Cancer-related fatigue in a couples’ context
Müller, Fabiola
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Clinically distinct trajectories of fatigue and their
longitudinal relationship with the disturbance of
personal goals following a cancer diagnosis
British Journal of Health Psychology, 2017, 22(3), 627-643 Fabiola Müller, Marrit A. Tuinman, Moniek Janse, Josué Almansa, Mirjam A. G. Sprangers, Ans Smink, Adelita V. Ranchor, Joke Fleer,
Abstract
Objectives: Most studies on fatigue in patients with cancer aggregate its prevalence
and severity on a group level, ignoring the possibility that subgroups of patients may differ widely in their development of fatigue. This study aimed to identify subgroups of patients with clinically distinct trajectories of fatigue from diagnosis to 18 months post-diagnosis. As fatigue might trigger goal disturbance, the study also identified trajectories of concrete and abstract goal disturbance, and longi-tudinally examined their co-occurrence with fatigue.
Design: Prospective design with quantitative and qualitative method of data collection.
Methods: Patients with colorectal cancer (n = 183) reported on their levels of
fa-tigue and goal disturbance shortly after diagnosis (T1) and at 7 months (T2), and 18 months (T3) post-diagnosis. Growth mixture model analyses were performed to identify trajectories of fatigue and goal disturbance. Guidelines for the clinical relevance of fatigue were applied.
Results: Four clinically distinct trajectories of fatigue were identified as follows:
(1) persistent severe fatigue (25.4%), (2) moderate fatigue (56.1%), (3) no fatigue (13.8%), and (4) rapidly improving fatigue (4.7%). The majority of patients with cancer reported high disturbance of their concrete goals, while high disturbance of abstract goals was less evident. Fatigue and concrete goal disturbance co-occurred longitudinally.
Conclusions: The fatigue and goal disturbance experienced from diagnosis to 18
months post-diagnosis differ considerably for subgroups of patients with cancer. Fatigue and concrete goal disturbance are persistent burdens in the majority of patients. Investigating symptom burden beyond average trends can guide clini-cians to identify patients most in need for treatment. Targeting goal disturbance might benefit the psychological well-being in patients suffering from persistent symptoms.
Statement of Contribution
What is already known on this subject?
• Fatigue is a common and distressing symptom at all stages of the cancer experience. • Earlier studies suggest that many patients recover from fatigue after treat-ment completion. • Patients with cancer experience disturbance in their personal goals, which is related to poor psychological well-being.
What does this study add?
• Developments of fatigue and goal disturbance differ between subgroups of patients with cancer but co-occur within these subgroups. • About 30% of the patients experience severe fatigue after diagnosis, of which only few patients recover within 18 months post-diagnosis. • Targeting goal disturbance might benefit patients with severe and ongoing symptoms.
2
Introduction
Fatigue is a highly distressing symptom many patients with cancer experience at diagnosis and throughout their treatment (Goedendorp, Gielissen, Verhagen, Peters, & Bleijenberg, 2008; Prue, Rankin, Allen, Gracey, & Cramp, 2006). Al-though many patients seem to recover, approximately one quarter report fatigue after the completion of their cancer treatment (Goedendorp, Gielissen, Verhagen, & Bleijenberg, 2013; Servaes, Gielissen, Verhagen, & Bleijenberg, 2007). This long-term fatigue may persist for many years (Harrington, Hansen, Moskowitz, Todd, & Feuerstein, 2010; Minton & Stone, 2008) and is related to a substantial loss of quality of life (Donovan, McGinty, & Jacobsen, 2013; Kluthcovsky et al., 2012; Schmidt et al., 2012). As the population of patients treated for cancer is growing, there is a pressing need to identify specifically those patients most affected by fatigue. While most studies aggregate fatigue prevalence and severity at a group level, the primary aim of the current study was to identify and describe subgroups of patients with clinically relevant differences in the severity and development of their fatigue from diagnosis to survivorship. As fatigue might elicit the disturbance of personal goals, we also examined distinct trajectories of concrete and abstract goal disturbance and investigated their longitudinal associations with fatigue.
Empirical evidence suggests that fatigue in patients with cancer is most evident during active treatment and improves thereafter (e.g., Schmidt et al., 2015; Wang et al., 2014). This knowledge is derived mainly from cross-sectional stud-ies and longitudinal studstud-ies reporting on average developments of fatigue on the group level. These studies provided important insights into the extent of the fa-tigue problem and recognized that distinct (e.g., demographic and clinical) risk factors are responsible for the initiation and persistence of fatigue (Goedendorp et al., 2013; Schmidt et al., 2015; Servaes, Verhagen, & Bleijenberg, 2002). How-ever, research among other patient groups, most prominently pain (e.g., Axén & Leboeuf-Yde, 2013; Enthoven et al., 2016), demonstrated that studies aggregating symptom developments on a group level can disguise clinically and theoretically relevant differences between subgroups of patients. Similarly, the development of fatigue may relevantly differ for subgroups of patients from diagnosis to survivor-ship (Donovan, Small, Andrykowski, Munster, & Jacobsen, 2007).
