Skip to main content

Treatment Satisfaction and Adherence to Oral Chemotherapy in Patients With Cancer

Publication: Journal of Oncology Practice

Abstract

Purpose:

Although patients with cancer overwhelming prefer oral to intravenous chemotherapy, little is known about adherence to oral agents. We aimed to identify the rates and correlates of adherence in patients with diverse malignancies.

Materials and Methods:

Ninety patients with chronic myeloid leukemia or metastatic renal cell carcinoma, non–small-cell lung cancer, or breast cancer enrolled in this prospective, single-group, observational study of medication-taking behaviors. Adherence was measured via self-report and with an electronic pill cap (Medication Event Monitoring System cap). Patients completed surveys regarding symptom distress, mood, quality of life, cancer-specific distress, and satisfaction with clinician communication and treatment at baseline and 12-week follow-up.

Results:

As measured by the Medication Event Monitoring System, patients took, on average, 89.3% of their prescribed oral chemotherapy over the 12 weeks. One quarter of the sample was less than 90% adherent, and women were more adherent than men (mean difference, 9.59%; SE difference, 4.50%; 95% CI, −18.65 to −0.52; P = .039). Improvements in patient symptom distress (B = −0.79; 95% CI, −1.41 to −0.18), depressive symptoms (B = –1.57; 95% CI, –2.86 to –0.29), quality of life (B = 0.38; 95% CI,0.07 to 0.68), satisfaction with clinician communication and treatment (B = 0.73; 95% CI, 0.49 to 0.98), and perceived burden to others (B = −1.28; 95% CI, −2.20 to −0.37) were associated with better adherence. In a multivariate model, improved treatment satisfaction (B = 0.71; 95% CI, 0.48 to 0.94) and reduced perceived burden (B = −0.92; 95% CI, −1.76 to −0.09) were the strongest indicators of better adherence.

Conclusion:

Women and patients who reported increased treatment satisfaction and reduced burden to others were more adherent to oral chemotherapy. Interventions that help patients improve communication with clinicians and reduce burden may optimize oral chemotherapy adherence.

Introduction

The use of oral chemotherapy has revolutionized oncology care, not only in prolonging survival but also in improving quality of life (QOL) and reducing care burden for patients.1 However, home administration of treatment presents challenges for patients and clinicians with regard to maintaining patient safety, monitoring toxicities, and ensuring adherence.2,3 Adherence (taking medications as prescribed) is assumed to be a necessary component of achieving successful outcomes.4 Despite patient preference for oral chemotherapy relative to intravenous infusion, preliminary evidence suggests that adherence rates are variable and less understood.3,5,6
Poor adherence is concerning because of the diminished therapeutic efficacy of chemotherapy with lower exposure to drug,7 influencing risk for recurrence and mortality.8-10 Ineffective patient-clinician communication,11,12 increased use of health care resources,13 higher outpatient visits and hospitalization rates, and longer inpatient stays14-16 are associated with suboptimal adherence to medication in general. Additionally, a number of patient factors (eg, age, beliefs about medication, mental health), disease factors (eg, comorbidities, disease severity), treatment factors (eg, adverse effects, toxicities), and system factors (eg, relationship with clinician, medication cost) are associated with poor adherence.6,17,18 Specifically, patients with depression15,19 or greater adverse effects20,21 are less adherent to treatment, and lower adherence has been observed in situations in which the physician dominates discussions.22,23 More work is needed to identify correlates of nonadherence to oral chemotherapy specifically.
The need to address poor adherence has increasing significance as the development and use of oral agents in cancer treatment rise. However, interventions to improve adherence have been limited in theoretic rationale and methodologic rigor.6 The identification of patient, disease, treatment, and system factors that are potentially modifiable is necessary to guide intervention development. Furthermore, understanding these factors in the context of a longitudinal study may clarify the relationships. The aims of this study were to determine the rates of adherence to oral chemotherapy in a sample of adult patients with diverse malignancies and identify salient patient-, disease-, treatment-, and system-related correlates of adherence.

Materials and Methods

Study Design

From March 8, 2011, to November 5, 2012, patients receiving oral anticancer therapy for chronic myeloid leukemia (CML) or for metastatic renal cell carcinoma, non–small-cell lung cancer, or breast cancer participated in a single-arm, prospective, longitudinal study of medication-taking behaviors. Patients were recruited from the outpatient clinic at a cancer center at a major medical center in Boston, Massachusetts. The study was approved by the institutional review board of the hospital.

