Trajectories of Fatigue, Glycated Hemoglobin, Diabetes Self-management, and Quality of Life in People With a Dual Diagnosis of Operable Pancreatic Tumor and Diabetes Mellitus: A Longitudinal Mixed-methods Study
Trajectories of Fatigue, Glycated Hemoglobin, Diabetes Self-management, and Quality of Life in People With a Dual Diagnosis of Operable Pancreatic Tumor and Diabetes Mellitus: A Longitudinal Mixed-methods Study
In people with both burdens of OPT and DM, fatigue is the most common and deliberating symptom that likely impacts their quality of life and diabetes self-management. However, little is known about the longitudinal relationships among fatigue, its influencing factors, glycemic status, diabetes self-management, and quality of life in people with both OPT and DM during the first-year post-operation. Thus, the purposes of this study are to (1) describe the trajectories of fatigue, HbA1c, diabetes self-management, and quality of life in people with OPT and DM within 12 months post-operation; (2) examine the relationships among the trajectories of fatigue, its influencing factors, diabetes self-management, and quality of life in people with OPT and DM; and (3) understand the experiences of people with both OPT and DM regarding changes in challenges with diabetes, fatigue, diabetes self-management, diabetes care needs, and quality of life.
Pancreatic cancer is a devastating disease, and surgery is one of the treatment options with the goal of curing the disease. However, people with operable pancreatic tumor (OPT) often have a high prevalence of comorbid diabetes mellitus (DM) that is likely to result in increased glucose fluctuation and poor glycemic control. Importantly, in people with both burdens of OPT and DM, fatigue is the most common and deliberating symptom that likely impacts their quality of life and diabetes self-management. However, little is known about the longitudinal relationships among fatigue, its influencing factors, glycemic status, diabetes self-management, and quality of life in people with both OPT and DM for 12 months of follow-up. Thus, the purposes of this study are to (1) describe the trajectories of fatigue, HbA1c, diabetes self-management, and quality of life in people with OPT and DM within 12 months of follow-up; (2) examine the relationships among the trajectories of fatigue, its influencing factors, diabetes self-management, and quality of life in people with OPT and DM; and (3) understand the experiences of people with both OPT and DM regarding changes in challenges with diabetes, fatigue, diabetes self-management, diabetes care needs, and quality of life. Guided by the Theory of unpleasant symptoms, this two-year study will use a longitudinal concurrent mixed methods design. Participants will be recruited from an outpatient pancreatic surgical department at a medical center in northern Taiwan using purposive sampling. For the quantitative component, data will be collected using structured questionnaires over five time points (baseline, 3, 6, 9, and 12 months post baseline),The Chinese version of a structured questionnaire, including the demographic and clinical characteristics form, Diabetes Distress Scale, Center for Epidemiological Studies Depression Scale, Multidimensional Scale of Perceived Social Support, Fatigue Symptom Inventory, Summary of Diabetes Self-Care Activities, and the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire will be used. For data analysis, latent growth curve modeling under a structural equation modeling framework using Mplus version 8.6 will be performed. A sample of 115 participants with both OPT and DM will be recruited. To illuminate our understanding of the findings from the quantitative component of the study, the qualitative component will use semi-structured qualitative interviews to explore the changes in challenges with diabetes, fatigue, diabetes self-management, diabetes care needs, and quality of life for a subsample of 20 participants who reported their fatigue intensity scores as 4 or more on the Fatigue Symptom Inventory concurrently. Qualitative content analysis will be used to analyze the qualitative data.
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