Thus far, only a few studies have investigated distinct developments of fa-tigue in patients with cancer over time (Bødtcher et al., 2015; Donovan et al., 2007;
Schmidt et al., 2012; Servaes et al., 2007). These few studies, however, assessed developments of fatigue only after treatment completion, relied on retrospective reports on fatigue, or had a rather short follow-up period (i.e., 8 months after di-agnosis). For example, Schmidt et al. (2012) identified in their retrospective study a large group of patients without substantial fatigue from diagnosis to long-term survivorship, another subgroup with the commonly expected recovery after treat-ment completion and patients who had either a delayed recovery after treattreat-ment completion or remained fatigued even 6 years post-diagnosis. Another limitation of the above-mentioned studies is that they focused only on patients with breast cancer, which could be misleading, as women may be more likely to experience fa-tigue during and after cancer treatment than men (Husson et al., 2015; Oerlemans et al., 2013; Prue et al., 2006). Despite their limitations, these studies suggest that the development of fatigue can differ considerably for subgroups of patients, which calls for further investigation with prospective data.
Patients with cancer might not only differ in the development of their fa-tigue but also in the degree to which they are confronted with difficulties in achiev-ing their personal goals. Personal goals represent everythachiev-ing a person values and wants to achieve and as such provide meaning and structure to an individual’s life (Austin et al., 1996; Carver & Scheier, 2011; Wrosch, Scheier, Carver, & Schulz, 2003). Patients with cancer have been found to experience disturbances in achiev-ing their personal goals (Offerman, Schroevers, van der Velden, de Boer, & Pruyn, 2010; Stefanic, Caputi, & Iverson, 2014), which is related to impaired psychological well-being (Avis et al., 2012; Hullmann, Robb, & Rand, 2016). This is in line with the Illness Intrusiveness framework postulating that disturbances of one’s valued activities and interests is a key mechanism explaining how physical disease trans-lates into poor psychological outcomes such as depression and distress (Devins, 2010; Devins, Bezjak, Mah, Loblaw, & Gotowiec, 2006). Importantly, personal goals are assumed to differ in their level of abstraction (Austin et al., 1996; Carver & Scheier, 1998) and might thus be differently affected in patients with cancer. That is, goals can be differentiated into concrete short-term goals such as ‘accompany my partner to his doctor’s appointment tomorrow’ and related abstract goals such as ‘being a caring partner’. Abstract goals are assumed to be most important to a person as they are closely related to an individual’s core values and identity (Austin et al., 1996; Wrosch et al., 2003). Like fatigue, goal disturbance seems to improve
in patients with cancer over time (Janse, Ranchor, Smink, Sprangers, & Fleer, 2015; Janse, Sprangers, Ranchor, & Fleer, 2016; Pinquart, Fröhlich, & Silbereisen, 2008; Stefanic et al., 2014), but insights into its distinct developments, particularly the disturbance of concrete versus abstract goals, are lacking but needed to increase our knowledge of how patients’ goals are differently affected by cancer over time. Most notably, despite the evidence that both fatigue and goal disturbance are distressing and prevalent in patients with cancer and both show a comparable development over time, little research has considered whether fatigue and goal disturbance are related to each other. According to the theory of life-span devel-opment (Heckhausen, Wrosch, & Schulz, 2010), the attainability of goals depends on an individual’s resources to pursue them (e.g., time, cognitive, and physical abilities; Wrosch et al., 2003). Cancer-related fatigue represents a major limitation of such resources as it is typically characterized by impaired energy, cognition, and mood (Servaes et al., 2002). There is some preliminary evidence that high levels of fatigue (Sohl, Levine, Case, Danhauer, & Avis, 2014) or its indicators such as a lack of energy and concentration problems (Stefanic et al., 2014) are indeed positively associated with difficulties in achieving valued activities and important goals, respectively. Particularly concrete goals that are pursued on a daily basis and rely heavily on these resources (Carver & Scheier, 1998) might be disturbed by fatigue. The association of fatigue with the disturbance of abstract goals may be less pronounced, as abstract goals can be achieved via several pathways, that is, through the achievement of several related concrete goals (Wrosch et al., 2003). Investigating whether disease-related symptoms such as fatigue are longitudinally related to the disturbance of either concrete or abstract goals can help to reveal the mechanisms through which disease impacts psychological well-being.
In short, this prospective study among patients with colorectal cancer aimed to (1) identify and describe clinically distinct trajectories of fatigue from diagnosis to 18 months post-diagnosis, (2) identify trajectories of concrete and abstract goal disturbance, and (3) examine the associations of the trajectories of fatigue with the trajectories of concrete and abstract goal disturbance. Beyond the general trend of recovery from fatigue and goal disturbance, we expected to identify subgroups of patients with either no, persistent high, and high but improving trajectories. Fatigue and especially concrete goal disturbance were expected to develop concur-rently in a way that patients with high fatigue also report high goal disturbance.
