Pratheepawanit N, Salek MS, Finlay IG. The applicability of quality of life assessment in palliative care: comparing two quality of life measures. Palliat Med 1999;13:325 – 34. [3] Kaasa S, Loge JH. Quality of life in palliative care: principles and practice. Palliat Med 2003;17:11 – 20. [4] Andrykowski MA, Brady MJ, Hunt JW. Positive psychosocial adjustment in potential bone marrow transplant recipients: cancer as a psychosocial transition. Psychooncology 1993;2:261 – 76. [5] Bach JR, Tilton MC. Life satisfaction and well-being measures in ventilator assisted individuals with traumatic tetraplegia. Arch Phys Med Rehab 1994;75:626 – 32. [6] Breetvelt IS, Van Dam FSAM. Underreporting by cancer patients: the case of response shift. Soc Sci Med 1991;32:981 – 7. [7] Friedland J, Renwick R, McColl M. Coping and social support as determinants of quality of life in HIV/AIDS. AIDS Care 1996;8: 15 – 31. [8] Sneeuw KCA, Aaronson NK, Sprangers MAG, Detmar SB, Wever LDV, Schornhagel JH. The value of caregiver ratings in evaluation the quality of life of patients with cancer. J Clin Oncol 1996;15: 1206 – 17. [9] Ubel PA, Loewenstein G, Hershey J, Baron J, Mohr T. Do nonpatients underestimate the quality of life associated with chronic health conditions because of a focusing illusion? Med Decis Making 2001;21:190 – 9. [10] Duric V, Stockler M. Patients’ preferences for adjuvant chemotherapy in early breast cancer: a review of what makes it worthwhile. Lancet Oncol 2001;2:691 – 7. [11] Sprangers MA, Schwarz CE. Integrating response shift into healthrelated quality-of-life research: a theoretical model. Soc Sci Med 1999;48:1507 – 15. [12] Cox K. Assessing the quality of life of patients in phase I and II anticancer drug trials: interviews versus questionnaires. Soc Sci Med 2003;56:921 – 34. [13] Van Stehl HF, Maille AR, Colland VT, et al. The relative health related quality of life of veterans with Parkinson’s disease. Qual Life Res 2003;12:133 – 45. [14] Weiser M, Geise A, Hadzionerovic D, et al. Health-related quality of life of patients with multiple organ dysfunction: individual changes and comparison with normative population. Crit Care Med 2003;31: 1094 – 101. [15] Berhard J, Hurny C, Maibach R, Herrman R, Laffer U. Quality of life as subjective experience: reframing of perception in patients with colon cancer undergoing radical resection with or without adjuvant chemotherapy. Ann Oncol 1999;10:775 – 82. [16] Sprangers MAG, Van Dam FSAM, Broersen J, Lodder L, Wever L, Visser MRM, Oosterveld P, Smets EMA. Revealing response shift in longitudinal research on fatigue The use of the thentest approach. Acta Oncol 1999;38:709 – 18. [17] Bernhard J, Lowry A, Maibach R, Hurny C, et al. Response shift in the perception of health for utility evaluation: an explorative investigation. Eur J Cancer 2001;37:1729 – 35. [18] Hagedoorn M, Sneeuw KCA, Aaronson NK. Changes in physical functioning and quality of life in patients with cancer: response shift and relative evaluation of one’s condition. J Clin Epidemiol 2002;55: 176 – 83. [19] Robling M, Hood K. Response shift, responsiveness or recall bias? Br J Gen Pract 2002;52:585. [20] Juraskova I, Butow P, Robertson R, Sharpe L, McLeod C, Hacker N. Post treatment sexual rehabilitation; a qualitative insight. Psychooncology 2003;12:267 – 79. [21] Schwartz CE, Sprangers MA. Methodological approaches for assessing response shift in longitudinal health-related quality of life research. Soc Sci Med 1999;48:1531 – 48.
