Scientometric Study on Education, Training, and Exercise
Prepared for: Jerzy Jarmasz , Defence Scientist, DRDC Toronto [email protected] Alain Auger, Lead, S&T Outlook, DRDC Ottawa [email protected] Prepared by: Brenda Brady Strategic Information Analyst Knowledge, Information and Technology Services Branch National Research Council Canada [email protected]
NRC-KM Project #: BB16-10
DRDC Project #: FE22071714
Report submitted on: November 14, 2016
NRC-KM employees make every effort to obtain information from reliable sources. However, we assume no responsibility or liability for any decisions based upon the information presented.
Scientometric Study on Education, Training, and Exercise November 2016
[This page intentionally left blank]
Page 2 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table of Contents
1 Executive Summary ...... 6 2 Background ...... 8 2.1 Key Issues ...... 8 2.2 Key Questions ...... 8 3 Introduction ...... 9 4 Educational Technology ...... 9 4.1 Workplace Training ...... 11 4.2 Military Training and Technology ...... 11 4.3 U.S. Department of Defense Research Programs in the Area of Education and Training ...... 12 5 Physical Education and Training ...... 16 5.1 Physical Education and Fitness Publications: Topic Clusters ...... 18 5.2 Military Fitness and Physical Education ...... 24 5.2.1 Current U.S. Military Research Programs on Physical Fitness ...... 27 5.3 Physical Education and Fitness: Research Momentum ...... 28 5.4 Summary of Physical Education Topics and Trends ...... 33 6 Learning-Conducive Technologies ...... 33 6.1 Learning-Conducive Technologies: Top Terms Cluster Maps ...... 38 6.2 Military Research into Learning-Conducive Technologies ...... 45 6.3 Learning-Conducive Technologies: Research Momentum ...... 49 6.4 Summary of Learning-Conductive Technologies and Trends ...... 54 7 Holistic Approaches to Training and Learning ...... 55 8 Drivers, Barriers, and Opportunities in Education, Training, and Exercise ...... 57 8.1 Physical Education and Fitness ...... 57 8.2 Learning-Conducive Technologies ...... 58 9 Summary and Conclusions ...... 61 10 References ...... 63 11 Appendix A: Attachments ...... 78 12 Appendix B: Methodology ...... 78 12.1 Bibliographic Databases ...... 78 12.2 Market Reports, Web Sites and U.S. Defense Department Research Justifications ...... 81 12.3 Analysis ...... 81 12.3.1 Top Terms Cluster Maps ...... 82 12.3.2 Subject Groups and Sub-Dataset Comparisons ...... 82 12.3.3 R&D Momentum Indicator ...... 83
Page 3 of 85
Scientometric Study on Education, Training, and Exercise November 2016
List of Tables
Table 1. Summary of Findings ...... 7 Table 2. U.S. Army Research Lab: R&D Topics Related to Human Performance and Training ...... 14 Table 3. FY 2017 RDT&E Topics on Physical Training and Fitness ...... 27 Table 4. Selected U.S. DoD RDT&E Program Elements, FY2017 ...... 47 Table 5. Physical Fitness and Training: Drivers, Barriers, Opportunities ...... 59 Table 6. Learning Conducive Technologies: Drivers, Barriers, Opportunities ...... 60 Table 7. Search Terms: Physical Education and Fitness ...... 79 Table 8: Search Terms: Learning-Conducive Technologies ...... 80
List of Figures
Figure 1. Gartner's Hype Cycle for Education, 2016 ...... 10 Figure 2. Physical Education & Fitness, Top 321 Terms Cluster Map, Filter of 20% ...... 19 Figure 3. Physical Education & Fitness, Top Terms Cluster Map: Detail for Supplements ...... 20 Figure 4. Physical Education & Fitness, Top Terms Cluster Map: Detail for Sensors, Wearables, Smartphones ...... 21 Figure 5. Physical Education & Fitness, Top Terms Cluster Map: Detail for Exergames ...... 22 Figure 6. Physical Education & Fitness, Top Terms Cluster Map: Detail for Cognition Topics ...... 23 Figure 7. Physical Education & Fitness, Top Terms Cluster Map: Detail for Military Personnel ...... 23 Figure 8. Top Terms in the Military Subset of Physical Education Publications ...... 25 Figure 9. Physical Education & Fitness: Top 25 Subject Groups ...... 29 Figure 10. Research Momentum of Physical Education & Fitness Subject Groups, 2011-2015 ...... 30 Figure 11. Research Momentum of Physical Education & Fitness Subject Groups: Hot/Emerging Quadrants ...... 31 Figure 12. Research Momentum of Physical Education & Fitness Subject Groups: New/Disappearing Quadrant ...... 32 Figure 13. Physical Education & Fitness: Context-Aware and Vitamins, # Publications, 2011-2015 ...... 32 Figure 14. Learning-Conducive Technologies, Top Terms Cluster Map, Filter of 20% ...... 39 Figure 15. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Electrical Stimulation . 40 Figure 16. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Nootropic Drugs ...... 41 Figure 17. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Ethics and Drugs ...... 42 Figure 18. Ethical Discussions (n=139): Share by Technology Type ...... 43 Figure 19. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Supplements & Exercise ...... 43 Figure 20. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Military Personnel ...... 44 Figure 21. Learning-Conducive Technologies: Military Subset, Top Terms ...... 45 Figure 22. Learning Conducive Technologies: Top Subject Groups, ≥ 50 Publications ...... 49 Figure 23. Transcranial Direct Current Stimulation: No. of Publications, 2011-2015 ...... 50 Figure 24. Learning Conducive Technologies: Research Momentum, 2011-2015 ...... 51 Figure 25. Learning Conducive Technologies: Research Momentum, Hot/Emerging Quadrants ...... 52 Figure 26. Learning Conducive Technologies: Research Momentum, New/Disappearing Quadrant ...... 53 Figure 27. No. of Publications for Bio/Neurofeedback, Games, and Resilience Training, 2011-2015 ...... 54 Figure 28. U.S. TRADOC Human Dimension Integration Framework ...... 55
Page 4 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 29. Physical Education & Fitness Dataset: Global Dataset & Military Subset, No. Publications & Share of Subject Groups...... 82 Figure 30. Learning-Conducive Technologies Dataset: Global Dataset & Military Subset: No. Publications & Share of Subject Groups ...... 83
Page 5 of 85
Scientometric Study on Education, Training, and Exercise November 2016
1 EXECUTIVE SUMMARY DRDC commissioned this study on education and training in order to identify trends and research gaps in several areas of interest to defence agencies. For this project, two sub-topic areas were selected: physical education and fitness training, and technologies to induce or encourage an optimal state of learning. The study began with an overview of educational technologies generally, and then conducted a bibliometric analysis based on data retrieved from scientific and technical publications databases, complemented by a review of market reports and relevant research projects currently underway in the U.S. Department of Defense.
Key findings:
1. There are some significant differences between the technology landscapes for generic educational technology and physical education. Platform technologies are the norm for education generally, but not for fitness training. Physical education is dominated by two main technology streams – dietary supplements and wearable sensors or fitness trackers – and driven by demand from consumers and elite athletes, and not necessarily by institutional requirements. The diffusion of wearable technology is boosting interest in information and communications technologies, such as remote monitoring and cloud computing.
2. Technologies to induce a state of learning readiness are commercially available, but are less commonly applied to healthy subjects. Many of these technologies are the object of controversy, especially when they cross the increasingly blurry boundary between “therapy” and “enhancement”. The key technology streams are dietary supplements, nootropic drugs, and electrical brain stimulation.
3. Holistic approaches are well supported in doctrine, but there is little evidence of truly integrative approaches in practice, probably because of the complexity of learning, and the difficulties of scaling and operationalizing such approaches. Several technologies which act in a systemic fashion may be candidates for an holistic approach.
4. Drivers and barriers are similar for both physical education and learning-conducive technologies: the requirement for “readiness” and aspirations for advanced performance are drivers; safety, regulation, and ethical considerations act as restraints.
5. There are many and varied research opportunities in: foundational science (e.g., neuroscience, epigenetics, sleep, and nutrition), metrics, computational models, non-invasive sensors and brain-computer interfaces, safe (plant-based) supplements, and so on.
A summary of findings with respect to each of the key questions is provided in Table 1.
Page 6 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 1. Summary of Findings
Key Question Findings Established and Comprehensive learning platforms are well established in the field. emerging research The future of educational technology is continuous (anytime/anywhere), areas and technologies personalized, digital, adaptive, connected, collaborative, and social. in the field of education Workplace training platforms differ somewhat from academic educational and training technology. Workplace learning technology places more emphasis on social media, collaboration, productivity tools and performance support. Virtual and augmented reality plays an increasing role in “multi-sensory” and occupational learning. Military training is increasingly reliant on live, virtual, and constructive approaches (simulations) for operational training. Established and Key technologies are dietary supplements and sensors and other wearable/mobile emerging technologies technology. Research momentum is much more pronounced for electronic devices for physical or fitness and information technologies. Social media and ubiquitous technologies such as training smartphones are being harnessed to encourage behavioural change. There is an absence of full-featured platforms technologies. Military research is focused on validation of standardized tests. Obesity and overweight have been identified as epidemiological trends, but are not the primary focus of current technological research. Established and Key technologies include dietary supplements, nootropic drugs, and electrical brain emerging technologies stimulation. Most technologies were first applied in rehabilitative settings. that monitor, stimulate, Of the key technologies, electrical brain stimulation shows the greatest research or otherwise encourage momentum over the past five years. a state conducive to Technologies to enhance/induce better performance in healthy individuals face learning strong ethical challenges, as well as a lack of foundational/clinical research. Evidence of a holistic Holistic approaches are supported by doctrine, but there is little evidence of their approach to training practical application. Certain technologies which are systemic in their effect may be candidates for a more holistic approach. These include dietary supplements and electrical brain stimulation. Combinatory approaches (multiple technologies applied concurrently) are also reported in the literature. Differential effects and the complexities of learning make a holistic approach difficult to operationalize. Drivers, barriers, and Drivers: aspiration for optimal physical and cognitive performance; desire to opportunities for accelerate training timelines and render it effective and efficient; training demands of education and training military missions; standards-based performance; demographic and epidemiological technologies factors such as obesity, increasing combat role for women; and national initiatives in neuroscience. Barriers: in some cases, a lack of clinical evidence, and differential effects; validity of certain technologies (e.g., sensor signals); cost; privacy and security concerns; regulation; difficulties with scaling a personal approach; ethical concerns. Opportunities: foundational research in neuroscience, epigenetics; brain-computer interface; validation studies; development of educational platforms for physical education training; focus on hot issues such as obesity or injury; incorporation of physical fidelity in live-virtual-constructive training; holistic or combinatory approaches; metrics performance optimization.
Page 7 of 85
Scientometric Study on Education, Training, and Exercise November 2016
2 BACKGROUND
In order to assist with long-term R&D planning and the prioritization of research topics, scientometric studies are being commissioned by DRDC to provide a high-level overview of global research and development activity in certain scientific domains. These studies will assist DRDC in uncovering and understanding the potential impact of new research on future defense and security capabilities and operations.
This scientometric study will support an upcoming Canadian Army workshop and a presentation on future science and technology trends in learning and training. Within the broad domain, and to make effective use of project resources, two sub-topics were chosen as areas of focus: 1.) technologies and science in support of physical exercise training, and 2.) emerging and potentially game-changing technologies that have the potential to induce states conducive to learning of all types.
Thus, this study will examine broad trends and technologies in the area of education and training, with a focus on novel technologies for physical training, as well as technologies which monitor, stimulate, or otherwise encourage a state conducive to learning. The report will examine market and technical literature to investigate trends, drivers, and impediments to adoption. The study should also identify potentially game-changing outliers. The ensemble of analyses will suggest future directions for research into optimal and effective military training for DRDC and its clients, based on trends seen in the literature. 2.1 Key Issues The objective of this scientometric study is to detect and categorize or summarize R&D activities from the available literature in order to identify established and emerging trends and technologies. Results of this project will be used to ascertain future trends and technologies that could have a significant impact on the defence community in the area of training and education. 2.2 Key Questions 1. In brief, and based on a review of market literature, review articles, and industry reports, what are the current and emerging research areas and technologies in the field of education and training?
2. What are the established and emerging technologies for physical education or fitness training?
3. What are the established and emerging technologies that monitor, stimulate, or otherwise encourage a state conducive to learning, both cognitive and physical?
4. Is there any evidence of a holistic approach to training or performance, one which links and/or encompasses training methods and technologies for cognitive or physical performance? If so, which technologies and methods support a holistic approach?
5. What are the drivers and barriers, strengths, weaknesses, and opportunities for education and training technologies identified in questions 2-4?
Page 8 of 85
Scientometric Study on Education, Training, and Exercise November 2016
6. What are the SPEED (Social, Political, Economic, Environmental and Defence) factors impacting education and training?a
3 INTRODUCTION
To answer the questions set forth in the mandate, a variety of sources were consulted. These included market reports and roadmaps, review articles, research solicitations from the U.S. Department of Defense, as well as two datasets of bibliographic references for scientific and technical publications.
For key question 2 (physical education and fitness), the final dataset comprised 4,646 records. For key question 3 (learning-conducive technologies), the final dataset comprise 1,902 records.
A complete description of the search methodology, sources, and processing tools is included below in Section 12.
4 EDUCATIONAL TECHNOLOGY
Reports consulted for this project characterize the future of education as continuous (anytime, anywhere), digital, personalized, adaptive, connected, collaborative and social. Platform technologies, such as learning management systems, are well-established in the field. They combine learning objects and experiences with learner-facing applications that enable personal, adaptive, and social functionality. Learning platforms also offer provide authoring, assessment and administrative tools.
Educational technology can be considered a subset of the lucrative and burgeoning Internet of Things (IoT). According to a 2016 report from Frost and Sullivan, the educational IoT was valued at more than US$11 billion in 2015. This connected learning system will prepare students for the workforce and cater to their lifelong learning needs. Digital and dynamic learning systems, hyper-connectivity and actionable analytics (based on data from outcomes, learning paths, entry/exit patterns) will gradually move the educational sector away from a one-size-fits-all learning model to one that is personalized, adaptive, and competency based. Students will learn anytime and anywhere, using a variety of devices, and at their level of proximal development. Increased communication and collaboration will enable a more accessible global learning experience.1,2
A dazzling array of technologies supports this vision: laptops, smartphones, iPads, speech technologies, augmented reality, streaming videos, cloud computing, machine-to-machine communications, social media, artificial intelligence, diagnostic/prescriptive/predictive analytics, and cybersecurity solutions, to name but a few. Even though the education industry is still primarily in the “watch and learn” phase of implementing an education IoT, with the bulk of implementations in the higher education and vocational segments, educational institutions are expected to increase their information and communications technology (ICT) budgets by 11% in 2017.3
Renowned IT consultancy Gartner largely concurs with the Frost and Sullivan analysis. According to Gartner, the education industry invested a record US$3.76 billion in the first three quarters of 2015, with investments driven by the need to retain learners, improve outcomes, and make education both a For the Emerging Technology Snapshot (trend card), to be produced after completion of the main report.
Page 9 of 85
Scientometric Study on Education, Training, and Exercise November 2016 scalable and affordable. Gartner’s annual hype cycle for education (Figure 1) depicts technologies such as Li-Fi (light fidelity or optical wireless communications), virtual reality, smart machines, affective computing, MOOCs (massively open online courses) and competency-based platforms as being at the rising or “breakthrough” phase of adoption.b Big data and analytics are at a more advanced stage of adoption, and gamification, BYOD (bring your own device) and mobile learning (via smartphones) have entered the mainstream.4
Figure 1. Gartner's Hype Cycle for Education, 2016 (Permission for use of this image in this report was granted by Gartner Inc. The original can be found in [5].)
Gartner’s top ten strategic technologies for higher education in 2016 are:5
1. Adaptive, personalized learning: enabled by algorithms which adjust content. 2. Predictive analytics: to predict behaviours or outcomes, improve success and increase retention. 3. Customer relationship management: to track and manage relations with constituents at all phases of student life cycle, and to engage parents, alumni, corporations, and others. 4. Exostructure: architectures and standards for interoperability with other systems in the cloud. The exostructure leverages partnerships, content, tools and services in the educational ecostructure. 5. Open microcredentials: the use of badges or points for granting credentials for skills learned at the sub-diploma level, across all environments.
b For a description of Gartner’s hype cycle methodology, a portrayal of how technologies may evolve over time (based on the views of subject matter experts and industry participants) see http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp .
Page 10 of 85
Scientometric Study on Education, Training, and Exercise November 2016
6. Digital assessment: digital methods to create, report, administer tests and exams. 7. Smart machines: to generate analytics and advice, to boost productivity, and to target complex data sources such as Facebook, Twitter feeds, as well as large volumes of traditional data. 8. Open education resource ecosystem: to leverage content that is freely findable and available. 9. Listening and sensing technology: comprises a broad collection of virtual capabilities that range from proximity beacons to social listening and sentiment analysis through capture and interpretation of social activities, such as tweets, to technologies that operate in the IoT. Used to understand student movements and sentiments on campus and to deliver content (and largely modeled on similar retail “listening” technology). 10. Collaboration technology: for research, education, and outreach. Not a new trend, but one which has grown in importance, as the educational ecosystem grows more global and hyper- connected. 4.1 Workplace Training Workplace learning occurs outside the larger and more traditional K-12 or higher education environment. Like generic educational delivery, it will be continuous, virtual, collaborative, and heavily dependent on information technology. Bersin by Deloitte, a consultancy specializing in human resource management and training, describes a “continuous learning stack” which enterprises may leverage organizational learning. The stack includes some technologies specifically designed for learning, such as massive open online courses (MOOCs), e-books, and learning portals, as well as others – notably social media and platforms, search and “help” software – which are easily adapted for learning purposes. Productivity tools, such as search and texting applications, and performance support platforms (embedded coaching and instructional tools) are also featured in Bersin’s interpretation.6
Another key workplace training technology will be augmented and virtual reality (AR/VR). Gartner notes that AR/VR training has been pioneered in medical schools, and is beginning to have an impact in athletics.4,7 Frost and Sullivan also identifies AR/VR as having significant future potential in delivering complex training applications and immersive learning experiences in the workplace: “As multi-sensory learning has always been beneficial in transmitting information, many industries are expected to turn to AR as the prominent source of instructional learning.” 8,9 Frost and Sullivan predicts that AR/VR technology will converge with smartphones and wearables, widening the horizon for AR/VR and making for a more diverse range of applications.8 They also estimate that the the market for AR/VR in education will be worth US$15 billion by 2025.10
In another 2016 report, Goldman Sachs places a lower value on the 2025 educational AR/VR market (US$0.7 billion), but states that AR/VR will become a “standard tool” in educational institutions and in workplace training. The report also notes that AR/VR is a common feature of military training.11
4.2 Military Training and Technology A 2000 overview of military training published by the Institute for Defense Analyses notes that “military training is distinguished from other forms of training by its emphases on discipline, just-in-case preparation [i.e., structured around emergency and/or combat scenarios, which may only comprise 10% of actual operational conditions], and the training of collectives.”12 Such training must also: address a broad range of military careers, from administrative support to special operations; reflect changing and uncertain environments; and develop proficiency in new platforms and weaponry ranging from communications systems to unmanned aerial systems. The desired endstate for today’s warriors is
Page 11 of 85
Scientometric Study on Education, Training, and Exercise November 2016
“readiness”, at once physical, psychological, and skill-based. As well as being effective, today’s training must also be affordable, realistic, and delivered in the shortest timespan possible.13-15 Since 2000, military budgets, including those for training, have faced significant reductions, while global confrontations and the need for well-prepared troops continue to increase.15,16
Over the last decade, a key strategy of military training has been the provision of live, virtual and constructive (LVC) training, defined as follows:17
Live: a simulated exercise that utilises personnel and actual operational equipment and systems in a training environment (usually a military operated training range). Virtual: a simulated exercise that utilises simulated equipment that replicates the capabilities of real operational equipment, providing personnel with necessary learning in motor, decision making and communications skills. Constructive: a simulated exercise where people, assets and capabilities are generated and controlled by computers.