Methods
Participants
Available data were part of a larger study on goal adjustment in patients with cancer (Janse, van Faassen, et al., 2015; Janse, Fleer, Smink, Sprangers, & Ranchor, 2016; Janse, Ranchor, et al., 2015; Janse, Ranchor, Smink, Sprangers, & Fleer, 2016; Janse, Sprangers, et al., 2016). Newly diagnosed patients with colorectal cancer were recruited at four hospitals in the Netherlands. Patients younger than 18 years, those not able to understand Dutch and those with cognitive impairment, a psy-chiatric disorder, and drug or alcohol problems were excluded from participation. Of the 622 eligible patients, 497 were offered information, and 228 signed the informed consent form (response rate 45.9%). A detailed flow chart of the patient recruitment process is reported elsewhere (Janse, Fleer, et al., 2016). As this study aimed to investigate developments of fatigue and goal disturbance from diagnosis until early survivorship, we excluded patients who were lost to follow-up due to their health condition (n = 25; too ill/deceased). We also excluded patients who dropped out of the study (n = 8; mainly loss of interest) and those who finished the study but did not have complete data on the primary outcome measures (n = 3), resulting in a final sample of 183 patients with cancer. The Medical Ethical Committee of the University Medical Center Groningen approved the study (RUG 2009-4461).
Procedure
Patients with a confirmed colorectal cancer diagnosis were recruited between September 2011 and March 2013. A nurse or physician briefly introduced the study to eligible patients and handed them written information on the study, an informed consent form, and a prepaid envelope. In case patients did not respond within 2 weeks, they were called by the research assistant. After informed consent was given, patients were assigned to a trained interviewer. The interviews were scheduled approximately 1 month after cancer diagnosis (T1) and at 7 months (T2) and 18 months (T3) post-diagnosis. Interviews were conducted in person at a location convenient to the patient, most often their home.
Measures
Demographic and clinical variables. Age, gender, level of education, and relation-ship status were measured by means of self-reporting. Information regarding clini-cal variables was retrieved from the Netherlands Cancer Registry. The cancer site was dichotomized into (1) colon carcinoma and (2) a carcinoma located in the rectum, rectum sigmoid, or anus. Treatment was dichotomized into (1) surgery only and (2) surgery and additional treatment (chemo- and/or radiotherapy). Pa-tients with a permanent or temporary stoma were categorized as having a stoma (yes). Information on the cancer stage was dichotomized into (1) good prognosis (stage I, stage II) and (2) poor prognosis (stage III, stage IV).
Fatigue. Fatigue was assessed at all three measurement points with the fa-tigue symptom subscale of the cancer-specific EORTC QLQ-C30, version 3 (Aar-onson et al., 1993). Three questions measured fatigue during the past week (‘Did you need to rest?’, ‘Have you felt weak?’, ‘Were you tired?’). Responses were scored on a 4-point Likert scale ranging from (1) ‘not at all’, (2) ‘a little’, (3) ‘quite a bit’, to (4) ‘very much’. Following the scoring manual, scores were linearly transformed into a 0-100 scale with higher scores indicating higher levels of fatigue (Fayers et al., 2001). The internal consistency of the fatigue scale was good (range α = .85 - .86). Norm scores of the Dutch general population and established guidelines for relevant differences between groups and changes over time were applied to define the fatigue trajectories. A fatigue score of 15.5 is considered normal in Dutch aged 60-69 years, averaged for men and women (van de Poll-Franse et al., 2011). A deviation from this norm of at least 5 points is considered small-sized, of at least 13 points is considered medium-sized and of at least 19 points is considered large-sized (Cocks et al., 2011). Within trajectories, a deviation over time of at least 5 points (deterioration) and 4 points (improvement) is considered small-sized, of at least 10 points (deterioration) and 9 points (improvement) is considered medium-sized, and of at least 15 points (deterioration) is considered large-sized (Cocks et al., 2012). Although Cocks et al. (2012) could not estimate the size of a large improvement, we considered an improvement of at least 15 points as large-sized, similar to the estimate of a large-sized deterioration.
Goal disturbance. At each interview, patients were asked to name three to ten personal goals (i.e., plans, activities or projects) they were currently trying to achieve. For each stated goal, patients were asked to respond to one question: ‘To
which degree does your illness hinder you in achieving your goal?’ Responses could be given on a 10-point Likert scale ranging from (1) ‘not at all’ to (10) ‘very’. Two independent raters categorized the level of abstraction of each goal. This variable was dichotomized into (1) concrete short-term goals (e.g., gardening, spend time with my grandchildren) and (2) abstract goals (e.g., stay involved in the community, maintain good quality of life). The raters labelled 1,263 goals as concrete and 514 goals as abstract. A mean goal disturbance score was calculated for concrete and abstract goals at each measurement point (range 1-10), with a higher score indicating higher goal disturbance. Intraclass correlations [ICC] were calculated to estimate the internal consistency of the items measuring concrete goal disturbance (range ICC = .37 - .65) and abstract goal disturbance (range ICC = .34 - .58). As no norm scores or established guidelines for the measurement of goal disturbance exist, statistical testing was used to define goal disturbance trajectories.