Participants

Eligible patients had been prescribed an oral tyrosine kinase inhibitor chemotherapy, endocrine therapy, or capecitabine at least 2 weeks before enrollment (Appendix, online only), had Eastern Cooperative Oncology Group performance status ≤ 2,24 were ≥ 18 years of age, and were able to read and respond to questions in English. The 2-week period gave patients time to stabilize on their medication and reduced the risk of dropout related to discontinuation of oral chemotherapy as a result of adverse effects. Patients with comorbid delirium, dementia, or active and untreated psychotic, bipolar, or substance-dependence disorders were not eligible because of their inability to consent.

Procedure

Study staff screened for eligibility with the electronic health record (EHR) and obtained permission from oncology clinicians to approach eligible patients during clinic visits or by telephone. Interested and eligible patients signed written informed consent in person. Participants completed self-report measures at enrollment (baseline) and 12-week follow-up (postassessment) using paper-based questionnaires or the Health Insurance Portability and Accountability Act–compliant Research Electronic Data Capture survey tool.25 At baseline, participants received an electronic pill cap and bottle to monitor adherence throughout the 12 weeks. Participants received financial reimbursements for each assessment ($100 total).

Measures

Sociodemographics

Participants reported sociodemographic information such as age, sex, health insurance, pharmacy benefits, and out-of-pocket monthly oral chemotherapy costs. We collected information about cancer, clinical characteristics, and oral chemotherapy type from the EHR.

Objective adherence monitoring

All participants received the Medication Event Monitoring System26 pill cap and bottle (MEMSCap) to store medication, which electronically records the date and time of bottle openings. A rate of adherence was operationalized as the percentage of prescribed pills taken4: number of cap openings compared with number of expected openings during the study. For patients on interval dosing schedules, as opposed to continuous (eg, daily), we reviewed the EHR to verify scheduled medication breaks so that patients were not penalized on off-cycle days. Although no consensus exists regarding adequate adherence, investigators use 80% to 95%27 as achievable and acceptable. For this study, we examined adherence rates using a 90% threshold.8,28

Self-reported adherence

Self-reported adherence was assessed with the Adherence subscale of the Cancer Therapy Satisfaction Questionnaire (CTSQ).29 The CTSQ is a 21-item measure that evaluates beliefs about medical care and aspects of nonadherence. Patients are asked to rate items such as how often did you “have trouble remembering to take your cancer therapy pills” on a scale of 1 (never) to 5 (always). The CTSQ has good psychometric properties for use in patients with cancer30 and demonstrated good reliability in our sample (α = .82).

Cancer symptoms and treatment adverse effects

Distress related to adverse effects was assessed using the Symptom Distress Scale, a validated 13-item instrument that measures the frequency and intensity of cancer-related symptoms, including fatigue, pain, nausea, and insomnia.31 The Symptom Distress Scale demonstrated good reliability in our sample (α = .80).

Mood

Anxiety and depressive symptoms were measured using the Hospital Anxiety and Depression Scale, a valid and reliable 14-item measure that has been used extensively in oncology32 and has strong psychometric properties.33 The Hospital Anxiety and Depression Scale showed strong reliability in our sample (α = .85).

QOL

The Functional Assessment of Cancer Therapy–General (FACT-G) was administered to assess four domains of QOL: physical, social/family, emotional, and functional. The FACT-G is a validated 27-item instrument that has been used with patients diagnosed with various cancer types.34 The scale demonstrated strong reliability in our sample (α = .90).

Cancer-specific psychological distress

Cancer-specific psychological distress was assessed with the Cancer Worries Inventory (CWI), a 24-item measure that evaluates distress using five subscales: death and dying, perceived burden to friends and family, spirituality, chemotherapy, and treatment. The CWI has good psychometric properties35 and showed strong reliability in our sample (α = .92).

Satisfaction with clinician communication and treatment

The Functional Assessment of Chronic Illness Therapy–Treatment Satisfaction–Patient Satisfaction (FACIT-TS-PS) was administered to assess patients’ satisfaction with clinician communication and treatment. This 33-item scale assesses patients’ perceived confidence and trust in their physicians and nurses, ability to communicate and participate in decision making, and thoroughness of explanations received by clinicians (Appendix).36 This assessment has been validated for use in cancer37 and showed strong reliability in our sample (α = .90).