The training environment may combine different elements of LVC and may be entirely synthetic, a complete recreation of the real world conditions (including sensory and cognitive stressors) in realistic training scenarios encountered by both individuals and teams.17,18 It may also be distributed or embedded at the point of need, e.g., within flight displays.19,20 Frost and Sullivan has stated that demand for increasingly complex training and realistic virtual environments is driving innovation in mixed reality training (with live training supplemented by virtual environments) and portable solutions: “By 2020, nearly all exercises will include some sort of mixed reality constructs and/or devices. As the lines between live, virtual and constructive training continue to blur, different segments will need to be developed to more accurately define the training and simulation market structure.”21
Not only can LVC training be immersive and engaging, it is safer and can reduce costs substantially since it simulates expensive or risky real-world environments and platforms such as warships. 15,17 With asymmetric and conventional conflicts increasing and defence budgets shrinking, Frost and Sullivan estimates that the value of the global LVC market will reach US$8.09 billion by 2023, with a “stable” compound annual growth rate (CAGR) of 0.7% from 2014-2023.15 4.3 U.S. Department of Defense Research Programs in the Area of Education and Training Although a literature search on military training technologies was not commissioned for this study, Broad Agency Announcements (BAAs) and Research, Development, Test and Evaluation (RDT&E) budget requests published by the U.S. Department of Defense (DoD) were consulted, in order to identify potential future research tangents. While these are only representative of the U.S. military research agenda, these documents may also be considered as a proxy for the future objectives of its coalition partners.c
Reproduced below in Table 2 are selected research topic solicitations in the area of human performance from the U.S. Army’s current Broad Agency Announcement and RDT&E reports.22,23 As well as touching on many of the educational technologies described above for all segments of education – e.g., c Many of the same topic areas are cited in documentation on NATO’s multimedia library on Education and Training: http://www.natolibguides.info/training/home
Page 12 of 85
Scientometric Study on Education, Training, and Exercise November 2016 personalized, adaptive, distributed, and digital training – the research solicitations confirm the strong military interest in improving simulated (LVC) environments. They also suggest gaps and new avenues in the field of training and performance, such as: practical applications of neuro/cognitive science; development of human-computer interfaces; the use of learner data, models and analytics; studies of the impacts of LVC on team training; and the definition of training requirements for future autonomous systems.
Page 13 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 2. U.S. Army Research Lab: R&D Topics Related to Human Performance and Training
Program Element Description Soldier Performance Proposals are requested involving … R&D that advances and improves human factors Research design principles and guidance for sensory, perceptual, cognitive, and physical performance while providing the … information necessary for effectively designing systems that are best suited to the operator, maintainer, or trainer. Proposals for technology for collecting sensory, cognitive and physical performance data (including biomechanics data) in field environments are also requested. Live, Virtual, This effort develops and investigates LVC training technologies to inform an Constructive interactive, seamless training environment. In FY 2017, it will design and develop Simulations physics-based dynamic effect algorithms and synthetic terrain components to enable the Army's One World Environment for training; develop a cloud-based architecture to support adaptive training; develop prototype technologies, such as performance assessment, mobile application use, data analytics, and social media and assess impact on training and education for operational systems; validate methods to measure, track, and manage general learning outcomes that will feed a continuous adaptive learning model; and design and develop artificial intelligence algorithms to enable intelligent and believable behaviors of virtual characters that can be reused across virtual, constructive, virtual and gaming domains. Neuroscience The US Army Research Lab (ARL) is seeking to understand the nature of the human- system interactions and use that information combined with technological advancements… to create enhanced and total novel interaction technologies for tomorrow's Soldier. This effort requires the translation of laboratory- based neuroscience research and methods to militarily-relevant environments, to include training and virtual environments, in order to enable system designs that exploit the capabilities of both Soldier's brain function and systems to maximize Soldier-system performance. Research is focused on the capability to sense, process, and extract critical information about brain activity in dynamic, complex environments; characterizing and modeling neural processes in real-world contexts and those processes critical to Army-relevant operational tasks; and creating and extending novel brain-computer interaction technologies which have the potential to revolutionize the basic interactions Soldiers have with their systems and each other. This effort also addresses the rapid technological advances in areas such as sensing, computation, robotics, microelectronics, and computer and network information systems that are outstripping the capabilities of the human brain and limiting both Soldier-system effectiveness and the Army's ability to fully exploit investments in these new technologies. Also of interest are new approaches for understanding and predicting the behavior of individuals and groups, especially those that elucidate the neurobiological basis of behavior and decision making; new approaches for training individuals and teams, including embedded training and simulation; and understanding and improving team performance. Social/Cognitive ARL’s efforts focus on two areas: socio-technical network operations and network- Network Science enabled cognition with both areas sharing the goal of the improving cognitive performance for collaboration and decision making in complex network-enabled operations. The research contributes to the development of theory, measures, models, and understanding of social networks and the cognitive implications of those networks and ultimately will guide the design of human-team-system interaction aligned with future operational systems.
Page 14 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Program Element Description Adaptive and Proposals are requested to design, develop, apply, and evaluate artificially-intelligent Intelligent Training agent technologies (e.g., computer-based tutors, virtual humans, process agents and Technologies authoring tools/methods) to enhance training effectiveness and reduce associated training support costs. The goal of this research is to enhance the realism, adaptability and decision-making skills of artificially-intelligent computer-based tutors and virtual humans to support one-to-one and one-to-many training experiences where human support is limited, impractical, or completely unavailable. Technical challenges include the development/application of intelligent agents that can adapt in complex, ill- defined domains; understanding natural language in multi-sided conversations with trainees; rapid authoring of effective computer-based tutors for individuals and teams, and realistic virtual humans. Anticipated capabilities include computer-based tutors on par or better than expert human tutors and realistic virtual humans that are so visually and cognitively realistic that they are indistinguishable from humans. Advanced Training The Simulation and Training Technology Center is investigating innovative, license-free Technologies tools, and techniques to train and educate Soldiers and leaders in individual and team knowledge, skills, attributes, and abilities in order to execute full spectrum operations in an era of persistent conflict. Whitepapers are solicited for areas such as: Next- Generation Learning Management Systems; user-friendly authoring tools with minimum train-up time and that support the rapid development of learning principle- based content across several platforms (e.g. game-based, mobile, virtual worlds); Content Management Systems that are data-driven and capable of taking doctrine, training requirements, historical records, and other user inputs, creating a linkage between the set, and auto-generating content for courseware across multiple, varied platforms at once; Social Media and Social Networks for Learning in and out of the classroom; and other innovative learning technologies in support of the Army Learning Model. Simulation In order to support future training, experimentation, and acquisition decisions there is Interoperability a need to refine and demonstrate advances in computer science to develop next Technologies and generation architectures for Modeling and Simulation (M&S), including considerations Future Architectures for Live, Virtual, Constructive, and Gaming environments. Immersive Learning We seek advances in virtual environment research that improves the sense of presence and engagement in a virtual environment, improves the realism of a virtual environment and the ability to visualize information, improves the user’s experience and within the environment, and provides the ability for multiple users to edit the environment on-the-fly. The user interface should not limit but rather should augment the virtual experience by providing realistic feedback from experiences within the virtual environment. The interface should also provide subtle environmental effects to the user and improve the user’s ability to navigate through and within the environment through natural means. FY 2017 plans: will investigate the effectiveness of using realistic human driven avatars (puppeteering) in training applications on improving human performance; investigate effectiveness of current Army applied virtual distributed learning training; identify capability gaps in small team training as it relates to the Army’s Synthetic Training Environment; develop automated authoring tools to supplement traditional classroom training with computer-guided tutoring per ALM; and mature intelligent tutoring system domain models to represent Army training domains; assess effectiveness of instructional models for unit-level team tutoring.
Page 15 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Program Element Description Future Autonomy: This effort will research and develop simulation architectures, tools, and models that Optimizing Training can represent current and future semi and fully autonomous systems. The Strategies architecture, tools and models will enable the evaluation of the training impacts (i.e., cognitive, physiological, and team coordination) of future autonomous systems and technologies on individual, crew, and unit tasks. The training demands of systems that are increasingly complex, intelligent, and self-adaptive far exceed those of legacy systems that require training of primarily procedural tasks. This is compounded by parallel increases in autonomy and responsibility at lower echelons. FY 2017 plans: will conduct experiments to assess effectiveness of best practice training strategies for autonomous systems.
5 PHYSICAL EDUCATION AND TRAINING
A brief review of market literature in the area of sports or other physical education and training did not identify any comprehensive and full-featured platforms, akin to the learning management systems and portals seen for academic and workplace learning. Instead, two major thrusts of commercial activity targeting consumers and professional athletes were found:
1) sports nutrition and supplements, and 2) wearable computing and mobile devices such as smartphones used to monitor personal biometrics and performance or team performance (technique).
With regard to sports nutrition and supplements, the market definition provided by BCC Research includes proteins, carbohydrates, fats, minerals, vitamins, amino acids, stimulants such as caffeine or guarana, herbal supplements and a host of other products.d These are ingested both before and after exercise to boost performance, improve recovery, build muscle and increase physical stamina and strength. Traditionally consumed by professional athletes, these products are becoming more and more appealing to ordinary consumers as the focus of market demand shifts towards maintaining weight, refreshment, boosting consumption of protein and improving mental performance.24
According to BCC Research, consumption of sports nutrition products and energy supplements is driven by: the global trend to urbanization, according to which more consumers are exposed to health clubs in urban areas; increasing health awareness and desire for weight management; growth in the numbers of “lifestyle” consumers who favour health related products; and aggressive marketing and branding campaigns featuring elite athletes and media personalities.
Challenges to the use of sports nutrition products include safety issues, undesirable side effects, regulation, and (unsubstantiated) claims. BCC estimates that the 2020 market value of these products will be US$65 billion, with a CAGR of 10% between 2015 and 2020.24
In the area of wearable computing or portable devices, the public’s love affair with fitness trackers is accelerating adoption of the broader category of wearables (which may include clothing with embedded d Some commercially available foods, drinks, or supplements also contain substances such as anabolic steroids or synthetic human growth hormone, which are banned by the World Anti-Doping Agency.
Page 16 of 85
Scientometric Study on Education, Training, and Exercise November 2016 biometric sensors) for both personal and medical use. Management consultancy Accenture has estimated that the number of U.S. consumers who own a wearable fitness device will rise from 22% in 2016 to 43% by 2020.25 Consumer-style wearables can monitor heart rate, location/acceleration, sleep, skin temperature, etc., and can communicate with the user and with other devices, often via fitness apps on smartphones. Most of these devices are not regulated by public health agencies, as they are deemed to be “low risk” wellness products.26
According to Frost and Sullivan, the 2020 value of the global market for wearables in healthcare will be US$18.9 billion. It will be split into two segments: 1) a wellness, fitness and sport category ($10.6 billion in 2020, with a five-year CAGR of 27.8%), and 2) medical/clinical category ($8.3 billion, with a five-year CAGR of 32.9%). A major restraint on adoption is increasing concern about the privacy and security of personal data, especially in cloud or mobile environments.27
Frost and Sullivan also projects the global value of wearables in sports (i.e., for elite athletes and professionals as well as enthusiastic amateurs) to be US$12.11 billion in 2020, with a five-year CAGR of 23%. Frost and Sullivan states: “The use of wearables in sports is being increasingly seen as a source of competitive advantage. Adoption of wearables is gaining traction, as athletes and trainers are highly appreciative of the benefits that wearables offer. The increasing miniaturization of sensors and affordability of cloud computing is making wearables a potent weapon in training.” Drivers for adoption include injury prevention, adoption of evidence-based training techniques, and growing numbers of “pro-sumer” athletes. Restraints include questions around the accuracy and robustness of the devices, lack of evidence concerning effectiveness of solutions or return on investment, and insufficient awareness on the part of some coaches.28
In another study published by Lux Research in 2016, the authors found that a third of users stopped using their device within six months, possibly due to poor usability, bulkiness or lack of appeal, but “ultimately the lack of actionable insights is considered these technologies’ biggest pain point.” Sensors need to include more intelligence, non-invasiveness, and functionality, according to Lux; “invisible” or unobtrusive form factors such as gels, tattoos, or miniaturized devices are to be preferred; and multi- function, multiplatform devices and effective algorithms are also required. Furthermore, “the ability to bring about a behavioural change is the holy grail of sports wearables.” As wearables edge towards more medical grade features and quality, regulation will also likely be an issue.29
Page 17 of 85
Scientometric Study on Education, Training, and Exercise November 2016
5.1 Physical Education and Fitness Publications: Topic Clusters To assess trends and outliers for physical education and fitness training as described in the research literature, searches were conducted in a variety of databases, using the search strategy described in Section 12.1Error! Reference source not found.. A total of 4,646 publication records were retrieved. A omposite keyword field was created from the descriptor field and words and phrases extracted from the title field. Terms were then cleaned and ranked. The top 321 terms (those with ≥ 30 publications) were used to make a map showing topical clusters and their rate of correlation (co-occurrence within a single document). In the cluster analysis, one term, Physical exercise, was omitted because of its large size (1,375 publications) and its high degree of correlation with other topics.
A complete description of the clustering methodology appears in Section 12, and an enlarged version of the map included here (Figure 2) is contained in the Tableau workbook appended to this report. In the map, nodes are sized relative to the number of publications linked to a topic. On the connecting line (edge) between nodes, a number indicates the coefficient of correlation.
Figure 2 shows the top terms clustered and filtered to a correlation coefficient of 20%, such that the most important terms and inter-relationships are portrayed. The largest and most highly correlated cluster of topics appears in red at the mid-right position in the graphics: it describes a variety of topics related to dietary supplements. A detail of this cluster is reproduced below in Figure 3.
On the left there appear several multi-coloured (turquoise, magenta, purple) clusters referring to a variety of information and communication technologies (ICTs): electronics, sensors, smartphone apps, wearables, exergames, etc. Details for these clusters appear below in Figures 4 and 5.
While these two major topic areas, supplements (focused more on the human subject) and ICTs (focused more on the technology to collect data and deliver insights), appear to be virtually equal in size and importance, an analysis of research momentum (see below, Section 5.3) clearly indicates that growth in research interest is associated much more with the sensors/electronics than with supplements and nutrition.
Page 18 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 2. Physical Education & Fitness, Top 321 Terms Cluster Map, Filter of 20%
Page 19 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 3 shows the central importance of Dietary supplements in the data and its association with sub- topics such as Nutrition, Endurance training, Exercise testing, Musculoskeletal system, Protein, etc. Many of the articles in this cluster report on the results of clinical trials or other experiments, newly identified biomarkers and blood tests for fatigue, links with performance improvements, the immunological effects of supplementation, adverse effects, associations of exercise with muscle damage, and so on.30-33 Ergogenic agents (i.e., performance enhancing drugs) are also seen in a turquoise cluster at top right, linked to Caffeine and Stimulants.34-36
Figure 3. Physical Education & Fitness, Top Terms Cluster Map: Detail for Supplements
Page 20 of 85
Scientometric Study on Education, Training, and Exercise November 2016
At the left of the main map there appears a somewhat less tightly linked assortment of nodes discussing the use of wearables, sensors, etc., in physical activity (detail shown in Figure 4). One of the most common sensor types used in these applications is the Accelerometer, which is used to measure speed and motion. A significant number of documents discussing accelerometers also mention a technical issue related to their use, that of Reliability and validity. For instance, a 2013 publication states: “No definitive evidence currently exists to indicate which are the most valid and reliable accelerometer models for use with different kind people.” 37 The theme of validity recurs in 2016 titles, with the technology still being found wanting in measurement accuracy.38-40
Information technologies used to process sensor data and to recognize and classify activities, such as Machine learning or Artificial intelligence, also appear at the left of the detail.41-43 Figure 4 also shows the connection between certain technologies such as Smartphones or Social media for health promotion or other applications, such as weight management, 44 which require behavioural change or persuasion.45,46 The publications also report several instances of applications which exploit both wearable technologies and social media for motivating “social workouts” and increasing persistence.47-49 This recalls the social elements in the workplace learning stack described by Bersin by Deloitte.6
Figure 4. Physical Education & Fitness, Top Terms Cluster Map: Detail for Sensors, Wearables, Smartphones
Page 21 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 5 provides a detail showing Sports and Sports training (yellow nodes) and their connection with games, in blue. Interactive games are cited in these publications for their motivational effect and the greater engagement they provide. A well-designed interface (such as haptic feedback or other featured interactivity) is regarded as a key characteristic.50-53
Not shown in the cluster is a node for Virtual reality. Although this topic appeared in the “top” list for the dataset, with 143 records, correlations with other topics fell below the filtration level of 20%, so it was excluded from the map. The strongest correlations for Virtual reality were with Simulation (19%), Exergames (17%), User interfaces (17%), Interactive technologies (16%), and Sports training (15%). Articles for the Virtual reality node described avatars and realism, immersive qualities and personalization, as well as its contribution to effectiveness in sports training.54-56
Figure 5. Physical Education & Fitness, Top Terms Cluster Map: Detail for Exergames
Page 22 of 85
Scientometric Study on Education, Training, and Exercise November 2016
At the top right of the main map, an isolated cluster of Cognition-related topics appears (Figure 6). Since the primary search strings used to retrieve the physical education data had to do with fitness and exercise, it may not be surprising that these terms, although in the “top” of the list of descriptors, do not correlate strongly with other topics in the map. In the second dataset retrieved for this project and discussed later in this report (publications on human enhancement and performance optimization), a stronger connection between cognition and exercise is seen. The publications underlying Figure 7 do broach similar topics, for example, the growing evidence for synergies and connections between exercise activity and improved cognitive function. 53,57,58
Figure 6. Physical Education & Fitness, Top Terms Cluster Map: Detail for Cognition Topics
A final detail from the main map (Figure 7) shows connections between topics such as Military personnel and Military training with other subject terms. The defence nodes link indirectly with Wearable devices (in green), but the main cluster, in orange, includes topics such as Obesity, Body mass, Body mass index (BMI), and so on. Studies in this group are predominantly epidemiological in nature, and do not often describe interventions or technological “fixes.59-62 Further discussion of the link between military personnel and body weight is included in the next section of this report.
Figure 7. Physical Education & Fitness, Top Terms Cluster Map: Detail for Military Personnel
Page 23 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Notably missing from the list of top terms and from the cluster map are references to integrated, full- featured “platforms” for the delivery of physical education or fitness training. Where references do exist in the dataset, they usually refer to web-based, cloud-hosted prototypes incorporating rich multimedia, simulations, sensor data, and analytics.63-66 5.2 Military Fitness and Physical Education Physical fitness training has been a staple of all defence training for well over a century. New recruits are subjected to rigorous boot camps and are re-tested regularly throughout their military career. Beginning from an approach that favoured assorted drills and physical labour tasks, the curriculum has gradually evolved into standardized tests that combine push-ups, pull-ups, runs, climbing and jumping drills, along with other functional tasks (e.g., stretcher carrying) that reflect the operational and occupational requirements.67,68
The current Canadian minimal fitness standard, delivered through the Fitness for Operational Requirements of Canadian Armed Forces Employment (FORCE) program, uses four test components: a sandbag lift, intermittent weight-carrying shuttles, 20-metre rushes, and a sandbag drag.69 Since 1980, the U.S. Army uses a Basic Training Physical Fitness Test (also known as the Army Physical Fitness Test or APFT) for new recruits: it is a series of timed push-ups, sit-ups, and a two-mile run. The validity of the APFT test is currently under review, under scrutiny for its ability to measure endurance, strength, and mobility.70 Physical training continues through the military occupational life cycle as part of general “readiness” assurance.
To assess the research landscape for the military domain, a subset of 436 titles was created from the comprehensive basic physical training dataset of 4,646 publications. These publications featured either military author affiliations or discussed military fitness training or personnel.