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[22] Dijkers MP. Individualization in quality of life measurement: instruments and approaches. Arch Phys Med Rehab 2003;84:S3 – S13. [23] Hickey AM, Bury G, Bradley F, O’Kelly FD, O’Boyle CA. A new short form individual quality of life measure (SEIQoL-DW): application in a cohort of individuals with HIV/AIDS. BMJ 1996;313:29 – 33. [24] O’Boyle CA. The schedule for the evaluation of individual quality of life. Internat J Ment Health 1994;23:3 – 23. [25] Campbell S, Whyte F. The quality of life of cancer patients participating in phase I clinical trials using SEIQoL-DW. J Adv Nurs 1999;30:334 – 5.
[26] Browne JP, O’Boyle CA, McGee HM, McDonald NJ, Joyce CRB. Development of a direct weighting procedure for quality of life domains. Qual Life Res 1997;6:301 – 9. [27] Cella DF, Tulsky DS, Gray G, Sarafian B, et al. The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol 1993;11:570 – 9. [28] Carver CS, Scheier MF. Scaling back goals and recalibration of the affect system are processes in normal adaptive self-regulation: understanding the bresponse shift Q phenomena. Soc Sci Med 2000; 50:1715 – 22.
Changes in quality of life in patients with advanced cancer Evidence of response shift and response restriction Louise Sharpea,T, Phyllis Butow b, Clair Smithb, David McConnellb, Stephen Clarkec a
Clinical Psychology Unit F12, School of Psychology, The University of Sydney, NSW Australia b Medical Psychology Research Unit, The University of Sydney, NSW Australia c Department of Medical Oncology, Royal Prince Alfred Hospital, NSW Australia Received 19 April 2004; accepted 15 February 2005
Abstract Objective: Response shift is a process argued to facilitate adjustment to illness. This study investigated the relationship between response shift and adjustment. Methods: Fifty-six patients with metastatic cancer were interviewed using SEIQoLDW and asked to nominate the five areas of most importance to them. Surviving patients were re-interviewed 3 (n=38) and 6 months (n=28) later. Results: The majority of patients showed evidence of restricted priorities close to the diagnosis of metastatic cancer. Approximately half the sample shifted their
priorities from one area to another over time. Response shift was found to be helpful for those who nominated life domains that were poorly rated, but unhelpful, for those who shifted from a highly rated life domain. Conclusions: These results suggest that response shift is common during adjustment to illness. However, response shift can be helpful or unhelpful depending upon the context. The clinical and theoretical implications of these findings are discussed. D 2005 Elsevier Inc. All rights reserved.
Keywords: Response shift; Cancer; Quality of life; Adjustment
Introduction The preservation of quality of life is a primary goal of interventions in the management of cancer [1], particularly when treatment is given with palliative intent [2,3]. However, as quality of life has been increasingly investigated, a number of research findings that appear to be counterintuitive have emerged. For example, it is well documented that quality of life is not always highly correlated with physical function, and some patients maintain a good quality of life in the presence of considerable physical incapacity [4,5]. Indeed, in some studies, the quality of life of patients with advanced illness has been demonstrated to be equivalent to that of healthy people [6]. It has been demonstrated that lay people, carers and health providers T Corresponding author. Clinical Psychology Unit F12, The University of Sydney NSW 2006 Australia. Tel.: +61 2 9351 4558; fax: +61 2 9351 7328. E-mail address: [email protected] (L. Sharpe). 0022-3999/05/$ – see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2005.02.017
underestimate patients’ quality of life compared with patient ratings [7–9]. Furthermore, patients frequently indicate that they are willing to tolerate unpleasant side effects from treatments even where negligible benefits to survival exist [10]. To explain these paradoxical findings, Sprangers and Schwartz [11] introduced the concept of bresponse shift.Q According to response shift models, all individuals differ in areas of their life that they most value and their expectation of achievements across life domains. Those whose expectations are met within the areas that they deem as most important are those who are argued to report a good quality of life [11]. When an individual experiences a change in circumstance, such as a serious health threat, the areas that seem important to them are likely to be subject to change. Similarly, the level of achievement in a particular area of life is likely to be less than it was previously. Response shift occurs as part of adjustment to illness when persons are able to shift their priorities and expectations in line with their changed circumstances.
