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The Effects of Being Informed About Chemotherapy-Related Cognitive Symptoms With And Without Self-Affirmation on Perceived Cognitive Symptoms of Breast Cancer Patients: A Randomized Prospective, Longitudinal Study

Open AccessPublished:March 26, 2022DOI:https://doi.org/10.1016/j.clbc.2022.03.001

      Abstract

      Background

      Informing patients about chemotherapy-related cognitive symptoms (CRCS) may increase perceived cognitive symptoms. This longitudinal randomized study evaluated this Adverse Information Effect (AIE) in breast cancer patients and examined whether self-affirmation (SA) can reduce AIEs (ClinicalTrials.gov identifier: NCT04813965).

      Patients and Methods

      Before (neo) adjuvant chemotherapy, 160 newly diagnosed breast cancer patients were randomly allocated to receive: standard information on side-effects (control), standard information with additional information about CRCS (information), or standard and additional information with a subsequent self-affirmative text (information+SA). Online-questionnaires assessed the perceived frequency (MOS-cog) and severity (MDASI-cog) of cognitive symptoms before chemotherapy (baseline, T0), and 2.5-months (T1) and 6.5-months (T2) post-chemotherapy. Higher scores indicate less frequent, and more severe symptoms, respectively. Baseline-to-follow-up analyses using a mixed-effects modeling approach compared groups over time.

      Results

      At T0-T2, 148, 140 and 133 patients responded, respectively (attrition rates: 8%, 5%, 5%). Frequency (ES = -0.36, P =.003) and severity (ES = 0.54, P <.001) of symptoms worsened from baseline to T1, without differences between groups. At T2, symptom frequency remained stable for informed (ES=-0.3, P =.021) and self-affirmed (ES=-0.3, P =.019) patients, but returned to baseline levels for controls. At T2, symptom severity remained increased for informed patients (ES = 0.3, P =.006), but normalized for self-affirmed patients (ES = 0.2, P =.178) and controls.

      Conclusion

      No AIEs occurred until T2. The initial overall increase in perceived cognitive symptoms recovered at T2 for controls, but not for patients who received additional information about CRCS. Self-affirmation attenuated these longer-term AIEs for the perceived severity but not the frequency of symptoms.

      Keywords

      Introduction

      Many cancer patients (17%-75%) experience chemotherapy-related cognitive symptoms (CRCS), sometimes lasting well into the survivorship period.
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      Previous randomized
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      AIE
      AIE = adverse information effect
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      The influence of priming and pre-existing knowledge of chemotherapy-associated cognitive complaints on the reporting of such complaints in breast cancer patients.
      • Schagen SB
      • Das E
      • Vermeulen I.
      Information about chemotherapy-associated cognitive problems contributes to cognitive problems in cancer patients.
      This randomized prospective study examined the short- and longer-term effects of communication about potential CRCS on perceived cognitive symptoms in newly diagnosed breast cancer patients about to begin (neo) adjuvant chemotherapy. Our first aim was to evaluate the effect over time of providing patients before chemotherapy-initiation with additional written information about CRCS on perceived cognitive symptoms. Building on findings that perceived cognitive symptoms of breast cancer patients increased after receiving cognitive side-effect information,
      • Jacobs W
      • Das E
      • Schagen SB.
      Increased cognitive problem reporting after information about chemotherapy-induced cognitive decline: the moderating role of stigma consciousness.
      • Schagen SB
      • Das E
      • van Dam FSAM.
      The influence of priming and pre-existing knowledge of chemotherapy-associated cognitive complaints on the reporting of such complaints in breast cancer patients.
      • Schagen SB
      • Das E
      • Vermeulen I.
      Information about chemotherapy-associated cognitive problems contributes to cognitive problems in cancer patients.
      we hypothesized that communicating about CRCS before chemotherapy-initiation will result in short- and longer-term AIEs on patients' perceived cognitive symptoms after chemotherapy completion. Our second aim was to translate the beneficial effects of self-affirmation to the oncology domain, and to examine the efficacy of a newly developed, text-integrated self-affirmation intervention in reducing such potential AIEs. We hypothesized that adding a textual self-affirmation to pre-treatment information about CRCS would reduce AIEs on patients' perceived cognitive symptoms.

      Patients And Methods

      Study Design and Procedure

      In this randomized, longitudinal, online survey study, patients were accrued in ten hospitals in the Netherlands between March 20, 2014 and September 23, 2016. See Figure 1 for the CONSORT diagram. Prior to chemotherapy, the treating medical oncologist or nurse specialist identified potentially eligible patients and informed them about the study using a standardized research description and information leaflet (see S1). The study coordinator contacted interested patients by telephone to further explain the study and verify in- and exclusion criteria. Patients were informed as much as possible about the study aims, without causing interference with the main research objectives. Hence, the research descriptions did not specifically mention CRCS or the hypotheses, but explained that the trajectory of treatment experiences and the effects of different information conditions on patients’ well-being were evaluated. No further interactions took place, apart from reminding non-responders by telephone. Before chemotherapy (baseline, T0) and at 2.5 months (T1) and 6.5 months (T2) post-chemotherapy, a 30-minute survey was conducted independently online via a standardized email containing a survey link and personal login code. All patients provided written consent.
      Figure 1
      Figure 1Consort flow diagram.
      Notes. (a) Medical records were investigated after T2, which showed that 11 patients did not meet the inclusion criteria of the study at T0 (previous cancer treatment n = 10, alcohol use >3 a day n = 1). The number of participants not meeting the inclusion criteria did not differ across experimental conditions (control n = 5, information n = 1, information+SA n = 5; χ2(2,148) = 3.26, P = .196). Following the intention-to-treat principle, all these randomized patients with data on at least one of 3 data waves were included in the analyses (n = 148). (b) Twelve patients withdrew from participation shortly after opening the baseline survey link and consequent randomization, but before providing any post-randomization data (n = 6), and/or having signed consent (n = 10), and/or being exposed to the experimental manipulation (at least 6 patients were exposed to one of the experimental text screens, but for the other 6 this was not recorded by the survey program). These patients were equally distributed across the 3 experimental conditions (n = 12 of n = 160: control n = 2, information n = 3, information+SA n = 7; χ2(2,160) = 3.64, P = .162). The proportion of patients who declined after randomization per arm was: 3.77% (2/53) for the control condition, 5.66% (3/53) for the information condition, and 12.96% (7/54) for the information+SA condition. (c) Drop outs after T0 (n = 15 of n = 148: n = 6 control, n = 5 information, n = 4 information+SA) were equally distributed across the 3 experimental groups (χ2(2,148) = 0.29, P = .867). (d) Following completion of the third questionnaire, patients were explicitly informed about the specific aims, experimental conditions, and main outcomes of this study. Next, patients were given the option to withdraw their data with a second passive informed consent option. There were no patients who requested for their data to be removed from the analyses.
      At baseline, all participants first read a standardized general introduction to the online survey, after which randomization took place by a computer program using an algorithm developed by the University's IT expert that included time of entry, previous allocations, and previous completions. This program was linked to Qualtrics,
      where patients completed the online surveys. Outside of the study, all patients received usual medical care and standard treatment information from their clinician, but in order to influence their baseline informational status they were randomly allocated to one of 3 different online experimental manipulations (for details see Textbox 1 and S1):
      Control group:
      • -
        Received a neutral written introduction to the survey with no reference to CRCS.
      Information group:
      • -
        Received additional written information about potential CRCS.
      Information+SA
      SA = self-affirmation
      (SA = self-affirmation) group:
      Patients received these texts only once, directly after randomization at baseline. Next, all groups completed an identical questionnaire. At 2.5 months and 6.5 months post-chemotherapy, all groups received identical general introductions and online surveys, without any experimental manipulations. Health care professionals were blinded to group assignment, but due to the nature of the study patients were not. However, patients were not explicitly informed about the study's main outcomes, experimental conditions, specific aims and hypotheses, and the content of the texts that they did not receive until after T2, when they were debriefed and had the option to withdraw their data (second passive consent; see S1).