Statistical Analyses

Patient characteristics were described with measures of central tendency using SPSS software (version 22.0; SPSS, Chicago, IL). We operationalized adherence rate as the percentage of medication taken as prescribed, including thresholds for the proportion of participants who were less than 90% adherent over the course of the study (poor adherence). The relationship between the MEMSCaps adherence rate and self-reported CTSQ adherence was assessed using bivariate Pearson product-moment correlation coefficients. Differences in adherence rates based on patient characteristics were examined using independent samples t tests and one-way analyses of variance.
We examined whether changes from baseline to 12 weeks in patient-reported symptom distress, mood symptoms, QOL, cancer-specific distress, and satisfaction with clinician communication and treatment were associated with MEMSCaps adherence rates. Linear regressions were conducted to investigate whether a change in each variable was associated with adherence, interpreting the unstandardized B coefficients. Estimates (with 95% CIs) were considered statistically significant based on a two-sided α of .05. A final multivariable model was tested including significant predictors of adherence to determine the degree of association of each variable change with adherence over and above the effects of the other variables. Previous power calculations used incremental R2 (small, F2 = 0.02; medium, F2 = 0.15; large, F2 = 0.35) and indicated 80% power to detect a medium effect size (F2 = 0.17) in incremental R2 for each predictor with 80 participants, six predictors, and an α of .05.

Results

Patient Characteristics

A total of 90 patients participated (CML, n = 17 [18.9%]; metastatic renal cell carcinoma, n = 25 [27.8%]; metastatic non–small-cell lung cancer, n = 24 [26.7%]; and metastatic breast cancer, n = 24 [26.7%]). Demographic and treatment-related characteristics are summarized in Table 1. Participants were on average 58 years of age and were approximately evenly distributed by sex, with slightly more women. A majority were white, not Hispanic, at least college educated, and partnered. More than half were retired, unemployed, or on disability. All participants had health insurance, and most had a pharmacy benefit that covered all or part of their oral chemotherapy expenses.
Table 1. Patient Demographic and Clinical Characteristics (N = 90)

Adherence and Correlates

Adherence measured by MEMSCaps was available for 82 participants. Reasons for missing MEMSCaps data were: taken off of oral chemotherapy (n = 4), MEMSCap malfunction (n = 1), lost to attrition (n = 1), died (n = 1), and lost to follow-up (n = 1). No differences were found between participants who had MEMSCaps data and those who did not (all P > .05). Mean MEMSCaps adherence was 89.3% (standard deviation [SD], 19.1%), with a median of 96.9%. Perfect adherence (100% taken) was observed in only 24.4% of the sample (n = 22). Using 90% as a cutoff for poor adherence, 25.6% (n = 23) were poorly adherent. For participants who were poorly adherent using this cutoff, mean adherence was 67.8% (SD, 25.5%). The proportion of patients by cancer type who were less than 90% adherent is illustrated in Figure 1. No patients took more than the prescribed amount of medication.
Fig 1. Proportion of patients with poor adherence by cancer type (poor adherence defined as < 90% adherent). NOTE. Percentages are out of total number of participants with available Medication Event Monitoring System data (n = 82).
MEMSCaps adherence was associated with subjective adherence on the patient-reported CTSQ-Adherence subscale at baseline- (r = 0.34; P = .002) and postassessment (r = .45; P < .001). Significant differences were found in adherence based on sex, such that women (mean, 93.48%; SD, 10.8%) had greater adherence than men (mean, 83.90%; SD, 25.25%; mean difference, 9.59%; SE difference, 4.50%; t (45.10) = −2.13; 95% CI, −18.65 to −0.52; P = .039). There was a marginal, nonsignificant difference in adherence in patients on a continuous (mean, 92.15%; SD, 14.79%) versus those on a interval schedule (mean, 82.31%; SD, 25.83%; mean difference, 9.85%; SE difference, 5.62%; t (29.45) = 1.75; 95% CI, −1.64 to 21.33; P = .090). No differences in adherence were found for other baseline or patient characteristics.

Adherence and Changes in Symptom Distress, Mood, QOL, Burden, and Treatment Satisfaction

