Research Critique Abstract Presentation

Oncology Nursing Society 44th Annual CongressApril 11–14, 2019 • Anaheim, CA

1Research

1. Examining Medication Beliefs Among Patients Receiving Oral Oncolytic Agents Using a Derived Model of Self-Regulation Victoria Marshall, PhD, RN University of South Florida Tampa, FL [email protected]

2. Using the Dynamic Symptoms Model to Explore a Comprehensive List of Cancer Pain Research-Related Variables Jia-Wen Guo, PhD, RN University of Utah Salt Lake City, UT [email protected]

Research Critique Abstract PresentationSaturday, April 13 • 2:45–4 pm

Note one action you’ll take after attending this session: ________________________________________________________________________________________________________________________________________________

ONS 44th Annual Congress

Research (Marshall) 1

Victoria K. Marshall, PhD, RNAssistant Professor

University of South Florida

• This research was funded by the American Nurses Foundation, Jean E. Johnson Award• Coauthors:• Rebecca Lehto, PhD, RN• Charles W. Given, PhD• Barbara Given, PhD, RN, FAAN• Alla Sikorskii, PhD

Disclosures



• Use of oral oncolytic agents (OAs) is increasing.1• OAs now account for nearly 60% of all new cancer treatments

approved by the Food and Drug Administration1

• Patients receiving OAs must demonstrate self-efficacy in the management of:• Complex OAA regimens• Adherence• Symptom/side effect management• Monitoring and reporting of drug toxicities• Potential drug-drug or food-drug interactions• Polypharmacy2

Background & Significance

Background & Significance • Patients receiving OAs face challenges without the close observation

of oncology health care providers that can influence medication beliefs.2

• Medication beliefs are a critical component to medication taking behavior and help establish a foundation for how individuals cope with their illness.3,4,5

• Medication beliefs have been consistently linked to patient outcomes such as symptoms, side effects and adherence across chronic diseases including cancer,3,4,5 but less is known about factors influencing medication beliefs across the treatment trajectory.

Medication Beliefs

• Medication beliefs are defined as an individual’s perceptions regarding the benefits and concerns of treatment with medication that arise from cognitive representation of illness.5• Have both positive and negative components



Purpose

• To examine medication beliefs for the first 12 weeks after initiating a new oral oncolytic agent (OAs) using a derived Model of Self-Regulation.

Aims 1-3

Using longitudinal data from baseline, 4, 8, and 12 week interviews:1. To examine whether positive and negative components of medication

beliefs change over time 2. To examine the summed symptom severity and interference indices on the

positive and negative components of medication beliefs over time 3. To explore the influence of depressive symptoms and cognitive

effectiveness on the positive and negative components of medication beliefs over time

Aims 4-5

Using 12-week interview data for those completing Beliefs about Medicine Questionnaire:

1. To explore the relationship of documented adverse events on positive and negative components of medication beliefs at 12 weeks since the initiation of a new oral oncolytic agent

2. To determine the effect that permanent physician-directed oral oncolytic agent stoppages have on the positive and negative components of medication beliefs by exploring whether patients who experience a permanent physician-directed OA stoppage differ in their medication beliefs compared to those who have not experienced a permanent physician-directed OA stoppage



Conceptual Framework6

Methods• Secondary data from a National Cancer Institute-funded randomized

controlled trial testing an intervention to promote symptom management and OA adherence, Improving Adherence to Oral Cancer Agents and Self Care of Symptoms Using an IVR, (1R01CA162401-O1A1).7

• Patients meeting the following criteria were recruited from six United States Comprehensive Cancer Centers:

• Received a new OA prescription

• ≥ 21 years of age

• Cognitively intact

• English speaking

• Able/willing to complete phone calls

• Eastern Cooperative Oncology Group performance8 score of 0–2 or a Karnofsky score9 ≥ 50

Methods

• Instruments/Measures • Beliefs about Medicine Questionnaire10

• Cancer Symptom Experience Inventory11

• Comorbid conditions requiring medication• Center for Epidemiologic Studies Depression Scale (CES-D)12

• Attentional Function Index13

• Physician-directed OA stoppages via Medical record audit• Adverse events measured via Common Terminology Criteria for Adverse

Events (CTCAE)14 and confirmed by medical record audit



Analyses Aims 1-3

• Linear mixed effects models (LME) were used to relate each Beliefs about Medicine Questionnaire (BMQ) subscale to fixed explanatory and time-varying covariates (symptoms, cognitive effectiveness, depressive symptoms). • Least squares means of the BMQ subscales were output from the

LME and evaluated for change over time.