A frequent topic of discussion in the subset of publications is the constant revision of the tests, training programs, and standards used to assess military fitness. Many of the recent publications attempt to validate current methods and standards, or discuss how they apply to changing military populations (for example, studies of how gender differences may align with training and standards).71-77
Page 24 of 85
Scientometric Study on Education, Training, and Exercise November 2016
A list of top terms (≥ 40 publications) in the military subset appears in Figure 8.
Military personnel 268 Physical fitness 237 Physical exercise 139 Physiology 114 Military applications 85 Physical education and training 68 Training 66 Injuries 58 Military training 52 Endurance 47 Body mass 46
Body composition 46 TopTerms Endurance training 45 Performance (Human) 44 Strength (Physiology) 42 Risk factors 42 Exercise testing 42 Body mass index (BMI) 42 Stress (Physiology and Psychology) 41 Muscle strength 41 Physical activity 40 0 50 100 150 200 250 300 No. Publications, 2011-2016
Figure 8. Top Terms in the Military Subset of Physical Education Publications
In the military subset, there is far less discussion of technological innovations applied to fitness training than is the case for the complete dataset. For instance, in the comprehensive dataset, from subject groups created from terms, technology subject groups are referenced in 76% of the data; for the military subset, the technology subject groups appear in only 55% of the records.e
In the military subset, the most frequently cited solutions involve either supplement use (around 108 publications or 25% of the military subset when all supplement-related subjects are combined) or sensors, fitness trackers or other wearables (92 titles or 21% of the military subset when all ICT-related terms are combined). These proportions are roughly similar to those seen in the global dataset.
Data sourced from outside the publications dataset acknowledge that supplement use is widespread among Canadian military personnel. In the recently published Department of National Defence Health and Lifestyle Information Survey of Canadian Armed Force, the overall (self-reported) usage rate for all supplements, including energy drinks, protein powders, weight loss products, vitamins, minerals, and other supplements, was 74.7% in 2013-2014. Supplement usage was higher in females, in younger members of the forces, and for land-based troops.78 In another recent U.S. study (2016), it was reported e A complete listing of the subject groups, with numbers of publications and the percentage coverage in the datasets (both the global physical education and fitness dataset and the military subset, appears in Section 12.3.2 of this report.
Page 25 of 85
Scientometric Study on Education, Training, and Exercise November 2016 that 60-70% of military personnel used dietary and nutritional supplements, compared to about 50% of the civilian population.79
Studies in the military subset of data retrieved for this project discuss the use of specific substances (e.g., beta-alanine, quercetin, leucine, caffeine, vitamins and minerals), possible side effects, and the impact of supplements on health and overall physical capacity.80-85 A research program outside the chronological bounds of this study (DARPA’s Peak Soldier Performance Program, dated 2004) also investigated endurance and strength building nutrients and stimulants that could be used to improve physical performance.86
Wearables, body sensors, and vital signs measurement of various types are mentioned in around 21% of the military publications dataset, more often in the context of measuring basic physiologic indicators and validating training than as a tool for personalizing training or delivering interventions.87-90 However, a trial of one commercial body-worn sensor (BlackGhost) is described in an article on how the data collected can be used to measure performance and inform future training,91 and several articles describe interventions or coaching based on wearables that are designed to improve fitness and lower risk.92-94 Several of the publications mention the importance of lightweight, energy-efficient, unobtrusive and non-invasive form factors, such as tattoos, as the wave of the future for sport and military populations.95-98 It should also be noted that fitness trackers, while popular among military personnel, are only beginning to be officially recognized and permitted (in certain circumstances) by military agencies.99
Somewhat surprisingly, given the military emphasis on virtualization and simulation in overall training, it does not appear that augmented and virtual reality correlate strongly with physical education in the military research literature. Virtual and/or augmented reality are cited in only 10 publications in the military subset. One recent publication from the Tianjin University of Technology and Education describes how AR/VR could have beneficial effects (e.g., increasing engagement) in physical training for military and general populations.100 A 2013 thesis from the U.S. Naval Postgraduate School describes VR physical education training as “next-generation” 95 and a 2016 U.S. Army Research Laboratory study examines the use of avatars and their capacity for driving affective changes in motivation and attitude.101
As noted above, a far greater concern in the military publications subset is the prevalence and impact of obesity on the military population. Fully 22% of the publications in the military subset, versus 11.2% in the global physical fitness dataset, mention some aspect of obesity or overweight. These publications study prevalence, cause, effect (risk factors), and in some cases, intervention or training programs.102-107
As a point of additional interest, in self-reported data from the recently published Department of National Defence Health and Lifestyle Information Survey of Canadian Armed Forces Personnel 2013/2014, 49% of those surveyed identified as being overweight, and 25% stated that they considered themselves obese, representing a slight increase over the previous (2008) survey.78 In 2015 data from the U.S. Defense Health Agency, 73% of personnel were classified as overweight or obese.108 A 2016 article in Army Technology also suggests that fitness trackers could help alleviate the situation, but also points out the security risks (i.e., the broadcast of GPS location) of using such devices in the field.109
Page 26 of 85
Scientometric Study on Education, Training, and Exercise November 2016
5.2.1 Current U.S. Military Research Programs on Physical Fitness Few examples of technology-based innovations in the area of physical training appear in this year’s DoD research and development budget requests (Research, Development, and Test and Evaluation, or RDT&E reports). Curiously, none of the current RDT&E reports reviewed mentioned research programs targeting weight management, although the Pentagon is known to be reviewing fitness standards in light of the obesity epidemic.110
Some examples of fitness/physical performance research topics sourced from this year’s RDT&E reports for the U.S. Army and Navy appear below in Table 3.23,111 While the research proposals do make some mention of the use of electronics to monitor fitness in training environments, the greater emphasis appears to be on injury prevention and defining the physical components of simulated training. The collection of soldier-level data described in these projects will support optimal training and potentially, a degree of personalization. In the Table below, technology terms and end-uses are in boldface.
Table 3. FY 2017 RDT&E Topics on Physical Training and Fitness
Agency Title Description U.S. Army Flexible Electronics Program Will conduct applied research on the integration of electronics, power components, and sensors on non-traditional flexible substrates. The applied research shall support the demonstration of Army-relevant sensors on flexible substrates for robust monitoring of the human state. The flexible electronics programs efforts will extend physiological monitoring beyond the single-user, fitness- focused commercial perspective by supporting the Army goal to monitor the Soldier in training environments, determine soldier unique states, apply advance modeling to optimize the team performance based on individual uniqueness, and then apply resource distribution processes in real-time. U.S. Army Training and Soldier Will determine the level of physical, perceptual, and cognitive Performance interaction necessary for a simulated environment to effect performance similar to the operational environment. The program will characterize the appropriate use of different classes of simulated environments to ensure valid results and will develop guidelines for using mobility platforms in simulators to induce physical and cognitive stress that is representative of the operational environment. U.S. Army Continuous Multi-Faceted This effort will investigate technologies that provide the foundation Soldier Characterization for for future Army systems to adapt to individual Soldier’s states, Adaptive Technologies behaviors, and intentions in real-time. Develop novel approaches to individualize adaptive systems through enhanced interfaces, interactions, or interventions that capitalize on prediction methods; and decrease time-to-train, augment physical, cognitive, and social performance, and improve human-network interactions.
Page 27 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Agency Title Description U.S. Army Injury Prevention and This effort evaluates and assesses the effects of repetitive motion Reduction - Musculoskeletal during military operations and training on the human body; will Injury Prevention provide mathematical models to predict the likelihood of physical injuries following continuous operations and muscle fatigue; evaluates current standards for return-to-duty; and establishes improved medical test methods with the goal of rapid return to duty of Warfighters following injury. U.S. Navy Human Performance, Completed development of physical conditioning assessment and (Marine Training, and Education training optimization methods to improve warfighter performance. Corps) Completed efforts to incorporate the effects of nutritional and functional fitness into models and simulations in the Distributed Operations Virtual Toolkit. Initiated a unified theory of warrior resilience and fitness to enhance performance and mitigate injuries at the infantry small unit level. Will complete design and development of methods for establishing optimal training intervals for the Marine Corps Martial Arts Program (MCMAP) for improvement in physical performance and warrior mindset. Will initiate the development of measures of training effectiveness that connect training tasks with measures of performance under various stressors.
5.3 Physical Education and Fitness: Research Momentum As a preliminary step to assessing the research momentum of topics in the physical education dataset, subject groups were created from the list of terms. These groups aggregate terms with very similar content. Some groups have generic names (e.g., Dietary supplements), while others with substantial numbers of publications are quite specific (e.g., Antioxidants), and might be considered a sub-type of the generic group. Subject group assignments are not mutually exclusive. A complete listing of the subject groups appears in Section 12.3.2 of this report
The top 25 (of a total of 37) subject groups are shown in Figure 9. As seen in the topic cluster maps, electronics and sensors are featured prominently here, along with different types of dietary supplements.
Page 28 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Sensors 768 Biometrics/Biomarkers 766 Dietary supplements 765 Monitoring technologies 573 Accelerometers/Pedometers 538 Info & communications technologies (ICT) 488 Games (Video/Computer) 445 Smartphones, mobile devices, apps 435 Nutrition 313 Wearable devices 300 Protein 287 Doping/Ergogenics 279 Dietary carbohydrates 272 Interface (HCI or BCI) 235 Virtual or augmented reality 171 Steroids/Hormones 168
Fitness trackers 154 TechnologySubject Groups Vitamins 144 Computer vision/Image processing 136 Stimulants (Caffeine, Nicotine, etc.) 133 Antioxidants 128 Algorithms 128 Activity recognition 125 Wireless technologies 119 Feedback 118 0 200 400 600 800 1000 No. of Publications, 2011-2016
Figure 9. Physical Education & Fitness: Top 25 Subject Groups
To assess the research momentum of the technology subject groups, standardized values were calculated to determine the relative increase in the number of publications and the relative numbers of publications. A complete description of the indicator is provided in Section 12.3.3 of this report. Essentially, it plots the standard deviation of standardized measures of publication counts and velocity (the rate of publication increase) on two axes.
Page 29 of 85
Scientometric Study on Education, Training, and Exercise November 2016
A view of the 36 topics with 30-665 publications over the years 2011-2015, plotted across four quadrants, appears in Figure 10. Notably, some of the largest topics, especially those related to Dietary supplements, Dietary carbohydrates, and Nutrition, experienced the least rate of growth: they appear in the upper left, or “established topic” quadrant. One topic related to foundational scientific understanding, Biometrics/Biomarkers, also appears in the Established quadrant, as do Games and Accelerometers/Pedometers.
Figure 10. Research Momentum of Physical Education & Fitness Subject Groups, 2011-2015
Page 30 of 85
Scientometric Study on Education, Training, and Exercise November 2016
By way of contrast, many of the subject groups which plot in the Hot or Emerging quadrants (Figure 11) are topics which feature information and communication technologies (ICTs), such as Sensors, Smartphones, Wearable devices, Fitness trackers, Interface, Monitoring technologies, and so on. Several processing techniques related to sensors and trackers also appear here: Personalization, Classification, AI and machine learning, Computer vision/Image processing, Algorithms, GPS/GIS technology, etc. Publications linked to these nodes discuss themes such as methods to increase recognition of movement and accuracy of measurement,112-115 personalization as a means to increase engagement and combat “high levels of abandonment” (i.e., disuse of fitness trackers after a short period of time)116,117, or the technical challenges of integrating GPS data from smartphones or other devices in studies of physical activity.118-120 The Social media/Networks publications often describe how such media can be used to influence behaviours, promote healthy lifestyles, stage interventions and boost activity.121-123 Feedback is also seen as a key component in successful training interactions.124,125 Many of the topics shown here are also cited in recent market reports, such as Frost and Sullivan’s 2016 Next Generation Sensors for Wearables and Smart Phones, which pointed to growing demand for accuracy, intelligence and personalization in wearable technology.126
On a slightly less high-technology front, articles tagged with Sleep science describe research that employs devices to monitor sleep, add data to a bank of personal informatics, and relate sleep duration and quality to overall performance.127-130 Once again, this agrees with Frost and Sullivan’s view that fitness trackers will enable an ecosystem of connected health – an ecosystem that includes sleep data and correlates this data to overall fitness activity.131
Figure 11. Research Momentum of Physical Education & Fitness Subject Groups: Hot/Emerging Quadrants
Page 31 of 85
Scientometric Study on Education, Training, and Exercise November 2016
In the lower-left quadrant, which captures topics with lower-than-average research momentum and publication numbers, we see several of the topics describing supplements or other performance enhancing drugs, as well as several information technology topics. For instance, the node for Virtual or augmented reality describes some of the challenges associated with creating VR/AR fitness applications: these may include motion sickness, realism, and appropriate levels of coaching and interactivity.132-134 The Context-aware node describes how context-aware data processing can add to the quality and validity of mobile fitness apps.135-137
Figure 12. Research Momentum of Physical Education & Fitness Subject Groups: New/Disappearing Quadrant
Publication velocity for most topics in this quadrant flagged slightly from 2014 to 2015. Only Context- aware and Vitamins showed an increase in publication numbers in the last year of the period measured (Figure 13). The Vitamins node frequently references vitamins C and D.138-140 40 32 35 30 34 30 25 22 Context-aware 20 19 15 Vitamins 10 8 10 No.of Publications 5 7 3 3 0 2011 2012 2013 2014 2015 Year
Figure 13. Physical Education & Fitness: Context-Aware and Vitamins, # Publications, 2011-2015
Page 32 of 85
Scientometric Study on Education, Training, and Exercise November 2016
5.4 Summary of Physical Education Topics and Trends Consumer and professional athletic fitness trends are driving innovations in the physical education market. In contrast to the wider field of educational technology for either academic or workplace environments, our search did not identify comprehensive and full-featured platforms, akin to learning management systems, MOOCs or portals, in the field of physical education. Apart from a few (mostly consumer) apps and sensing technologies, there was little evidence of continuous training solutions, social media tie-ins, or micro-credentialling schemes which have begun to surface in workplace training.
Our research identified two major solution or technology streams related to today’s physical education: dietary supplements and wearable or mobile technologies to monitor personal or team biometrics. In the last five years, greater research interest has been accorded to the information technology and electronics topics, with sub-topics such as Fitness trackers and Smartphones demonstrating greater than average momentum, possibly because of their portability and user-friendliness. Accurate measurements are still a challenge for these technologies, however.
The research and market publications show that personal biometric data are being collected and fed into training systems, and interventions or feedback are being designed to perfect technique and influence fitness behaviour and lifestyle. Exergames are being exploited for their ability to engage and motivate. With regard to dietary supplements, the data reflected research interest in safe supplements, especially those derived from natural products. The research publications continue to document risk factors related to steroids and other banned substances.
Military physical education research is focused largely on the validation of existing standardized tests, and “big data” are only just beginning to have an impact, mostly as a means of optimizing training. RDT&E descriptions sourced from the U.S. Department of Defense show that as well as being used to design group training programs, individual-level data could ultimately lead to more precise, personal training. Epidemiological studies of weight management for military personnel are featured in the literature, but there is little evidence that current military research is targeting the issue of obesity via technological solutions.
6 LEARNING-CONDUCIVE TECHNOLOGIES
Question three of the project mandate required research and analysis on the topic of “established and emerging technologies that monitor, stimulate, or otherwise encourage a state conducive to learning, both cognitive and physical”, hereafter referred to as Learning-conducive technologies. While one could argue that good learning design -- based on a strong curriculum, appropriate sequencing, adaptation, immersive environments, and personalization -- is the best way to guarantee effective learning, the focus here is on technologies which act on the learner, enhancing his or her capacity to learn and thus preparing the ground for a successful training experience.
A description of the search terms used to capture the sometimes nebulous concept of how to “prime” a receptive learning state is provided in Section 12 of this report. While the search strategy used terms that combined aspects of enhancement with learning, it also employed keywords for technologies known to be used or investigated in the field, such as nootropic drugs or transcranial electrical stimulation. Searches on specific technologies yielded far more results than the more conceptual approach (“technologies conducive to learning”), and may in fact, have biased some aspects of the
Page 33 of 85
Scientometric Study on Education, Training, and Exercise November 2016 analysis by placing the emphasis on known methods and omitting or downplaying others which may be lesser known. However, the picture that does emerge, and one which is validated by a comparison with U.S. Department of Defense RDT&E topics, is one in which the foundational science is nascent. Certain scientific disciplines (such as neuroscience and epigeneticsf) are making significant contributions, and the focus of current research is on a handful of key technologies, such as transcranial direct current stimulation (tDCS) and nootropic drugs.
Initial bibliographic search results also included many records which described technologies or methods developed for therapeutic applications, such as rehabilitation after a stroke, or treatment of other medical conditions where either cognition or movement are affected (e.g., Parkinson’s disease or schizophrenia). Although these results were manually removed from the dataset used for analysis, it is clear that advances in rehabilitation or remediation may also lead to technologies which optimize health in normal individuals; at present, however, there are still many ethical concerns regarding enhancement or optimization of healthy persons.
A previous NRC-STI report for DRDC on the topic of human optimization, while broader in scope, covered some of the same conceptual and technological ground. In that report, the author found that a great deal of optimization research was related to human factors and cognitive science. In addition, there was a strong thread in the research documenting ethical considerations.141
While our research did not retrieve market reports in the traditional (commercial) sense, there is considerable interest in the topic of human enablement and enhancement, both physical and cognitive. As an example, a recent Frost and Sullivan report on human enablement and enhancement technologies across multiple industries and sectors (automotive, aerospace, entertainment, biomedical, defence) segmented the available or emerging technologies into two main streams: invasive (cognition-enhancing nootropics, “electroceuticals”g) and non-invasive (brain-computer interface and exoskeletons). Key findings of the Frost and Sullivan report include:142
There is a gradual convergence of nootropics with other dietary supplements, as well as a trend to their use with “healthy working professionals”, and not just athletes or persons with diagnosed medical conditions. Market demand is rising for natural enhancement ingredients in nootropics (e.g., ginseng or green tea). There is growing acceptance of technologies such as transcranial direct current stimulation (tDCS). The future will feature less focus on electroencephalograms (EEGs) as a means of measuring brain activity, and more emphasis on implantable, semi-implantable and non-invasive brain- computer interfaces. f Epigenetics is the science of changes and connections in gene expression. In this case, genomic profiles, physical exercise, pharmaceuticals, or stimulation may modify synaptic plasticity, signalling, etc., associated with physical performance or cognitive ability. g Electroceuticals are broadly defined by Frost and Sullivan as “products or techniques that modify functions of the human body through electrical stimulation”. They may include electrode-based neurostimulation as well as neuroprosthetics meant to replace a biological function. The University of Berkeley’s biomedical engineering lab is also developing “neural dust”, tiny implantable wireless sensors that can be used to monitor and stimulate nerves and muscles (see http://news.berkeley.edu/2016/08/03/sprinkling-of-neural-dust-opens-door-to- electroceuticals/).
Page 34 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Market drivers include demand for safe and non-invasive solutions, and continuing, widespread aspiration for cognitive enhancement. Barriers include: general low awareness of the available technologies, ethical concerns regarding the enhancement of healthy individuals; regulatory barriers; prohibitive costs; and the fact that the majority of cognitive enhancers are available by prescription only, or require supervision using trained professionals.
In their predictions for information technology in the coming five years, IT consultancy IDC has forecast the emergence of a “4th platform”, in which digital technologies designed to enhance will penetrate the human body, combining information technology with human biosystems. IDC also predicts that these digital technologies will be able to engineer biological systems at the cellular and subcellular level, leading to:143
Augmented sensing Augmented memory and cognition Augmented biostructure Augmented mobility Augmented immunity
In spite of the promise of enhancement, as suggested above, there are also some significant restraints. A widely cited review article from 2009 sets out some of the barriers in the area of cognitive enhancement. The underlying science is described as “highly experimental” and with small effect size. At that time, the authors estimated that regulatory and policy frameworks were ill-prepared to deal with issues raised by unconventional methods (nootropics, stimulation, genetic modification) of enhancement. They also stated:144
Cognitive enhancements are relatively new, and consequently there does not exist a large body of “received wisdom” about their potential uses, safety, efficacy, or social consequences; Enhancements could potentially have enormous leverage (consider the cost-benefit ratio of a cheap pill that safely enhances cognition compared to years of extra education); They are sometimes controversial; They currently face specific regulatory problems, which may impede advances; and They may eventually come to have important consequences for society and even, in the longer run, for the future of humankind.