Statistical analysis
A Wilcoxon signed rank test was conducted in IBM SPSS (Version 23) to test changes over time in fatigue and concrete and abstract goal disturbance on group level. Growth mixture model [GMM] analyses in LatentGold 4.5 (Vermunt & Magidson, 2005) were applied to identify subgroups of patients with different trajectories over time for fatigue and concrete and abstract goal disturbance (Ram & Grimm, 2009). Time was treated as a categorical variable; thus, no a priori as-sumptions about the shapes of the time trends were made. GMMs were estimated with an increasing number of trajectories. To handle missing data of patients who did not report concrete or abstract goals at all measurement points, GMMs were estimated using maximum likelihood on all available data. The following four criteria were applied to choose the best fitting models: First, the Bayesian infor-mation criterion [BIC] and the Akaike inforinfor-mation criteria [AIC and AIC3] were inspected, with lower values indicating better fit. Second, the best models were checked to assure that each trajectory had a substantial size (≥ 5%; Henselmans et al., 2010). Third, it was examined whether trajectories represented relevant dif-ferences in terms of their severity and development. Fourth, entropy-values were inspected, with values closer to one indicating good separation of trajectories and accurate classification of individuals within those trajectories (Ram & Grimm,
2009). See Figure S1 for graphs with alternative numbers of trajectories for the three outcome measures.
Demographic and clinical characteristics were analysed as descriptors of fatigue trajectories using mean values for continuous descriptors and frequen-cies for categorical descriptors, both weighted by the estimated individual tra-jectory membership probabilities. To assess the association between fatigue and goal disturbance, a model was fitted in which the trajectory membership of goal disturbance was predicted by the trajectory membership of fatigue. The trajectory parameters were fixed for both fatigue and goal disturbance based on the best model chosen, and only the effect of fatigue trajectory membership on goal dis-turbance trajectory membership was estimated. The associations with the highest probabilities are discussed.
Results
Sample characteristics
The 183 patients had an average age of 64.2 years (range 38-93) and were mostly male (60.7%). The majority had a partner (82.5%), and 45.4% had a medium edu-cational level. Approximately 60% of the patients were diagnosed with a colon carcinoma, and most (57.4%) had a good prognosis. The prognosis of nine patients was unknown. Thirty-five per cent of the patients had a permanent or temporary stoma, and more than half of the patients (54.6%) received additional treatment (chemo- and/or radiotherapy). The treatment of two patients was unknown. Developments of fatigue
As expected, fatigue showed significant improvements over time on the group level but varied in its severity and development for subgroups of patients (see Table 1). The fatigue model with four trajectories showed the best fit according to the AIC and AIC3 (see Table 2). Although the smallest trajectory (4.7%) was just below the 5% criterion for trajectories of meaningful sizes, this trajectory was retained, as it was theoretically meaningful. As shown in Figure 1a, Trajectory 1, persis-tent severe fatigue (25.4%), showed large-sized deviations from population norms within 18 months post-diagnosis, a non-relevant deterioration between T1 and T2 (p = .47), and a small-sized improvement between T2 and T3 (p = .19). Trajectory 2, moderate fatigue (56.1%), showed medium-sized deviations from population
norms at T1 and steady yet non-significant (p = .38, p = .16) gradual improvements, resulting in fatigue scores approaching norm values at T3. Trajectory 3, no fatigue (13.8%), showed stable (p = .50, p = .68) fatigue scores below population norms. Trajectory 4, rapidly improving fatigue (4.7%), showed large-sized deviations from population norms at diagnosis, a large-sized improvement between T1 and T2 (p = .008), and a small-sized improvement between T2 and T3 (p = .27), resulting in fatigue scores below population norms at T3.
Table 3 shows demographic and clinical characteristics of the total sample and the four fatigue trajectories. On average, patients within the persistent severe fatigue trajectory were the youngest (61.9 years), more often highly educated (46.4%), and less often partnered (70.7%) compared to patients within the other trajectories. The gender distribution across the trajectories of fatigue appeared similar to that of the total sample with more men than women in each trajectory. The majority of patients within the no fatigue trajectory had a good prognosis and only received surgical treatment.
Developments of concrete goal disturbance
On the group level, concrete goal disturbance showed an improvement over time but differed for subgroups of patients (see Table 1). Based on the fit statistics (see Table 2) and relevant differences in the emerging trajectories, a concrete goal dis-turbance model with three trajectories was chosen (see Figure 1b). As shown in Table 1, Trajectory 1, high concrete goal disturbance (60.3%), showed high concrete goal disturbance with a significant improvement between T2 and T3 (p < .001). Trajectory 2, improving concrete goal disturbance (33.5%), showed high concrete goal disturbance within 1 month post-diagnosis (T1), a significant improvement between T1 and T2 (p < .001), resulting in low concrete goal disturbance at T2 and T3. Trajectory 3, low concrete goal disturbance (6.2%), showed stable (p = .81, p = .18) low concrete goal disturbance within 18 months post-diagnosis.