Participants who reported reduced symptom distress had greater MEMSCaps adherence; for every 0.79-point reduction in symptom distress, there was a 1% increase in adherence (unstandardized B = −0.79; SE, 0.31; 95% CI, −1.41 to −0.18; P = .012; R2 = 0.09). Reductions in depressive symptoms were also associated with greater adherence (B = −1.57; SE, 0.64; 95% CI, −2.86 to −0.29; P = .017; R2 = 0.08); however, changes in anxiety were not associated with adherence (B = −0.92; SE, 0.69; 95% CI, −2.29 to 0.46; P = .188; R2 = 0.02). On the FACT-G, those who reported increased QOL had better adherence (B = 0.38; SE = 0.15; 95% CI, 0.07 to 0.68; P = .015; R2 = 0.08). On the CWI, participants who reported reduced perceived burden to friends and family had greater adherence (B = −1.28; SE, 0.46; 95% CI, −2.20 to −0.37; P = .006; R2 = 1.0). Changes in other CWI subscales were not associated with adherence. Finally, participants who reported improved satisfaction with clinician communication and treatment had better adherence (B = 0.73; SE, 0.12; 95% CI, 0.49 to 0.98; P < .001; R2 = 0.33). Improvements in treatment satisfaction accounted for 33% of the variance in adherence to oral chemotherapy.
A multivariable model was estimated to determine the strongest predictors of adherence. This model explained 41% of the variance in adherence (F = 11.14 [5, 67]; P < .001; Table 2). Improvements in treatment satisfaction predicted adherence above other predictors in the model (B = 0.71; SE, 0.12; 95% CI, 0.48 to 0.94; P < .001). In addition, perceived burden to family and friends remained a significant predictor of adherence above other variables (B = 0.92; SE, 0.42; 95% CI, −1.76 to −0.09; P = .03). When change in treatment satisfaction and perceived burden were included in the model, changes in symptom distress, depressive symptoms, and QOL no longer predicted adherence.
Table 2. Linear Regression Model: Change in Symptom Distress, Depressive Symptoms, Quality of Life, Worry Burden, and Treatment Satisfaction Affects Adherence With MEMSCaps