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Hence, response shift is defined as the change in an individual’s internal standards, values or conceptualisations that occur in response to a particular catalyst, such as ill health [11]. That is, when an individual becomes ill, they change their expectations through recalibration, prioritising different goals and/or life domains or completely changing their definition of what is important, to accommodate their change in circumstances. It has been argued that these changes are necessary in the face of deteriorating health to maintain optimism and good quality of life when the person can no longer function at the previous levels. Thus, response shift is a psychological process that allows an individual to maintain acceptable quality of life in the face of deteriorating health. Response shift has proved a popular concept, with numerous papers referring to the phenomena to explain counterintuitive findings [12–14]. In addition, a small number of research papers have developed paradigms to test hypotheses derived from models of response shift, such as the bthentestQ [11]. In this paradigm, patients rate some aspect of quality of life (e.g., total quality of life [15]; fatigue [16]; health utility [17]) before and after treatment. Following treatment, patients also rerate quality of life prior to the intervention. The discrepancy between the pre- and postratings of quality of life is used to determine response shift. Evidence consistently shows that patients overestimate their prior functioning after treatment, when their current functioning has declined. This results in no apparent change in ratings following treatment, despite the patient’s inferred reports of a decline from pretreatment levels. Researchers have assumed that this is evidence that the patients’ expectations have been recalibrated in light of their lowered level of function. However, the thentest paradigm is not without problems. First, response shift can only be inferred from the discrepancy between patients’ ratings [18]. There are other potential explanations, such as recall bias, which could account for the reported findings [19]. Second, even if one assumes that the discrepancies reported in the thentest are response shift, only changes in expectations are assessed. Sprangers and Schwartz [11] argue that response shift occurs not only when expectations change, but also if changes in values or conceptualisations occur. For example, it may be that, prior to a diagnosis of serious illness, a person’s priority is work. They may have valued their work role highly and placed considerable emphasis in their working life. However, when they become ill, work may no longer take priority, with the person valuing family and health more than they had previously. There are considerable anecdotal and qualitative data that support the view that patients reprioritise their life in response to illness. For example, most women report little initial impact of surgery for gynaecological cancer on their sexuality, due to a shift in focus onto survival [20]. To date, however, quantitative research has not addressed whether, when faced with deteriorating health, there is a shift in values or priorities. Furthermore, there has been no research
that has addressed whether response shift is associated with better adjustment. Schwartz and Sprangers [21] proposed various methodologies that would allow response shift in values and conceptualisations to be assessed. One method they described was the repeated use of individualised measures of quality of life over the course of an illness to determine whether the criteria by which patients judge their quality of life changes [26]. Individualised quality of life measures ask patients what they deem important to their overall quality of life, rather than assuming that what is important to a group of patients (or physicians) can be taken to represent quality of life for each individual [22]. One example of an individualised quality of life measure is the SEIQoL-DW [23]. The SEIQoL-DW requires participants to nominate the five most important areas of their life to their quality of life and weight their relative importance. In doing so, the SEIQoL-DW directly assesses the values that underlie judgements that participants make about their quality of life. Although the SEIQoL-DW has been used in other research with terminally ill patients [24], it has not been previously used in the same sample over time. However, repeated administration of the SEIQoL-DW in a group of patients dealing with a particular catalyst would allow a direct test of whether the phenomena of response shift can be observed through changes in conceptualisations and values. The main advantage of this method is that it allows the determination of whether priorities change in the face of ill health, which is not afforded by other paradigms such as the thentest. However, it does not provide an assessment of whether expectations are also changed. Hence, the SEIQoL-DW allows an examination of whether the qualitative, but not the quantitative, aspects of life quality change over time. In the present study, we hypothesized that response shift would be observed among patients who have been diagnosed with terminal cancer and are being treated with palliative intent. We hypothesized that patients would change their priorities over the course of their illness and that response shift would be associated with improvement in overall quality of life.