The authors conclude:
“Conventional” means of cognitive enhancement, such as education, mental techniques, neurological health, and external systems, are largely accepted, while “unconventional” means— drugs, implants, direct brain-computer interfaces—tend to evoke moral and social concerns. However, the demarcation between these two categories is problematic and may increasingly blur. It might be the newness of the unconventional means, and the fact that they are currently still mostly experimental, which is responsible for their problematic status rather than any essential problem with the technologies themselves. As society gains more experience with currently unconventional technologies, they may become absorbed into the ordinary category of human tools.
Page 35 of 85
Scientometric Study on Education, Training, and Exercise November 2016
A more recent (2015) review article points to a continuing tension between pharmacological (e.g., caffeine, modafinil, methylphenidate) and non-pharmacological enhancements such as computer training, sleep, or physical exercise itself:145
We find that all of the techniques described can produce significant beneficial effects on cognitive performance. However, effect sizes are moderate, and consistently dependent on individual and situational factors as well as the cognitive domain in question. Although meta- analyses allowing a quantitative comparison of effectiveness across techniques are lacking to date, we can conclude that PCE [pharmaceutical cognitive enhancement] is not more effective than NPCE [non-pharmaceutical]. .. Even though their actual effectiveness seems similar, in the general public PCE is perceived as fundamentally different from NPCE, in terms of effectiveness, but also in terms of acceptability, with benefits mainly to be found in the area of improved cognition.
As well as enhancing psychomotor performance, several of the technologies under consideration, notably tDCS, are touted as a means of improving cognition and motor skills.146,147 Another 2015 article also pointed to issues of perception and risk-benefit:148
This portable technology, which involves applying weak direct currents to the scalp via saline- soaked sponge electrodes, appears rather safe with medical supervision, reasonably effective across a range of brain functions, and accessible to an interested public. These features have led to its growing implementation in both research and clinical settings, as well as with home users…the public’s perception regarding tDCS has shifted from misunderstanding to cautionary realism. This change in attitude suggests that as the technology has become more grounded within the public domain, there has been a shift from a focus on an emergent technology to one on its applications and risk-benefit profile… it appears important to inform the public accurately on the short-and long-term consequences of tDCS on healthy individuals and on the plausibility of enhancement effects.
Around the world, there are several high-level initiatives which are funding brain science, with the goal of accelerating development of neurotechnologies. These include the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative in the United States, combining federal institutes, universities and industry, and the European Community’s Human Brain Project, a consortium of 116 institutions. According to a November 2016 National Science Foundation solicitation, as well as stimulating research into new therapies, these projects will integrate:149
…insights gained from neuroscience and cognitive science with those from rapidly changing technologies [which] will lead to significant innovations that are inspired by or directed toward the brain. These may include technologies for imaging, sensing, recording, or affecting real-time brain activity and behavior; brain-inspired computing paradigms; brain-computer interfaces; augmented and adaptive systems (e.g., for communication, prosthetics, learning, education, or performance); functional neurotechnologies; and other computational and bioengineered systems.
Deeper understanding of cognitive and neural processes in realistic, complex environments will in turn lead to:149
Page 36 of 85
Scientometric Study on Education, Training, and Exercise November 2016
adaptive processes during complex physical, social, and educational interactions; flexibility and contextual aspects of cognitive, biological, and machine learning; experimental paradigms leveraging immersive environments (e.g., virtual reality) or other simulation or synthesis methods; mobile technologies for cognitive and neural processing and data gathering; and cyber-human interactions such as human-robot symbiosis.
However, a July 2016 survey conducted by the Pew Research Institute found that the American public expressed more worry than optimism about potential human enhancements such as gene editing, brain chips, or synthetic blood, for healthy people.150 It is clear that as these technologies advance, the boundaries between therapeutic uses and healthy human enhancement are blurring, and will be tested. Ethical considerations will be a constant.151
All of the findings summarized here are apparent in the scientometric analyses which follow.
Page 37 of 85
Scientometric Study on Education, Training, and Exercise November 2016
6.1 Learning-Conducive Technologies: Top Terms Cluster Maps A second search on learning-conducive technologies was conducted in bibliographic databases and using the search strategy described in Section 12Error! Reference source not found.. The search retrieved a otal of 1,902 bibliographic references, after articles describing mainly therapeutic applications were removed.
A topic cluster map, showing the top 315 terms and their correlation (filtered to a correlation coefficient of 20%) is shown in Figure 14. An enlarged version is included in the accompanying Tableau workbook. One large and hyper-connected node for Transcranial direct current stimulation (tDCS), has been omitted from the main red cluster in the map, as it was deemed to be redundant.
Once again, the image shows several large clusters devoted to the principal technologies (tDCS in red, nootropics in turquoise, and supplements in purple). While much of the literature on learning conducive technologies describes cognitive benefits (delivered mainly through nootropics and electrical stimulation), the research publications also attest to interest in improving motor performance, chiefly through the effect of supplement use, and to a lesser extent through electrical stimulation. In the main map, clusters for electrical stimulation and nootropics, while largely self-contained, are indirectly linked through large nodes entitled Cognition, Cognitive enhancement, and Memory.
Page 38 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 14. Learning-Conducive Technologies, Top Terms Cluster Map, Filter of 20%
Page 39 of 85
Scientometric Study on Education, Training, and Exercise November 2016
A closer look at the largest (red) cluster (Figure 15) provides some insights into electrical brain stimulation. Our research revealed a huge upsurge of interest in this technology, with studies of its “boosting” effects on learning mainly conducted under controlled laboratory conditions. While results have been promising, differential effects are reported depending on the individual, the region targeted, the task, the anode location, the frequency, short vs. long-term memory, etc.152-155 Transcranial direct current stimulation is the most frequently studied sub-type of electrical stimulation in the data.
Also documented at the right-hand edge of this cluster is the relationship between Brain-derived neurotrophic factor (BDNF) and Neuroplasticity: intense exercise is known to up-regulate production of BDNF, which in turn improves cognitive performance.58 Increased release of BDNF may also be behind tDCS’s effects. 156
Figure 15. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Electrical Stimulation
Page 40 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Links from the right side of the red cluster lead to a collection of highly inter-connected turquoise nodes (Figure 16) describing the effects of pharmaceutical enhancements, principally Nootropic drugs. These nodes often discuss off-label uses of prescription drugs such as Ritalin (widely used by student populations, but also reported in workplace environments).157-159 Clinical trials and laboratory experiments for a host of natural ingredients (e.g., ginseng, Brassica or Bacopa derivatives) are also discussed; many of these also hold promise in anti-ageing markets.160-162
Figure 16. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Nootropic Drugs
Page 41 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Below the main Nootropic drugs cluster, there appears a series of orange nodes related to the ethical and regulatory aspects of pharmaceutical enhancement in healthy individuals (Figure 17). Some of the keywords used by the authors (Addiction, Substance abuse) indicate the general disapproving tone of these discussions. In these publications, the use of drugs such as modafinil (Provigil), Ritalin, amphetamines, or other “smart drugs” in healthy subjects is referred to as “academic doping”, “illicit” or “Viagra for the brain”.163-166
Figure 17. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Ethics and Drugs
Page 42 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Ethical aspects permeate the dataset, but are discussed more frequently in the context of pharmaceuticals. In fact, when all of the pharmaceutical enhancement terms are combined in a group comprising 846 articles (44% of the dataset), and all of the electrical stimulation publications are similarly combined (772 publications, or 40% of the dataset), and these are crossed with the group for Ethics (139 titles, or 7.3% of the dataset) one can see that it is chiefly the topic of pharmaceutical enhancement that is attracting ethical disapprobation. The topic of genetic engineering is almost absent in the data, appearing in only five records. According to one author, “genetic intervention would require morally intolerable experimentation.”167
1.44% Pharmaceutical enhancement 28.06% Electrical brain stimulation 70.50% Other
Figure 18. Ethical Discussions (n=139): Share by Technology Type
Far less controversy surrounds the use of “natural” supplements such as coenzyme Q10, nitrates, carnitine, omega fatty acids, or proteins to boost physical performance (Figure 19). Many of the publications in the purple cluster for dietary supplements also discuss biomarkers and their relation to exercise, fatigue, and supplement use.168-170
Figure 19. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Supplements & Exercise
Page 43 of 85
Scientometric Study on Education, Training, and Exercise November 2016
In a final detail from the learning-conducive technologies cluster map, a small group of nodes depicts the “military” content as expressed through the top terms. A concern for psychological aspects is apparent here. The publications also discuss whether resilience training and “comprehensive fitness” can enable military readiness.171-173 Certain cognitive enhancers (e.g., glutamatergic N-methyl d- aspartate NMDA receptor agonists) are cited as altering epigenetic and neurotrophic mechanisms in fear extinction in PTSD-affected personnel.174,175
Figure 20. Learning-Conducive Technologies, Top Terms Cluster Map: Detail for Military Personnel
Page 44 of 85
Scientometric Study on Education, Training, and Exercise November 2016
6.2 Military Research into Learning-Conducive Technologies A small subset (111 records) of records with military affiliations or subjects extracted from the main dataset features an almost equal division of interest between cognitive and physiological enhancement. The top terms in the military subset are shown in Figure 21. The special requirements imposed by a highly stressful environment are also apparent in this list, and resilience and the role played by mental health training are also prominent.176,177 Experimentation with transcranial direct current stimulation (e.g., its use in training pilots, UAV operators and image analysts) is also documented as a key area of military interest.178-184 Military personnel 41 Cognition 26 Resilience 24 Transcranial direct current stimulation (tDCS) 22 Physiology 22 Training 21 Military applications 20 Stress (Physiology and Psychology) 18 Psychology 17 Posttraumatic stress disorder (PTSD) 16 Dietary supplements 15 Psychological aspects 14 Coping behavior 13 Task performance 12 Resilience training 12 Physical exercise 12 Mental health 12 Army 12 Brain 11 Sleep deprivation 10 Physical fitness 10
Cognitive performance 10 Top Terms: Learning Conducive Technologies, Military Conducive TopLearning Terms: Technologies, Subset Military 0 5 10 15 20 25 30 35 40 45 No. Publications
Figure 21. Learning-Conducive Technologies: Military Subset, Top Terms
It is well known that over the past few decades, the U.S. Defense Advanced Research Projects Agency (DARPA) has sponsored research into technologies that lead to improved cognitive function or motor performance. For instance:
Peak Soldier Performance Program (2007): an investigation into genetic variation, energy- producing mitochondria, supplements and their effect on cognition in warfighters under stress, suffering from hunger or sleep deprivation.185-187 Accelerated Learning Program (2007-2012): developed novel training paradigms, including those leveraging brain-computer interfaces (BCI), to accelerate improvements in human performance. The program measured neural and other physiological correlates of task learning,
Page 45 of 85
Scientometric Study on Education, Training, and Exercise November 2016
combined with feedback, with an end goal of producing a two-fold increase in an individual's learning rate. Researchers also succeeded in identifying specific brain regions, activity patterns, and networks associated with the acquisition of complex tasks, utilizing these findings to accelerate learning of those tasks by a factor of two.188 Neurotechnology for Intelligence Analysts (2005-2014): recorded “target detection” brain signals to improve the efficiency of imagery analysis.188 Narrative Networks (N2) Program (2011-2015): developed new techniques to quantify the neural signatures of narratives on human cognition and behavior, including initial development of a closed-loop BCI system that adapts a narrative in response to a listener's EEG signals. Such a system would have numerous applications to training and human performance domains.188 Neuro Function, Activity, Structure, and Technology (NeuroFAST) (2015- ): funded by the Obama administration’s brain science initiative, Neuro-FAST aims to develop novel optical methods to enable real-time functional recording of thousands of neurons, with single-neuron resolution, in awake, behaving animals, and to register connections between neurons (circuity). “If successful, Neuro-FAST will support pioneering research into brain function over a wide range of spatial and temporal scales to better characterize and mitigate threats to the human brain and facilitate development of brain-in-the-loop systems to accelerate and improve functional behaviors.”188,189 Neural Engineering System Design (NESD) (2015- ): “…aims to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the human brain and the digital world. The interface would serve as a translator, converting between the electrochemical language used by neurons in the brain and the ones and zeros that constitute the language of information technology… the envisioned devices will have the potential to be practical outside of a research setting will require integrated breakthroughs across numerous disciplines including neuroscience, synthetic biology, low-power electronics, photonics, medical device packaging and manufacturing, systems engineering, and clinical testing.”190
A DoD-sponsored Consortium for Health and Military Performance (CHAMP) also conducts basic and clinical research in military human performance, so as to inform the development of educational products, clinical products, clinical care pathways, operational guidelines, and health policies. CHAMP is devoted to the integration, translation and dissemination of information on all topics related to human performance optimization and Total Force Fitness (TFF). It is a collaborative effort among operational, medical, and research communities and policy makers.h
A similar initiative at the U.S. National Aeronautics and Space Administration (NASA), entitled Human Health and Performance, seeks to optimize astronaut performance in space by examining factors such as diet, age, exercise, supplements, physiological condition (biostatistics), and countermeasures applicable to long-term space environments.191-193 Trends identified in the most recent (2012) NASA Health and Human Performance Strategy include a growing number of participants in spaceflight, an aging workforce, advances in commercial diagnostic and treatment modalities, environmental monitoring, and information management. A key goal is to “optimize the integrated human system risk management process and to continue to use the system to prioritize risks and execute tasks.”194
h An extensive list of CHAMP-sponsored publications appears at https://www.usuhs.edu/mem/champ- publications, and current research studies are listed at https://www.usuhs.edu/mem/champ-current-research- studies .
Page 46 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Selected research programs from this year’s RDT&E reports, and relevant to the Learning-Conducive Technologies theme, are listed below in Table 4.23,195,196 Collectively, the U.S. research programs demonstrate a strong interest in basic and translational neuroscience, and a desire to optimize warfighter health and cognitive performance in stressful conditions and in the context of accomplishing complex tasks. Many of the programs will deliver advances in both training and rehabilitation, or will support man-machine teaming (e.g., UAV operation). Novel human/brain-computer interfaces are also featured. References to specific domains of science, technologies and applications appear in boldface in the table.
Table 4. Selected U.S. DoD RDT&E Program Elements, FY2017
Agency Program Element Description DARPA Enhancing Will explore and develop stimulation methods and non-invasive devices to Neuroplasticity promote synaptic plasticity that is expected to impact higher cognitive functions; will both create an anatomical and functional map of the underlying biological circuitry that mediates plasticity and optimize stimulation and training protocols to enable long-term retention. DARPA Neuroscience Leverages recent advances in neurophysiology, neuro-imaging, cognitive Technologies science, molecular biology, and modeling of complex systems to sustain and protect the cognitive functioning of the warfighter faced with challenging operational conditions. Targets the warfighter’s ability to multitask, and characterizes dynamics of human cognitive functions such as memory, learning, and decision making. DARPA Quantitative Models Will establish a functional mathematical basis on which to build future advances of the Brain in cognitive neuroscience, computing capability, and signal processing. This program will further exploit advances in the understanding and modeling of brain activity and organization to improve training of individuals and teams as well as identify new therapies for cognitive rehabilitation (e.g., Traumatic Brain Injury (TBI), Post-Traumatic Stress Disorder (PTSD). DARPA Performance Focuses on leveraging advances in and integration of sensors, computation, Optimization in and analytics to enable optimum human performance in complex Complex environments. Will employ body area networks, wearable displays, haptics, Environments and other novel forms of human-computer interfaces to enable optimal performance in a wide variety of activities from learning and training to specialized tasking, and to mitigate the effects of physical injury, age, and mental impairment. US Equipment for the Targets mathematical modeling, physical and cognitive performance, and Army Soldier other topic areas. In FY2016, the program explored enhancement of cognitive skills via trans-cranial direct current stimulation (t-DCS) and examined associated neural mechanisms responsible for skill improvement, with the goal of understanding whether t-DCS can complement Soldier training in improving cognitive and motor skills required for enhanced battle space awareness. US Translational Integrates neuroscience with traditional approaches to understanding Soldier Army Neuroscience behavior to enable systems designs that maximize Soldier performance.
Page 47 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Agency Program Element Description US Air Applied Develops technologies to enhance airman performance, airman-airman, and Force Neuroscience airman-machine collaboration, and system interaction in distributed decision- making environments. Conduct research to predict physiological impacts of high-stress/extreme environments. Defined neurophysiological, psychological, and genetic mechanisms and processes for developing guidelines to enhance warfighter cognitive resiliency and performance. Applied physiology computational modeling methods to predict high-stress/extreme environmental effects on the human. Developed augmentation techniques for improving performance in operational environments that include human- machine teaming. In FY2017: Begin development of an optimized sense and access suite of tools for enhanced airman performance in a laboratory environment. Begin design of an adaptive aiding tool for team workload balancing for enhanced team performance. Investigate distributed team mechanisms, techniques, and metrics for initial augmentation technology development. Define target biomarkers and associated sampling techniques for the development of real-time biomarker sensor technology for human performance assessment. Develop behavioral model of mechanisms of cognitive augmentation and stress resilience.
Page 48 of 85
Scientometric Study on Education, Training, and Exercise November 2016
6.3 Learning-Conducive Technologies: Research Momentum Research momentum analysis of Learning-Conducive Technologies was based on the rate of publication increase and total publication counts for 2011-2015 for 35 technology subject groups with 20-270 publications.i These subject groups include both some general areas of scientific inquiry (e.g., Sleep science, Nutrition) as well as methods (Resilience training, Mindfulness/Meditation, Cognitive training) known to be used for enhancement or optimization. Brain-derived neurotrophic factor (BDNF) is also included as a group, as it had a substantial number of publications and is being studied as a biomarker related to exercise and cognition. The top technology subject groups in the dataset of 1,902 publications are shown in Figure 22.
Transcranial brain stimulation 743 Stimulants 293 Nootropic drugs 275 Dietary supplements 224 Brain stimulation, Non-specific 200 Neuromodulation 179 Sleep science 175 Deep brain stimulation 174 Non-invasive brain stimulation 122 Methylphenidate (Ritalin) 95 Nutrition 83 Brain-derived neurotrophic factor (BDNF) 75 Biomarkers 75
TopSubject Groups Vitamins 72 Modafinil 70 Interface (BCI/HMI) 67 Amphetamines 66 Pharmaceutical cognitive enhancement 63 Caffeine 62 Antioxidants 58 Brain mapping 58 Bio/Neurofeedback 52 0 100 200 300 400 500 600 700 800 No. Publications Figure 22. Learning Conducive Technologies: Top Subject Groups, ≥ 50 Publications
i A complete list of subject groups created for the Learning-Conducive Technologies global dataset and military subset appears in Section 12.3.2 of this report.
Page 49 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Because of the size of the group for transcranial brain stimulation (743 publications, covering 39% of the entire dataset), it was removed from the research momentum analysis, since it would skew overall results. However, as shown in Figure 23, the number of publications on this topic have more than quadrupled over five years, signalling intense research interest in this technology, especially as it has been found to prime both motor learning and cognitive processes such as reaction, attention, object recognition, memory, and creativity.182 As described earlier in this report, and described in several corporate press releases, tDCS is being piloted by various defence agencies around the world as a method of boosting learning and physical ability.183,197-199
250
200 201
150 158 124 100
No.Publications 69 50 43
0 2011 2012 2013 2014 2015 Year
Figure 23. Transcranial Direct Current Stimulation: No. of Publications, 2011-2015
Page 50 of 85
Scientometric Study on Education, Training, and Exercise November 2016
The overall view of research momentum is shown in Figure 24. An enlarged version with quadrant filters appears in the accompanying Tableau workbook.
