Jones, Lynne C.

Abstract 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.

AbstractIntroductionConventional Z‐scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z‐scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z‐scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.MethodsIn this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).ResultsCorrected Z‐scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted‐R2.DiscussionNonlinearly corrected Z‐scores with respect to age, sex, and education with age‐varying residual standard deviation allow for improved detection of non‐normative extreme cognitive scores.