Methods Participants and procedure Consecutive patients who had been diagnosed with metastatic cancer within the last 3 months and were being treated with palliative intent were recruited into the study from three Medical Oncology Departments in Sydney, Australia. Participants who were unable to speak English, who had significant cognitive impairment or a history of psychosis were excluded. The project was approved by the Human Ethics Committees of participating hospitals and the University of Sydney.
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All assessments were conducted in the patient’s home through interview by a trained interviewer (CS). All interviews were conducted on all occasions by the same interviewer. The initial assessment (T1) was conducted within 3 months of diagnosis of metastases. Further assessments were completed 3 (T2) and 6 months later (T3). The participants completed two quality of life measures: the SEIQoL-DW and the Functional Assessment of Cancer Therapy (FACT-G). Eighty-seven eligible patients were identified, and 57 participants volunteered (66%). Data were missing for 1 patient, leaving a sample of 56 participants at T1. Of those eligible patients who declined participation, 14 (47%) refused because they were too ill, 4 stated they had too many other commitments (13%), 3 declined due to language problems (10%), 2 stated they were too old (7%), 2 were cognitively impaired (7%) and 1 reported that the study was too private. Reasons for refusal were not stated for the other two patients. Three and 6 months after the initial interview, participants were contacted again to complete the subsequent assessments. Thirty-eight participants (68%) were still available at T2, and 28 (50%) completed the assessment at T3. Fourteen patients died prior to the second assessment, and the remaining 5 withdrew due to illness. At 6 months, all eight patients who did not take part had died. Analyses were conducted to determine whether there were differences between participants completing one, two or three assessments. Significant differences emerged, such that patients completing fewer assessments were more likely to be male [ F(2,54) =4.584, P =.014] and a shorter estimated survival at study entry as estimated by doctors [ F(2,56) = 5.521, P = .007] and the patients’ themselves [ F(2,56) =5.515, P = .007]. No other differences emerged, although there was a trend for noncompleters to be more poorly educated [ F(2,56) =2.889, P = .064]. Measurements SEIQoL-DW [23] The SEIQoL-DW is a briefer version of the SEIQoL that was developed to assess the quality of life of patients from the individual’s perspective [24]. The SEIQoL-DW is based on a simple apparatus with five interlocking discs that represent the most important domains that a person identifies as contributing to their quality of life. Participants label each disc with an area of their life that they consider important. Participants are then requested to move each disc to represent the relative importance of each domain to their quality of life. Participants then rate themselves on each chosen domain on a scale from the worst to the best possible status (0–100). A single index can be calculated by multiplying the individual’s rating on each domain by its relative importance. In the present study, the domains chosen and the global index are reported. The SEIQoL-DW has been demonstrated to be
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valid, having moderate convergent validity with other weighted measures of quality of life (j=0.44) and moderate reliability (j= 0.51; [26]). Browne et al. [26] have reported that the majority of patients are able to elect five important life domains without further prompting. The majority of research, using the SEIQoL-DW, report similar ease of administration (e.g., Ref. [23]). The only study to find that patients had difficulty nominating important life domains involved patients entering Phase I drug trials for terminal cancer [25]. Although the patients in this trial had some difficulty spontaneously nominating life domains, when given the original instructions and the prompt sheet developed by the original authors [23,24], all but one patient was able to nominate five areas. The same set of instructions was used in the present study, and, if patients were unable to spontaneously nominate five areas, the prompt sheet was also provided to aid them to do so. Functional Assessment for Cancer Therapy (FACT-G; [27]) The FACT-G is a 33-item cancer-specific scale for the assessment of general quality of life. The FACT-G offers five subscales: physical function, functional ability, social functioning, emotional well-being and relationship with doctor. The subscales can be totalled to provide an overall rating of quality of life. Reliability coefficients for the FACT subscales and total FACT score are high, ranging from .65 to .89 for internal consistency and from .82 to .92 for test– retest reliability over a week [27].
