Improving medication adherence and outcomes in adult kidney transplant patients using a personal systems approach: SystemCHANGE™ results of the MAGIC randomized clinical trial.

This study determined if a SystemCHANGE™ intervention was more efficacious than attention control in increasing immunosuppressive medication adherence and improving outcomes in adult kidney transplant recipients during a 6-month intervention phase and subsequent 6-month (no intervention) maintenance phase. The SystemCHANGE™ intervention taught patients to use person-level quality improvement strategies to link adherence to established daily routines, environmental cues, and supportive people. Eighty-nine patients (average age 51.8 years, 58% male, 61% African American) completed the 6-month intervention phase. Using an intent-to-treat analysis, at 6 months, medication adherence for SystemCHANGE™ (median 0.91, IQR 0.76-0.96) and attention control (median 0.67, IQR 0.52-0.72) patients differed markedly (difference in medians 0.24, 95% CI 0.13-0.30, P < .001). At the conclusion of the subsequent 6-month maintenance phase, the gap between medication adherence for SystemCHANGE™ (median 0.77, IQR 0.56-0.94) and attention control (median 0.60, IQR 0.44-0.73) patients remained large (difference in medians 0.17, 95% CI 0.06-0.33, P = .004). SystemCHANGE™ patients evidenced lower mean creatinine and BUN at 12 months and more infections at 6 and 12 months. This first fully powered RCT testing SystemCHANGE™ to improve and maintain medication adherence in kidney transplant recipients demonstrated large, clinically meaningful improvements in medication adherence. Clinical Trial Registration: NCT02416479.

An equine-assisted therapy intervention to improve pain, range of motion, and quality of life in adults and older adults with arthritis: A randomized controlled trial.

RESEARCH AIM: To compare equine-assisted therapy to exercise education on pain, range of motion, and quality of life in adults and older adults with arthritis. BACKGROUND: Quality of life for adults and older adults is negatively impacted by arthritis pain, stiffness, and decreased function. Equine-assisted therapy provides unique movements to the rider's joints and muscles improving pain, range of motion, and quality of life and has improved outcomes in balance, gait, strength, functional mobility, and spasticity for older adults, stroke, spinal cord injury, and multiple sclerosis patients. No research has investigated the effects on adults and older adults with arthritis. METHODS: Twenty adults and older adults with arthritis recruited from rheumatology clinics participated in a randomized controlled trial for six weeks. Participants and research assistants were blinded to assignment. Standardized valid and reliable instruments were used to measure pain, range of motion, and quality of life targeting back, knees, shoulders, and hips. RESULTS: Mean age was 63.85 (SD 6.885, 53-75) years. Pain significantly improved in shoulders (p = 0.007), hips (p = 0.027), and back (p = 0.006), not knees (p = 0.061). Range of motion improved for back (p = 0.02), hips (p = 0.04), shoulders (p = 0.005) and not knees. Quality of life improved for upper limb (p = 0.002), lower limb (p = 0.021), and affect (p = 0.030), not social interaction and symptoms. CONCLUSION: This randomized controlled trial provides evidence that equine-assisted therapy decreases pain, and improves range of motion, and quality of life for adults and older adults with arthritis. Further fully powered research with cost/benefit outcomes would be beneficial.

On the Identifiability of 3- and 4-Parameter Item Response Theory Models From the Perspective of Knowledge Space Theory.

The present work aims at showing that the identification problems (here meant as both issues of empirical indistinguishability and unidentifiability) of some item response theory models are related to the notion of identifiability in knowledge space theory. Specifically, that the identification problems of the 3- and 4-parameter models are related to the more general issues of forward- and backward-gradedness in all items of the power set, which is the knowledge structure associated with IRT models under the assumption of local independence. As a consequence, the identifiability problem of a 4-parameter model is split into two parts: a first one, which is the result of a trade-off between the left-side added parameters and the remainder of the Item Response Function, e.g., a 2-parameter model, and a second one, which is the already well-known identifiability issue of the 2-parameter model itself. Application of the results to the logistic case appears to provide both a confirmation and a generalization of the current findings in the literature for both fixed- and random-effects IRT logistic models.

Sequential detection of learning in cognitive diagnosis.

In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broadly defined skills, when the objective is to hasten learning. If these fine-grained skills can be identified, a sharpened focus on learning and remediation can be achieved. The focus here is on how to detect when learning has taken place for a particular attribute and efficiently guide a student through a sequence of items to ultimately attain mastery of all attributes while administering as few items as possible. This can be seen as a problem in sequential change-point detection for which there is a long history and a well-developed literature. Though some ad hoc rules for determining learning may be used, such as stopping after M consecutive items have been successfully answered, more efficient methods that are optimal under various conditions are available. The CUSUM, Shiryaev-Roberts and Shiryaev procedures can dramatically reduce the time required to detect learning while maintaining rigorous Type I error control, and they are studied in this context through simulation. Future directions for modelling and detection of learning are discussed.