Efficacy of microcurrent therapy for treatment of acute knee pain: A randomized double-blinded controlled clinical trial.

OBJECTIVE: We would like to determine whether electrotherapy, specifically microcurrent therapy, increases function and decreases pain in people who have acute knee pain. DESIGN: Randomized, double-blinded, placebo-controlled clinical trial. SETTING: University laboratory and patient home. SUBJECTS: A total of 52 subjects (35 females and 17 males) with acute knee pain. INTERVENTION: Treatment group (n = 26) wore the active microcurrent therapy device at home for 3 hours per day for 4 weeks and the control group (n = 26) wore the placebo for 3 hours per day for 4 weeks. MAIN MEASURES: Numeric Pain Rating Scale (NPRS) and Short Form 12 (SF-12) health scale were used to measure the pain level and the functionality of the participants. Secondary assessments included musculoskeletal ultrasound imaging (MSK US) and Lower Extremity Functional Scale (LEFS). RESULTS: A total of 52 subjects completed the study; 26 in the treatment group and 26 in the control group. Microcurrent therapy significantly reduced pain over 4 weeks. Especially week three was significant (P < 0.01) after adjusting for the family-wise error rate. The analysis on SF-12 revealed those with microcurrent therapy showed an increasing trend in the improvement of physical function score until week three. CONCLUSION: An active microcurrent therapy device decreased knee pain and increased function. Microcurrent therapy may be an alternative or used with a pharmacological approach for people with acute knee pain.

An Intersection Database Facilitates Access to Complex Signalized Intersections for Pedestrians with Vision Disabilities.

A growing number of intersections and crosswalks pose barriers to pedestrians with vision disabilities. This project investigated the effects of providing verbal descriptions of intersections and crosswalks on the performance of street-crossing subtasks by individuals who are totally blind. The authors designed an intersection database containing information relevant to crossing subtasks such as finding and aligning with the crosswalk, deciding when to cross, remaining in the crosswalk, and recognizing the end of a crossing. The authors conducted an experiment with 22 blind adults at two intersections in Portland, Oregon. The intersections included crosswalks that varied widely in geometric and operational characteristics, including the presence or absence of accessibility features. In the no database condition, participants used their typical street-crossing procedures. In the database condition, participants additionally listened to database-generated descriptions of the intersections and crosswalks before crossing. The database descriptions had significant positive effects on some subtasks (primarily "crossing" subtasks such as deciding when to cross) and not others (primarily "wayfinding" subtasks such as remaining in the crosswalk). Participants' reports of the usefulness of specific features of the database were supported by the empirical findings. Implications of the findings for database development, transportation engineers, blind pedestrians, and orientation and mobility specialists are discussed.

Extracting Data from the Electronic Health Record of Patients with ADHD Reveals Pediatricians' Discussions of Educational Support and Document Collection.

Primary care physicians (PCPs) have an important role in the identification and management of Attention Deficit Hyperactivity Disorder (ADHD). There is a paucity of research on PCPs' practices related to the discussion of educational interventions. We conducted a retrospective chart review using Natural Language Processing to extract data on how often PCPs in an outpatient clinic: 1) discuss educational support with patients and caregivers; and 2) obtain educational records. About three-quarters of patients had at least one term related to educational support included in at least one note, but only 13 percent of patients had at least one educational record uploaded into the electronic health record (EHR). There was no association between having an educational document uploaded into the EHR and inclusion of a term related to educational support in a note. Almost half (48 percent) of these records were unclearly labeled. Further education of PCPs is warranted to increase discussions of educational support and obtaining educational records, as is collaboration with health information management professionals around labeling.

Estimating Finite Mixtures of Ordinal Graphical Models.

Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. However, ordinal variables are very popular in many areas of psychological science, and the population often consists of several different groups based on the heterogeneity in ordinal data. Driven by these needs, we introduce the finite mixture of ordinal graphical models to effectively study the heterogeneous conditional dependence relationships of ordinal data. We develop a penalized likelihood approach for model estimation, and design a generalized expectation-maximization (EM) algorithm to solve the significant computational challenges. We examine the performance of the proposed method and algorithm in simulation studies. Moreover, we demonstrate the potential usefulness of the proposed method in psychological science through a real application concerning the interests and attitudes related to fan avidity for students in a large public university in the United States.

Model-based clustering of time-evolving networks through temporal exponential-family random graph models.

Dynamic networks are a general language for describing time-evolving complex systems, and discrete time network models provide an emerging statistical technique for various applications. It is a fundamental research question to detect a set of nodes sharing similar connectivity patterns in time-evolving networks. Our work is primarily motivated by detecting groups based on interesting features of the time-evolving networks (e.g., stability). In this work, we propose a model-based clustering framework for time-evolving networks based on discrete time exponential-family random graph models, which simultaneously allows both modeling and detecting group structure. To choose the number of groups, we use the conditional likelihood to construct an effective model selection criterion. Furthermore, we propose an efficient variational expectation-maximization (EM) algorithm to find approximate maximum likelihood estimates of network parameters and mixing proportions. The power of our method is demonstrated in simulation studies and empirical applications to international trade networks and the collaboration networks of a large research university.

A review of dynamic network models with latent variables.

We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions. For each dynamic model, we also discuss its applications that have been studied in the literature, with the data source listed in Appendix. Based on the review, we summarize a list of open problems and challenges in dynamic network modeling with latent variables.