A New Mobile Application to Reduce Anxiety in Pediatric Patients Before Bone Marrow Aspiration Procedures.

OBJECTIVES: Insufficient preparation for children who are undergoing bone marrow aspiration can cause anxiety and negative outcomes. Nonpharmacological therapies have been proven to reduce fear in children who are undergoing painful procedures. We have therefore developed a mobile application to help reduce these patients' anxiety by providing them with procedural information and coping skills. METHODS: This single-blinded, randomized controlled trial included 60 patients age 5 to 12 years old who were undergoing bone marrow aspiration procedures in Thailand that were conducted between May 2015 and May 2016. Sixty participants were randomly assigned to the intervention group (mobile application added to usual care) or the control group (usual care only). Preprocedural anxiety levels were evaluated by visual analog scales (child anxiety visual analog scale); this was repeated in the intervention group immediately after patients used the mobile application. On the day of the procedure, the patients' cooperation levels were assessed by using the modified Yale Preoperative Anxiety Scale. The total amount of sedative drugs that were used was also recorded. The paired t test and the Wilcoxon signed rank test were used to analyze within-person change, whereas the t test and the Wilcoxon rank sum test were used for group comparisons. RESULTS: The child anxiety visual analog scale score of patients in the intervention group decreased significantly after they used the mobile application (P < .0012). The modified Yale Preoperative Anxiety Scale score of patients in the intervention group was significantly lower than that in the control group (P < .01). There was no difference in sedative use between the 2 groups. CONCLUSIONS: This mobile application possibly had effectiveness in routine use for reducing anxiety and increasing patients' cooperation in bone marrow aspiration procedures.

Cyber-workstation for computational neuroscience.

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Model development, testing and experimentation in a CyberWorkstation for Brain-Machine Interface research.

The CyberWorkstation (CW) is an advanced cyber-infrastructure for Brain-Machine Interface (BMI) research. It allows the development, configuration and execution of BMI computational models using high-performance computing resources. The CW's concept is implemented using a software structure in which an "experiment engine" is used to coordinate all software modules needed to capture, communicate and process brain signals and motor-control commands. A generic BMI-model template, which specifies a common interface to the CW's experiment engine, and a common communication protocol enable easy addition, removal or replacement of models without disrupting system operation. This paper reviews the essential components of the CW and shows how templates can facilitate the processes of BMI model development, testing and incorporation into the CW. It also discusses the ongoing work towards making this process infrastructure independent.

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.