Discussion

In this study, we measured oral chemotherapy adherence using MEMSCaps and described factors associated with adherence in patients with diverse malignancies. Mean oral chemotherapy adherence was less than 90%, with nearly one quarter of the sample considered poorly adherent, missing approximately one third of doses on average. Women had better adherence than men, a pattern that has been inconsistent.38,39 Additionally, although socioeconomic status15 and social support40 may be important drivers of adherence, we did not find education, work, income, or partnership status to be associated with adherence.
Improvements in symptom distress, depressive symptoms, and QOL were associated with greater adherence in bivariable analyses but were not significant in the multivariable model beyond the effects of improvements in treatment satisfaction and perceived burden to others. Oral chemotherapy adverse effects are often challenging to manage and can be as distressing as those from intravenous chemotherapy.1 Consistent with findings that the presence of adverse effects is strongly associated with poorer adherence,20,21,38,41 patients in our study who had reductions in symptom-related distress had better adherence. Similarly, depression42,43 and negative mood44 have repeatedly been shown to negatively influence adherence; therefore, it is not surprising that patients who reported a decrease in depressive symptoms correspondingly had better adherence. Relationships between anxiety and adherence have been mixed, with minimal evidence that elevated anxiety contributes to nonadherence.45 We did not find an association between change in anxiety and adherence. Our findings that patients reporting improved QOL had better adherence are in contrast to a previous study38 in patients with CML that showed that better QOL was associated with nonadherence to imatinib. Reductions in perceived burden to patients and families were also associated with better adherence and remained a significant predictor in the multivariable model. Although home medication administration may place additional caregiving burden on loved ones, patients may help to alleviate burden by taking medications as prescribed.
Improved satisfaction with clinician communication and treatment was the most robust predictor of better adherence. Those who reported improvements in perceptions of being understood and respected by their oncologist, being involved in decisions about their health care, possessing the ability to talk to staff when they needed to, and having trust and confidence in their oncologist were more likely to be adherent. This finding parallels research highlighting the importance of the patient-clinician relationship,42,46 patient-clinician communication,17 and patient satisfaction with information received.18 It is also consistent with work showing that patients’ attitudes toward adherence are associated with their understanding of the disease and treatment.47 Although poor communication can be detrimental,48 knowledge, information, and perceived active participation in treatment are empowering for patients, likely salient factors in supporting adherence.
Our findings highlight the importance of optimizing patient-clinician communication, which would increase treatment satisfaction and, per our data, potentially increase adherence. Although patients perceive oncologists to be a primary source of support, oncologists receive minimal training in interpersonal aspects of care and communication.49 Oncology is increasingly incorporating patient-centered care models that improve interpersonal skills, underscore the importance of the relationship with the patient and family, incorporate shared decision making, and stress the role of addressing patients’ concerns, needs, and QOL.50-52
This was a single-group, nonrandomized, observational study; therefore, we cannot determine that the changes in psychological variables influenced adherence behaviors. It is possible that patients with high adherence are simply more likely to report higher satisfaction with treatment. The restricted demographic and geographic variability in our sample limits the generalizability of the findings. In addition, certain risk factors for nonadherence were not represented in this sample of mostly white, English-speaking patients at an academic institution with relatively low out-of-pocket chemotherapy costs. The limited variability in socioeconomic status at this Massachusetts hospital may account for the lack of differences in adherence by sociodemographic factors. Our sample size was not large enough to include other covariates that may affect the observed relationships. Although there is no gold standard for adherence measurement, our use of MEMSCaps is advantageous as an objective, electronic measure, minimizing the common overestimates on self-report adherence surveys.6 However, a MEMSCap opening does not confirm medication taking. Furthermore, use of MEMSCaps may positively influence adherence because of awareness of behavioral observation.53 In addition, MEMSCaps are most efficient for patients on a daily schedule. Approximately one third of our sample was prescribed an interval schedule, complicating medication monitoring. However, with thorough EHR reviews, we programmed the MEMSCap database to account for medication breaks. Despite these limitations, the strong positive correlation between the CTSQ-Adherence subscale with MEMSCaps in this sample suggests that MEMSCaps captured true adherence.
The modifiable adherence-related factors identified in this study also inform the development of a tailored intervention, suggesting that it may be helpful to incorporate strategies that enhance satisfaction with clinician communication and treatment and minimize perceived burden to others. Furthermore, adherence may be improved by symptom management to reduce distress and psychosocial interventions that aim to improve mood and QOL. Unfortunately, rigorous, prospective, randomized controlled interventions to improve oral chemotherapy adherence are lacking.6,54 Limited studies have provided preliminary evidence for a treatment monitoring program20 and a multidisciplinary pharmaceutical care program.55
Replication of our findings is a necessary next step to confirm the importance of modifying these psychological and behavioral factors. Future research could involve the development of a theoretically and empirically based intervention to target adherence-associated factors and test outcomes in a randomized controlled trial design. For example, brief interventions that optimize adherence could be disseminated with the use of e-health interventions and may improve patient outcomes. In addition, interventions could be tailored to providers, with the goal of targeting empathic listening, effectively engaging the patient in decision making, and explaining rationale for treatment and potential adverse effects. For example, a study showed that oncologists who received a CD-ROM training on empathy and communication using examples from their own previously recorded visits had increased ability to empathize, and their patients reported higher trust and greater perceived empathy than patients whose oncologists did not receive the training.56 For patients, interventions should be individually tailored to the level of desired information57 and preferences for decision making (eg, active role, guided role, shared role).58 Interventions should also be guided by patient factors that limit the ability to provide patient-centered care, such as health literacy and numeracy, comfort with assertiveness, psychological health, and comorbidities.59
In this sample of patients with diverse malignancies prescribed oral chemotherapy, improvements in symptom distress, depressive symptoms, QOL, perceived burden on friends and family, and satisfaction with clinician communication and treatment were associated with greater adherence. Increased treatment satisfaction was the most robust predictor of adherence, suggesting the need to enhance patient-clinician relationships and implement patient-centered care models in oncology to ensure optimal outcomes.

Acknowledgment

Supported by Massachusetts General Hospital Cancer Center.

Authors' Disclosures of Potential Conflicts of Interest

Treatment Satisfaction and Adherence to Oral Chemotherapy in Patients With Cancer

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/journal/jop/site/misc/ifc.xhtml.

Jamie M. Jacobs

No relationship to disclose

Nicole A. Pensak

No relationship to disclose

Nora J. Sporn

No relationship to disclose

James J. MacDonald

No relationship to disclose

Inga T. Lennes

Honoraria: Blue Cross and Blue Shield of Massachusetts
Consulting or Advisory Role: Kyruus, InfiniteMD

Steven A. Safren

Patents, Royalties, Other Intellectual Property: Oxford University Press, Guilford Press, Springer

William F. Pirl

No relationship to disclose

Jennifer S. Temel

Research Funding: Pfizer (Inst)

Joseph A. Greer

Research Funding: Pfizer (Inst)

Appendix

Types of Oral Chemotherapies

Anastrozole, exemestane, axitinib, afatinib, crizotinib, disatinib, lenvatinib, letrozole, imatinib, sorafenib, nilotinib, sunitinib, tamoxifen, erlotinib, pazopanib, and capecitabine.