      Study Sample

      Eligible patients had a primary breast cancer diagnosis stage I-III, were scheduled to receive (neo) adjuvant chemotherapy, were 18 years or older, had sufficient command of the Dutch language, had Internet access, did not have a history of neurological or psychiatric symptoms that influenced cognitive functioning, had not been diagnosed with cancer in the past, did not use drugs, and did not drink more than 3 alcoholic beverages a day. Chemotherapy regimens followed the prevailing guidelines at that time and were mostly anthracycline and taxane based. Patients needed to complete the baseline survey before their first cycle.

      Measures

      We obtained sociodemographic and medical information through the baseline interview and online surveys, and additional treatment and comorbidity information through the medical records after T2. Primary outcomes were the perceived frequency and severity of cognitive symptoms. Secondary outcomes were other cancer-related symptoms, anxiety, depression and pre-existing knowledge (for details see Table 1). This study was part of a larger project: results for other primary and secondary outcomes will be described elsewhere (see S2).
      Table 1Study Constructs and Instruments
      ConstructInstrumentNumber of ItemsPossible ScoreCronbach's Alpha (α)/ Pearson's rCommentsExamplesAssessed at
      Perceived cognitive symptom frequencyMOS-cog scale (Medical Outcomes Study – scale) revised, subscale cognitive functioning.
      • Stewart AL
      • Ware JE.
      Measuring Functioning and Well-being: the Medical Outcomes Study Approach.
      6(1) never – (6) always: recoded to (0) always – (100) never cf. 85α = .89Participants indicated the frequency of experiencing a range of day-to-day problems in 6 aspects of cognitive functioning during the past week (including today). Scores were reverse coded, transformed to a 0-100 scale and then averaged.
      • Hays RD
      • Sherbourne CD
      • Mazel RM.
      User's Manual for the Medical Outcomes Study (MOS) Core Measures of Health-Related Quality of Life.
      Higher mean scores indicate better perceived cognitive functioning (range 0-100).
      Amount of time in past week (including today) became confused, reacted slowly to things, had difficulty reasoning, was forgetful, had trouble keeping attention, had difficulty concentrating.T0, T1, T2
      Perceived cognitive symptom severityTwo items of the M. D. Anderson Symptom Inventory multiple myeloma module (MDASI-MM, part 1).
      • Cleeland CS
      • Mendoza TR
      • Wang XS
      • Chou C
      • Harle MT
      • Morrissey M
      • et al.
      Assessing symptom distress in cancer patients: the M. D. Anderson Symptom Inventory.
      ,
      • Jones D
      • Vichaya EG
      • Shelley Wang X
      • Williams LA
      • Shah ND
      • Thomas SK
      • et al.
      Validation of the M. D. Anderson Symptom Inventory multiple myeloma module.
      2(0) symptom has not been present – (10) was as bad as you can imagine it could be)Pearson's r = .81, P < .001Patients reported the severity of 2 cognitive symptoms at their worst in the last 24 hours on a 0-10 scale, with 0 being ‘not present’ and 10 being ‘as bad as you can imagine’: difficulty remembering and difficulty paying attention (concentrating). Higher mean scores indicate more severe symptoms.Difficulty remembering; Difficulty paying attention (concentrating).T0, T1, T2
      AnxietyDutch version of the Hospital Anxiety and Depression Scale (HADS).
      • Spinhoven P
      • Ormel J
      • Sloekers PP
      • Kempen GI
      • Speckens AE
      • van Hemert AM.
      A validation study of the hospital anxiety and depression scale (HADS) in different groups of Dutch subjects.
      ,
      • Zigmond AS
      • Snaith RP.
      The hospital anxiety and depression scale.
      74-point scale (range 0-3)α = 0.83Higher sum scores (range 0-21 per subscale) indicate higher levels of anxiety.I get sudden feelings of panic.T0, T1, T2
      DepressionDutch version of the HADS.
      • Spinhoven P
      • Ormel J
      • Sloekers PP
      • Kempen GI
      • Speckens AE
      • van Hemert AM.
      A validation study of the hospital anxiety and depression scale (HADS) in different groups of Dutch subjects.
      ,
      • Zigmond AS
      • Snaith RP.
      The hospital anxiety and depression scale.
      74-point scale (range 0-3)α = 0.75Higher sum scores (range 0-21 per subscale) indicate higher levels of depression.I still enjoy the things I used to enjoy.T0, T1, T2
      Perceived severity of other cancer-related symptomsTwelve items of the 13-item core MDASI (part 1).
      • Cleeland CS
      • Mendoza TR
      • Wang XS
      • Chou C
      • Harle MT
      • Morrissey M
      • et al.
      Assessing symptom distress in cancer patients: the M. D. Anderson Symptom Inventory.
      12(0) symptom has not been present – (10) was as bad as you can imagine it could beα = 0.85Participants reported the severity of 12 symptoms at their worst in the last 24 hours on a 0–10 scale, with 0 being ‘not present’ and 10 being ‘as bad as you can imagine.’ Difficulty remembering was excluded. Higher mean scores indicate more severe symptoms.For example: ‘pain’.T0, T1, T2
      Pre-existing knowledgecf. 5-71(1) Not at all – (5) totallyn.a.Participants indicated at the end of the third survey, whether they had knowledge about the potential cognitive symptoms of cancer treatment prior to the experiment.To what extent do you have knowledge about the fact that some people may have memory and concentration problems during and after cancer and cancer treatment?T2

      Ethical Approval

      The Institutional Review Board of the Netherlands Cancer Institute served as the central ethical committee for all participating institutes and approved the study (PTC13.0541-M13WEL-NL43939.031.13). Originally, we wanted to investigate AIEs in 300 patients receiving adjuvant chemotherapy and 300 patients receiving neoadjuvant chemotherapy separately, and to have a 12- instead of 9-month interval between baseline and T2. However, because patient accrual lagged significantly behind expected numbers in the protocol the aimed intervals were shortened, and sample size calculations were adapted changing the aimed sample size from 300 per treatment group to 300 in total. The trial was retrospectively registered at ClinicalTrials.gov on March 21, 2021 (Identifier: NCT04813965).