Analyses Aims 4-5

• A regression analysis evaluated the relationship of adverse events*

with each BMQ subscale. • A t-test determined differences in medication beliefs between those

who experienced permanent OA stoppages and those who did not*. *Adverse events and permanent stoppages were only assessed for those who completed the 12 week BMQ

Findings: Aim 1-3• A total of 272 participants completed the baseline interview • Sample predominantly Caucasian (91%)• Diagnosed with stage four cancer (71%)• Mean age of 61.39 (SD = 2.22) years• Most prevalent cancers were gastrointestinal (32%) and breast cancer

(21%)• Kinase inhibitors (47%) and cytotoxics (35%) were the most frequent

forms of OA treatment



Findings: Aims 1-3• Beliefs that OA medication was necessary increased over time (mean

difference 0.112, SE=0.055, p = .04).

• Increased Concern beliefs were significantly associated with:• Patient-reported symptom severity (B = 0.009, SE = 0.001, p = <.01)• Patient-reported symptom interference (B = 0.010, SE = 0.001, p = <.01)• Depressive symptoms (B = 0.021, SE = 0.003, p = <.01)• Cognitive effectiveness (B = -0.006, SE = 0.001, p = <.01)• Chronic conditions requiring medications (B = 0.048, SE = 0.014, p = <.01)

Findings: Aims 4-5

• A total of 164 participants were included in the study

• Mean age 62.60 (SD = 10.46) year

• Predominantly Caucasian (88%)

• Breast (26%) and gastrointestinal cancers (23%) were most prevalent types of cancer

• Predominantly stage four cancer (72%)

Findings: Aims 4-5

• A regression analysis showed patients experiencing zero (B = 0.50, SE = 0.21, p = .02), one (B = 0.70, SE = 0.21, p = <.01), or two (B = 0.82, SE = 0.23, p = <.01) adverse events had significantly higher Necessity beliefs compared to those with three or more adverse events. • Independent t-tests followed by a regression analysis revealed

patients not experiencing a physician-directed OA stoppage had significantly higher Necessity beliefs (B = 0.80, SE = 0.23, p = <.01) compared to those who had experienced a permanent physician-directed OA stoppage.



Limitations• Sample was mostly Caucasian and a limited number of patients from diverse

ethnic and racial background were represented, thus limiting the generalizability of the findings.• Assessed medication beliefs over 12 weeks since initiating a new OA, a relatively

short considering the chronic nature of cancer and treatment with oral agents. • Aims 4-5 were tested using a cross sectional design and does not capture

potential changes in relationships with regards to adverse events and permanent stoppages on medication beliefs as they occur.• Sample size of patients completing the BMQ at 12 weeks after experiencing a

permanent physician-directed was restricted to 15 patients. • Only 8 adverse events were examined to explore their relationship with

medication beliefs and may have limited the ability to detect relationships with a wide array of adverse events that are typical among individuals with cancer who are receiving oral agents

Conclusions• Results support that the two components of medication beliefs are

influenced by different factors. • Nurses should elicit medication beliefs at each clinic visit, especially

when patients experience increasing levels of symptom severity/interference or adverse events, depressive symptoms, compromised cognitive effectiveness, and when patients experience a permanent physician-directed OA stoppage. • Screening medication beliefs can also serve to address ethical issues

regarding how long patients remain on OAs that cause more harm than benefit at the end of life. • Future research on medication beliefs is needed with more diverse

ethnic backgrounds.

Implications for Nursing

• Nurses should elicit medication beliefs at each clinic visit, especially when changes to the patient’s health are noted. • Screening medication beliefs can help weigh the benefits and risks of

OA treatment.• Understanding the factors that influence medication beliefs can help

nurses target patients at risk for nonadherence.



1.Center Watch. (2018). 2018 FDA Approved Drugs: Oncology. Retrieved from https://www.centerwatch.com/drug-information/fda-approved-drugs/year/2018.

2. Given, B. A., Spoelstra, S. L., & Grant, M. (2011, May). The challenges of oral agents as antineoplastic treatments. In

Seminars in Oncology Nursing (Vol. 27, No. 2, pp. 93–103). WB Saunders.

3. Banning, M. (2012). Adherence to adjuvant therapy in post-menopausal breast cancer patients: A review. European Journal of Cancer Care, 21(1), 10–19.

4. Bhattacharya, D., Easthall, C., Willoughby, K. A., Small, M., & Watson, S. (2012). Capecitabine non-adherence: Exploration of magnitude, nature and contributing factors. Journal of Oncology Pharmacy Practice, 18(3), 333–342.

5. Horne, R. (2003). Treatment perceptions and self-regulation. In The self-regulation of health and illness behavior. In

L. D. Cameron, & H. Leventhal (Eds.), The self-regulation of health and illness behavior (pp. 138–153). New York, NY: Routledge.