Results Demographic characteristics Patients in the present sample were diagnosed with a range of cancers, and all had metastases. Colorectal (28%) and lung cancer (18%) were the most common, with small numbers of patients (n=1–3) diagnosed with other primary cancers (see Table 1). The majority of the sample were receiving chemotherapy (60%), with 11% receiving radiotherapy, 14% receiving no treatment and the remainder receiving a combination of treatments (2%) or some other treatment (5%). Current medications are presented in Table 1. The average age of the participants was 64 years (range: 46 – 82; S.D.=8.6). Forty-nine percent were male and 51% female. Most patients were married (56%) or widowed (23%), with few being divorced (12%) or never married (7%). Most patients were Australian born (68%), with a 23% being of European origin and 1–3% born in Africa, America or Asia. More than half the sample (53%) had fewer than 10 years of education, with 24% completing 12 years and 23% completing tertiary study. Doctors estimated that 49% of the sample had less than a year to live, with 44% estimated to live between 12 and 24 months.
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Table 1 Diagnoses, treatments and medication of patients in the present sample Number of patients (%) Diagnosis Colorectal cancer Lung cancer Copy from the final version of Caregiver Burden Paper
18 (28) 13 (23)
Treatment No treatment Chemotherapy Radiotherapy Combination Other
8 34 6 1 3
(14) (60) (11) (2) (5)
Medication None General Morphine based Antidepressant medication
27 17 12 1
(47) (30) (21) (2)
Quality of life Quality of life scores were analysed to investigate changes over time, using a one-way analysis of variance (ANOVA). No significant differences were observed in FACT-G scores [ F(2,27) =0.42, P =.5]. In contrast, changes over time were observed on SEIQoL-DW [ F(2,27) =3.437, P =.047]. As can be seen from the means (Table 2), these changes indicated small but statistically significant improvements in global ratings of quality of life over 6 months for surviving patients. The percentage of participants nominating particular areas of their life as most important was calculated. Health and family were the two most frequently endorsed areas at each assessment. Health was nominated by 30% of the sample as the most important domain at T1, 35% at T2 and 41% at T3. A similar pattern was observed for family. Twenty-seven percent of the sample nominated family at T1, 30% at T2 and 38% at T3. Smaller proportions nominated other domains, including social, leisure, maintaining independence, psychological functioning, spirituality and work (Table 3). While examining the data on nominated domains, an interesting pattern emerged. Many patients were unable to identify five areas that they believed contributed to their quality of life, even after being given the prompt sheet. Indeed, at T1, only 9% of participants identified five domains, and 54% were unable to identify more than three.
Table 3 The proportion of the sample at each time nominating different life domains as the most important contributor to quality of life Domain
T1 (n=56) [%]
T2 (n=37) [%]
T3 (n=28) [%]
Health Family Social Leisure Independence Psychological Spiritual Work
29 27 7 7 16 7 1 5
35 30 3 0 16 14 0 3
41 38 3 0 0 14 0 3
This pattern was not stable across time (see Fig. 1). At T2, 21% identified five domains, and 42% were unable to identify more than three. By T3, 57% identified five areas, and only 21% identified three or fewer domains. To ensure that the global quality of life ratings derived from the SEIQoL-DW were still valid despite the restriction in the number of nominated areas, Pearson product–moment correlations between the SEIQoL-DW global quality of life score and FACT scores were calculated for T1. The two quality of life scores were highly correlated (r =.72, Pb.001), as was the total SEIQoL-DW score and all subscales of the FACT (r =.57–.67, P b.001), except doctor–patient relationship (r = .26, ns). To determine whether the trend evident in Fig. 1 represented a significant change in the number of areas that participants were able to nominate, a repeated measures ANOVA was conducted for T1, T2 and T3. The changes in the number of areas endorsed over time was significant [ F(2,27) =25.00, P b.001]. Participants nominated more areas of their life that were important to quality of life at each time point (Table 4). To investigate whether the number of areas nominated was associated with quality of life, participants were dichotomised into those able to elicit four or more areas of life (46%) and those unable to do so. An independent t test was performed to assess differences in quality of life scores. The differences between the groups were not significant either for FACT [t(1,56) = 1.534, P =.13] or 60 50 40 30 20 10
Table 2 Means (S.D.) for quality of life ratings at T1, T2 and T3
0
Measure
T1
T2
T3
FACT-G SIEQoLT
84.7 (15.5) 65 (23)
82.1 (15.6) 68 (20)
86.8 (17.3) 74 (20)
T Changes over time are significantly different at the level of P b.05.
