Development of a personalized shared decision-making tool for knee osteoarthritis and user-testing with African American and Latina women.

Background: Patients with chronic knee pain are often unaware of treatment options and likely outcomes-information that is critical to decision-making. A consistent framework for communicating patient-personalized information enables clinicians to provide consistent, targeted, and relevant information. Our objective was to user-test a shared decision-making (SDM) tool for chronic knee pain. Methods: A cross-functional team developed a Markov-based health economics model and tested the model outputs with patient panels, patient and clinician focus groups, and clinical specialists. The resulting SDM tool was user-tested in a parallel-designed, randomized controlled study with 52 African American and 52 Latina women from geographically representative areas of the US. Participants were randomized to counseling with or without the SDM tool. Feedback was collected at intervention and at 1 month after intervention and analyzed with Student's t-tests and Chi-squared analyses (alpha = 0.05). Results: Qualitative results indicated patients understood the material, rated the overall experience highly, and were likely to recommend the physician. The SDM group reported high satisfaction with the tool. A greater proportion of the SDM group (56%) reported increased physical activity over baseline at 1 month compared with the control group (33%) (P = 0.0005). New use of medications for knee pain (58% SDM; 49% control) did not differ significantly between groups (P = 0.15). Conclusion: Use of this innovative SDM tool was associated with high satisfaction and a significant increase in self-reported physical activity level at 1 month. The SDM tool may elicit behavioral changes to promote musculoskeletal health.

Examining the Association between Emotional Dysregulation and Treatment Burden.

Treatment burden contributes to suboptimal adherence and lower health-related quality of life among people diagnosed with chronic conditions, but little is known about how the psychological process of emotional dysregulation influences treatment burden. To explore this relationship, we conducted a descriptive, cross-sectional study comprised of 149 men and women diagnosed with one or more Centers for Medicare and Medicaid Systems defined chronic conditions recruited from a single primary care setting. Multivariate analyses demonstrated emotional dysregulation as an independent factor contributing to total (p < .001), medication (p = .007), and dietary (p < .001) treatment burden, with model effect sizes ranging from large (.43) to moderate (.21), controlling for other known antecedent factors of treatment burden. Emotional dysregulation was not a factor contributing to exercise related treatment burden. Our findings indicated emotional dysregulation as an important factor contributing to higher levels of total, medication, and dietary treatment burden associated with daily self-management regimens.

A Community and Technology-Based Approach for Hypertension Self-Management (COACHMAN) to Improve Blood Pressure Control in African Americans: Results from a Pilot Study.

PURPOSE: Adoption of technology has increased to support self-managing chronic diseases. However, behavioral interventions evaluating such technology have been understudied in African Americans with hypertension. The aim of this study was to explore a community and technology-based intervention for hypertension self-management (COACHMAN) intervention on blood pressure (BP) control and health-related quality of life (HRQoL) in African Americans with hypertension. METHODS: Sixty African Americans (mean age 60; 75% females) who were prescribed antihypertensive medications and owning a smartphone were randomized to the COACHMAN (n = 30) or enhanced usual care (n = 30) group for 12 weeks. COACHMAN is comprised of four components: web-based education, home BP monitoring, medication management application, and nurse counseling. Hypertension knowledge, self-efficacy, technology adoption/use, medication adherence, BP, and HRQoL scores were assessed. RESULTS: Mean systolic and diastolic BP at baseline was 150.49 (SD = 13.89) and 86.80 (SD = 13.39), respectively. After completing the 3-month intervention to improve hypertension self-management, the groups did not significantly differ in BP control and HRQoL. Clinically relevant BP reduction was observed in the intervention group. Paired t-test showed that mean medication-taking adherence scores significantly improved in the intervention group (P = 0.023) compared to the control group (P = 0.075). CONCLUSION: Using technology may have a positive impact on supporting hypertension self-management, particularly in medication-taking adherence. Further research is warranted in a larger sample and should include standardization of medication management to isolate the effects of behavioral interventions on changes in BP. CLINICALTRIALSGOV IDENTIFIER: NCT03722667.

Treatment burden as a predictor of self-management adherence within the primary care population.

PURPOSE: We aimed to (1) describe the amount of treatment burden experienced in the primary care population diagnosed with chronic conditions and (2) examine if cumulative and task-specific treatment burden were predictors of medication, exercise, and dietary adherence in patients diagnosed with chronic conditions. DESIGN: We conducted a prospective, descriptive, cross-sectional study. METHODS: We enrolled 149 men and women from a single primary care clinic. Participants completed self-report surveys with data collected between September 2019 and December 2019. Our primary statistical analyses consisted of multivariate regression modeling. RESULTS: The sample experience a moderate amount of treatment burden (M = 38.22; SD = 31.83). We found strong, negative correlations between both cumulative and task-specific burden in relation to medication, exercise, and dietary adherence (p < .001). Significant multivariate models (p < .001), controlling for sample demographics, demonstrated cumulative treatment burden predicted medication adherence, whereas task-specific burden predicted medication, exercise, and dietary adherence outcomes, with model effect sizes ranging from moderate (0.20) to large (0.54). CONCLUSIONS: Results demonstrate higher levels of cumulative and task-specific treatment burden predict medication, exercise, and dietary adherence within a sample diagnosed with various chronic conditions. These findings indicate the potential for using treatment burden screening in the clinical setting to identify individuals at risk for poor self-management adherence. Treatment burden screening also enables the provider to determine areas of high burden affecting self-management adherence in order to design an effective treatment plan using targeted interventions, resources, or education to reduce patient burden in order to improve adherence.