In the upper left quadrant, topics with large publication numbers but relatively stagnant increases over five years include three pharmaceutical approaches (Stimulants, Nootropic drugs, and Methylphenidate or Ritalin). Sleep science related to optimal learning or performance had an average of 31 publications per year, with the most recent titles focusing on subjects such as the effects of sleep deprivation on learning and memory, and dopamine receptor regulation of sleep, learning, and plasticity.200-203 Several studies also examined the effect of combinations of tDCS and sleep.204-207
Figure 24. Learning Conducive Technologies: Research Momentum, 2011-2015
Page 51 of 85
Scientometric Study on Education, Training, and Exercise November 2016
In the Hot/Emerging quadrants (Figure 25), the generic node for Dietary supplements has the greatest publication count as well as the greatest acceleration, relative to the other topics. Several supplement types (Fatty acids, Antioxidants, Vitamins) are also featured in the Emerging quadrant, as are Amino acids, referring to both amino acids as supplements and to their role as metabolic regulators.84,208,209 Performance enhancers known for their illicit use and hazardous effects (Steroids, and Hormones such as testosterone or human growth hormone) are also featured as Emerging.210,211 Other key areas of interest include Deep brain stimulation (invasive), Non-invasive brain stimulation, and Neuromodulation. In the case of the latter, the data describe a varied collection of technologies and methods – many of them related to tDCS or other electrical stimulation methods – which deliver insights and effects on how to control brain or other central nervous system activity.212-215
Along with a sizeable node for Interface, several non-pharmaceutical techniques appear in the Emerging quadrant: Mindfulness/Meditation and Cognitive training, with the latter sometimes also referred to as “brain training”. Studies on mindfulness report on attempts to enhance metacognitive regulation,216-218 while Cognitive training approaches use personalized, computer-based training to try to produce improvements in function.219-221
The placement of topics in these quadrants agrees with market reports such as Frost and Sullivan’s Innovations in Human Enablement and Enhancement Technologies (2016), which stressed technologies such as novel brain-computer interfaces,142 or programs such as the U.S. Air Force’s Applied Neuroscience, which seeks to “Define target biomarkers and associated sampling techniques for the development of real-time biomarker sensor technology for human performance assessment.”196
Figure 25. Learning Conducive Technologies: Research Momentum, Hot/Emerging Quadrants
Page 52 of 85
Scientometric Study on Education, Training, and Exercise November 2016
In the lower left or New/Disappearing quadrant (Figure 26), almost all of the topic groups refer to drugs, stimulants, or other supplements. The exceptions, and those which may more accurately fall into the “new” category, are:
Games: valued for their priming of attention, multi-tasking and reactive performance, and exploitation of affect in learning.222-224 Bio/Neurofeedback: publication numbers climbed from a low of five in 2011 to 17 in 2015; the most recent titles discuss closed loop systems which feed data back to the subject to allow for correction and control in rehabilitative settings as well as for healthy individuals.225-227 Resilience training: a node with low numbers and a relatively flat trajectory of increase, but with apparent higher significance for military personnel, as discussed above.
Figure 26. Learning Conducive Technologies: Research Momentum, New/Disappearing Quadrant
Page 53 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Publication counts for these three New/Disappearing topics for 2011-2015 are shown in Figure 27.
20
15
10 Bio/Neurofeedback
5 Games No.Publications 0 Resilience training 2011 2012 2013 2014 2015 Year
Figure 27. No. of Publications for Bio/Neurofeedback, Games, and Resilience Training, 2011-2015
6.4 Summary of Learning-Conductive Technologies and Trends
In summary, several major technological streams compete for research attention in the area of “learning-conducive technologies”: supplements, nootropic drugs, and electrical brain stimulation. Many of these approaches began as rehabilitative or remedial treatments, but are beginning to have an impact – and acceptance – as enhancing or “priming” technologies for healthy subjects. The boundaries between health and rehabilitation are blurring, although the research reveals a continuing and profound concern about the ethical aspects of enhancement technologies in healthy individuals.
Neuroscience, and in particular the study of brain circuitry and regulation as it relates to function, is a key enabler and the focus of much military research. Defence science also acknowledges the important role played by stress management and resilience in ensuring optimal performance.
Emerging technologies and areas of scientific inquiry include transcranial brain stimulation and other novel brain-computer interfaces, safe or natural dietary supplements, “soft” techniques such as mindfulness, and understanding of complex systems at the molecular and epigenetic level. Clinical trials and safety studies continue in all areas.
Page 54 of 85
Scientometric Study on Education, Training, and Exercise November 2016
7 HOLISTIC APPROACHES TO TRAINING AND LEARNING
The Cambridge English Dictionary defines “holistic” as “dealing with or treating the whole of something or someone and not just a part”.228 Thus, holistic approaches to training or learning would employ multiple technologies, supported by doctrine and applied in an integrated fashion, that address the full spectrum of soldier health and learning: mind and body, and targeting the systemic biological and psychological aspects of learning and performance.
Military agencies have long subscribed to a “performance triad” which highlights the contribution of sleep, activity, and nutrition to overall soldier health and performance. Both the Canadian and U.S. Armies espouse the performance triad and state that it is key to readiness, resilience, and injury avoidance: “Soldiers need to be fit, both physically and mentally, to respond to all challenges.”229,230
The U.S. Department of Defense (DoD) also promotes “total force fitness” but in 2010 stated that there continued to be a “capability gap” with regard to a strategic approach to human performance training and optimization. DoD identified eight integral components of fitness: social, behavioural, physical, environmental, medical, spiritual, nutritional and psychological fitness, but added that “traditionally, attention to fitness within the military has generally been targeted only to the physical domain”.231 Attempts continue to develop a life cycle, holistic approach to soldier wellbeing and performance. A more comprehensive approach would result in a whole new definition of “training” and the ultimate “end state”.231 The U.S. Army’s Human Dimension Strategy (2015) repeats many of the same themes: investments in cognitive, physical and social components are required to optimize human performance, enable strategic advantage and lead to effective decision-making. Personalized and holistic programs are prescribed to enable the human dimension.232 A graphic (Figure 28) from a U.S. Army Training and Doctrine Command (TRADOC) illustrates the holistic approach.233
Figure 28. U.S. TRADOC Human Dimension Integration Framework
Page 55 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Findings from our data suggest support – at least philosophically – for the idea of integrative, holistic, lifecycle training. For instance, as seen in Figure 21, much of the focus of “learning conducive” technologies for the military is directed towards psychological underpinnings of performance. The DoD CHAMP initiative has also described efforts to collect performance metrics across a spectrum of domains: physical performance, nutritional status, psychological status, cognitive performance, environmental challenges, sleep, and pain. The goal is to apply these metrics into operational practice, but this will require culture change and a paradigm shift.234-236
According to the CHAMP program director, “Leaders are uniquely postured in the military chain of command to directly influence a culture of fitness for a ready force, and promote the concept that service members are ultimately responsible [italics mine] for their fitness and performance.”236 This statement may lead us to believe that official doctrine can have a limited influence in encouraging total force fitness, and that efforts should also be directed to personnel at the individual level.
Our research identified some convergence of scientific disciplines that address human performance optimization at a systemic, and even a molecular, level. These include basic neuroscience, neurobiology, and epigenetics, nutrition, sleep science, and information science -- all building blocks for greater understanding and practical applications.143,237
Certain technologies described above, such as transcranial brain stimulation or dietary supplements, appear to have an effect on both cognition and motor performance, and so might support a holistic approach. Also, as reported in the literature, there is growing evidence of the beneficial influence of exercise on both cognition and affect.238-242
With the possible exception of some of the supplements and electrical brain stimulation, it does not appear that any single technology addresses all aspects of fitness, learning, and readiness. Neither does it appear that any single technology (again, with the possibility of transcranial stimulation, if proven safe and effective) will dramatically disrupt the status quo. Certain technologies such as wearables, social media, or games, may address neglected personal or behavioural aspects of training, leading to more of a “whole person” approach. A non-invasive brain-computer interface, with a closed feedback loop may enable advances in assessment and correction of physical performance and improved cognition.243 Several publications retrieved by our search also point to combinatory approaches – for instance, combinations of aerobics with supplements, of sensors with games, of drugs or tDCS with cognitive training, of kinesiology with epigenetic understanding -- which may advance integrative practice.244-250 Thus it appears likely that a holistic approach will proceed on multiple technology fronts.
However, the distinctness and lack of significant connectivity between the major technology clusters seen in topical maps produced for this project suggest that integrative or combined approaches remain the exception in the research literature, and not the rule. While a holistic approach may be enshrined in policy, the complexities and differential effects of certain technologies with regard to learning present challenges to operationalizing this approach. Holistic training today remains more in the realm of wishful thinking than common practice.
Page 56 of 85
Scientometric Study on Education, Training, and Exercise November 2016
8 DRIVERS, BARRIERS, AND OPPORTUNITIES IN EDUCATION, TRAINING, AND EXERCISE
In this section, Tables 5 and 6 list drivers, barriers, and opportunities (i.e., stated research/technical gaps) for Physical Education Training and Learning-Conducive Technologies, respectively. These lists were compiled using the ensemble of sources consulted for this report: market reports, scientific and research publications, and ongoing military research projects. Collective references are provided at the foot of each table.
This assessment was also prepared from a military, occupational or “professional athletics” perspective, i.e., findings do not necessarily reflect the drivers, markets or research agendas for K-12 schooling , post-secondary education, or consumer products.
8.1 Physical Education and Fitness Physical fitness is a topic area with repercussions at both the policy and the personal level. As noted above, the military takes great care to mandate total force fitness and to create the conditions which will lead to a high standard of physical readiness. However, this is also a domain where each warfighter must make a personal and ongoing commitment.
Physical education is well established and officially supported as a critical requirement for military personnel. There is no argument that physical fitness is critical, and standards set minimal requirements. New equipment and missions will guarantee a continuing need for this type of training. From point of view of defence trainers, any development that could deliver this training more efficiently and economically would be a welcome development. The increase in rates of obesity and overweight should also drive interest in delivering training to address this health and performance issue.
Military policies regarding the use of fitness trackers are only beginning to be developed, however our research pointed to some technical and security gaps such as signal validity and exposure of GPS position which may limit their use as tools to deliver and support military fitness training.
Supplement use, on an unofficial basis, is already widespread on the part of military personnel. Both the Department of National Defence and the U.S. Department of Defense follow the usage guidelines prepared by their respective public health regulatory agencies. At present, Canadian soldiers are only prohibited from using products with substances that are on the national Controlled Drugs and Substances Act: this includes ephedrine, anabolic steroids, and certain precursors. There are also documented issues with undeclared ingredients and misuse of amphetamines. In the United States, a DoD initiative entitled Operation Supplement Safety has a goal of educating personnel about the safe use of supplements.
Opportunities for physical education and fitness training exist in: assuring safety and developing safe alternatives; delivering fitness training at a personal level; gathering data and creating models which would enable the incorporation of greater physical fidelity in the Live, Virtual, and Constructive Training solutions increasing favoured by the military; and in creating technologies and platforms that feature a holistic approach. Known issues, such as obesity, or changing demographics (more women in combat roles) also offer opportunities for research. While fitness trackers and smartphone apps may be popular
Page 57 of 85
Scientometric Study on Education, Training, and Exercise November 2016 with individuals, there has been little investigation of how these could be officially and safely incorporated into service life or workforce training. The apparent efficacy of mobile and social tools in affecting behavioural change (e.g., supporting weight loss or lifelong fitness) might be better exploited to improve engagement and adherence to a healthy lifestyle. 8.2 Learning-Conducive Technologies Compared to physical education and fitness, most “learning conducive technologies” are at a much less mature state of development and social acceptance. Many such technologies already exist on the commercial market, or are being re-purposed in off-label use. However, enhancement technologies are frequently viewed with suspicion, with physicians, policymakers, and the public questioning their application to healthy individuals. A strong current of ethical discomfort runs through the literature and acts as a restraint.
Furthermore, increasing numbers of research publications are pointing to the significant differential effects of technologies such as transcranial stimulation on individual cognition or motor skill acquisition. Transcranial direct current stimulation may soon fall under the regulatory purview of agencies such as the Food and Drug Administration, as it is not classified as a “low risk” application.26
However, the current surge of neuroscience and epigenetic studies identified in this report should lead to greater understanding of the mechanisms underlying electrical stimulation and supplementation. Greater understanding would, in turn, enable their adoption (or elimination) as a means of “priming” an optimal learning state. Ongoing developments in the field of non-invasive brain-computer interfaces should also assist in this regard. If such technologies can be shown to be both safe and effective – and if requirements are clearly defined and validated – this should go some way in allaying ethical concerns and matching technologies to performance requirements.
Page 58 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 5. Physical Fitness and Training: Drivers, Barriers, Opportunities
Drivers Barriers Opportunities and Research Gaps • Physical employment standards (fitness • Accuracy (validity) of signals in many wearable • Sensor/signal validity tests) or portable devices • Multi-feature, multi-sensory, multi-form, multi-platform • Higher-than-normal requirements for • Cost of high-end motion-capture devices, sensing and measuring technologies fitness, especially in some military athletic wearables • Non-invasive, embedded or implanted sensors occupations (e.g., special operations) • Privacy of personal data from wearables • Closed-loop feedback • Increasing adoption and accessibility of • Data infrastructure required by streams of • Addition of intelligence, personalization, and persuasion consumer fitness trackers personal data to physical training • Strong fitness culture and awareness in • Security of fitness trackers (broadcasting • Exploitation of social data, platforms the military personal/GPS data) in the field • Predictive analytics • Obesity epidemic • Safety knowns and unknowns (e.g., • Large scale live-virtual-constructive training with high- • Injury prevention supplements) fidelity physical elements • New equipment, missions require • Illegality of certain supplements • Physical training and coaching delivered using virtual constant training • Physical training competes with other types reality • Need for accelerated training timelines (e.g., operational) of training in the military • Cross-career (lifecycle) performance metrics and training • Need for cost-effective training budget – and budgets are shrinking opportunities • Special conditions: heat, stress, weight- • Competition with rehabilitation research • Computational models of performance and abilities carriage, etc. • Motivation and adherence to physical fitness • Use of (big), field-based/operational metrics to align • Need for real-time data to inform regimes training with physical employment standards curricula, personalization, efficacy, • Regulation • Epidemiology: characterization of performance safety • Lack of consensus as to how to measure characteristics of individuals, injury, medical records • Women in the military workforce: need “fitness” integration for gender specific training • Difficulties with scaling a “personal” approach • Focus on interventions for problems such as obesity, • Difficulties with operationalizing holistic injury, older workforce training • Comparison and validation of programs by type: e.g., • Degree of individual responsibility vs. strength, endurance, high intensity training institutional demands • Genetic basis for high performance and/or injury potential • Characterization of performance requirements of environmental factors: heat, cold, altitude • Comparisons of male vs. female performance • Holistic and/or combinatory approaches
Collective references for Table 58,15,24,29,38,56,126,233,251-258
Page 59 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 6. Learning Conducive Technologies: Drivers, Barriers, Opportunities
Drivers Barriers Opportunities and Research Gaps • Aspiration for cognitive and • Differential response • Safe supplements based on plant extracts physical performance • R&D for enhancement competes with • Foundational research into neural connectomics enhancement therapeutic uses (network neuroscience) • Desire to accelerate skills • Limited understanding and lack of clinical • Genetic and non-genetic factors governing individual acquisition, minimize data/evidence differences, dosage and intensity of stimulation training time • Development is ahead of regulation and • Long term safety and efficacy studies • Growing complexity of scientific insight • Better protocols (sham stimulation and controls for (military) cognitive • Ethical concerns regarding enhancement, clinical trials) requirements requires high- especially in healthy subjects • Non-invasive, implanted brain-computer interface and level cognitive performance • Public attitudes, perception of enhancement prostheses • Intensity of physical • Safety concerns • Technology that integrates social and behavioural requirements • Off-label uses of drugs, OTC drugs factors into performance training • National initiatives in brain • Administration and dosage concerns • Personalized, closed-loop tech with feedback science (UK, US) will advance • Training/clinical supervision requirements – (actionable, adaptive) neuroscience knowledge in safety concerns is self-administered (e.g., • Adapting or repurposing therapeutic technologies for the field and enable tDCS) healthy subjects translational research • Regulation: new FDA guidance suggests • Resilience training and other methods/technologies to neurostimulation and blood-based address psychological factors physiological assessment technologies will be • Lack of metrics for human performance-optimization regulated • Validation studies for brain/cognitive training • Regulation of drugs • Mobile cognition measurement • Improper use of performance enhancers • Integrative, holistic methods, e.g., addressing the • Some cognitive enhancers are available by performance triad of sleep, nutrition and exercise – all prescription only aspects of the human dimension • Generally low awareness of technologies • Combination technologies to address enhancement, available for enhancement e.g. tDCS and supplements or nootropics • Well-defined and validated requirements.
Collective references for Table 6: 13,20,142,143,150,186,233,235,251,252,259-270
Page 60 of 85
Scientometric Study on Education, Training, and Exercise November 2016
9 SUMMARY AND CONCLUSIONS
The survey of market reports, research and scientific literature conducted for this study suggests that today’s physical education has not benefitted greatly from significant advances made in the broader field of education technology. There are obvious differences: physical education is, after all, primarily physical, with less emphasis on the knowledge components that appear as learning objects in today’s learning management systems. The field of physical education and fitness training is not yet served by the comprehensive and full-featured platform technologies, MOOCs or portals seen for K-12 and university education. Neither did the search find strong evidence for micro-credentials (badges or sub- diploma level credits) or lifelong, continuous learning schemes.
Instead, the field is dominated by two main technology streams – dietary supplements and wearable sensors or fitness trackers – and driven by demand from consumers and elite athletes, not necessarily institutional requirements. Dietary supplements are well-established in the market, and widely used by military personnel. Many such supplements contain banned substances, and if improperly used, may have serious adverse effects. Their use is also linked to illegal doping in sport, but the search continues for safe alternatives. In the last five years, interest in wearables/trackers has surged, and researchers are attempting to overcome the limitations and inaccuracies of the current sensing and measurement technologies. The diffusion of wearable technologies is also boosting interest in information and communications technologies such remote monitoring and cloud computing.
For the military, physical training is dictated by standardized tests, and the research literature is focused largely on validation of those tests. Research publications also include epidemiological studies of obesity and weight in military personnel, along with some interest in wearable technology, mostly as a means of collecting and analyzing performance data. It is likely that such data will be incorporated in future simulations and models which can be used to optimize training or deliver personalized solutions.
This study also examined technologies to induce optimal physical and cognitive learning states in individuals. Once again, several major technology streams were identified: electrical brain stimulation, dietary supplements, and various drugs classified as nootropics. Search results indicate that there is a well-established history of using such technologies in rehabilitation settings – for instance, in treating attention deficit disorder, stroke victims, neurological disease or psychological disorders. As the science behind these solutions advances, and as the boundaries blur between rehabilitation and the enhancement of healthy individuals, there are continuing and profound ethical concerns voiced in the literature, especially with regard to pharmaceutical enhancement.
In the last five years, there has been a great surge of interest in transcranial direct current stimulation (tDCS), with some evidence that it is safe and acts on pathways for both cognitive and motor ability. Transcranial stimulation is the focus of several ongoing military demonstrations investigating its use in improved performance.
Military research also features a strong emphasis on foundational neuroscience, i.e., understanding how neural networks and epigenetics are affected by technological interventions, and how technologies may induce peak performance. The research also shows strong interest in natural, plant-derived substances and on the interplay between physical exercise and improved cognition. Resilience training and other low-tech approaches, such as cognitive training or mindfulness training/meditation, are also cited as methods to induce a state of learning readiness.
Page 61 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Holistic approaches are supported in doctrine, and to some extent by research that addresses the “performance triad” or foundational “systems” science: neurobiology, sleep science, epigenetics, and the like. However, our research did not identify any examples of a truly integrated approach, one that addresses all types of military training from cradle to grave, possibly because of the complexities of learning, individual differences between learners, and the difficulties of operationalizing such as approach. Apart from a handful of technologies such as tDCS, there do not appear to be many candidates for a systemic solution that addresses all requirements in a holistic or integrated way.