1
2 Time 1
3 Time 2
4
5
Time 3
Fig. 1. The proportion of participants able to elicit one to five areas of their life as important to their overall quality of life.
L. Sharpe et al. / Journal of Psychosomatic Research 58 (2005) 497–504 Table 4 Mean difference scores between T1 and T2 quality of life scores for those who made a response shift and those that did not
8
Response shift
2
Present Absent
FACT difference scoreT 15.1 (17.4) 19.4 (21.5)
SEIQoL difference scoreT 6.7 (26) 6.4 (22)
T Difference scores were calculated by subtracting T1 from T2 scores; hence, a positive score is indicative of an improvement in quality of life.
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6 4 0 -2
Low rating
High rating
-4 -6 -8
SEIQoL-DW global scores [t(1,56) = 1.412, P =.17]. Furthermore, the number of areas nominated for the SEIQoLDW was unrelated to either measure (r b.07, P N.6). Response shifts The percentage of participants who changed the most important domain from T1 to T2 was calculated. At T1 and T2, 53% nominated the same area as most important, while the remaining 47% shifted their responses. A similar pattern emerged for changes in nominated area between T2 and T3. That is, of the 28 participants who were re-interviewed, 43% shifted their responses from one area to another, while 57% retained the same area as the most important domain. To determine whether shifting response was associated with improvements in quality of life, an independent samples t test was conducted on changes in quality of life as a function of response shift status. There were no significant differences between those who shifted their response and those who did not on either the SEIQoL-DW [t(1,37) = 1.600, P =.119] or FACT [t(1,37) =0.65, P = .5]. This result was surprising, and we hypothesized that it may be the nature of the response shift, rather than its presence or absence that determines whether it is helpful. That is, if patients shift their focus from an area that is negative to one that is more positive, the response shift will be helpful. However, the reverse would be true of a shift from a positive area to a negative one. Therefore, we dichotomised the sample according to their ratings for the most important domain. Most participants gave high ratings, with half the sample rating this domain N80/100. We conducted a 2 (response shift: present and absent)2 (ratings: high and low) ANOVA, with change in FACT-G score between T1 and T2 as the dependent variable. There was no main effect for response shift [ F(1,37) =1.375, P =.250], nor was there a main effect for rating of the most important domain [ F(1,37) = 0.001, P = .98]. However, the interaction between response shift and rating closely approached significance [ F(3,34) = 3.813, P =.06; Fig. 2]. For those who rated the most important domain as relatively poor and shifted their response, there was an average improvement of 6 (S.D.=15.2) on FACT-G at T2. This is in contrast to a mean deterioration of 11 (S.D.=15.9) on FACT-G for those with poor initial ratings who did not shift their response (ES=1.1). For those who initially gave high ratings and did not change their response, there was no
-10 -12 Response shift
No Response shift
Fig. 2. Change in quality of life scores on FACT-G between T1 and T2, as a function of initial ratings of most important domain (high z80, low b 80) and response shift. Positive scores reflect an improvement and negative scores reflect a deterioration.
change in quality of life (mean improvement=0.5, S.D.=17.8), with a small deterioration in their quality of life (mean deterioration=4.8, S.D.=16.1) for those who rated the domain highly and then shifted their response (ES = 0.3).
