This article will describe the evolution of the Ideal Symptoms Model, herein called the Dynamic Symptoms Model, and its use to model cancer-related symptoms since its initial publication in 2010. Discussion led to changes within the model to better describe the symptoms experience, its antecedents and consequences, and how interventions affect symptoms. Clinicians and symptom scientists can use the Dynamic Symptoms Model to visualize symptom influences and relationships with other variables over time and to formulate research questions and analytic plans.
Theories and conceptual models can be thought of as broad nets that attempt to rationalize, explain, and master a phenomenon within clinical nursing and interdisciplinary care. They can be used to guide a review of the literature and to formulate and organize research variables and relationships. Gaps in the literature can be identified and opportunities for additional research revealed (Fawcett, 2005). A variety of symptom models or theories exist, including the Theory of Symptom Management (Dodd et al., 2001), Theory of Unpleasant Symptoms (Lenz, Pugh, Milligan, Gift, & Suppe, 1997), Symptoms Experience Model (Armstrong, 2003), and Symptom Experiences in Time Theory (Henly, Kallas, Klatt, & Swenson, 2003). Most recently, the National Institute of Nursing Research identified a new National Institutes of Health Symptom Science Model to guide symptom science research (Cashion & Grady, 2015).
Brant, Beck, and Miaskowski (2010) compared and contrasted these symptom models and proposed a new Ideal Symptoms Model, herein called the Dynamic Symptoms Model, that could address the complex nature of symptoms, co-occurring symptoms and symptom interactions, and the longitudinal trajectories of symptoms that change over time. Since that initial publication, the authors and other nurse scientists have used the model to conceptualize symptoms and to study the relationships between antecedents, the symptoms experience, nursing interventions that influence the symptoms experience, and the consequences of deleterious symptoms. In addition, Brant has met with nursing doctoral students, symptom scientists, and interdisciplinary team members to discuss the model, refine components of the model, and clarify concepts and relationships within the model. Current literature and the evolution of symptom science have led to changes within the model. The purpose of this article is to discuss the most recent use of the model in oncology research and to further explicate various components within the model.
This model has received significant attention during the past six years by oncology nurse scientists and doctoral students who need a conceptual model or theory that incorporates changes in the symptoms experience over time. To the authors’ knowledge, the model has been cited 34 times, 14 of which were specific to the cancer symptoms experience. The most common use of the model was to inform conceptualization of symptom trajectories (Brant et al., 2011; Henly, Wyman, & Findorff, 2011; Keller, 2015; Pan et al., 2012) or patterns (Haisfield-Wolfe, Brown, Richardson, & Webster, 2015). Symptom clusters were discussed in two articles (Kim, Barsevick, Beck, & Dudley, 2012; Kim, McDermott, & Barsevick, 2014). Symptom interventions or self-care interventions were the focus of two articles (Alexander, Prabhu Das, & Johnson, 2012; Yeager, 2012). The symptoms discussed included psychological symptoms and their impact on quality of life (Albrecht, 2014; Gosselin, 2012; Matzka et al., 2016), along with physical symptoms, such as neuropathy and diarrhea (Faiman, 2015) and musculoskeletal symptoms (Davis, Carpenter, & Otte, 2016). Cancer types included in these citations were breast, lymphoma, lung, colorectal, multiple myeloma, and leukemia. Finally, a state of the science paper cited the Brant model as a framework for advancing symptom science (Davis et al., 2016). Of note, the Dynamic Symptoms Model was cited more often outside of the cancer setting for other chronic disease states (20 citations).
Because symptoms experience is complex, a model that illustrates this phenomenon is going to be highly complex to try to capture the longitudinal nature of the symptoms experience and highlight the concepts that give rise to and influence the symptoms experience over time. Initially, variables were laid out in the model in a minimalistic manner and concepts were not elaborated in great detail. For example, antecedents were listed in four categories: demographic, physiologic, psychological, and situational. No further explanation was provided for these antecedents. The symptoms experience, which included timing, distress, intensity, and quality, was found to have missing elements. As for interventions, additional thought was not given to the patient–family and provider–nurse interaction, nor interventions provided by others in the healthcare team. Finally, only four consequences were included in the model: quality of life, survival, function, and adjustment. These gaps in the model leave clinicians or scientists with unanswered questions about the model and leave room for omission and misinterpretation. Additions to the model are included in Table 1, and the newer revised Dynamic Symptoms Model is included in Figure 1. The authors added these descriptors to the model, not to make it more complex, but rather to clarify the meaning and relationships among components of the model and to improve its usability.