1 mo nt h p os t-7 mo nt hs p os t-18 mo nt hs p os t- di agn os is ( T1 ) di agn os is ( T2 ) di agn os is ( T3 ) T 1 - T 2 T 2 - T 3 T 1 - T 3 M ea n ( SE ) p Fa tig ue To ta l g ro up 34 .8 (2 .0 ) 29 .6 (1 .9) 25 .6 (1 .9) .0 17 .0 17 <. 001 D ist in ct tr aj ect or ie s Per sis ten t s ev er e ( 25 .4 % ) 56 .8 (5 .8 ) 60 .0 (5 .9) 52 .5 (6 .7 ) .47 .19 .54 M ode ra te (5 6. 1% ) 29 .3 (5 .2 ) 24 .2 (2 .5 ) 20 .9 (2 .7 ) .38 .16 .21 N o (1 3. 8% ) 2. 8 (1 .2 ) 1. 9 ( 1. 2) 2. 4 (1 .2 ) .50 .6 8 .78 Ra pi dl y i m pr ov in g ( 4. 7% ) 74 .7 (2 6. 8) 11 .7 (7. 2) 3. 4 (5 .6 ) .0 08 .27 .02 C onc re te g oa l d ist ur ba nc e To ta l g ro up 4. 9 ( 0. 2) 4.1 (0 .2 ) 2. 9 ( 0. 2) <. 001 <. 001 <. 001 D ist in ct tr aj ect or ie s H ig h ( 60 .3 %) 5. 6 ( 0. 3) 5. 6 ( 0. 4) 3. 8 ( 0. 3) .9 8 <. 001 <. 001 Impr ov in g ( 33. 5% ) 4. 2 ( 0. 5) 2. 0 ( 0. 3) 1. 5 (0 .1) <. 001 .02 <. 001 Lo w (6 .2 %) 1.1 (0 .1) 1.1 (0 .1) 1. 0 (0 .0) .81 .18 .23 Ta bl e 1 M ea n v al ue s, s ta nd ar d e rr or s a nd t es tin g o f d iff er en ce s o ve r t im e f or t he t ot al g ro up a nd f or t he d ist in ct t ra je ct or ie s o f f at ig ue , c on cr et e g oa l d ist ur ba nc e, an d a bs tra ct go al d ist ur ban ce
1 mo nt h p os t-7 mo nt hs p os t-18 mo nt hs p os t- di agn os is ( T1 ) di agn os is ( T2 ) di agn os is ( T3 ) T 1 - T 2 T 2 - T 3 T 1 - T 3 M ea n ( SE ) p A bs tr ac t g oa l d ist ur ba nc e To ta l g ro up 4.1 (0 .3 ) 4. 0 ( 0. 3) 3. 6 ( 0. 3) .67 .9 4 .50 D ist in ct tr aj ect or ie s Pe rs ist en t h ig h ( 21 % ) 6. 4 ( 0. 4) 7.6 (0 .4) 7.3 (0 .3 ) .01 9 .5 9 .0 39 La te im pr ov in g ( 13 .2 % ) 5. 7 ( 0. 2) 6.3 (0 .3 ) 1. 9 ( 0. 3) .15 <. 001 <. 001 Lo w (3 5. 9%) 1. 9 ( 0. 3) 1. 3 (0 .1) 1. 5 (0 .1) .0 82 .10 .38 Tr an sie nt (1 2% ) 7.4 (0 .8 ) 1. 5 ( 0. 3) 4. 0 (1 .5 ) <. 001 .13 .0 02 D el ay ed -o nset (8 .8 % ) 2. 0 ( 0. 5) 2.1 (0 .6 ) 7.7 (0 .7 ) .82 <. 001 <. 001 A cu te (9 .1% ) 1. 7 ( 0. 3) 8.1 (0 .6 ) 2. 0 ( 0. 4) <. 001 <. 001 .32 Ta bl e 1 (c on tin ue d)
2
Table 2
Fit statistics for trajectories of fatigue, concrete goal disturbance, and abstract goal disturbance
Number of
trajectories LL BIC AIC AIC3 Npar Entropy Trajectories of fatigue
1 -2522.2 5070.4 5054.4 5059.4 5 -2 -2483.7 5019.4 4987.3 4997.3 10 .71 3 -2447.6 4973.3 4925.2 4940.2 15 .77 4a -2436.1 4976.4 4912.2 4932.2 20 .79
Trajectories of concrete goal disturbance
1 -1210.9 2447.9 2431.8 2436.8 5 -2 -1168.4 2388.9 2356.8 2366.8 10 .80 3a -1135.1 2348.4 2300.3 2315.3 15 .72
Trajectories of abstract goal disturbance
1 -800.7 1622.2 1609.3 1613.3 4 -2 -776.1 1593.8 1568.1 1576.1 8 .53 3 -764.2 1591.0 1552.4 1564.4 12 .56 4 -746.1 1575.6 1524.2 1540.2 16 .62 5 -718.5 1541.3 1477.1 1497.1 20 .61 6a -703.5 1532.0 1455.0 1479.0 24 .64 7 -694.1 1534.0 1444.1 1472.1 28 .62 8 -685.6 1538.0 1435.3 1467.3 32 .62
Note. LL, log likelihood; BIC, Bayesian information criterion; AIC, Akaike information
crite-rion; Npar, number of parameters estimated in the model; -, Entropy cannot be computed for a one-trajectory model. a Chosen model based on the criteria formulated in the Statistical analysis
section. Fit indices for the models with five fatigue trajectories and four concrete goal disturbance trajectories are not reported. Due to the very small sizes of the emerging trajectories (< 1%), the parameters could not be reliably estimated.