Discussion The present study investigated the extent to which response shift in values and conceptualisations could be identified in a group of patients recently diagnosed with metastatic cancer. Furthermore, we investigated whether response shift was helpful in producing improvements in quality of life. The results support the fact that response shift is common among those recently diagnosed with terminal cancer. Indeed, half of the surviving sample changed the domain that they nominated as most important to their quality of life between T1 and T2. Initial analyses did not confirm that response shift is necessarily associated with positive adjustment. While response shift has often been described as a positive style of adjustment [11], other authors have suggested that response shift may be helpful or unhelpful depending on the context [27]. Intuitively, the degree that an area was rated positively could determine whether shifting focus to or from that area was helpful. Our results support this contention. That is, the interaction between rating their most important area positively versus negatively and response shift closely approached significance. This suggests that when people focus their attention on an area that is positive, this is adaptive and there is no need to shift response. In fact, shifting response from a highly rated area led to a small deterioration in quality of life. However, if a patient focused on an area of life that is less positively rated, then shifting their focus to another area led to an improvement in quality of life. Whereas, leaving the focus in that area was associated with deterioration in quality of life.
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We also found evidence of a particular mechanism associated with response shift, described as goal restriction [28]. That is, a large proportion of the sample were unable to nominate five areas that were important to their quality of life. The restriction of responses was particularly marked at T1. Over time, patients became significantly more able to nominate more domains. This finding was unexpected and has not been reported in the literature. In the original validation papers, Hickey et al. [23] found that all HIV/ AIDS patients nominated five areas that were important to their quality of life. Their sample consisted of 52 HIV positive patients receiving ambulatory care. Hence, their prognosis was better than that of the patients in this study. Differences in the samples may account for the different findings. Indeed, the only study to report patients having difficulty nominating five areas on SEIQoL-DW was in a small sample of patients in Phase I trials [24]. Although prognosis or diagnosis was not reported, it is likely that the sample were similar to patients in this study. In the study of Campbell and Whyte [25], only 1 of their 15 patients identified five areas spontaneously. However, with the use of the prompts, all but one of their respondents eventually identified five areas. In the present study, this was not the case. Despite the use of standard prompts, only 9% of patients were able to identify five areas important to their quality of life. One explanation that Campbell and Whyte [25] gave for the difficulty that their patients had in generating domains was that the patients’ disease may have restricted their focus in life. This explanation is similar to bscaling back goalsQ described by Carver and Scheier [28] as one process associated with response shift. They argued that when a goal becomes no longer viable, an individual scales back the goal to make it more achievable and avoid feeling as though they have failed [28]. Alternately, some goals are shifted from one domain to another to focus on areas where success is more likely. Our data provide evidence for both these processes. That is, near diagnosis of metastases (i.e., within 3 months), patients in our sample restricted their focus on a small number of life areas that were of most importance. Some patients maintained the same narrow focus across time, while others identified additional domains that became important over time. Response shift is described as a normal part of adjustment, which allows a person to adapt to their changing circumstances. Our results provide direct evidence that supports the contention that response shifts (and restriction) are common among individuals with metastatic cancer. We found that shifting one’s focus from one area to another can be helpful if the former area is not progressing well. However, response shift was not found to be universally helpful. For example, a person who becomes ill and focuses on their health will fare well if health improves. However, if their focus shifts from improving health to another area (e.g., conflict ridden family relationships), our results suggest that the shifted position may contribute to