The principal drivers for both fitness and optimal learning states in the military are the continuing and urgent need for operational readiness. In an era of shrinking budgets and expanding missions, there are also practical considerations: solutions must be scalable, affordable, effective, and must meet accelerated training timelines. There are also significant restraints or concerns in areas such as safety and ethics. In some fields of foundational understanding, such as neuroscience and epigenetics, insights which could improve both physical training and cognitive learning capacity are only just beginning to emerge.
Market documents and reported research gaps suggest multiple areas where considerable work remains to be done. Physical education could benefit from recent advances in sensor technology and analytics, and from the addition of personalization, intelligence, and persuasion to existing platforms. Virtual or mixed reality will likely experience higher levels of adoption, since these are so applicable to “physicality”. There is also a lack of basic epidemiological knowledge, and a need for cross-career performance metrics and training opportunities. Solutions could also be designed for problem areas such as obesity or targeting changing demographics in the military (older workers, more women in combat roles).
For both fitness training and in the area of technologies to prime learning, much work remains to be done in the area of potentially re-purposed therapeutic technologies. Several key technologies, such as safe and non-invasive brain-computer interfaces and feedback devices, may also lead to improved performance outcomes. Finally, there is a need for well-defined and validated military requirements and metrics, and for combinatory, integrated, and holistic approaches which address the whole person at an individual level.
Some of the most promising scientific or technological areas where DRDC may choose to direct its efforts include: transcranial direct current stimulation; multiple wearable and accurate sensors and apps; non-invasive brain-computer interfaces; safe, plant-based nutraceuticals; personalization technologies; virtual/augmented reality; and basic science in areas such as sleep, nutrition, and neuroscience.
Page 62 of 85
Scientometric Study on Education, Training, and Exercise November 2016
10 REFERENCES
1. Frost and Sullivan. Impact of IoT on Global Educational Technology. Mountain View, CA: Frost and Sullivan; 2016. 2. Frost and Sullivan. Insight on Digital Learning and Higher Education in North America. Mountain View, CA: Frost and Sullivan; 2016. 3. Frost and Sullivan. Navigating Digital Transformation in Education. Mountain View, CA: Frost and Sullivan; 2016. 4. Lowendahl J-M. Hype Cycle for Education, 2016. Stamford, CT: Gartner; 2016 5. Lowendahl J-M, Thayer T-LB, Morgan G. Top 10 Strategic Technologies Impacting Higher Education in 2016. Stamford, CT: Gartner; 2016 6. Bersin by Deloitte. Continuous Learning Technology Stack 2015: http://www.bersin.com/Lexicon/details.aspx?id=19495. 7. Walker M, Burton B, Cantara M. Hype Cycle for Emerging Technologies, 2016. Stamford, CT: Gartner; 2016 8. Frost and Sullivan. Augmented and Virtual Reality Applications in Healthcare. Mountain View, CA: Frost and Sullivan; 2016. 9. Frost and Sullivan. Innovations in Virtual Reality: IT, Computing and Communications. Mountain View, CA: Frost and Sullivan; 2016. 10. Frost and Sullivan. Digital Disruption in the Global Education Sector. Mountain View, CA: Frost and Sullivan; 2016. 11. Bellini H. Virtual and Augmented Reality: Understanding the Race for the Next Computing Platform. Washington, DC: Goldman Sachs; 2016: http://www.goldmansachs.com/our- thinking/pages/technology-driving-innovation-folder/virtual-and-augmented-reality/report.pdf. 12. Fletcher JD, Chatelier PR. An Overview of Military Training. Washington, DC: Institute for Defense Analyses; 2000: http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA408439. 13. Tangney J. Human Systems Roadmap Review. Washington, DC: U.S. Department of Defense Human Systems Community of Interest; 2016: http://www.defenseinnovationmarketplace.mil/resources/NDIA_Human_Systems_Conference_ 2016_HSCOI_DistroA_FINAL.pdf. 14. U.S. Army. Train to Win in a Complex World. Washington, DC: U.S. Army; 2016. 15. Frost and Sullivan. Analysis of the Global Military Training & Simulation Market. Mountain View, CA: Frost and Sullivan; 2015. 16. Deloitte. Global Defense Outlook, 2016. New York, NY: Deloitte; 2016: https://www2.deloitte.com/global/en/pages/public-sector/articles/gx-global-defense- outlook.html. 17. Frost and Sullivan. Military Training and Simulation Trends 2016-2030. Mountain View, CA: Frost and Sullivan; 2016. 18. Cichowski S. Live, Virtual and Constructive, a Common Platform for the Future Training System. Neuilly-sur-Seine, France: NATO Scientific and Technical Organisation; 2011. 19. Hoke J, Wenger J, Wolford B. Embedded LVC training: A distributed training architecture for live platforms. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2012 2012. 20. Santarnecchi E, Muller T, Rossi S, et al. Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex. 2016;75:33-43.
Page 63 of 85
Scientometric Study on Education, Training, and Exercise November 2016
21. Frost and Sullivan. US DoD Training and Simulation Market, Forecast to 2020. Mountain View, CA: Frost and Sullivan; 2016. 22. U.S. Army Research Laboratory. Broad Agency Announcement for Basic and Applied Scientific Research, 2012-2017. Research Triangle Park, NC: U.S. Army Contracting Command; 2016: https://www.arl.army.mil/www/pages/8/W911NF-12-R-0011-04.pdf. 23. U.S. Army. Research, Development, Test and Evaluation (RDT&E): Volumes 1-7. Washington, DC: U.S. Army; 2016: http://asafm.army.mil/search.aspx. 24. Kumar A. Sports Nutrition and High Energy Supplements: The Global Market. Wellesley, MA: BCC Research; 2015. 25. Accenture. Digital Health Solutions Expected to Save U.S. Healthcare System More Than $100 Billion Over Next Four Years, Accenture Finds Chicago. IL: Accenture; 2015. https://newsroom.accenture.com/news/digital-health-solutions-expected-to-save-us- healthcare-system-more-than-100-billion-over-next-four-years-accenture-finds.htm. 26. U.S. Food and Drug Administration. General Wellness: Policy for Low Risk Devices. Washington, DC: U.S. Food and Drug Administration; 2016: http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocum ents/ucm429674.pdf. 27. Frost and Sullivan. Wearable Technologies in Clinical and Consumer Health. Mountain View, CA: Frost and Sullivan; 2016. 28. Frost and Sullivan. Wearables in Sports: Revolutionizing the World of Sports. Mountain View, CA: Frost and Sullivan; 2016. 29. Ghersin N, Melnick J. From Tracking to Intelligence: Uncovering the Unmet Needs of Today's Athlete. Boston, MA: Lux Research; 2016. 30. Muggeridge DJ, Howe CCF, Spendiff O, Pedlar C, James PE, Easton C. A single dose of beetroot juice enhances cycling performance in simulated altitude. Medicine and Science in Sports and Exercise. 2014;46(1):143-150. 31. Galvan E, Walker DK, Simbo SY, et al. Acute and chronic safety and efficacy of dose dependent creatine nitrate supplementation and exercise performance. Journal of the International Society of Sports Nutrition. 2016;13(1):12. http://dx.doi.org/10.1186/s12970-016-0124-0. 32. Álvares TS, Conte Jr CA, Paschoalin VMF, et al. Acute L-arginine supplementation increases muscle blood volume but not strength performance. Applied Physiology, Nutrition and Metabolism. 2012;37(1):115-126. 33. Santos SA, Silva ET, Caris AV, Lira FS, Tufik S, dos Santos RVT. Vitamin E supplementation inhibits muscle damage and inflammation after moderate exercise in hypoxia. Journal of Human Nutrition and Dietetics. 2016. 34. Cheng CF, Hsu WC, Kuo YH, Shih MT, Lee CL. Caffeine ingestion improves power output decrement during 3-min all-out exercise. European Journal of Applied Physiology. 2016;116(9):1693-1702. 35. Bird SR, Goebel C, Burke LM, Greaves RF. Doping in sport and exercise: anabolic, ergogenic, health and clinical issues. Annals of Clinical Biochemistry. 2016;53(2):196-221. 36. Mora-Rodríguez R, Pallarés JG, López-Samanes A, Ortega JF, Fernández-Elías VE. Caffeine ingestion reverses the circadian rhythm effects on neuromuscular performance in highly resistance-trained men. PLoS ONE. 2012;7(4). 37. Santos-Lozano A, Garatachea N. Accelerometers: A physical activity assessment tool. Medicina dello Sport. 2013;66(4):497-511. 38. Huang Y, Xu J, Yu B, Shull PB. Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking. Gait and Posture. 2016;48:36-41.
Page 64 of 85
Scientometric Study on Education, Training, and Exercise November 2016
39. Chen MD, Kuo CC, Pellegrini CA, Hsu MJ. Accuracy of wristband activity monitors during ambulation and activities. Medicine and Science in Sports and Exercise. 2016;48(10):1942-1949. 40. Nelson MB, Kaminsky LA, Dickin DC, Montoye AHK. Validity of consumer-based physical activity monitors for specific activity types. Medicine and Science in Sports and Exercise. 2016;48(8):1619-1628. 41. Ferri C. Identifying the sport activity of GPS tracks. In: Proceedings from Procedia Computer Science; 2016. 42. Reiss A, Stricker D. Aerobic activity monitoring: Towards a long-term approach. Universal Access in the Information Society. 2014;13(1):101-114. 43. Kolosnjaji B, Eckert C. Neural network-based user-independent physical activity recognition for mobile devices. Lecture Notes in Computer Science Vol 93752015:378-386. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84983552237&partnerID=40&md5=b8674a4d7af3f75fdbd72aef31f6a2ac. 44. Jane M, Foster J, Hagger M, Pal S. Using new technologies to promote weight management: A randomised controlled trial study protocol. BMC Public Health. 2015;15(1). 45. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Medicine. 2014;44(5):671-686. 46. Eapen ZJ, Peterson ED. Can mobile health applications facilitate meaningful behavior change?: Time for answers. JAMA - Journal of the American Medical Association. 2015;314(12):1236- 1237. 47. Chen H, Lin TYT, Mu Q, Rau PLP. How online social network and wearable devices enhance exercise well-being of Chinese females? Lecture Notes in Computer Science. Vol 91812015:15- 21. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84951113432&partnerID=40&md5=0fecc61b50febfc7f494382851fd1d2f. 48. Sonne T, Jensen MM. Race by hearts: Using technology to facilitate enjoyable and social workouts. Lecture Notes in Computer Science. Vol 87702014:125-132. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84921415800&partnerID=40&md5=df5f7cb145143093d27d1f26ee6df811. 49. Stragier J, Vanden Abeele M, Mechant P, De Marez L. Understanding persistence in the use of Online Fitness Communities: Comparing novice and experienced users. Computers in Human Behavior. 2016;64:34-42. 50. Stach T, Graham TCN. Exploring haptic feedback in exergames. Lecture Notes in Computer Science Vol 6947 LNCS2011:18-35. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 80052785828&partnerID=40&md5=8cc61bff51214cb985d5d3381bed20d0. 51. Kim SY, Prestopnik N, Biocca FA. Body in the interactive game: How interface embodiment affects physical activity and health behavior change. Computers in Human Behavior. 2014;36:376-384. 52. Ketcheson M, Ye Z, Graham TCN. Designing for exertion: How heart-rate power-ups increase physical activity in exergames. In: Proceedings from CHI PLAY 2015 - Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play; 2015. 53. van der Niet AG, Smith J, Scherder EJA, Oosterlaan J, Hartman E, Visscher C. Associations between daily physical activity and executive functioning in primary school-aged children. Journal of Science and Medicine in Sport. 2015;18(6):673-677. 54. Staurset EM, Prasolova-Førland E. Creating a smart virtual reality simulator for sports training and education. Smart Innovation, Systems and Technologies. Vol 592016:423-433.
Page 65 of 85
Scientometric Study on Education, Training, and Exercise November 2016
55. Tuveri E, Macis L, Sorrentino F, Spano LD, Scateni R. Fitmersive games: Fitness gamification through immersive VR. In: Proceedings from Proceedings of the Workshop on Advanced Visual Interfaces AVI; 2016. 56. Kang SY, Yi KO, Kang SA, Lee AH. Personalized Exercise Prescription Utilizing Virtual Fitness. In: Kim KJ, Joukov N, eds. Information Science and Applications (ICISA) 2016. Singapore: Springer Singapore; 2016:1453-1458. 57. Rattray B, Smee D. Exercise improves reaction time without compromising accuracy in a novel easy-to-administer tablet-based cognitive task. Journal of Science and Medicine in Sport. 2013;16(6):567-570. 58. Moreau D. Brains and brawn: Complex motor activities to maximize cognitive enhancement. Educational Psychology Review. 2015;27(3):475-482. 59. Reyes-Guzman CM, Bray RM, Forman-Hoffman VL, Williams J. Overweight and obesity trends among active duty military personnel: A 13-year perspective. American Journal of Preventive Medicine. 2015;48(2):145-153. 60. Aandstad A, Hageberg R, Sæther Ø, Nilsen RO. Change in anthropometrics and aerobic fitness in air force cadets during 3 years of academy studies. Aviation Space and Environmental Medicine. 2012;83(1):35-41. 61. Bedno SA, Urban N, Boivin MR, Cowan DN. Fitness, obesity and risk of heat illness among army trainees. Occupational Medicine. 2014;64(6):461-467. 62. Gattis VM. Obesity: A Threat to National Security. Carlisle Barracks, PA: Army War College; 2011: http://www.dtic.mil/dtic/tr/fulltext/u2/a547350.pdf. 63. Wang QC, Wu HJ. Design research in motion cloud computing platform based on a training aid system. Applied Mechanics and Materials. 2014;635-637:1551-1554. 64. Peng S. Cloud-based sport training platform based on body sensor network. Journal of Software Engineering. 2015;9(3):586-597. 65. Huang CH, Chin SL, Hsin LH, Hung JC, Yu YP. A Web-based E-learning Platform for physical education. Journal of Networks. 2011;6(5):721-727. 66. Xue F. Design and implementation of visual simulation system of sports training. Advanced Materials Research. 2014;926-930:1771-1774. 67. Knapik JJ, East WB. History of United States Army physical fitness and physical readiness training. U.S. Army Medical Department Journal. 2014. 68. Reilly T. Canada's Physical Fitness Standard for the land force: A global comparison. Canadian Army Journal. 2010. https://www.cfmws.com/en/AboutUs/PSP/DFIT/Documents/Publications%20and%20Reports/C anada's%20Physical%20Fitness%20Standard%20for%20the%20Land%20Force.pdf. 69. Canadian Forces Support Unit. CAF Minimum Physical Fitness Standard. Ottawa, ON: Canadian Forces Support Unit (CFSU); 2016: http://www.cg.cfpsa.ca/cg- pc/Ottawa/EN/FitnessandSports/MilitaryFitness/CFexprestesting/Pages/NewCanadianArmedFor cesminimumphysicalfitnessstandard.aspx. 70. Slater S. Three-event APFT retained pending baseline Soldier physical readiness study. 2012. https://www.army.mil/article/86147/Three_event_APFT_retained_pending_baseline_Soldier_p hysical_readiness_study/. 71. Rohde U, Sievert A, Rüther T, Witzki A, Leyk D. Concept for a predeployment assessment of basic military fitness in the German Armed Forces. Journal of Strength and Conditioning Research. 2015;29:S211-S215.
Page 66 of 85
Scientometric Study on Education, Training, and Exercise November 2016
72. Larsson H, Tegern M, Monnier A, et al. Content validity index and intra- and inter- rater reliability of a new muscle strength/ endurance test battery for Swedish soldiers. PLoS ONE. 2015;10(7). 73. Vaara JP, Kokko J, Isoranta M, Kyröläinen H. Effects of added resistance training on physical fitness, body composition, and serum hormone concentrations during eight weeks of special military training period. Journal of Strength and Conditioning Research. 2015;29:S168-S172. 74. Kodesh E, Shargal E, Kislev-Cohen R, et al. Examination of the effectiveness of predictors for musculoskeletal injuries in female soldiers. Journal of Sports Science and Medicine. 2015;14(3):515-521. 75. Grier T, Canham-Chervak M, Anderson MK, Bushman TT, Jones BH. The effects of cross-training on fitness and injury in women. U.S. Army Medical Department journal. 2015:33-41. 76. Gagnon P, Spivock M, Reilly T, Mattie P, Stockbrugger B. The FORCE fitness profile - Adding a measure of health-related fitness to the Canadian Armed Forces operational fitness evaluation. Journal of Strength and Conditioning Research. 2015;29:S192-S198. 77. Tan M. Army takes a hard look at creating a combat readiness test. Army Times. 2016. https://www.armytimes.com/story/military/2016/05/11/army-takes-hard-look-creating- combat-readiness-test/83980166/. 78. Department of National Defence. Health and Lifestyle Information Survey of Canadian Armed Forces Personnel 2013/2014. Ottawa, ON: Department of National Defence; 2016: http://www.forces.gc.ca/assets/FORCES_Internet/docs/en/about-reports-pubs-health/health- and-lifestyle-survey.pdf. 79. Knapik JJ, Trone DW, Austin KG, Steelman RA, Farina EK, Lieberman HR. Prevalence, adverse events, and factors associated with dietary supplement and nutritional supplement use by US Navy and Marine Corps personnel. Journal of the Academy of Nutrition and Dietetics. 2016. 80. Zaǐtseva IP, Nosolodin VV, Zaǐtsev ON, et al. [Vitamin and mineral supplements in the diet of military personnel: effect on the balance of iron, copper and manganese, immune reactivity and physical work-capacity]. Voenno-medit{combining double inverted breve}sinskiǐ zhurnal. 2012;333(3):37-41. 81. Lieberman HR, Stavinoha T, McGraw S, White A, Hadden L, Marriott BP. Caffeine use among active duty US Army soldiers. Journal of the Academy of Nutrition and Dietetics. 2012;112(6):902-912.e904. 82. Carvey CE, Farina EK, Lieberman HR. Confidence in the efficacy and safety of dietary supplements among United States active duty army personnel. BMC Complementary and Alternative Medicine. 2012;12. 83. O'Fallon KS, Kaushik D, Michniak-Kohn B, Dunne CP, Zambraski EJ, Clarkson PM. Effects of quercetin supplementation on markers of muscle damage and inflammation after eccentric exercise. International Journal of Sport Nutrition and Exercise Metabolism. 2012;22(6):430-437. 84. Hoffman JR, Stout JR, Harris RC, Moran DS. β-Alanine supplementation and military performance. Amino Acids. 2015;47(12):2463-2474. 85. Austin KG, Farina EK, Lieberman HR. Self-reported side-effects associated with use of dietary supplements in an armed forces population. Drug Testing and Analysis. 2016;8(3-4):287-295. 86. Stoker L. Creating Supermen: battlefield performance enhancing drugs. Army Technology. 2013. http://www.army-technology.com/features/featurecreating-supermen-battlefield- performance-enhancing-drugs/. 87. Redmond JE, Cohen BS, Simpson K, Spiering BA, Sharp MA. Measuring physical activity during US Army Basic Combat Training: a comparison of 3 methods. U.S. Army Medical Department Journal. 2013:48-54.