Figure 1. Trajectories of (a) fatigue, (b) concrete goal disturbance and, (c) abstract goal disturbance
from 1 month to 18 months post-diagnosis.
Ta bl e 3 D em og ra ph ic a nd c lin ica l c ha ra ct er ist ic s o f t he f ou r t ra je ct or ie s o f f at ig ue a nd t he t ot al s am pl e Tr aj ec tor ie s of fa tig ue Per sis ten t s ev er e M ode ra te No Ra pi dl y i mpr ov -in g To ta l s am pl e (n = 4 7) (n = 1 03 ) (n = 2 5) (n = 9 ) (n = 1 83 ) D em og ra ph ic ch ar ac te ri st ic s A ge ( me an , S D , i n y ea rs ) 61 .9 (9. 7) 64 .7 (1 1.7 ) 66 .7 (8 .5 ) 63 .4 (1 1. 2) 64 .2 (1 0. 8) G en de r, m al e ( n, % ) 28 (5 9. 6) 61 (5 9.7 ) 17 (6 5. 1) 6 ( 65 ) 111 (6 0. 7) Ed uc at io n ( n, % ) L ow 7 (1 5. 6) 12 (1 1. 5) 10 (3 9.9 ) 3 (3 3. 3) 32 (1 7.5 ) M ed iu m 18 (3 8) 52 (5 0. 8) 9 (3 4. 5) 4 (5 1. 6) 83 (4 5. 4) H ig h 22 (4 6. 4) 39 (3 7.7) 7 (2 5. 6) 1 ( 15 .1) 68 (3 7.2 ) Re lat io ns hi p s tat us , p ar tne re d ( n, % ) 33 (7 0. 7) 87 (8 4. 5) 23 (9 0. 1) 9 (9 9.7 ) 15 1 ( 82 .5 ) C lin ic al c ha ra ct er ist ic s ( n, % ) Pr og no sis , g oo d 24 (5 2. 2) 58 (5 6. 5) 20 (7 7) 3 (3 8. 7) 10 5 ( 57. 4) C anc er sit e, c ol on ca nc er 26 (5 5. 8) 62 (60 .2 ) 17 (6 6. 7) 5 ( 63) 11 0 ( 60 .1) Tr ea tme nt , s ur ge ry o nl y 17 (3 5. 7) 45 (4 3. 9) 16 (6 3) 4 (4 0. 3) 81 (4 4. 3) Sto m a, y es 15 (3 2. 3) 41 (4 0) 5 (1 8. 7) 3 (3 7) 64 (3 5) N ot e. Th e n um be r o f p at ie nt s r ep or te d w ith in e ac h t ra je ct or y a re r ou nd ed , a s t he a na ly sis g iv es e st im at io ns o f g ro up s iz es o nl y.
Developments of abstract goal disturbance
The improvement of abstract goal disturbance on the group level was not signifi-cant (see Table 1). An abstract goal disturbance model with six trajectories was chosen (see Table 2, Figure 1c). The majority of patients (Trajectory 1-3; 70.1% of the total sample) showed trajectories comparable to those of concrete goal dis-turbance, representing persistent high (Trajectory 1), late improving (Trajectory 2), and low (Trajectory 3) abstract goal disturbance. The other three trajectories showed different patterns of improvement and deterioration with each having a peak in abstract goal disturbance at a different measurement point: Trajectory 4, transient abstract goal disturbance (12%), showed high abstract goal disturbance at T1, an improvement between T1 and T2 (p < .001) and a deterioration between T2 and T3 (p = .13). Trajectory 5, delayed-onset abstract goal disturbance (8.8%), showed low levels of abstract goal disturbance at T1 and T2 and a deterioration between T2 and T3 (p < .001). Trajectory 6, acute abstract goal disturbance (9.1%), showed low levels of abstract goal disturbance at T1 and T3 with a sharp peak of abstract goal disturbance at T2 (ps < .001).
Associations between trajectories of fatigue and trajectories of concrete goal disturbance
As expected, fatigue and concrete goal disturbance appeared to be strongly as-sociated, which is reflected in the mostly concurrent developments across time (see Table 4). Patients within the trajectories of persistent severe fatigue (98%) and moderate fatigue (56%) had the highest probability of experiencing concurrent high concrete goal disturbance. Interestingly, patients without fatigue were most likely to experience improving concrete goal disturbance (80%), that is high con-crete goal disturbance at diagnosis that improves thereafter. Patients with rapidly improving fatigue experienced either concurrent improving concrete goal distur-bance (41%) or high concrete goal disturdistur-bance (47%).
Table 4
Fatigue trajectory membership predicting concrete and abstract goal disturbance trajectory mem-bership
Trajectories of concrete
goal disturbance Trajectories of abstract goal disturbance High Improv-ing Low
Persis-tent high
Late
improv-ing Low Tran-sient De- layed-onset Acute Trajectories of fatigue Persistent severe .98 .0 .02 .51 .13 .03 .18 .08 .08 Moderate .56 .40 .05 .12 .15 .41 .10 .11 .11 No .05 .80 .15 .03 .07 .90 .0 .0 .0 Rapidly improving .47 .41 .12 .31 .0 .39 .31 .0 .0
Note. Values represent probabilities.