      Statistical Analysis

      Baseline characteristics (eg, education level: low, medium, high; medium is reference) and factors that could potentially influence perceived cognitive symptoms (eg, time between assessments) were compared using chi-square tests for categorical variables, and independent-samples t-tests or univariate analysis of variance for continuous variables. Questionnaire scores were calculated according to published scoring algorithms.
      To analyze between-group differences in change over time in symptom frequency and severity, we conducted baseline to follow-up analyses (short-term effect: T0 to T1 and longer-term effect: T0 to T2) using a mixed-effects modelling approach with random intercept, maximum likelihood solution and an autoregressive covariance structure
      • Baayen RH
      • Davidson DJ
      • Bates DM.
      Mixed-effects modeling with crossed random effects for subjects and items.
      in R version 3.4.2
      R Core Team
      R: a language and environment for statistical computing.
      using the package lme4.
      • Bates D
      • Maechler M
      • Bolker B
      • Walker S.
      Fitting linear mixed-effects models using lme4.
      The control group served as the reference category.
      If despite randomization there were between-group differences in baseline characteristics (see Table 2), or in other potential influencing factors (eg, chemotherapy regimen, see Table 2 and S2), these were corrected for when they significantly correlated with the outcome measure, and were compared with models without adjustment using the Bayesian Information Criterion (BIC)
      • Schwarz G.
      Estimating the dimension of a model.
      and the Akaike's Information Criterion (AIC).
      • Akaike H.
      Information theory and an extension of the maximum likelihood principle.
      Both are used to compare non-nested models and both penalize the number of model parameters. The BIC also penalizes small sample sizes.
      • Singer JD
      • Willett JB.
      Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence.
      Models with lower BIC or AIC values are considered to be better fitting models. If difference between models were ≤2, we chose the most parsimonious model.
      • Cohen J.
      Statistical Power Analysis for the Behavioural Sciences.
      ,
      • Raftery A.
      Bayesian model selection in social research.
      Table 2Baseline Clinical and Sociodemographic Characteristics, And Treatment Characteristics and Secondary Outcomes at T1 And T2
      Experimental condition
      NControl