Functional Assessment of Chronic Illness Therapy

The Functional Assessment of Chronic Illness Therapy–Treatment Satisfaction–Patient Satisfaction measure includes five subscales: Explanations, Interpersonal, Comprehensive Care, Nurses, and Trust. Patients respond to items such as “Did your doctor give explanations that you could understand?” “Did you have an opportunity to ask questions?” “Did you feel that the treatment staff worked together towards the same goal?” and “Did you have confidence in your doctor?” Patients rate each item on a Likert-type scale (0 = no, not at all; 1 = yes, but not as much as I wanted; 2 = yes, almost as much as I wanted; 3 = yes, and as much as I wanted). Items are summed for a total score.

References

1.
Borner M, Scheithauer W, Twelves C, et al: Answering patients’ needs: Oral alternatives to intravenous therapy. Oncologist 6:12-16, 2001 (suppl 4 )
2.
Weingart SN, Flug J, Brouillard D, et al: Oral chemotherapy safety practices at US cancer centres: Questionnaire survey. BMJ 334:407, 2007
3.
Weingart SN, Brown E, Bach PB, et al: NCCN Task Force report: Oral chemotherapy. J Natl Compr Canc Netw 6:S1-S14, 2008 (suppl 3 )
4.
Given BA, Spoelstra SL, Grant M: The challenges of oral agents as antineoplastic treatments. Semin Oncol Nurs 27:93-103, 2011
5.
Partridge AH, Avorn J, Wang PS, et al: Adherence to therapy with oral antineoplastic agents. J Natl Cancer Inst 94:652-661, 2002
6.
Greer JA, Amoyal N, Nisotel L, et al: A systematic review of adherence to oral antineoplastic therapies. Oncologist 21:354-376, 2016
7.
Bedell CH: A changing paradigm for cancer treatment: The advent of new oral chemotherapy agents. Clin J Oncol Nurs 7:5-9, 2003 (suppl )
8.
Bhatia S, Landier W, Shangguan M, et al: Nonadherence to oral mercaptopurine and risk of relapse in Hispanic and non-Hispanic white children with acute lymphoblastic leukemia: A report from the Children’s Oncology Group. J Clin Oncol 30:2094-2101, 2012
9.
Makubate B, Donnan PT, Dewar JA, et al: Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality. Br J Cancer 108:1515-1524, 2013
10.
Ganesan P, Sagar TG, Dubashi B, et al: Nonadherence to imatinib adversely affects event free survival in chronic phase chronic myeloid leukemia. Am J Hematol 86:471-474, 2011
11.
Waterhouse DM, Calzone KA, Mele C, et al: Adherence to oral tamoxifen: A comparison of patient self-report, pill counts, and microelectronic monitoring. J Clin Oncol 11:1189-1197, 1993
12.
Avorn J, Monette J, Lacour A, et al: Persistence of use of lipid-lowering medications: A cross-national study. JAMA 279:1458-1462, 1998
13.
Wu EQ, Johnson S, Beaulieu N, et al: Healthcare resource utilization and costs associated with non-adherence to imatinib treatment in chronic myeloid leukemia patients. Curr Med Res Opin 26:61-69, 2010
14.
Chan DC, Shrank WH, Cutler D, et al: Patient, physician, and payment predictors of statin adherence. Med Care 48:196-202, 2010
15.
Lebovits AH, Strain JJ, Schleifer SJ, et al: Patient noncompliance with self-administered chemotherapy. Cancer 65:17-22, 1990
16.
Barron TI, Connolly R, Bennett K, et al: Early discontinuation of tamoxifen: A lesson for oncologists. Cancer 109:832-839, 2007
17.
Back AL, Arnold RM, Baile WF, et al: Approaching difficult communication tasks in oncology. CA Cancer J Clin 55:164-177, 2005
18.
Efficace F, Baccarani M, Rosti G, et al: Investigating factors associated with adherence behaviour in patients with chronic myeloid leukemia: An observational patient-centered outcome study. Br J Cancer 107:904-909, 2012
19.
DiMatteo MR, Lepper HS, Croghan TW: Depression is a risk factor for noncompliance with medical treatment: Meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med 160:2101-2107, 2000
20.
Gebbia V, Bellavia M, Banna GL, et al: Treatment monitoring program for implementation of adherence to second-line erlotinib for advanced non-small-cell lung cancer. Clin Lung Cancer 14:390-398, 2013
21.
Spoelstra SL, Given BA, Given CW, et al: An intervention to improve adherence and management of symptoms for patients prescribed oral chemotherapy agents: An exploratory study. Cancer Nurs 36:18-28, 2013
22.
Bartel SB: Safe practices and financial considerations in using oral chemotherapeutic agents. Am J Health Syst Pharm 64:S8-S14, 2007 (suppl 5 )
23.
Stevenson FA, Cox K, Britten N, et al: A systematic review of the research on communication between patients and health care professionals about medicines: The consequences for concordance. Health Expect 7:235-245, 2004
24.
Oken MM, Creech RH, Tormey DC, et al: Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 5:649-655, 1982
25.
Harris PA, Taylor R, Thielke R, et al: Research electronic data capture (REDCap): A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42:377-381, 2009
26.
Stirratt MJ, Dunbar-Jacob J, Crane HM, et al: Self-report measures of medication adherence behavior: Recommendations on optimal use. Transl Behav Med 5:470-482, 2015
27.
Osterberg L, Blaschke T: Adherence to medication. N Engl J Med 353:487-497, 2005
28.
Krolop L, Ko YD, Schwindt PF, et al: Adherence management for patients with cancer taking capecitabine: A prospective two-arm cohort study. BMJ Open 3:e003139, 2013