Associations between trajectories of fatigue and trajectories of abstract goal disturbance
The association between fatigue and abstract goal disturbance appeared to be less pronounced than the relation between fatigue and concrete goal disturbance as the distribution of probabilities did not show the same discernible concurrent pattern across time (see Table 4). However, some relevant associations occurred. Patients with persistent severe fatigue had a high probability (51%) of experienc-ing concurrent persistent high abstract goal disturbance. Patients within the no fatigue trajectory had the highest probability (90%) of experiencing concurrent low abstract goal disturbance. Individuals with moderate fatigue (41%) and rapidly im-proving fatigue (39%) were most likely to experience low abstract goal disturbance.
Discussion
This prospective study described clinically distinct trajectories of fatigue from cancer diagnosis to 18 months post-diagnosis and examined their associations with trajectories of concrete and abstract goal disturbance. As expected, on the group level, fatigue and goal disturbance improved in the 18 months following diagnosis but differed considerably for subgroups of patients. Partly in accordance with our hypotheses, we identified subgroups of patients with low and high fatigue
and goal disturbance. However, only a small subgroup of patients showed the expected recovery from fatigue. Fatigue and especially concrete goal disturbance appeared to develop concurrently.
The majority of patients experienced substantially elevated levels of fatigue compared to Dutch population norms. One quarter of the patients experienced severe levels of fatigue persisting long after their cancer diagnosis, as has been found in earlier research (Goedendorp et al., 2013; Servaes et al., 2007). Like Bødtcher et al. (2015), we were able to demonstrate that these individuals ex-perienced severe fatigue from diagnosis onwards. Another half of the patients experienced moderate yet gradually improving fatigue from diagnosis onwards. A small group of patients did not experience any clinically relevant fatigue during the 18 months following diagnosis. The majority of those patients received surgi-cal treatment only, which may explain the absence of fatigue, as some treatment modalities, especially chemotherapy, are related to an increased risk for fatigue (Abrahams et al., 2016; Goedendorp et al., 2012; Husson et al., 2015). Interestingly, of those patients with severe fatigue at diagnosis, only a very small group (rapidly improving fatigue) showed the expected recovery while the majority remained se-verely fatigued. Future studies should investigate which characteristics distinguish patients who recover from those who remain severely fatigued. Unfortunately, due to small sample sizes within trajectory groups, potential predictors could not be tested for statistical significance. However, in line with earlier studies (Abrahams et al., 2016; Husson et al., 2015), our findings suggest that having a partner could be beneficial, while we could not find an indication for the vulnerability of women to fatigue as suggested by previous studies (Husson et al., 2015; Oerlemans et al., 2013; Prue et al., 2006). A vast majority of patients experienced high concrete goal disturbance while only a small group experienced persistent high abstract goal disturbance. This indicates that concrete goals are more often threatened in patients with cancer than abstract goals. If a particular concrete goal gets disturbed, individuals may formulate and pursue an alternative concrete goal to be able to maintain the re-lated abstract goal (Wrosch et al., 2003). For example, the abstract goal of being a caring partner can be achieved by accompanying your partner to a doctor’s ap-pointment or alternatively by giving a compliment. However, the low prevalence of high abstract goal disturbance in our study should be considered as preliminary
2
as some patients (n = 32) did not report any abstract goals and could therefore not be scored on abstract goal disturbance. Further, the three trajectories of con-crete goal disturbance identified in our study mirror the trajectories of illness intrusiveness identified by Sohl et al. (2014) among patients with breast cancer following diagnosis. Illness intrusiveness is a concept similar to goal disturbance as it measured the degree to which cancer interfered with important life domains. However, the percentage of patients within the high concrete goal disturbance trajectory was much larger in our study (60.3%) than the percentage of patients in the high trajectories of disturbed life domains identified by Sohl et al. (2014; < 18%). This difference in group sizes might be due to the operationalization and categorization of goals in our study. As opposed to Sohl et al. (2014), patients in our study did not respond to broad life domains (e.g., work, health, sex life) but could freely mention their personal goals and rate those on goal disturbance. Categorizing these goals on their level of abstraction and analysing the concrete goals separately might better tap into actual daily impairments of persons dealing with disease than predefined life domains do. As expected, fatigue and concrete goal disturbance co-occurred longitudi-nally in the majority of patients with cancer. Fatigue and abstract goal disturbance showed a less clear concurrent pattern. The majority of patients with both per-sistent severe and moderate fatigue experienced high concrete goal disturbance. Patients with persistent severe, but not moderate, fatigue were also very likely to experience high abstract goal disturbance. This indicates that only severe and persistent levels of fatigue have the potential to interfere with both concrete goals and important abstract goals. That is, the resources for goal attainment might be so severely limited in these patients that even alternative pathways to reaching their abstract goals (i.e., through other related concrete goals) get blocked. Patients without clinically relevant fatigue were unlikely to experience abstract goal dis- turbance but, interestingly, were likely to experience high concrete goal distur-bance at diagnosis. Their concrete goals might be disturbed because receiving a cancer diagnosis and initiating treatment may disturb concrete goals independent of fatigue by limiting other crucial resources for goal attainment such as time and evoking emotional distress (Heckhausen et al., 2010).
