About 18% of children in industrialized countries suffer from anxiety. We designed a mobile neurofeedback app, called Mind-Full, based on existing design guidelines. Our goal was for young children in lower socio-economic status schools to improve their ability to self-regulate anxiety by using Mind-Full. In this paper we report on quantitative outcomes from a sixteen-week field evaluation with 20 young children (aged 5 to 8). Our methodological contribution includes using a control group, validated measures of anxiety and stress, and assessing transfer and maintenance. Results from teacher and parent behavioral surveys indicated gains in children's ability to self-regulate anxiety at school and home; a decrease in anxious behaviors at home; and cortisol tests showed variable improvement in physiological stress levels. We contribute to HCI for mental health with evidence that it is viable to use a mobile app in lower socio-economic status schools to improve children's mental health.

Millions of children have challenges with anxiety that negatively impact their development, education and well-being. To address this challenge, we developed version 2.0 of Mind-Full, a wearable, mobile neurofeedback system, designed to teach young children to learn to self-regulate anxiety. We present a mixed methods evaluation of a seven week long intervention in schools. We report on a subset of outcome measures related to 10 children's anxiety and stress in the classroom and describe mediating socio-technological processes that may have impacted outcomes. Findings showed improvement in children's ability to self-regulate anxiety and reduced cortisol levels for some children. Qualitative findings suggested that children who made multimodal connections during system mediated learning and had teacher support for learning transfer responded well to the intervention. We suggest that framing mental health app design as a distributed, adaptive, socio-technological system enables designers to better meet individual's unique and changing mental health needs.

Brain--computer interface applications (BCIs) utilizing neurofeedback (NF) can make invisible brain states visible in real time. Learning to recognize, modify, and regulate brain states is critical to all children's development and can improve learning, and emotional and mental health outcomes. How can we design usable and effective NF BCIs that help children learn and practice brain state self-regulation? Our contribution is a list of challenges for this emerging design space and a conceptual framework that addresses those challenges. The framework is composed of five interrelated strong concepts that we adapted from other design spaces. We derived the concepts reflectively, theoretically, and empirically through a design research process in which we created and evaluated a NF BCI, called Mind-Full , designed to help children living in Nepal who had suffered from complex trauma learn to self-regulate anxiety and attention. We add rigor to our derivation methodology by horizontally and vertically grounding our concepts, that is, relating them to similar concepts in the literature and instantiations in other artifacts. We illustrate the generative power of the concepts and the inter-relationships between them through the description of two new NF BCIs we created using the framework for urban and indigenous children with anxiety and attentional challenges. We then show the versatility of our framework by describing how it inspired and informed the conceptual design of three NF BCIs for different types of self-regulation: selective attention and working memory, pain management, and depression. Last, we discuss the contestability, defensibility, and substantiveness of our conceptual framework in order to ensure rigor in our research design process. Our contribution is a rigorously derived design framework that opens up this new and emerging design space of NF BCI's for children for other researchers and designers.

Children in North America are more likely to suffer from attentional challenges than any other mental health issue. Studies suggest that neurofeedback treatments may be useful for helping these children learn to self-regulate. Applying neurofeedback treatments in real-world, school settings poses a challenge, however, as these environments are often noisy and filled with distractions. The addition of ambient audio to neurofeedback systems may help reduce these disruptions. Further, research suggests that certain auditory treatments, such as binaural beats and white noise, may improve children's focus and aid memory recall. In the following paper we present the theories supporting this idea as well as a mixed methods framework for evaluating whether sound can help children focus while learning to self-regulate using a neurofeedback system. Specifically, we wish to investigate whether these treatments may help children (1) achieve an attentive state sooner and (2) maintain an attentive state for longer, when compared to the same system without sound.