29.
Abetz L, Coombs JH, Keininger DL, et al: Development of the cancer therapy satisfaction questionnaire: Item generation and content validity testing. Value Health 8:S41-S53, 2005 (suppl 1 )
30.
Trask PC, Tellefsen C, Espindle D, et al: Psychometric validation of the cancer therapy satisfaction questionnaire. Value Health 11:669-679, 2008
31.
McCorkle R: The measurement of symptom distress. Semin Oncol Nurs 3:248-256, 1987
32.
Moorey S, Greer S, Watson M, et al: The factor structure and factor stability of the hospital anxiety and depression scale in patients with cancer. Br J Psychiatry 158:255-259, 1991
33.
Zigmond AS, Snaith RP: The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 67:361-370, 1983
34.
Cella DF, Tulsky DS, Gray G, et al: The Functional Assessment of Cancer Therapy scale: Development and validation of the general measure. J Clin Oncol 11:570-579, 1993
35.
D’Errico G, Galassi J, Schanberg R, et al: Development and validation of the Cancer Worries Inventory. J Psychosoc Oncol 17:119-137, 2008
36.
Webster K, Cella D, Yost K: The Functional Assessment of Chronic Illness Therapy (FACIT) measurement system: Properties, applications, and interpretation. Health Qual Life Outcomes 1:79, 2003
37.
Peipert JD, Beaumont JL, Bode R, et al: Development and validation of the Functional Assessment of Chronic Illness Therapy Treatment Satisfaction (FACIT TS) measures. Qual Life Res 23:815-824, 2014
38.
Noens L, van Lierde MA, De Bock R, et al: Prevalence, determinants, and outcomes of nonadherence to imatinib therapy in patients with chronic myeloid leukemia: The ADAGIO study. Blood 113:5401-5411, 2009
39.
Darkow T, Henk HJ, Thomas SK, et al: Treatment interruptions and non-adherence with imatinib and associated healthcare costs: A retrospective analysis among managed care patients with chronic myelogenous leukaemia. Pharmacoeconomics 25:481-496, 2007
40.
DiMatteo MR: Social support and patient adherence to medical treatment: A meta-analysis. Health Psychol 23:207-218, 2004
41.
Figueiredo Junior AG, Forones NM: Study on adherence to capecitabine among patients with colorectal cancer and metastatic breast cancer. Arq Gastroenterol 51:186-191, 2014
42.
Ruddy K, Mayer E, Partridge A: Patient adherence and persistence with oral anticancer treatment. CA Cancer J Clin 59:56-66, 2009
43.
Sedjo RL, Devine S: Predictors of non-adherence to aromatase inhibitors among commercially insured women with breast cancer. Breast Cancer Res Treat 125:191-200, 2011
44.
Bender CM, Gentry AL, Brufsky AM, et al: Influence of patient and treatment factors on adherence to adjuvant endocrine therapy in breast cancer. Oncol Nurs Forum 41:274-285, 2014
45.
De Geest S, von Renteln-Kruse W, Steeman E, et al: Compliance issues with the geriatric population: Complexity with aging. Nurs Clin North Am 33:467-480, 1998
46.
Stanton AL, Petrie KJ, Partridge AH: Contributors to nonadherence and nonpersistence with endocrine therapy in breast cancer survivors recruited from an online research registry. Breast Cancer Res Treat 145:525-534, 2014
47.
Hollywood E, Semple D: Nursing strategies for patients on oral chemotherapy. Oncology (Williston Park) 15:37-39, discussion 40, 2001 (suppl 2 )
48.
Schneider SM, Hess K, Gosselin T: Interventions to promote adherence with oral agents. Semin Oncol Nurs 27:133-141, 2011
49.
Hietanen P, Aro AR, Holli K, et al: Information and communication in the context of a clinical trial. Eur J Cancer 36:2096-2104, 2000
50.
Tirodkar MA, Acciavatti N, Roth LM, et al: Lessons from early implementation of a patient-centered care model in oncology. J Oncol Pract 11:456-461, 2015
51.
Mead N, Bower P: Patient-centredness: A conceptual framework and review of the empirical literature. Soc Sci Med 51:1087-1110, 2000
52.
Schapira L: Communication skills training in clinical oncology: The ASCO position reviewed and an optimistic personal perspective. Crit Rev Oncol Hematol 46:25-31, 2003
53.
McCambridge J, Witton J, Elbourne DR: Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects. J Clin Epidemiol 67:267-277, 2014
54.
Mathes T, Antoine SL, Pieper D, et al: Adherence enhancing interventions for oral anticancer agents: A systematic review. Cancer Treat Rev 40:102-108, 2014
55.
Simons S, Ringsdorf S, Braun M, et al: Enhancing adherence to capecitabine chemotherapy by means of multidisciplinary pharmaceutical care. Support Care Cancer 19:1009-1018, 2011
56.
Tulsky JA, Arnold RM, Alexander SC, et al: Enhancing communication between oncologists and patients with a computer-based training program: A randomized trial. Ann Intern Med 155:593-601, 2011
57.
Lobb EA, Butow PN, Meiser B, et al: Tailoring communication in consultations with women from high risk breast cancer families. Br J Cancer 87:502-508, 2002
58.
Shields CG, Morrow GR, Griggs J, et al: Decision-making role preferences of patients receiving adjuvant cancer treatment: A University of Rochester Cancer Center community clinical oncology program. Support Cancer Ther 1:119-126, 2004
59.
Balogh EP, Ganz PA, Murphy SB, et al: Patient-centered cancer treatment planning: Improving the quality of oncology care—Summary of an Institute of Medicine workshop. Oncologist 16:1800-1805, 2011