      (n = 51)
      Information

      (n = 50)
      Information+SA

      (n = 47)
      F / χ2P
      Age (M, SD) a14853.09 (8.49)50.19 (9.36)53.65 (8.81)2.150.12
      Education level (No. %) b147 (n = 1 not classified)Low (Verhage 1-3)7 (14.0%)2 (4.0%)1 (2.1%)10.04.040*
      Middle (Verhage 4-5)26 (52.0%)27 (54.0%)19 (40.4%)
      High (Verhage 6-7)17(34.0%)21 (42.0%)27 (57.4%)
      Employment status at T0 (No. %)148Yes (full or part-time)12 (23.5%)12 (24.0%)15 (31.9%)5.440.245
      No21 (41.2%)12 (24.0%)12 (25.5.%)
      Temporarily not18 (35.3%)26 (52.0%)20 (42.6%)
      Marital status at T0 (No. %)148Single/widowed/divorced6 (11.8%)4 (8.0%)10 (21.3%)3.860.145
      Married/in a relationship45 (88.2%)46 (92.0%)37 (78.7%)
      Days since diagnosis (M, SD)14875.10 (29.73)75.27 (31.61)83.52 (29.76)1.210.302
      Breast cancer subtype (No. %)148Triple negative10 (19.6%)4 (8.0%)5 (10.6%)7.10.311
      HER2+ER+ and/or PR+7 (13.7%)5 (10.0%)9 (19.1%)
      HER2+ER- and PR-0 (0.0%)2 (4.0%)2 (4.3%)
      HER2- ER+ and/or PR+34 (66.7%)39 (78.0%)31 (66.0%)
      Breast cancer stage (No. %)147 (n = 1 not classified)Stage I16 (31.4%)14 (28.6%)20 (42.6%)3.060.548
      Stage II29 (56.9%)27 (55.1%)23 (48.9%)
      Stage III6 (11.8%)8 (16.3%)4 (8.5%)
      Chemotherapy regimen planned (No. %)148FEC/DOC25 (49.0%)19 (38.0%)16 (34.0%)11.30.08
      TAC9 (17.6%)16 (32.0%)13 (27.7%)
      AC/PAC11 (21.6%)15 (30.0%)11 (23.4%)
      Other c6 (11.8%)0 (0.0%)7 (14.9%)
      Adjuvant or neoadjuvant chemotherapy (No. %)148Adjuvant46 (90.2%)41 (82.0%)45 (95.7%)4.830.089
      Neoadjuvant5 (9.8%)9 (18.0%)2 (4.3%)
      Menopausal status (No. %)148Pre22 (43.1%)27 (54.0%)21 (44.7%)4.240.375
      Post26 (51.0%)21 (42.0%)26 (55.3%)
      Unknown d3 (5.9%)2 (4.0%)0 (0.0%)
      Age of menopause (M, SD)69 (n = 4 unknown)47.15 (5.57)49.30 (4.46)48.61 (5.95)0.960.388
      Hormonal contraceptive use (eg, pill, Mirena; No. %)148Yes48 (94.1%)45 (90.0%)43 (91.5%)0.590.745
      No3 (5.9%)5 (10.0%)4 (8.5%)
      Comorbidity (medical records; No. %)148No23 (45.1%)24 (48.0%)23 (48.9%)0.160.923
      Yes28 (54.9%26 (52.0%)24 (51.1%)
      Comorbidity (medical records; No. %)148Cardiovascular disease0.080.962
      Yes15 (29.4%)15 (30.0%)15 (31.9%)
      No36 (70.6%)35 (70.0%)32 (68.1%)
      Diabetes Mellitus0.010.994
      Yes3 (5.9%)3 (6.0%)3 (6.4%)
      No48 (94.1%)47 (94.0%)44 (93.6%)
      Depression40.135
      Yes4 (7.8%)0 (0.0%)2 (4.3%)
      No47 (92.2%)50 (100.0%)45 (95.7%)
      Other1.310.519
      Yes22 (43.1%)19 (38.0%)15 (31.9%)
      No29 (56.9%)31 (62.0%)32 (68.1%)
      Medication use at T0 (medical records; No. %)148Cardiovasular0.510.774
      Yes11 (21.6%)8 (16.0%)9 (19.1%)
      No40 (78.4%)42 (84.0%)38 (80.9%)
      Anti-diabetic0.330.848
      Yes2 (3.9%)2 (4.0%)2 (2.1%)
      No49 (96.1%)48 (96.0%)46 (97.9%)
      Psychotropic0.260.877
      Yes6 (11.8%)6 (12.0%)7 (14.9%)
      No45 (88.2%)44 (88.0%)40 (85.1%)
      Pain medication3.870.144
      Yes2 (3.9%)1 (2.0%)5 (10.6%)
      No49 (96.1%)49 (98.0%)42 (89.4%)
      Perceived severity of other cancer-related symptoms at T0 (M, SD; range 0-10)1461.38 (1.19)1.40 (1.19)1.12 (1.21)0.840.436
      Pre-existing knowledge at T2 (M, SD; range 1-5)1313.61 (1.28)4.13 (.97)3.86 (1.03)2.490.087
      Anxiety (HADS-A) at T0 (M, SD; range 0-21)1464.80 (3.24)5.84 (3.50)4.87 (3.36)1.450.239
      Depression (HADS-D) at T0 (M, SD; range 0-21)1462.20 (2.36)2.02 (1.92)2.70 (2.73)1.080.343
      Alcohol use at T0 (No. %)147Yes (≤3 per day)40 (78.4%)36 (72.0%)33 (71.7%)2.830.587
      Yes (>3 per day)0 (0.0%)0 (0.0%)1 (2.2%)
      No11 (21.6%)14 (28.0%)12 (26.1%)
      Time in months between T0 and T1 (M, SD; range 4.10-15.29)1406.50 (1.94)6.37 (1.86)6.40 (1.42)0.070.934
      Time in months between T0 and T2 (M, SD; range 7.56-24.82)13310.44 (2.52)10.10 (1.74)10.96 (3.36)1.20.306
      Time until first chemotherapy cycle in days at T0 (M, SD; range 0-50.12) e1488.97 (12.53)5.33 (6.74)7.65 (11.24)1.560.213
      Time in months since chemotherapy completion at T1 (M, SD; range 0-11.53) f1402.63 (2.18)2.31 (1.84)2.44 (1.33)0.360.698
      Time in months since chemotherapy completion at T2 (M, SD; range 0-21.38) g1336.38 (3.14)6.05 (1.84)6.95 (3.42)1.110.332
      Radiotherapy yes/no (No. %)147 (n = 1 missing)Yes41 (80.4%)44 (89.8%)36 (76.6%)3.070.216
      No10 (19.6%)5 (10.2%)11 (23.4%)
      Herceptin yes/no (No. %)148Yes8 (15.7%)7 (14.0%)11 (23.4%)1.670.434
      No43 (84.3%)43 (86.0%)36 (76.6%)
      Endocrine treatment yes/no (No. %)147 (n = 1 missing)Yes39 (78.0%)44 (88.0%)39 (83.0%)1.770.412
      No11 (22.0%)6 (12.0%)8 (17.0%)
      Type of endocrine treatment received (No. %)122 (n = 26 no ET or missing)Tamoxifen34 (87.2%)33 (75.0%)33 (84.6%)2.650.618
      Aromatase inhibiter1 (2.6%)3 (6.8%)1 (2.6%)
      Tamoxifen and aromatase inhibiter4 (10.3%)8 (18.2%)5 (12.8%)
      Endocrine treatment at T0 yes/no (No. %)147 (n = 1 missing)Yes0 (0.0%)0 (0.0%)2 (4.3%)4.310.116
      No50 (100.0%)50 (100.0%)45 (95.7%)
      Endocrine treatment at T1 yes/no (No. %)142Yes28 (58.3%)31 (64.6%)34 (73.9%)2.550.28
      No20 (41.7%)17 (35.4%)12 (26.1%)
      Endocrine treatment at T2 yes/no (No. %)137Yes34 (70.8%)34 (75.6%)34 (77.3%)0.540.762
      No14 (29.2%)11 (24.4%)10 (22.7%)
      Type of surgery (No. %)146 (n = 2 missing)Breast conserving31 (62.0%)25 (51.0%)24 (51.1%)3.660.454
      Mastectomy h18 (36.0%)22 (44.9%)23 (48.9%)
      Axillary/sentinel lymph node dissection1 (2.0%)2 (4.1%)0 (0.0%)
      Notes. (a) M = mean; SD = standard deviation. (b) Education level low: 1 = did not finish primary school, 2 = finished primary school, 3 = did not finish secondary school, middle: 4 = finished secondary school, low level, 5 = finished secondary school, medium level, high: 6 = finished secondary school, highest level, and/or college degree, 7 = university degree.
      • Van der Elst WIM
      • van Boxtel MP
      • van Breukelen GJ
      • Jolles J.
      Rey's verbal learning test: normative data for 1855 healthy participants aged 24–81 years and the influence of age, sex, education, and mode of presentation.
      • Verhage F.
      Intelligence and Age: Research on Dutch People Aged Twelve to Seventy-Seven Years Old.
      (c) ‘Other’ includes: PAC, AC, AC/Carboplatin/PAC, FEC/Xeloda(capecitabine)/PAC, DOC, AC/DOC. PAC = Paclitaxel; AC = Doxorubicin/Cyclophosphamide; FEC = 5-Fluorouracil/Epirubicin/Cyclophosphamide, DOC = Docetaxel; TAC = Docetaxel/Doxorubicin/Cyclophosphamide. (d) ‘Menopausal status unknown’ includes for example no menstruation due to Mirena or continuous use of oral contraceptives. (e) Nineteen patients completed the baseline survey on the same day as they received their first cycle or after receiving their first cycle of chemotherapy. These patients were equally distributed across groups (χ2(2,148) = 0.067, P = .967). (f) At T1, six patients were not yet finished with chemotherapy. For these patients, time since chemotherapy completion was coded as ‘0’ months. (g) At T2, one patient was not yet finished with chemotherapy. For this patient, time since chemotherapy completion was coded as ‘0’ months. (h) ‘Mastectomy’ also includes a mastectomy following breast conserving surgery. * < .05. ** < .01. *** < .001.
      Differences in mean change scores over time between treatment and control groups were accompanied by standardized effect sizes (ES) calculated based on the t-test statistic: (2*t)/(√degrees of freedom). Effect sizes of 0.2 were considered small, 0.5 moderate, and 0.8 large.
      • Cohen J.
      A power primer.
      The analyses were conducted on an intention-to-treat (ITT) basis.