Since its inception, the Dynamic Symptoms Model has provided a foundation to discuss symptom science and model changes in symptoms experiences of patients with cancer. As symptom science continues to evolve, dynamic symptoms models to illustrate patients’ symptoms experiences will continue to evolve. More models need to be tested and evaluated to identify missing variables and better understand the relationships between and among them, as well as the directionality of these relationships.
Albrecht, T.A. (2014). Physiologic and psychological symptoms experienced by adults with acute leukemia: An integrative literature review. Oncology Nursing Forum, 41, 286–295. doi:10.1188/14.ONF.286-295
Alexander, J., Prabhu Das, I., & Johnson, T.P. (2012). Time issues in multilevel interventions for cancer treatment and prevention. Journal of the National Cancer Institute. Monographs, 44, 42–48.
Armstrong, T.S. (2003). Symptoms experience: A concept analysis. Oncology Nursing Forum, 30, 601–606. doi:10.1188/03.ONF.601-606
Brant, J.M., Beck, S., & Miaskowski, C. (2010). Building dynamic models and theories to advance the science of symptom management research. Journal of Advanced Nursing, 66, 228–240. doi:10.1111/j.1365-2648.2009.05179.x
Brant, J.M., Beck, S.L., Dudley, W.N., Cobb, P., Pepper, G., & Miaskowski, C. (2011). Symptom trajectories during chemotherapy in outpatients with lung cancer colorectal cancer, or lymphoma. European Journal of Oncology Nursing, 15, 470–477. doi:10.1016/j.ejon.2010.12.002
Cashion, A.K., & Grady, P.A. (2015). The National Institutes of Health/National Institutes of Nursing Research intramural research program and the development of the National Institutes of Health Symptom Science Model. Nursing Outlook, 63, 484–487. doi:10.1016/j.outlook.2015.03.001
Davis, L.L., Carpenter, J.S., & Otte, J.L. (2016). State of the science: Taxane-induced musculoskeletal pain. Cancer Nursing, 39, 187–196. doi:10.1097/NCC.0000000000000273
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Faiman, B. (2015). Peripheral neuropathy and diarrhea symptoms in patients with multiple myeloma (Doctoral dissertation). Retrieved from https://etd.ohiolink.edu/!etd.send_file?accession=case1417619492&dispos…
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Haisfield-Wolfe, M.E., Brown, C., Richardson, M., & Webster, K. (2015). Variations in symptom severity patterns among oropharyngeal and laryngeal cancer outpatients during radiation treatment: A pilot study. Cancer Nursing, 38, 279–287.
Henly, S.J., Kallas, K.D., Klatt, C.M., & Swenson, K.K. (2003). The notion of time in symptom experiences. Nursing Research, 52, 410–417. doi:10.1097/00006199-200311000-00009
Henly, S.J., Wyman, J.F., & Findorff, M.J. (2011). Health and illness over time: The trajectory perspective in nursing science. Nursing Research, 60(3, Suppl.), S5–S14. doi:10.1097/nnr.0b013e318216dfd3
Keller, G.P. (2015). Determining level and trajectory of change in reported attentional function in women with breat cancer receiving chemotherapy, a pilot study. Scholar Archive. Retrieved from http://bit.ly/2bcMK9l
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Kim, H.J., McDermott, P.A., & Barsevick, A.M. (2014). Comparison of groups with different patterns of symptom cluster intensity across the breast cancer treatment trajectory. Cancer Nursing, 37, 88–96. doi:10.1097/NCC.0b013e31828293e0
Lenz, E.R., Pugh, L.C., Milligan, R.A., Gift, A., & Suppe, F. (1997). The middle-range theory of unpleasant symptoms: An update. Advances in Nursing Science, 19(3), 14–27.
Matzka, M., Mayer, H., Köck-Hódi, S., Moses-Passini, C., Dubey, C., Jahn, P., . . . Eicher, M. (2016). Relationship between resilience, psychological distress and physical activity in cancer patients: A cross-sectional observation study. PLOS One, 11, e0154496. doi:10.1371/journal.pone.0154496
Pan, H.H., Lin, K.C., Ho, S.T., Liang, C.Y., Lee, S.C., & Wang, K.Y. (2012). Factors related to daily life interference in lung cancer patients: A cross-sectional regression tree study. European Journal of Oncology Nursing, 16, 345–352.
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Brant is a nurse scientist at the Billings Clinic Hospital in Montana; Dudley is a professor in the School of Health and Human Sciences at the University of North Carolina in Greensboro; Beck is a professor and the Robert S. and Beth M. Carter Endowed Chair in the College of Nursing at the University of Utah in Salt Lake City; and Miaskowski is a professor in the School of Nursing at the University of California, San Francisco. No financial relationships to disclose. Brant can be reached at firstname.lastname@example.org, with copy to editor at ONFEditor@ons.org.