Information & Authors

Information

Published In

Journal of Oncology Practice
Pages: e474 - e485
PubMed: 28398843

History

Published online: April 11, 2017

Permissions

Request permissions for this article.

Authors

Affiliations

Jamie M. Jacobs [email protected]
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Nicole A. Pensak
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Nora J. Sporn
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
James J. MacDonald
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Inga T. Lennes
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Steven A. Safren
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
William F. Pirl
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Jennifer S. Temel
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL
Joseph A. Greer
Massachusetts General Hospital Cancer Center and Harvard Medical School; Boston University School of Public Health, Boston, MA; University of Colorado Denver–Anschutz Medical Campus, Aurora, CO; University of California Los Angeles, Los Angeles, CA; University of Miami, Coral Gables; and University of Miami Miller School of Medicine, Miami, FL

Notes

Corresponding author: Jamie M. Jacobs, PhD, Massachusetts General Hospital Cancer Center, 55 Fruit St, Yawkey Center, Suite 10B, Boston, MA 02114; e-mail: [email protected].

Author Contributions

Conception and design: Inga T. Lennes, Steven A. Safren, Jennifer S. Temel, Joseph A. Greer
Collection and assembly of data: Nora J. Sporn, Joseph A. Greer
Data analysis and interpretation: Jamie M. Jacobs, Nicole A. Pensak, James J. MacDonald, Inga T. Lennes, Steven A. Safren, William F. Pirl, Joseph A. Greer
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors

Metrics & Citations

Metrics

Altmetric

Citations

Article Citation

Download Citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format





Download article citation data for:
Jamie M. Jacobs, Nicole A. Pensak, Nora J. Sporn, James J. MacDonald, Inga T. Lennes, Steven A. Safren, William F. Pirl, Jennifer S. Temel, Joseph A. Greer
Journal of Oncology Practice 2017 13:5, e474-e485

View Options

View options

PDF

View PDF

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Personal login Institutional Login

Purchase Options

Purchase this article to get full access to it.

Purchase this Article

Subscribe

Subscribe to this Journal
Renew Your Subscription
Become a Member

Media

Figures

Other

Tables

Share

Share

Share article link

Share