Page 67 of 85
Scientometric Study on Education, Training, and Exercise November 2016
88. National Aeronautics Space Administration. Monitors Track Vital Signs for Fitness and Safety: National Aeronautics and Space Administration;2012. 89. Simpson K, Redmond JE, Cohen BS, et al. Quantification of physical activity performed during US Army Basic Combat Training. U.S. Army Medical Department Journal. 2013:55-65. 90. Connor SO, McCaffrey N, Whyte E, Moran K. The novel use of a SenseCam and accelerometer to validate training load and training information in a self-recall training diary. Journal of Sports Sciences. 2015. 91. Solon O. Black Ghost military training system uses big data to improve soldiers' performance. Wired. 2013. http://www.wired.co.uk/article/equivital-black-ghost. 92. Talbot LA, Metter EJ, Morrell CH, Frick KD, Weinstein AA, Fleg JL. A pedometer-based intervention to improve physical activity, fitness, and coronary heart disease risk in national guard personnel. Military Medicine. 2011;176(5):592-600. 93. Bleser G, Steffen D, Reiss A, Weber M, Hendeby G, Fradet L. Personalized physical activity monitoring using wearable sensors. Lecture Notes in Computer Science 2015;8700:99-124. 94. Fravell M, Nasser K, Cornum R. The Soldier Fitness Tracker: Global delivery of comprehensive soldier fitness. American Psychologist. 2011;66(1):73-76. 95. Sadagic A. Next generation of physical training environments: Bringing in sensor systems and virtual reality technologies. Lecture Notes in Computer Science 2013;8027 LNAI:717-726. 96. Bandodkar AJ, Wang J. Non-invasive wearable electrochemical sensors: A review. Trends in Biotechnology. 2014;32(7):363-371. 97. Bandodkar AJ, Molinnus D, Mirza O, et al. Epidermal tattoo potentiometric sodium sensors with wireless signal transduction for continuous non-invasive sweat monitoring. Biosensors and Bioelectronics. 2014;54:603-609. 98. Zois DS, Levorato M, Mitra U. Energy-efficient, heterogeneous sensor selection for physical activity detection in wireless body area networks. IEEE Transactions on Signal Processing. 2013;61(7):1581-1594. 99. Bacon L. Marines can now wear fitness trackers in more military facilities. Marine Corps Times. 2016. http://www.thingser.com/links/marinecorpstimes-com-story-military-2016-05-31- marines-can-now-wear-fitness-trackers-more-military-facilities-85206020. 100. Zhang K, Liu SJ. The application of virtual reality technology in physical education teaching and training. In: Proceedings from 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI); ; 10-12 July 2016, 2016. 101. Hudson I, Hurter J. Avatar types matter: Review of avatar literature for performance purposes. Lecture Notes in Computer Science 2016;9740:14-21. 102. Collée A, Clarys P, Geeraerts P, Dugauquier C, Mullie P. Body mass index, physical activity, and smoking in relation to military readiness. Military medicine. 2014;179(8):901-905. 103. Horner F, Bilzon JL, Rayson M, et al. Development of an accelerometer-based multivariate model to predict free-living energy expenditure in a large military cohort. Journal of Sports Sciences. 2013;31(4):354-360. 104. Shrestha M, Combest T, Fonda SJ, Alfonso A, Guerrero A. Effect of an accelerometer on body weight and fitness in overweight and obese active duty soldiers. Military Medicine. 2013;178(1):82-87. 105. Sporis G, Harasin D, Bok D, Matika D, Vuleta D. Effects of a training program for special operations battalion on soldiers' fitness characteristics. Journal of Strength and Conditioning Research. 2012;26(10):2872-2882.
Page 68 of 85
Scientometric Study on Education, Training, and Exercise November 2016
106. Newton Jr RL, Han H, Stewart TM, Ryan DH, Williamson DA. Efficacy of a pilot internet-based weight management program (H.E.A.L.T.H.) and longitudinal physical fitness data in army reserve soldiers. Journal of Diabetes Science and Technology. 2011;5(5):1255-1262. 107. Leyk D, Witzki A, Willi G, Rohde U, Rüther T. Even one is too much: Sole presence of one of the risk factors overweight, lack of exercise, and smoking reduces physical fitness of young soldiers. Journal of Strength and Conditioning Research. 2015;29:S199-S203. 108. Attwood E. Overhauling military fitness standards, implementing new programs. Athletic Business. 2016. http://www.athleticbusiness.com/military/overhauling-military-fitness- standards-implementing-new-programs.html. 109. Baker B. Fat fighters: can tech tackle the global military obesity problem? Army Technology. 2016. http://www.army-technology.com/features/featurefat-fighters-can-tech-tackle-the- global-military-obesity-problem-4951081/. 110. Daniels J. Fit to fight? Pentagon mulls changes in fitness standards as obesity rates rise. CNBC News. 2016. 111. U.S. Navy. Research, Development, Test and Evaluation (RDT&E): Volumes 1-7. Washington, DC: U.S. Army; 2016: http://www.secnav.navy.mil/fmc/fmb/Pages/Fiscal-Year-2017.aspx. 112. Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y. Physical human activity recognition using wearable sensors. Sensors (Switzerland). 2015;15(12):31314-31338. 113. Arif M, Kattan A, Ahamed SI. Classification of physical activities using wearable sensors. Intelligent Automation and Soft Computing. 2015:1-10. 114. Ren X, Ding W, Crouter SE, Mu Y, Xie R. Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning. Applied Intelligence. 2016;45(2):512-529. 115. Guiry JJ, Van De Ven P, Nelson J. Classification techniques for smartphone based activity detection. In: Proceedings from Proceedings of the 11th IEEE International Conference on Cybernetic Intelligent Systems 2012, CIS 2012; 2012. 116. Harrison D, Marshall P, Bianchi-Berthouze N, Bird J. Activity tracking: Barriers, workarounds and customisation. In: Proceedings from Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2015. 117. Kuru A, Forlizzi J. Engaging experience with physical activity tracking products. Lecture Notes in Computer Science 2015;9186:490-501. 118. Jankowska MM, Schipperijn J, Kerr J. A framework for using GPS data in physical activity and sedentary behavior studies. Exercise and Sport Sciences Reviews. 2015;43(1):48-56. 119. Incel OD, Kose M, Ersoy C. A review and taxonomy of activity recognition on mobile phones. BioNanoScience. 2013;3(2):145-171. 120. Castellano J, Casamichana D. Sport with global positioning devices (GPS): Applications and limitations. Revista de Psicologia del Deporte. 2014;23(2):355-364. 121. Edwards HM, McDonald S, Zhao T, Humphries L. Design requirements for persuasive technologies to motivate physical activity in adolescents: A field study. Behaviour and Information Technology. 2014;33(9):968-986. 122. Goyder E, Hind D, Breckon J, et al. A randomised controlled trial and cost-effectiveness evaluation of 'booster' interventions to sustain increases in physical activity in middle-aged adults in deprived urban neighbourhoods. Health Technology Assessment. 2014;18(13):1-209. 123. Rovniak LS, Sallis JF, Kraschnewski JL, et al. Engineering online and in-person social networks to sustain physical activity: Application of a conceptual model. BMC Public Health. 2013;13(1). 124. Park C, Lee D, Jang M, Kim J, Kang JH. A socially interactive robot using social skills in physical training. In: Proceedings from 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence; 2015.
Page 69 of 85
Scientometric Study on Education, Training, and Exercise November 2016
125. Lin PH, Intille S, Bennett G, et al. Adaptive intervention design in mobile health: Intervention design and development in the Cell Phone Intervention for You trial. Clinical Trials. 2015;12(6):634-645. 126. Frost and Sullivan. Next Generation Sensors for Wearables and Smart Phones. Mountain View, CA: Frost and Sullivan; 2016. 127. Kolla BP, Mansukhani S, Mansukhani MP. Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices. 2016;13(5):497-506. 128. Chakraborty B, Yoshida T. Design of a framework for wellness determination and subsequent recommendation with personal informatics. In: Proceedings from AAAI Spring Symposium - Technical Report; 2016. 129. Roane BM, Van Reen E, Hart CN, Wing R, Carskadon MA. Estimating sleep from multisensory armband measurements: Validity and reliability in teens. Journal of Sleep Research. 2015;24(6):714-721. 130. Purta R, Mattingly S, Song L, et al. Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits. In: Proceedings from International Symposium on Wearable Computers, Digest of Papers; 2016. 131. Cotrupe J. Wearing Your Heart (Rate) on Your Sleeve: How Fitness Trackers and Big Data are Giving the World a Running Start toward Connected Health. Mountain View, CA: Frost and Sullivan; 2016. 132. Lugrin JL, Landeck M, Latoschik ME. Avatar embodiment realism and virtual fitness training. In: Proceedings from 2015 IEEE Virtual Reality Conference, VR 2015 - Proceedings; 2015. 133. Shaw LA, Wünsche BC, Lutteroth C, Marks S, Callies R. Challenges in virtual reality exergame design. In: Proceedings from Conferences in Research and Practice in Information Technology Series; 2015. 134. Bao W, Su QF. Design of simulation system for VR physical education. In: Proceedings from ISME 2015 - Proceedings of the Information Science and Management Engineering III; 2015. 135. Miao F, Cheng Y, He Y, He Q, Li Y. A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the smartphone. Sensors (Switzerland). 2015;15(5):11465- 11484. 136. Hamper A. A context aware mobile application for physical activity promotion. In: Proceedings from Proceedings of the Annual Hawaii International Conference on System Sciences; 2015. 137. Mortazavi B, Pourhomayoun M, Ghasemzadeh H, Jafari R, Roberts CK, Sarrafzadeh M. Context- aware data processing to enhance quality of measurements in wireless health systems: An application to MET calculation of exergaming actions. IEEE Internet of Things Journal. 2015;2(1):84-93. 138. Scholten SD, Sergeev IN. Vitamin D and physical performance in athletes. Immunology, Endocrine and Metabolic Agents in Medicinal Chemistry. 2015;14(3):152-161. 139. Popovic LM, Mitic NR, Miric D, Bisevac B, Miric M, Popovic B. Influence of vitamin c supplementation on oxidative stress and neutrophil inflammatory response in acute and regular exercise. Oxidative Medicine and Cellular Longevity. 2015;2015. 140. Shanely RA, Nieman DC, Knab AM, et al. Influence of vitamin D mushroom powder supplementation on exercise-induced muscle damage in vitamin D insufficient high school athletes. Journal of Sports Sciences. 2014;32(7):670-679. 141. Wiseman E. Scientometric Study on Human Optimization. Ottawa, ON: National Research Council of Canada; 2015. 142. Frost and Sullivan. Innovations in Human Enablement and Enhancement Technologies. Mountain View, CA: Frost and Sullivan; 2016.
Page 70 of 85
Scientometric Study on Education, Training, and Exercise November 2016
143. Gens F. IDC FutureScape: Worldwide IT Industry 2017 Predictions. Framingham, MA: IDC; 2016. 144. Bostrom N, Sandberg A. Cognitive enhancement: Methods, ethics, regulatory challenges. Science and Engineering Ethics. 2009;15(3):311-341. 145. Caviola L, Faber NS. Pills or push-ups? Effectiveness and public perception of pharmacological and non-pharmacological cognitive enhancement. Frontiers in Psychology. 2015;6(DEC). 146. Summers JJ, Kang N, Cauraugh JH. Does transcranial direct current stimulation enhance cognitive and motor functions in the ageing brain? A systematic review and meta- analysis. Ageing Research Reviews. 2016;25:42-54. 147. Strickland E. Olympic athletes are electrifying their brains, and you can, too. IEEE Spectrum. 2016. http://spectrum.ieee.org/biomedical/bionics/olympic-athletes-are-electrifying-their- brains-and-you-can-too. 148. Cabrera LY, Reiner PB. Understanding public (mis)understanding of tDCS for enhancement. Frontiers in Integrative Neuroscience. 2015;9(APR). 149. National Science Foundation. Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS): Program Solicitation 2016: https://www.nsf.gov/pubs/2017/nsf17519/nsf17519.htm?WT.mc_id=USNSF_179. 150. Funk C, Kennedy B, Sciupac E. U.S. Public Wary of Biomedical Technologies to 'Enhance' Human Abilities. Washington, DC: Pew Research Center; 2016: http://assets.pewresearch.org/wp- content/uploads/sites/14/2016/07/PS_2016.07.26_Human-Enhancement-Survey_FINAL.pdf. 151. Giglio F, Spagnolo A. Is there a clear boundary between therapy and human enhancement in medical practive? International Journal of Bioethics and Health Policy. 2016;1. 152. Ehsani F, Bakhtiary AH, Jaberzadeh S, Talimkhani A, Hajihasani A. Differential effects of primary motor cortex and cerebellar transcranial direct current stimulation on motor learning in healthy individuals: A randomized double-blind sham-controlled study. Neuroscience Research. 11// 2016;112:10-19. 153. Goel V, Eimontaite I, Goel A, Schindler I. Differential modulation of performance in insight and divergent thinking tasks with tDCS. Journal of Problem Solving. 2015;8(1):23-35. 154. Scheldrup M, Greenwood PM, McKendrick R, et al. Transcranial direct current stimulation facilitates cognitive Multi-Task performance differentially depending on anode location and subtask. Frontiers in Human Neuroscience. 2014;8(SEP). 155. Lage C, Wiles K, Shergill SS, Tracy DK. A systematic review of the effects of low-frequency repetitive transcranial magnetic stimulation on cognition. Journal of Neural Transmission. 2016:1-12. 156. Chaieb L, Antal A, Ambrus GG, Paulus W. Brain-derived neurotrophic factor: its impact upon neuroplasticity and neuroplasticity inducing transcranial brain stimulation protocols. Neurogenetics. 2014;15(1):1-11. 157. Lamkin M. Cognitive enhancements and the values of higher education. Health Care Analysis. 2012;20(4):347-355. 158. Whetstine LM. Cognitive enhancement: Treating or cheating? Seminars in Pediatric Neurology. 2015;22(3):172-176. 159. Busardò FP, Kyriakou C, Cipolloni L, Zaami S, Frati P. From clinical application to cognitive enhancement: The example of methylphenidate. Current Neuropharmacology. 2016;14(1):17- 27. 160. Pitt J, Leung Y. Cognitive effects of nutraceuticals. Nutraceuticals: Efficacy, Safety and Toxicity2016:29-48.
Page 71 of 85
Scientometric Study on Education, Training, and Exercise November 2016
161. Anantha Lakshmi J, Satyavati D. A study on nootropic activity of methanolic extract of Brassica oleraceae var. Caulorapa bulb in rodents. Pharmacologyonline. 2015;3(2015-DECEMBER):107- 115. 162. Benson S, Downey LA, Stough C, Wetherell M, Zangara A, Scholey A. An acute, double-blind, placebo-controlled cross-over study of 320 mg and 640 mg doses of Bacopa monnieri (CDRI 08) on multitasking stress reactivity and mood. Phytotherapy Research. 2014;28(4):551-559. 163. Lucke JC, Bell SK, Partridge BJ, Hall WD. Academic doping or Viagra for the brain? the history of recreational drug use and pharmacological enhancement can provide insight into these uses of neuropharmaceuticals. EMBO Reports. 2011;12(3):197-201. 164. LaBuzetta JN. Moving beyond methylphenidate and amphetamine: The ethics of a better "smart drug". American Journal of Bioethics. 2013;13(7):43-45. 165. Franke AG, Bonertz C, Christmann M, et al. Non-medical use of prescription stimulants and illicit use of stimulants for cognitive enhancement in pupils and students in Germany. Pharmacopsychiatry. 2011;44(2):60-66. 166. Blank C, Brunner J, Kreische B, Lazzeri M, Schobersberger W, Kopp M. Performance-enhancing substance use in university students: motives, attitudes, and differences in normative beliefs. Journal of Substance Use. 2016:1-7. 167. Ford K, Glymour C. The enhanced warfighter. Bulletin of the Atomic Scientists. 2014;70(1):43-53. 168. Tonoli C, Heyman E, Buyse L, et al. Neurotrophins and cognitive functions in T1D compared with healthy controls: Effects of a high-intensity exercise. Applied Physiology, Nutrition and Metabolism. 2014;40(1):20-27. 169. Myburgh KH. Polyphenol supplementation: Benefits for exercise performance or oxidative stress? Sports Medicine. 2014;44(SUPPL.1):S57-S70. 170. Hecksteden A, Heinze T, Faude O, Kindermann W, Meyer T. Validity of lactate thresholds in inline speed skating. Journal of Strength and Conditioning Research. 2015;29(9):2497-2502. 171. Seligman MEP, Fowler RD. Comprehensive Soldier Fitness and the future of psychology. American Psychologist. 2011;66(1):82-86. 172. Cornum R, Matthews MD, Seligman MEP. Comprehensive Soldier Fitness: Building resilience in a challenging institutional context. American Psychologist. 2011;66(1):4-9. 173. Griffith J, West C. Master resilience training and its relationship to individual well-being and stress buffering among Army National Guard soldiers. Journal of Behavioral Health Services and Research. 2013;40(2):140-155. 174. Kaplan GB, Moore KA. The use of cognitive enhancers in animal models of fear extinction. Pharmacology Biochemistry and Behavior. 2011;99(2):217-228. 175. Stackman Jr RW, Zhang G, Sgeirsdóttir HN, Cohen SJ, Munchow AH, Barrera MP. Stimulation of serotonin 2A receptors facilitates consolidation and extinction of fear memory in C57BL/6J mice. Neuropharmacology. 2013;64:403-413. 176. Zimmermann F. Mindfulness-based practices as a resource for health and well-being. Medical Acupuncture. 2015;27(5):349-359. 177. Vakili V, Brinkman WP, Morina N, Neerincx MA. Characteristics of successful technological interventions in mental resilience training. Journal of Medical Systems. 2014;38(9). 178. McKinley RA, McIntire L, Bridges N, Goodyear C, Weisend MP. Acceleration of image analyst training with transcranial direct current stimulation. Behavioral Neuroscience. 2013;127(6):936- 946. 179. Nelson JM, McKinley RA, McIntire LK, Goodyear C, Walters C. Augmenting visual search performance with transcranial direct current stimulation (tDCS). Military Psychology. 2015;27(6):335-347.