deteriorating quality of life despite improving health. Whether response restrictions are helpful or not also depends on the context in which is not clear from the present data. Among the limitations of the study is the fact that we did not assess psychological functioning. However, there was no relationship between the number of areas nominated in the SEIQoL-DW and emotional well-being or other aspects of quality of life. Another limitation is the sample size, particularly given attrition over time and in subgroup analyses (e.g., response shifters vs. nonshifters). To minimise the problems of power, we focused the analyses on changes between T1 and T2, but the fact remains that some effects approached but did not reach significance. This confirms that the current study is somewhat underpowered. High rates of attrition among terminally ill patients are inevitable, but limit the relationships between variables that can be investigated, particularly complex, multiply determined relationships, such as response shift. Nonetheless, the effect size for change in quality of life for response shifts where the initial nominated area was rated poorly was large (ES=1.1) although considerably smaller for those initially rating their nominated area as positive (ES=0.3). This suggests that in larger samples, response shifts were likely to be found to be significantly associated with differences in quality of life, particularly for those who initially nominate an area where quality of life is rated as being poorer. Second, although the sample included a consecutive sample of patients with different cancers, the degree to which the phenomena identified are generalisable to patients with localised cancer or other illnesses is unknown. This is particularly so because more men than women were unavailable for follow-up. This may be due to the different types of cancer that are overrepresented among men, such that their mortality rate is higher and they were either unavailable for subsequent assessments due to death or ill health. Nonetheless, this means that in subsequent assessments, women were overrepresented. The level of education among participants was also relatively low, with more than half the sample completing less than 10 years of education. Results may be different for more highly educated participants. Another issue is that the patients in this study were initially assessed within 3 months of the diagnosis of metastases, however, it is possible that some patients had already experienced a response shift that was not captured in the time frame available for the research. Furthermore, it may be that previous response shifts were associated with the initial diagnosis of cancer, rather than the diagnosis of metastases. Future research is needed to determine the timeframe on making response shifts and/or restrictions and how common these processes are in other illnesses or earlier stages of cancer. Despite these limitations, the present study offers original data that have important methodological, clinical and theoretical implications. One advantage of the SEIQoL-DW is that bIf the respondent nominates fewer than five cues,
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the system allows for a corresponding number of segments to be manipulatedQ [25]. One problem with standardised quality of life assessment is that participants cannot indicate the importance of different areas to their quality of life [1,22]. The fact that so many patients did not report valuing a large number of domains suggests that this characteristic of the SEIQoL-DW is advantageous. Furthermore, the fact that the SEIQoL-DW score remained highly correlated with FACT-G suggests that it is appropriate for patients (if they choose) to restrict the number of domains that they nominate without compromising the validity of the assessment. At a theoretical level, this study is the first to examine response shifts in values and conceptualisations in adjustment to metastatic cancer. The strengths of the study include a consecutive sample of patients with different cancers, all of whom had recently received the diagnosis of metastatic cancer and its prospective nature. The present study is the first to demonstrate direct evidence of a shift over time in the values that are important to determining patients’ quality of life. Moreover, our results suggest that this process can be helpful to the quality of life of the patient when the shift is away from an area that is not being successfully negotiated. Furthermore, the present study reveals evidence of what we have termed response restriction—whereby participants reconceptualise their quality of life with reference to a few very important domains. While response restriction was common in this sample of patients, the degree to which it was helpful to adjustment is unclear and should be the focus of future research. The present results have clinical implications for how health care workers can facilitate adjustment to illness in patients with advanced cancer. Our results suggest that trying to encourage patients to focus on areas of their life that are positive and set goals in areas that are highly valued and progressing well would be helpful. However, clinicians need to be aware that shifting priorities only appears to be helpful if the current area that is prioritised is not progressing well. Hence, if patients are invested in an area that that they value and feel is positive, patients can be encouraged to continue to prioritise that area. On the other hand, according to our results, if the area is not progressing well, helping patients to make shifts in their values should facilitate better adjustment. Similarly, on the basis of the current findings, there does not appear to be any reason to discourage patients from focusing on only one or two important aspects of their life, especially if patients view these areas positively.
Acknowledgments This study was supported by a grant from the NSW Cancer Council. We would like to express our sincere thanks to the patients, for the generous time that they gave to take part in this study.
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