      Results

      Sample Characteristics at Baseline

      Figure 1 depicts patient flow and reasons for decline, not meeting inclusion criteria, withdrawal and dropout. Two-hundred-and-one patients were informed about the study and agreed to be contacted, of whom 195 could be reached. One patient did not meet the eligibility criteria and 25 declined after receiving additional information. Another 9 withdrew consent before randomization, resulting in 160 randomized patients. Another 12 withdrew from participation shortly after being randomized, but before providing post-randomization data (n = 6), and/or signing consent (n = 10), and/or reading the experimental text (n = 6 were exposed to the manipulation; n = 6 not recorded). Some patients received the survey and consent request simultaneously because of a prompt start with chemotherapy, and could start the survey and the consequent randomization procedure without having consented beforehand. In total, 148 patients agreed to participate and responded at baseline, 140 responded at T1 and 133 at T2. Table 2 shows baseline clinical and sociodemographic characteristics, as well as relevant treatment-related information and secondary outcomes at follow-ups. Follow-ups took place on average 2.46 (SD = 1.81, range = 0-11.53, median = 1.95) and 6.45 (SD = 2.88, range = 0-21.38, median = 5.68) months after chemotherapy completion, without group differences (see Table 2). Despite randomization, we found baseline group differences for education level (χ2(4,147) = 10.04, P = .040). The control group included more low-educated patients than other groups (see Table 2). Examining correlations between perceived cognitive symptoms and potential influencing factors (see S2) showed that education level was significantly correlated with symptom frequency at T1 (Pearson's r = -0.19, P = .026; all other P’s ≥ .154). Here, symptom frequency at T1 increased with higher education levels. All other potential influencing factors were equally distributed across groups and were not added to the analyses (see Table 2 and S2).

      Effects Of Group and Time on The Perceived Frequency and Severity of Cognitive Symptoms

      Table 3 shows the results from the ITT analysis and shows the difference in mean change in symptom frequency between the information and control group, and between the information + SA and control group from T0 to T1 and from T0 to T2 (see Figure 2 for a graphic representation). Results are described for the model excluding education level, because it had a better fit and effects of education level were not significant. The main effect of time (first contrast) was significant (P = .003), indicating that symptom frequency worsened from T0 to T1 for all groups, with a small effect size (ES = -0.36). No significant between-group differences in short-term change (T0-T1) were found, as all groups showed a similar short-term worsening in symptom frequency. Significant differences were found in longer-term change (T0-T2) between the control group and the information and information+SA groups (ES = -0.3, P = .021 and ES = -0.3, P = .019 respectively), where the increased experience of symptom frequency persisted for patients in both the information and information+SA groups and controls recovered almost to baseline. No other effects were observed.
      Table 3Mean Scores at Baseline, T1 And T2, And Between-Group Differences for Mixed-Effects Models of Primary Study Outcomes
      AssessmentBetween-Group Differences
      T0T1T2T0-T1T0-T2
      NMSDNMSDNMSDMean changeSEPES aMean changeSEPES
      Symptom frequency
      MOS-cog total147140133
      Control5076.2714.994769.9319.524574.8116.04
      Information5075.7315.504866.1117.154566.4420.75-2.73.2.396-0.1-7.63.3.021-0.3
      Information+SA4776.3115.624566.8118.414366.4314.79-2.53.2.440-0.1-7.83.3.019-0.3
      Symptom severity
      MDASI-cog total146140132
      Control501.521.70473.092.67442.102.01
      Information491.561.91483.602.34453.582.750.50.5.3220.11.40.5.0060.3
      Information+SA471.431.84453.242.44432.702.330.20.5.6190.10.70.5.1780.2
      Notes. Bold font indicates significant overall interaction effect between group and time and significantly different contrast (T0-T1; T0-T2). Control group is reference group. Reported are the means and standard deviations. Reported are the unadjusted models; education level was not added to the model. MOS-cog scores range from 0-100. MDASI-cog scores range from 0-10. Higher scores on the MOS-cog scale indicate a lower frequency of perceived cognitive symptoms. Higher scores on the MDASI-cog scale indicate a higher severity of perceived cognitive symptoms. * < .05, ** < .01, *** < .001.
      Abbreviations: SE = Standard Error; n = number of patients; SA = self-affirmation condition; M = mean; SD = standard deviation; ES = effect size; MOS-cog = revised Medical Outcomes Study – cognitive functioning subscale of Stewart and Ware
      • Stewart AL
      • Ware JE.
      Measuring Functioning and Well-being: the Medical Outcomes Study Approach.
      ; MDASI-cog = two items of the M. D. Anderson Symptom Inventory multiple myeloma module cf.
      • Cleeland CS
      • Mendoza TR
      • Wang XS
      • Chou C
      • Harle MT
      • Morrissey M
      • et al.
      Assessing symptom distress in cancer patients: the M. D. Anderson Symptom Inventory.
      ,
      • Jones D
      • Vichaya EG
      • Shelley Wang X
      • Williams LA
      • Shah ND
      • Thomas SK
      • et al.
      Validation of the M. D. Anderson Symptom Inventory multiple myeloma module.
      ; T0 = baseline assessment; T1 = 2.5 months after chemotherapy completion; T2 = 6.5 months after chemotherapy completion.
      (a) Effect size was calculated based on the t test statistic: (2*t)/sqrt(df).
      Figure 2
      Figure 2Interaction between experimental condition and time on (A) the perceived frequency of cognitive symptoms (MOS-cog) and (B) the perceived severity of cognitive symptoms (MDASI-cog).
      Notes. Higher frequency scores indicate less frequent cognitive symptoms. Higher severity scores indicate more severe cognitive symptoms. Y-axis for symptom frequency represents the mean of 6 MOS-cog items ranging from 0 to100. Y-axis for symptom severity represents the mean of two MDASI-cog items ranging from 0 to 10. Bars represent Standard Errors. Abbreviations: SA = self-affirmation; C = control condition; Info = information condition; Info+SA = information+SA condition; T0 = baseline assessment; T1 = 2.5 months after chemotherapy completion; T2 = 6.5 months after chemotherapy completion.
      For symptom severity, we report on the model without education level because of its better fit and because there was no significant correlation with (all P’s ≥ .319) or effect of education level. Also, for symptom severity the main effect of time (first contrast) was significant (ES = 0.54, P < .001), but again no significant between-group differences in short-term change were found, as all groups showed a similar short-term increase in symptom severity. Significant differences were found in longer-term change between controls and informed patients (ES = 0.3, P = .006), where informed patients remained increased in their symptom severity and controls recovered towards baseline (Table 3, Figure 2). No significant differences were found in longer-term change between controls and the information+SA group (ES = 0.2, P = .178). The level of symptom severity in the information + SA group followed a similar path as in the control group, and the information + SA group partially but significantly recovered towards baseline. No other effects were found.