6. Marshall, V.K. Lehto, R.H., Given, C.W, Given, B.A., Sikorskii, A. (2019). Conceptualisation of Medication Beliefs

among Patients with Advanced Cancer Receiving Oral Oncolytic Agents Using a Theory Derivation Approach. European Journal of Cancer Care. Advanced online publication. dOI: 10.1111/ecc.12988

7. Given, B.A., & Given, C.W. (2013–2017). Improving adherence to oral cancer agents and self care of symptoms using an IVR (1Ro1CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from

https://clinicaltrials.gov/ct2/show/NCT02043184.

8. Oken, M. M., Creech, R. H., Tormey, D. C., Horton, J., Davis, T. E., McFadden, E. T., & Carbone, P. P. (1982). Toxicity and response criteria of the Eastern Cooperative Oncology Group. American Journal of Clinical Oncology, 5(6), 649–

655. doi:10.1097/00000421-198212000-00014.

References

References9. Karnofsky, D. A. (1949). The clinical evaluation of chemotherapeutic agents in cancer. Evaluation of Chemotherapeutic Agents. In C. M. MacLeod (Ed.), Evaluation of Chemotherapeutic Agents. New York, NY: Columbia University Press.10. Horne, R., Weinman, J., & Hankins, M. (1999). The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psychology & Health, 14(1), 1–24. doi:10.1080/08870449908407311.11. Given, B., Given, C. W., Sikorskii, A., Jeon, S., McCorkle, R., Champion, V., & Decker, D. (2008). Establishing mild, moderate, and severe scores for cancer-related symptoms: How consistent and clinically meaningful are interference-based severity cut-points? Journal of Pain and Symptom Management,35(2), 126–135. doi:10.1016/j.jpainsymman.2007.03.012.12. Radloff LS. (1977). The CES-D Scale: A self report depression scale for research in the general population. Applied Psychological Measurement, 1(3): 385–401.13. Cimprich, B., Visovatti, M., & Ronis, D. L. (2011). The Attentional Function Index—a self-report cognitive measure. Psycho-Oncology, 20(2), 194-202.14. US Department of Health and Human Services. (2009). Common terminology criteria for adverse events (CTCAE) version 4.0. National Institutes of Health, National Cancer Institute, 4(03). www.evs.nci.nih.gov/ftp1/CTCAE/CTCAE_4.03_2010-06-14_QuickReference_5x7.pdf. Accessed October 5, 2017.

https://www.centerwatch.com/drug-information/fda-approved-drugs/year/2018

https://clinicaltrials.gov/ct2/show/NCT02043184

http://dx.doi.org/10.1097/00000421-198212000-00014

http://dx.doi.org/10.1080/08870449908407311


Research (Guo) 1

Jia-Wen Guo, PhD RNAssistant Professor, University of Utah College of Nursing

Using the Dynamic Symptoms Model to Explore a Comprehensive List of Cancer Pain Research-Related Variables

• I have nothing to disclose.

Disclosures


Research (Guo) 2

qDescribe the need of identifying cancer pain management research related concepts

qUnderstand how to apply the Dynamic Symptoms Model to extract the concepts related to cancer pain management research

qDescribe the application of the using cancer pain management research related concepts

Presentation Takeaways

NIH NATIONAL INSTITUTE OF NURSING RESEARCH K01NR016948

Funding source

Mentors• Katherine Sward• Susan Beck• Gary Donaldson• Wendy Chapman• Lewis Frey

Acknowledgements


Research (Guo) 3

Background

• Cancer pain is multidimensional • The optimal strategy: Personalized cancer pain care

• Personalized cancer pain care requires a broad understanding of the factors influencing cancer pain• Existing research outcomes could be used to develop

personalized cancer pain care • A comprehensive understanding of the list of existing cancer

pain related variables is useful

Dynamic Symptoms Model (DSM)

• A model specifically for studying cancer related symptoms,

Brant, J . M ., D ud ley , W . N ., Beck , S ., & M iasko w sk i, C . (2016 ). Evo lu tio n o f the D ynam ic Sym p to m s M o de l. O nco l N urs Fo rum , 43 (5 ), 651-654 . do i:10 .1188/16 .O N F.651-654

Purpose

Generate a comprehensive list of variables used in managing cancer pain• Facilitate an understanding of the details of

complex multidimensional factors in cancer pain research by using the DSM as a framework


Research (Guo) 4

Methods

•Systematic scoping review •Content analysis

Methods: Systematic scoping review•Eligibility Criteria• Inclusion criteria• Published research articles• Published since 1990• Written in English• Full-text access • Cancer pain as part of the title, keyword list, and outcome variables• Randomized controlled trails and review

•Exclusion criteria• Topics focused on molecular biology, drug testing, or cost calculations