The major strength of this study is that we investigated trajectories of fatigue prospectively from diagnosis until 18 months later in a gender-mixed cancer
popu-lation. Using norm values and evidence-based guidelines for the interpretation of fatigue scores allowed us to identify subgroups of patients with clinically relevant differences in the severity and development of their fatigue. Similarly, trajectories of concrete and abstract goal disturbance were assessed and their longitudinal associations with fatigue have been examined.
Despite these strengths, the findings should be interpreted in light of some limitations. First, even though we aimed to start data collection as soon as pos-sible after diagnosis and to follow patients until survivorship, some patients had already started their treatment before T1 and others had a long treatment period or might have had a recurrence. Approximately 30% of the patients started their last treatment (mainly surgery only) before T1 and three patients started their last treatment after T2. However, for the majority of patients, the three measure-ment points coincide with the time of diagnosis (T1), treatmeasure-ment (T2), and early survivorship (T3).
Second, although our results and that of others (Sohl et al., 2014; Stefanic et al., 2014) suggest that symptoms as fatigue might trigger goal disturbance, other research designs are needed to draw more firm conclusions regarding the direc-tionality of their relationship. Daily diary methods can disentangle the temporal order of fatigue and goal disturbance and may provide stronger evidence for the disrupting effect of fatigue on personal goals within individuals in daily life.
Third, due to computation problems, models with more trajectories than those selected for fatigue (four trajectories) and concrete goal disturbance (three trajectories) could not be reliably estimated. Studies with larger sample sizes might identify additional relevant trajectories. However, the identified trajectories of fa-tigue (Bødtcher et al., 2015; Donovan et al., 2007; Servaes et al., 2007) and concrete goal disturbance (Sohl et al., 2014) resemble those identified in earlier research, which indicates that the most relevant trajectories have been identified in our study. However, future studies may extend the follow-up period beyond 18 months after diagnosis as there are indications that some patients may show a delayed onset of or a delayed recovery from fatigue in the months or years after treatment completion (Andrykowski, Donovan, Laronga, & Jacobsen, 2010; Oerlemans et al., 2013; Schmidt et al., 2012).
Our findings have implications for clinical practice and research on cancer-related fatigue and the broader field of adjustment to chronic disease. First, this
study demonstrated that the general recovering trend of fatigue, implied by many studies, is an oversimplification. This oversimplification may misguide practition-ers and result in serious cases of fatigue remaining undetected in clinical practice. Patients with severe fatigue at diagnosis appear highly likely to remain fatigued. Those patients should be identified early and treatment options should be dis-cussed. The large group of patients with moderate fatigue should be especially closely monitored, as they experienced elevated fatigue 18 months post-diagnosis that may require support in some patients. These findings are in accordance with recent guidelines from the National Comprehensive Cancer Network (NCCN, 2016) that call for initiating screening for fatigue early and continual monitoring until after treatment completion.
Second, beyond cancer research, evidence is accumulating that various symptoms (i.e., depression, anxiety, pain) vary relevantly in their development for subgroups of individuals (Ng, Tan, Mooppil, Newman, & Griva, 2015; Wes-seling et al., 2015) and are predictive of more distal negative outcomes (i.e., poor psychiatric and social health; Musliner, Munk-Olsen, Eaton, & Zandi, 2016). These trajectories can be successfully predicted by demographic (i.e., age, gender), clini-cal (i.e., co-morbidities, pain sites) and psychosocial (i.e., catastrophizing, social support) variables (Musliner et al., 2016; Rzewuska, Mallen, Strauss, Belcher, & Peat, 2015; Wesseling et al., 2015). Our findings extend this research and call for more studies directed towards analysing symptom developments beyond their average trend and to identify their predictors to guide clinical practice to timely target individuals in highest need for treatment.
Third, a growing body of research supports the important role of personal goals for psychological well-being among various patient groups (Devins, 2010; Hullmann et al., 2016) and indicate that disturbances in identity-relevant aspects of life, which are similar to inherently important abstract goals in our study, are most detrimental for one’s psychological well-being (Abraído-Lanza & Revenson, 2006). As our study suggests that the most severe symptoms disturb both con-crete and abstract goals, relieving goal disturbance might be a valuable approach to protect the patient’s psychological well-being if the symptom burden cannot be reduced. That is, patients suffering from severe and persistent fatigue should be supported in applying strategies to pursue their goals despite their limited re-sources (e.g., by recruiting external rere-sources for practical support) or alternatively
to disengage from former goals and formulate and pursue more attainable ones (Heckhausen et al., 2010).
Supporting information
Figure S1. Graphs of models with alternative numbers of trajectories for (a) fatigue, (b) concrete goal disturbance and (c) abstract goal disturbance from 1 to 18 months post-diagnosis.
Acknowledgments
This work was supported by the Dutch Cancer Society (RUG 2009-4461). Medi-cal records were supplied by the Netherlands Cancer Registry, managed by the Comprehensive Cancer Centre.
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