      Discussion

      This study suggests that no AIEs or self-affirmation effects on perceived cognitive symptoms occurred at 2.5 months after chemotherapy. In line with previous studies eg,
      • Janelsins MC
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      Cognitive complaints in survivors of breast cancer after chemotherapy compared with age-matched controls: an analysis from a nationwide, multicenter, prospective longitudinal study.
      the perceived frequency and severity of cognitive symptoms increased shortly after chemotherapy. This short-term worsening of perceived symptoms occurred in all groups, independent of the kind of information patients received before chemotherapy-initiation. Informed and self-affirmed patients showed a similar short-term increase in perceived symptoms as controls. However, at longer-term, this initial increase in perceived symptom frequency and severity persisted for the information group, while controls recovered. This suggests that AIEs on both symptom frequency and severity occurred at 6.5 months post-chemotherapy and that they can develop over time, even when not present initially. This adds to previous cross-sectional studies demonstrating immediate AIEs on (perceived) cognitive symptoms in subgroups of cancer patients years after treatment.
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      The influence of priming and pre-existing knowledge of chemotherapy-associated cognitive complaints on the reporting of such complaints in breast cancer patients.
      • Schagen SB
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      Moreover, it stresses the importance of investigating the course of AIEs over time. Others already suggested that the influence of pre-treament expectations and nocebo/placebo information on patient-reported outcomes and side-effects can last up until years after treatment,eg,
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      ,
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      Is it best to expect the worst? Influence of patients' side-effect expectations on endocrine treatment outcome in a 2-year prospective clinical cohort study.
      sometimes with increasing expectation effects over time when negative pre-treatment expectations are confirmed by the experience of high initial side-effects.
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      Is it best to expect the worst? Influence of patients' side-effect expectations on endocrine treatment outcome in a 2-year prospective clinical cohort study.
      Although with much shorter follow-ups, AIEs also developed over time in an 8-day study comparing pain ratings of participants who were informed about increased pain to no-information controls.
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      • May A.
      Insular cortex activity is associated with effects of negative expectation on nociceptive long-term habituation.
      Ratings were identical until day 5, but then habituated and reduced for controls, while they remained constant for informed participants.
      Against expectations, self-affirmation did not reduce AIEs on the longer-term increased experience of symptom frequency, which persisted for both the informed and self-affirmed patients, while controls recovered. However, symptom severity followed a similar path from baseline to T2 for self-affirmed patients and controls, indicating that patients who self-affirmed partially, but significantly recovered towards baseline. This suggests that informing patients about CRCS before treatment may elicit a certain self- or identity threat, resulting in a persisted increase in the experience of symptom frequency and severity at 6.5 months post-chemotherapy, that can be partially reduced for symptom severity at T2 by inviting patients to restore their self-integrity at baseline. Future studies should investigate why AIEs and self-affirmation effects only occurred after a significant time delay. In line with our findings, a recent unpublished trial in gastrointestinal cancer patients found positive effects of a nocebo education intervention on the experienced chemotherapy-related adverse events at 12-weeks follow-up, but not yet at 10-days follow-up.
      • Michnevich TL
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      Preventing adverse events of chemotherapy by educating patients about the nocebo effect (RENNO study)–study protocol of a randomized controlled trial with gastrointestinal cancer patients.
      It should also be examined why self-affirmation did not reduce the observed AIEs on symptom frequency at T2. Self-affirmation reduces stress,
      • Dutcher JM
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      • Levine JM
      • et al.
      Neural mechanisms of self-affirmation's stress buffering effects.
      and its benefits are thought to mostly occur when the domain under threat is important to the individual
      • Sherman DK
      • Cohen GL.
      The psychology of self-defense: self-affirmation theory.
      and the threatening information is personally relevant.
      • MCQueen A
      • Klein WMP.
      Experimental manipulations of self-affirmation: a systematic review.
      Potentially, symptom severity is a greater indication of the experienced symptom burden or distress and of the extent to which patients value their cognition than the prevalence of symptoms. In symptom research, frequency and severity are generally considered and included as separate but often related components of the symptom experience eg,
      • Portenoy RK
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      • McCarthy Lepore J
      • Friedlander-Klar H
      • Kiyasu E
      • et al.
      The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress.
      but empirical study of their distinction,
      • Schneider S
      • Stone AA.
      Distinguishing between frequency and intensity of health-related symptoms from diary assessments.
      or the degree to which these and other components contribute to overall distress is limited.
      • Kirkova J
      • Walsh D
      • Rybicki L
      • Davis MP
      • Aktas A
      • Jin T
      • et al.
      Symptom severity and distress in advanced cancer.
      Hypothetically, some components contribute to distress more than others and are therefore more sensitive to stress-reducing interventions.
      Consistent with the suggestion that patients who expect cancer-related stigmatization are vulnerable to AIEs,
      • Jacobs W
      • Das E
      • Schagen SB.
      Increased cognitive problem reporting after information about chemotherapy-induced cognitive decline: the moderating role of stigma consciousness.
      ,
      • Brown RP
      • Pinel EC.
      Stigma on my mind: individual differences in the experience of stereotype threat.
      ,
      • Jacobs W
      • Schagen SB
      • Thijssen M
      • Das E.
      Preventing adverse information effects on health outcomes: a self-affirmation intervention reduced information-induced cognitive decline in gastrointestinal cancer patients.
      our findings demonstrate that it may be worthwhile to further examine the role of identity processes in AIEs, and to translate psychological constructs such as self-affirmation to the clinical context. Others already have suggested to interconnect the separate but overlapping research fields of social expectancies (stereotype threat) and treatment expectations in clinical contexts,
      • Schwarz KA
      • Pfister R
      • Büchel C.
      Rethinking explicit expectations: connecting placebos, social cognition, and contextual perception.
      but thus far this integration had not occurred.
      Our findings stress the importance of examining the impact of interventions over time, and on various outcomes. Interventions that were effective in laboratory settings need to be translated to actual clinical practice. Further exploration of textual self-affirmations in reducing AIEs seems valuable. They may be more suitable for clinical practice than traditional procedures and can for example be added to patient leaflets. Although beneficial effects of non-textual self-affirmations on health-related variables have been widely established eg,
      • Creswell JD
      • Lam S
      • Stanton AL
      • Taylor SE
      • Bower JE
      • Sherman DK.
      Does self-affirmation, cognitive processing, or discovery of meaning explain cancer-related health benefits of expressive writing?.
      ,
      • Yildirim M
      • Gulsoy H
      • Batmaz M
      • Ozgat C
      • Yesilbursali G
      • Aydin R
      • et al.
      Symptom management: the effects of self-Affirmation on chemotherapy-related symptoms.
      studies using text-incorporated self-affirmations are scarce; the best form, timing, target group, and aimed outcomes need to be further explored.
      Although effects may be small in magnitude, doctors should be attentive for AIEs when informing patients about side-effects and should be aware that such AIEs may develop after some time. It is too early for specific recommendations about when and how to communicate about CRCS in clinical practice, but inviting patients to self-affirm immediately before or after discussing CRCS may reduce some of the potential AIEs.eg,
      • Cohen GL
      • Sherman DK.
      The psychology of change: self-affirmation and social psychological intervention.
      Self-affirmations are brief and could be feasibly implemented in a clinical setting, while maintaining informed consent. Although the translation to clinical practice needs further investigation, clinicians could for example use verbal self-affirmation strategies when discussing symptoms with patients, add written self-affirmations to leaflets, or educate patients about AIEs
      • Evers AWM
      • Colloca L
      • Blease C
      • Gaab J
      • Jensen KB
      • Atlas LY
      • et al.
      What should clinicians tell patients about placebo and nocebo effects? Practical considerations based on expert consensus.
      and self-affirmation. Patients could self-affirm during doctor-patient interactions, or could request less or more tailored information in trying to minimize AIEs.