Methods: Systematic scoping review• Search strategy• Literature databases• PubMed, CINAHL, PsycINFO, and the Cochrane

• Determine core idea and search terms based on the eligibility criteria

• Desired core ideas: cancer pain, pain management, randomized controlled trial

• Undesired ideas: non-cancer, cost-effectiveness

• Search terms • Mesh term (e.g., Pain Management[Mesh])

• Keywords (e.g., pain care [Title/Abstract])


Research (Guo) 5

Example of formatting a search formula from search terms

Core Search term s Search form ula

Cancer pain pain[M esh] AND neoplasm s[M esh] “cancer pain”[M esh]“cancer pain”[Title/Abstract]

“cancer related pain”[Title/Abstract]“oncology pain”[Title/Abstract]“oncology related pain” [Title/Abstract

((((((pain[M esh] AND neoplasm s[M esh]))) OR “cancer pain”[M esh]) OR “cancer related pain”[Title/Abstract]) OR “cancer

pain”[Title/Abstract]) OR “oncology pain”[Title/Abstract]

Methods: Systematic scoping review • Study selection process• Using EndNote

Methods: Systematic scoping review


Research (Guo) 6

Methods: Systematic scoping review •Methodology quality appraisal• We did not appraise methodology quality or risk of bias of the

included articles, which is consistent with guidance on scoping review • Moreover, the purpose of the study is to extract the cancer

pain-related variables but not applying the outcome of the knowledge from the studies.

Elo , S ., & Kyngas, H . (2008 ). The qua lita tive co ntent ana lys is p ro cess . J A dvN urs , 62 (1 ), 107-115 . do i:10 .1111/j.1365-2648 .2007 .04569 .x

MethodsTo address rigor• Inter-rater reliability (IRR) examined the extent to which the two raters obtained the same results when they • selected the included articles•developed the coding schema


Research (Guo) 7

Results

•144 articles were eligible•55 of them were used for

the content analysis• Saturation was achieved with no new

codes emerging after 55 articles• The IRR values > 0.80

PRISMA Flow Diagram

0

Results

•10 domains based on DSM1. Characteristics of pain 2. Symptoms other than pain3. Demographic4. Physiologic5. Psychological6. Social7. Spiritual8. Environmental9. Intervention of pain10. Overarching consequences

Example concepts for 1. Characteristics of pain • pain relief• pain severity or intensity• pain duration• pain site or location• pain trajectories or patterns• pain interference• pain diagnosis description

• e.g., breakthrough pain


Research (Guo) 8

Example concepts for 2. Symptoms other than pain• Nausea and vomiting• Constipation• Diarrhea• Dyspnea• Fatigue• More….(over 50 items)

Example concepts for 3. Demographic domain• Age• Sex/gender• Race• Ethnicity• Marital status• Education• Language• Occupation• Social economic status

Example concepts for 4. Physiologic domain• Cancer diagnosis related information

• e.g., family history, site of cancer, stage of cancer

• Cancer treatment• Biomarker or lab data• Life expectancy• Mobility or physical dysfunction• Life expectancy• Perception issue

• e.g., visual impairment, hearing impairment


Research (Guo) 9

Example concepts for 5. Psychological domain• Psychological distress (e.g., emotional distress, psychiatric issues)• Substance abuse• Cognitive ability• Adherence of treatment• Self-efficacy• Awareness of cancer diagnosis• Suicide ideation• Life experience (e.g., combat or war)

Example concepts for 6. Social domain• Social isolation/connections• Social support

Example concepts for 7. Spiritual domain• Spirituality well-being• Religion


Research (Guo) 10

Example concepts for 8. Environmental domain• Residential location (e.g., Zip code)• Hospitalization reason• Information access (how patient access the information, e.g.,

email, phone)• Healthcare setting (e.g., outpatient)

Example concepts for 9. Intervention domain• Complementary and alternative medicine (CAM)

• e.g., music therapy, aromatherapy, massage, Chinese medicine

• Education program for pain• Pain medication • Radiotherapy for pain

Example concepts for 10. Overarching consequences• Quality of life• Functional or performance status • Patient satisfaction• Quality of pain management• Survival or death


Research (Guo) 11

Conclusions

•DSM is a useful framework for identifying a comprehensive list of cancer pain research related variables

•The findings of this study can help researchers to consider other possible variables to be included in personalized cancer pain care studies

• Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research methodology, 8(1), 19-32.

• Brant, J. M., Dudley, W. N., Beck, S., & Miaskowski, C. (2016). Evolution of the

dynamic symptoms model. Oncology Nursing Forum, 43(5), 651-654. doi:10.1188/16.ONF.651-654

• Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. doi:10.1111/j.1365-2648.2007.04569.x

References

Research Critique Abstract Presentation

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