      Limitations

      All patients received standard care by a variety of health care providers. Therefore, information about possible side-effects may have differed between patients, and patients may or may not have been informed about CRCS by their health care provider. However, pre-existing knowledge about CRCS recorded at T2 did not differ across groups.
      Patients received the experimental information online, rather than during a medical consultation. Surveys were completed independently online at a self-chosen moment. Consequently, there was limited control over the setting and timing of survey completion and follow-up assessments were regularly delayed, resulting in greater intervals between assessments than planned.
      Because patient accrual lagged significantly behind expected numbers in the protocol the aimed intervals between surveys were shortened and sample size calculations were adapted. The adapted power calculation was based on a prior study on AIEs in breast cancer patients with effect sizes of f = 0.25-0.27 (ηp2 = 0.06-0.07),
      • Jacobs W
      • Das E
      • Schagen SB.
      Increased cognitive problem reporting after information about chemotherapy-induced cognitive decline: the moderating role of stigma consciousness.
      an alpha of 0.05, a power of 0.80, and 3 groups, and showed that 132-156 patients were needed for the main effects in the present 3×3 mixed design. Our sample was sufficient to assess main effects of the experimental manipulation, and within-subjects effects of time.
      Twelve patients withdrew from participation shortly post-randomization and were excluded. They were equally distributed across conditions. All other available data was analyzed according to the ITT principle.
      Further, the occurrence and strength of the self-affirmation effects may have been influenced by the timing of the intervention. Our self-affirmation intervention was presented to patients directly after instead of prior to the threatening information about CRCS. Previous research on the contextual factors that influence the effectiveness of self-affirmation showed that for self-affirmation to be effective, the intervention should be placed in temporal proximity to a psychological threat, that is soon before it occurs or as it takes place,
      • Ferrer RA
      • Cohen GL.
      Reconceptualizing self-affirmation with the trigger and channel framework: lessons from the health domain.
      so before the start of a defensive response.
      • Critcher CR
      • Dunning D
      • Armor DA.
      When self-affirmations reduce defensiveness: timing is key.
      Affirming after a threat attenuated defensiveness only if the defensive conclusion was not yet reached.
      • Critcher CR
      • Dunning D
      • Armor DA.
      When self-affirmations reduce defensiveness: timing is key.
      So potentially, affirming before instead of after the information about CRCS will have had a greater effect on symptom severity, and may not have resulted in null-effects for symptom frequency.

      Future Directions

      Despite our study's shortcomings and small effect sizes, we showed that relatively small variations in the written pre-treatment information presented to patients only once and not even by their own doctor may have a significant effect on patients' perceived cognitive symptoms in the future, and that adding a brief written self-affirmation may partially reduce such longer-term AIEs on symptom severity. Strengths of this study are the longitudinal design, the inclusion of a standard-information control group, and the interdisciplinary approach.
      In future, AIEs could be studied in an offline face-to-face setting, more closely resembling the natural procedure of informing patients about side-effects. This will also enable more control over the setting and timing of survey completion. Post-randomization exclusions need to be avoided, although they may be legitimate under certain circumstances even in ITT trials, for example when patients were not exposed to the intervention.
      • Fergusson D
      • Aaron S
      • Guyatt G
      • Hebert P.
      Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis.
      Comparable to other cancer and cognition studies, healthy and no-chemotherapy controls could be included to determine (clinically) significant cognitive impairment, and to clarify previous mixed findings regarding chemotherapy experience as a risk factor for AIEs.
      • Schagen SB
      • Das E
      • van Dam FSAM.
      The influence of priming and pre-existing knowledge of chemotherapy-associated cognitive complaints on the reporting of such complaints in breast cancer patients.
      ,
      • Schagen SB
      • Das E
      • Vermeulen I.
      Information about chemotherapy-associated cognitive problems contributes to cognitive problems in cancer patients.
      It could be worthwhile to compare AIEs and perceived symptoms between neoadjuvant and adjuvant groups. Being informed about CRCS directly after diagnosis, a time known to be especially stressful,
      • Hermelink K
      • Untch M
      • Lux MP
      • Kreienberg R
      • Beck T
      • Bauerfeind I
      • et al.
      Cognitive function during neoadjuvant chemotherapy for breast cancer.
      may have a different impact on symptom reporting and the occurrence of AIEs than receiving such information at a later time. In addition, patients were assessed during or shortly after the treatment phase. Longer-term post-treatment measurements could indicate whether effects persist or normalize beyond the treatment phase.
      Finally, as the effects of self-affirmation depend on the context in which the intervention is introduced, and the intervention will be most (or only) effective when certain key conditions such as ‘timeliness’ are met,
      • Ferrer RA
      • Cohen GL.
      Reconceptualizing self-affirmation with the trigger and channel framework: lessons from the health domain.
      future studies with optimized designs should further explore under what conditions and at what times self-affirmation is (most) effective in attenuating AIEs on breast cancer patients’ perceived cognitive symptoms. For example, it could be explored whether self-affirming prior to the information about CRCS can also attenuate AIEs on longer-term symptom frequency,
      • Ferrer RA
      • Cohen GL.
      Reconceptualizing self-affirmation with the trigger and channel framework: lessons from the health domain.
      and whether repeated self-affirmations throughout the (post) treatment period are more effective than affirming only once before the start of chemotherapy.
      • Binning KR
      • Cook JE
      • Greenaway VP
      • Garcia J
      • Apfel N
      • Sherman DK
      • et al.
      Securing self-integrity over time: self-affirmation disrupts a negative cycle between psychological threat and academic performance.
      Also other factors that potentially could influence the effectiveness of the intervention could be studied, such as the form and content of the intervention
      • Iles IA
      • Gillman AS
      • Ferrer RA
      • Klein WM.
      Self-affirmation inductions to reduce defensive processing of threatening health risk information.
      and at what times throughout the (post) treatment period they should be delivered to patients, such as at periods of heightened stress or key transition points.
      • Easterbrook MJ
      • Harris PR
      • Sherman DK.
      Self-affirmation theory in educational contexts.

      Conclusion

      Providing breast cancer patients with additional written information about potential CRCS before the start of chemotherapy has a small but significant adverse impact on their perceived frequency and severity of cognitive symptoms at 6.5 months after chemotherapy, but not at 2.5 months post-chemotherapy. An additional self-affirming paragraph can attenuate the longer-term AIEs for the perceived severity, but not the frequency of symptoms. AIEs appeared to develop over time and a brief psychological intervention may be useful in reducing some but not all AIEs on breast cancer patients' perceived cognitive symptoms in clinical practice. Randomized prospective studies need to further investigate to what extent pre-treatment information about CRCS has a temporary or longer-lasting influence on patients’ future (perceived) symptoms, and how such potential AIEs can be reduced. With the numbers of breast cancer survivors increasing
      • Maajani K
      • Jalali A
      • Alipour S
      • Khodadost M
      • Tohidinik HR
      • Yazdani K.
      The global and regional survival rate of women with breast cancer: a systematic review and meta-analysis.
      and patients expressing the need for pre-treatment information about CRCS,
      • Myers JS.
      Chemotherapy-related cognitive impairment: the breast cancer experience.
      it will be all the more important for doctors to look for ways to communicate about CRCS without adding to the problem while upholding informed consent.
      Textbox 1. Experimental texts at baseline (translated from Dutch).
      1. General introduction text, seen by all experimental conditions at baseline
      We welcome you to the first questionnaire of the CONTEXT study. This questionnaire is about experiences during the treatment of cancer. We would like to ask you to complete this questionnaire. It will approximately take 30 minutes in total to complete the survey, but you can take as much time as you want. We would like to ask you to complete the survey in one go. In approximately 6 and 12 months, we will contact you again to complete a similar questionnaire. For more information about this study please read the study's patient information leaflet or contact the research coordinator < name, contact details >. Your data will be treated confidentially and anonymously. When you click on the ‘next’ button, you will first see a short text. Please read this text carefully. After that, the survey will start. Thank you very much for your cooperation.
      2a. Information condition
      For many people who are treated for cancer, chemotherapy is an important part of their treatment. Chemotherapy can have several side-effects, such as cognitive problems. Cognitive problems (thinking problems) are for instance memory- or concentration difficulties. We know from experience that some people have cognitive problems during or after chemotherapy. Research shows that chemotherapy can lead to changes in the brain. These changes can cause concentration- and memory problems and can lead to a reduced information processing speed. The goal of this study is to learn more about the relationship between chemotherapy and cognitive problems. This study is important in order to being able to prevent such problems in future.
      2b. Patients in the information+SA condition received the exact same first 2 paragraphs as patients in the information condition, but for the information+SA group the following paragraph was added
      Not everyone experiences cognitive problems. Many individuals have a good memory- and concentration ability after chemotherapy. In addition, every individual has a unique combination of talents and gains satisfaction from things that are important to him or her, such as spending time with family and children, friends, political involvement, enjoying nature, indulging your passion in hobbies and sports, expanding one's knowledge of art or religion. Every individual is unique.
      2c. Control condition
      Individuals who are treated for breast cancer have different experiences before, during and after treatment. The goal of this study is to learn more about these experiences. It is important to study which experiences people have and how these experiences change over time, in order to being able to better support and help individuals who are treated for breast cancer in the future.
      Note. See S1 for all original texts in Dutch.

      Clinical Practice Points

      Previous studies showed that breast cancer patients who were informed about potential chemotherapy-related cognitive symptoms (CRCS), subsequently indicated more cognitive symptoms in daily life and showed lower verbal memory performance compared to uninformed patients, irrespective of age, education level or negative affect.
      • Jacobs W
      • Das E
      • Schagen SB.
      Increased cognitive problem reporting after information about chemotherapy-induced cognitive decline: the moderating role of stigma consciousness.
      • Schagen SB
      • Das E
      • van Dam FSAM.
      The influence of priming and pre-existing knowledge of chemotherapy-associated cognitive complaints on the reporting of such complaints in breast cancer patients.
      • Schagen SB
      • Das E
      • Vermeulen I.
      Information about chemotherapy-associated cognitive problems contributes to cognitive problems in cancer patients.
      The findings of the current multicentre, randomized, longitudinal study show how merely informing first-time breast cancer patients about potential cognitive side effects of chemotherapy before treatment may add to the experience of these side effects at 6.5 months after chemotherapy. Moreover, adding a self-affirmation intervention to the information attenuated such longer-term Adverse Information Effects (AIEs) for the perceived severity of symptoms. Although effects were small in magnitude, doctors and patients should be aware of AIEs when informing or being informed about side-effects and should know that such AIEs may develop after some time. It is too early for specific recommendations about when and how to communicate about CRCS in clinical practice, but using self-affirmation techniques when discussing CRCS may be an effective way to reduce some of the potential AIEs. Self-affirmation interventions are brief and could be feasibly implemented in a clinical setting while maintaining informed consent. In future, the best form, timing, target group, and aimed outcomes of such interventions need to be further investigated.

      Ethics approval and consent to participate

      The Institutional Review Board of the Netherlands Cancer Institute served as the central ethical committee for all participating institutes and approved the study (PTC13.0541-M13WEL-NL43939.031.13). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All participants provided written informed consent.

      Data availability

      The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

      Acknowledgements

      The authors thank all patients for their participation. We thank M. Temizkan, MD, R.M. Bijlsma, MD, A. L.Th. Imholz, MD, PhD, R.J.B. Blaisse MD, W.A. van der Steeg MD, PhD, E. Siemerink, MD, PhD, Q.C. Rossum-Schornagel MD, for their contributions to the patient recruitment. We thank all physicians and nurses of the Netherlands Cancer Institute, St. Antonius Hospital, Meander Medical Center, Hospital Group Twente, Isala Hospital, Deventer Hospital, Rijnstate Hospital, Fransiscus Gasthuis & Vlietland, St. Jansdal Hospital and University Medical Center Utrecht, for providing patients for this study. We thank K. Jóźwiak PhD for the statistical advice. We thank W. Stoop for developing the first part of the online survey. We thank L. van Lent and the other research assistants for helping with data collection.

      Funding

      This work was supported by the Dutch Cancer Society (KWF) [grant number: 2016-8178] and the Dutch Pink Ribbon Foundation [grant number: 2011.WO34.C123 ]. The funding sources had no involvement in the study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.

      Disclosure

      The authors have stated that they have no conflicts of interest.

      CRediT authorship contribution statement

      Wendy Jacobs: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Project administration. Sanne B. Schagen: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition. Susanne M. Brouwer: Formal analysis, Writing – review & editing. Jacobien M. Kieffer: Formal analysis, Writing – review & editing. Inge O. Baas: Resources, Writing – review & editing. Maartje Los: Resources, Writing – review & editing. Gabe S. Sonke: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Funding acquisition. Enny Das: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition.

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