Page 72 of 85
Scientometric Study on Education, Training, and Exercise November 2016
180. Nelson JT, McKinley RA, Golob EJ, Warm JS, Parasuraman R. Enhancing vigilance in operators with prefrontal cortex transcranial direct current stimulation (tDCS). NeuroImage. 2014;85:909- 917. 181. McKinley RA, Bridges N, Walters CM, Nelson J. Modulating the brain at work using noninvasive transcranial stimulation. NeuroImage. 2012;59(1):129-137. 182. McKinley AR, McIntire L, Nelson J, Goodyear C. The effects of transcranial direct current stimulation (tDCS) on training during a complex procedural task. Advances in Intelligent Systems and Computing. Vol 4882017:173-183. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84986290419&partnerID=40&md5=b2c99f0b5f1476d22c52d3851bde4c3a. 183. Choe J, Coffman BA, Bergstedt DT, Ziegler MD, Phillips ME. Transcranial direct current stimulation modulates neuronal activity and learning in pilot training. Frontiers in Human Neuroscience. 2016;10(34). http://journal.frontiersin.org/article/10.3389/fnhum.2016.00034. 184. US Air Force testing electrical brain stimulation to boost alertness and reduce stress of drone and fighter pilots and found a 30% boost in performance Next Big Future. 2016. http://www.nextbigfuture.com/2016/11/us-air-force-testing-electrical-brain.html. 185. Shachtman N. Supercharging soldiers' cells. Wired. 2007. https://www.wired.com/2007/03/supercharging_s/. 186. Burman-Fink M. The rise and decline of military human enhancement. Science Progress. 2011. https://scienceprogress.org/2011/01/the-rise-and-decline-of-military-human-enhancement/. 187. Coulter ID, Newberry S. Multicomponent Dietary Supplements for the Military. Santa Monica, CA: RAND Center for Military Health POlicy Research; 2011: http://www.rand.org/content/dam/rand/pubs/conf_proceedings/2011/RAND_CF298.pdf. 188. Miranda RA, Casebeer WD, Hein AM, et al. DARPA-funded efforts in the development of novel brain–computer interface technologies. Journal of Neuroscience Methods. 2015;244:52-67. http://www.sciencedirect.com/science/article/pii/S0165027014002702. Accessed 4/15/. 189. Sanchez JC. Neuro Function, Activity, Structure, and Technology (Neuro-FAST) 2016: http://www.darpa.mil/program/neuro-function-activity-structure-and-technology. 190. U.S. Defense Advanced Research Projects Agency. Bridging the Bio-Electronic Divide. Washington, DC: DARPA; 2016: http://www.darpa.mil/news-events/2015-01-19. 191. Monitors Track Vital Signs for Fitness and Safety: National Aeronautics and Space Administration, ;2012. 192. Hackney KJ, Scott JM, Hanson AM, English KL, Downs ME, Ploutz-Snyder LL. The astronaut- athlete: Optimizing human performance in space. Journal of Strength and Conditioning Research. 2015;29(12):3531-3545. 193. Norsk P, Baumann D. Human Health Countermeasures (HHC) Element Management Plan: Human Research Program: National Aeronautics and Space Administration;2012. 194. U.S. National Aeronautics and Space Administration. NASA Human Health and Performance Strategy Washington, DC: U.S. National Aeronautics and Space Administration,; 2012: http://www.nasa.gov/sites/default/files/atoms/files/nasa_human_health_and_performance_st rategy_2012.pdf. 195. U.S. Defense Advanced Research Projects Agency. Research, Development, Test and Evaluation (RDT&E): Volume 1. Washington, DC: DARPA; 2016: http://www.darpa.mil/attachments/DARPAFY17PresidentsBudgetRequest.pdf 196. U.S. Air Force. Research, Development, Test and Evaluation (RDT&E): Volumes 1-4. Washington, DC: U.S. Air Force; 2016: http://www.saffm.hq.af.mil/Budget. 197. Halo Neuroscience. Department of Defense Selects Halo Sport to Train Special Ops Forces. San Francisco, CA: Halo Neuroscience; 2016. https://medium.com/halo-neuroscience-company-
Page 73 of 85
Scientometric Study on Education, Training, and Exercise November 2016
news/department-of-defense-selects-halo-sport-to-train-special-ops-forces- e0fd3b8d8c6a#.xasgn28u4. 198. HRL Laboratories. HRL Demonstrates the Potential to Enhance the Human Intellect's Capacity to Learn New Skills. . Malibu, CA: HRL Laboratories; 2016. http://www.hrl.com/news/2016/0210/. 199. Forces TV. SAS Could Use Shocking Brain Technology. Forces TV. 2016. 200. França ASC, Lobão-Soares B, Muratori L, et al. D2 dopamine receptor regulation of learning, sleep and plasticity. European Neuropsychopharmacology. 2015;25(4):493-504. 201. Sachdeva A, Kumar K, Anand KS. Non pharmacological cognitive enhancers – Current perspectives. Journal of Clinical and Diagnostic Research. 2015;9(7). 202. Fullagar HHK, Skorski S, Duffield R, Hammes D, Coutts AJ, Meyer T. Sleep and athletic performance: The effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Medicine. 2015;45(2):161-186. 203. Borragán G, Urbain C, Schmitz R, Mary A, Peigneux P. Sleep and memory consolidation: Motor performance and proactive interference effects in sequence learning. Brain and Cognition. 2015;95:54-61. 204. Saebipour MR, Joghataei MT, Yoonessi A, Sadeghniiat-Haghighi K, Khalighinejad N, Khademi S. Slow oscillating transcranial direct current stimulation during sleep has a sleep-stabilizing effect in chronic insomnia: A pilot study. Journal of Sleep Research. 2015;24(5):518-525. 205. Sahlem GL, Badran BW, Halford JJ, et al. Oscillating square wave transcranial direct current stimulation (tDCS) delivered during slow wave sleep does not improve declarative memory more than sham: A randomized sham controlled crossover study. Brain Stimulation. 2015;8(3):528- 534. 206. Li LM, Uehara K, Hanakawa T. The contribution of interindividual factors to variability of response in transcranial direct current stimulation studies. Frontiers in Cellular Neuroscience. 2015;9(MAY). 207. Reis J, Fischer JT, Prichard G, Weiller C, Cohen LG, Fritsch B. Time- but not sleep-dependent consolidation of tDCS-enhanced visuomotor skills. Cerebral Cortex. 2015;25(1):109-117. 208. Freitas HR, da Silva Pereira A, da Silva Ramos T. The effects of acute/chronic glutamine and glutamine peptide supplementation on the performance and immune function in young active adult athletes. Current Nutrition and Food Science. 2015;11(4):315-322. 209. Ostojic SM. Guanidinoacetic acid as a performance-enhancing agent. Amino Acids. 2016;48(8):1867-1875. 210. Nieß AM, Striegel H, Wiesing U. Doping and ergogenic substance use in fitness sports. Deutsche Zeitschrift fur Sportmedizin. 2014;65(2):29-33. 211. Gaffney G. Performance enhancement drugs and sports supplements: A review of the evidence. Nutrition and Enhanced Sports Performance: Muscle Building, Endurance, and Strength2014:151-159. 212. Pecchinenda A, Ferlazzo F, Lavidor M. Modulation of selective attention by polarity-specific tDCS effects. Neuropsychologia. 2015;68:1-7. 213. Roy LB, Sparing R, Fink GR, Hesse MD. Modulation of attention functions by anodal tDCS on right PPC. Neuropsychologia. 2015;74:96-107. 214. Taya F, Sun Y, Babiioni F, Thakor N, Bezerianos A. Brain enhancement through cognitive training: A new insight from brain connectome. Frontiers in Systems Neuroscience. 2015;9(APR). 215. Cohen-Maximov T, Avirame K, Flöel A, Lavidor M. Modulation of gestural-verbal semantic integration by tDCS. Brain Stimulation. 2015;8(3):493-498. 216. Tan LF, Dienes Z, Jansari A, Goh SY. Effect of mindfulness meditation on brain-computer interface performance. Consciousness and Cognition. 2014;23(1):12-21.
Page 74 of 85
Scientometric Study on Education, Training, and Exercise November 2016
217. Valkanova V, Eguia Rodriguez R, Ebmeier KP. Mind over matter - What do we know about neuroplasticity in adults? International Psychogeriatrics. 2014;26(6):891-909. 218. Banks JB, Welhaf MS, Srour A. The protective effects of brief mindfulness meditation training. Consciousness and Cognition. 2015;33:277-285. 219. Peretz C, Korczyn AD, Shatil E, Aharonson V, Birnboim S, Giladi N. Computer-based, personalized cognitive training versus classical computer games: A randomized double-blind prospective trial of cognitive stimulation. Neuroepidemiology. 2011;36(2):91-99. 220. Snowball A, Tachtsidis I, Popescu T, et al. Long-term enhancement of brain function and cognition using cognitive training and brain stimulation. Current Biology. 2013;23(11):987-992. 221. Chaldogeridis A. Cognitive training supported by information and communication technologies. In: Proceedings from Proceedings of 2015 International Conference on Interactive Mobile Communication Technologies and Learning; 2015. 222. Pamme V, Simon J, Wilding K, Keller S, Scherer R. Designing for engaging BCI training: A jigsaw puzzle. In: Proceedings from Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play; 2015. 223. Anguera JA, Gazzaley A. Video games, cognitive exercises, and the enhancement of cognitive abilities. Current Opinion in Behavioral Sciences. 2015;4:160-165. 224. Dobrowolski P, Hanusz K, Sobczyk B, Skorko M, Wiatrow A. Cognitive enhancement in video game players: The role of video game genre. Computers in Human Behavior. 2015;44:59-63. 225. Abiri R, McBride J, Zhao X, Jiang Y. A real-time brainwave based neuro-feedback system for cognitive enhancement. In: Proceedings from ASME 2015 Dynamic Systems and Control Conference; 2015. 226. Choi K, Kim I, Min BK. A feedback training system using cognitive brain-computer interface. In: Proceedings from 3rd International Winter Conference on Brain-Computer Interface; 2015. 227. Scharnowski F, Weiskopf N. Cognitive enhancement through real-time fMRI neurofeedback. Current Opinion in Behavioral Sciences. 2015;4:122-127. 228. Cambridge University. Cambridge English Dictionary. 2016: http://dictionary.cambridge.org/dictionary/english/holistic. 229. Canadian Army. Canadian Army Integrated Performance Strategy: Canadian Army performance Triad (CAP3): Leader's Guide. Ottawa, ON: Canadian Army: http://strongproudready.ca/missionready/wp-content/uploads/cap3-leaders-guide-en.pdf. 230. U.S. Army. Performance Triad: A leader's Guide and Planner. Washington, DC: U.S. Army; 2013: http://armymedicine.mil/Documents/LeadersGuide_Planner_08_07_2013.pdf. 231. Land B. Current Department of Defense guidance for Total Force Fitness. Military Medicine. 2010;175. 232. U.S. Army. Army Human Dimension Strategy. Washington, DC: U.S. Army; 2015: http://usacac.army.mil/sites/default/files/publications/20150524_Human_Dimension_Strategy _vr_Signature_WM_1.pdf. 233. U.S. Army Training and Doctrine Command. The U.S. Army Human Dimension Concept. Fort Eustis, VA: TRADOC; 2014: http://www.tradoc.army.mil/tpubs/pams/tp525-3-7.pdf. 234. Deuster P. Consortium for Health and Military Performance (CHAMP). Bethesda, MD: CHAMP; 2015: http://herl.pitt.edu/symposia/adaptive- reconditioning/presentations/DEUSTER_CHAMP2015forJune5_2015R.pdf. 235. Nindl BC, Jaffin DP, Dretsch MN, et al. Human performance optimization metrics: Consensus findings, gaps, and recommendations for future research. Journal of Strength and Conditioning Research. 2015;29:S221-S245.
Page 75 of 85
Scientometric Study on Education, Training, and Exercise November 2016
236. Deuster PA, O'Connor FG. Human performance optimization: Culture change and paradigm shift. Journal of Strength and Conditioning Research. 2015;29:S52-S56. 237. Roco MC, Bainbridge WS. Converging Technologies for Improving Human Performance. Dordrecht, Netherlands: Springer; 2002: http://www.wtec.org/ConvergingTechnologies/Report/NBIC_report.pdf. 238. Hogan CL, Mata J, Carstensen LL. Exercise holds immediate benefits for affect and cognition in younger and older adults. Psychology and Aging. 2013;28(2):587-594. 239. Gomez-Pinilla F, Hillman C. The influence of exercise on cognitive abilities. Comprehensive Physiology. 2013;3(1):403-428. 240. Monteiro-Junior RS, da Silva Figueiredo LF, Maciel-Pinheiro PDT, et al. Acute effects of exergames on cognitive function of institutionalized older persons: a single-blinded, randomized and controlled pilot study. Aging Clinical and Experimental Research. 2016:1-8. 241. Pesce C, Croce R, Ben-Soussan TD, et al. Variability of practice as an interface between motor and cognitive development. International Journal of Sport and Exercise Psychology. 2016:1-20. 242. Heijnen S, Hommel B, Kibele A, Colzato LS. Neuromodulation of aerobic exercise-A review. Frontiers in Psychology. 2016;6(JAN). 243. Galway L, McCullagh P, Lightbody G, Brennan C, Trainor D. The potential of the brain-computer interface for learning: A technology review. In: Proceedings from Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 20152015. 244. Fosty B, Ben-Sadoun G, Sacco G, et al. Accuracy and reliability of the RGB-D camera for measuring walking speed on a treadmill. Gait and Posture. 2016;48:113-119. 245. Gilleen J, Michalopoulou PG, Reichenberg A, et al. Modafinil combined with cognitive training is associated with improved learning in healthy volunteers - A randomised controlled trial. European Neuropsychopharmacology. 2014;24(4):529-539. 246. Luft CDB, Pereda E, Banissy MJ, Bhattacharya J. Best of both worlds: Promise of combining brain stimulation and brain connectome. Frontiers in Systems Neuroscience. 2014;8(JULY). 247. Schättin A, Baur K, Stutz J, Wolf P, de Bruin ED. Effects of physical exercise combined with nutritional supplements on aging brain related structures and functions: A systematic review. Frontiers in Aging Neuroscience. 2016;8(JUN). 248. Looi CY, Duta M, Brem AK, Huber S, Nuerk HC, Cohen Kadosh R. Combining brain stimulation and video game to promote long-term transfer of learning and cognitive enhancement. Nature Scientific Reports. 2016;6. 249. Elmasry J, Loo C, Martin D. A systematic review of transcranial electrical stimulation combined with cognitive training. Restorative Neurology and Neuroscience. 2015;33(3):263-278. 250. Santarnecchi E, Brem AK, Levenbaum E, Thompson T, Kadosh RC, Pascual-Leone A. Enhancing cognition using transcranial electrical stimulation. Current Opinion in Behavioral Sciences. 2015;4:127-178. 251. Zambraski EJ, Hydren JR. Research Consensus: Defining Research Gaps and Priorities. Natick, MA: US Army Research Institute of Environmental Medicine; 2014: http://www.usariem.army.mil/assets/docs/research/rcdrgp_2014.pdf. 252. Hydren JR, Zambraski EJ. International research consensus: Identifying military research priorities and gaps. Journal of Strength and Conditioning Research. 2015;29:S24-S27. 253. Huang J, Deng W. Application prospect of big data in military physical education and sports. Lecture Notes in Electrical Engineering. 2016;406:627-635.
Page 76 of 85
Scientometric Study on Education, Training, and Exercise November 2016
254. U.S. Department of Defense. Human Systems Community of Interest. Washington, DC: Department of Defense; 2016: http://defenseinnovationmarketplace.mil/resources/HumanSystemsCOIPoster.pdf. 255. Frost and Sullivan. Wearables in Sports. Mountain View, CA: Frost and Sullivan; 2016. 256. Longmuir PE, Tremblay MS. Top 10 research questions related to physical literacy. Research Quarterly for Exercise and Sport. 2016;87(1):28-35. 257. Nindl BC, Sharp MA. Third International Congress on Soldiers' Physical Performance: Translating state-of-the-science soldier research for operational utility. The Journal of Strength & Conditioning Research. 2015;29:S1-S3. http://journals.lww.com/nsca- jscr/Fulltext/2015/11001/Third_International_Congress_on_Soldiers__Physical.1.aspx. 258. Frost and Sullivan. The Global Future of Workplace Technology, Forecast to 2025. Mountain View, CA: Frost and Sullivan; 2016. 259. McCullagh P. Mind, the gap. Communications in Computer and Information Science. 2016;621:102-112. 260. Adler AB, Delahaij R, Bailey SM, et al. NATO survey of mental health training in army recruits. Military Medicine. 2013;178(7):760-766. 261. Park JL, Fairweather MM, Donaldson DI. Making the case for mobile cognition: EEG and sports performance. Neuroscience and Biobehavioral Reviews. 2015;52:117-130. 262. Bassett DS, Medaglia JD, Sinnitt-Armstrong W, Zurn P. Mind Control: Frontiers i nGuiding the Mind. Philadelphia, PA: University of Pennsylvania; 2016: https://arxiv.org/pdf/1610.04134v1.pdf. 263. Allen AL, Strand NK. Cognitive Enhancement and Beyond: Recommendations from the Bioethics Commission. Trends in Cognitive Sciences. 2015;19(10):549-551. 264. Kelly TK, Masi R, Walker BA, Knapp SA, Leuschner KJ. As Assessment of the Army's Tactical Human Optimization, Rapid Rehabilitation and Reocnditioning Program. Santa Monica, CA: RAND Corporation; 2013: http://www.rand.org/content/dam/rand/pubs/technical_reports/TR1300/TR1309/RAND_TR130 9.pdf. 265. Bain L, Norris SP, Stroud C. Non-Invasive Neuromodulation of the Central Nervous System: Opportunities and Challenges: Workshop Summary. Washington, DC: National Academies Press; 2016. 266. Abdulkader SN, Atia A, Mostafa M-SM. Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal. 2015;16(2):213-230. http://www.sciencedirect.com/science/article/pii/S1110866515000237. Accessed 7//. 267. Maslen H, Savulescu J, Douglas T, Levy N, Kadosh RC. Regulation of devices for cognitive enhancement. The Lancet. 2013;382(9896):938-939. 268. Maslen H, Douglas T, Cohen Kadosh R, Levy N, Savulescu J. The regulation of cognitive enhancement devices: extending the medical model. Journal of Law and the Biosciences. 2014;1(1):68-93. http://jlb.oxfordjournals.org/content/1/1/68.abstract. Accessed March 1, 2014. 269. Brain stimulation is getting popular with gamers -- is it time to regulate it? The Conversation. 2016. http://theconversation.com/brain-stimulation-is-getting-popular-with-gamers-is-it-time- to-regulate-it-66845. 270. Todorovic M. FDA issues final guidance on general wellness: Neurostimulation and blood-based physiological assessment draw a short straw. Lux Research Analyst Insight, 2016.
Page 77 of 85
Scientometric Study on Education, Training, and Exercise November 2016
11 APPENDIX A: ATTACHMENTS
The following files are provided as separate attachments to this report:
Appendix 1: Tableau Workbook of Large-Scale Graphics
To view the file, first download a free copy of Tableau Reader software at http://www.tableau.com/products/reader
12 APPENDIX B: METHODOLOGY 12.1 Bibliographic Databases Literature searches were conducted in the databases listed below.
Scopus Defense Technical Information Center (DTIC) NATO Scientific and Technical Organisation: Publications database National Technical Information Service (NTIS)
Tables 7 and 8 list the concepts and keywords used in the search strategies. Truncation, used to retrieve variant word forms, is indicated by the asterisk (*) character. Terms in columns 1-3 were combined using Boolean and proximity operators and several iterations of strategies were applied in order to maximize the return of relevant records while minimizing false hits. The search targeted the title, abstract, and descriptor fields of the bibliographic records, and was limited to the current (2001-) five years.
Terms excluded after retrieval: rehabilitation, schizophrenia, autism, cognitive deficits, diabetes, stroke, ischemia, COPD, asthma, aged/elderly/geriatric, sarcopenia, cerebral palsy, Down’s syndrome, Alzheimer’s, Parkinson’s, dementias, disabilities, epilepsy, bipolar, depression, tinnitus, cognitive impairment or deficits, child/children/pediatric.
For key question 2 (physical education and fitness), the final dataset comprised 4,646 records. For key question 3 (learning-conducive technologies), the final dataset comprise 1,902 records.
Page 78 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 7. Search Terms: Physical Education and Fitness
Concept 1 Concept 2 Concept 3 Physical (education or Technolog* Fitbit* or jawbone or smartphone* or fitness or training) Trend* smartwatch* Fitness (education or Roadmap Virtual (reality or coach*) OR VR training OR coach* OR Innovat* (virtual or personal) assistant* aerobic*) Augmented reality OR AR Exercise* NEAR training Games OR exergames OR gamif* Endurance training Personal* Athlet* NEAR train* Digital OR online or elearning or mlearning Sport* NEAR train* OR web-based OR Internet Strength training Fitness NEAR app* (Agility or mobility) training Activity NEAR track* Performance training Fitness NEAR track* Basic training Sensor* or biosensor* or sensing Military training AND Acceleromet* or pedometer* (fitness OR physical OR (neuro* or cognit*) NEAR (enhance* or endurance) stimulat*) Physical readiness Brain computer interface OR BCI Human computer interface OR HCI Interface Transcranial direct current stimulat* (Brain or neuro* NEAR stimulat* Feedback* Avatar* Big data OR analytic* OR bioinformat* Motion NEAR analy* Social (media OR network*) Supplement* or stimulant* or doping or steroid*
Page 79 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Table 8: Search Terms: Learning-Conducive Technologies
Concept 1 Concept 2 Concept 3 Learning Conducive Transcranial direct current stimulation Learner Learner state OR TDCS Student Receptiv* (brain or neuro*) NEAR stimulat* Education Optim* Electric* stimulat* Training Potentiat* Neurobiology Cognition Cogniti* NEAR (enhance* OR Neurophysiology Fitness function*) Neuroplasticity Neuroenhanc* Neuromodulat* Neuroplasticity Neuroprim* Executive function Brain boosting Attention (Brain or mental) train* Focus Smart (pills OR drugs) Concentration Genetic engineering Precondition* Ritalin Enabl* NEAR technolog* Adderall Memory Dexedrine OR amphetamine* Skill* NEAR acquisition Methylphenidat* Modafinal Stimulant* Caffeine Supplement* GABA Glutamate* Nutrit* BDNF Or “brain derived neurotropic factor” or neurotrophin* Cortisol Dietary Sleep or circadian Mindfulness Meditat* Exercis*
Page 80 of 85
Scientometric Study on Education, Training, and Exercise November 2016
12.2 Market Reports, Web Sites and U.S. Defense Department Research Justifications In addition to the bibliographic databases listed above, the following market/web sources were also consulted:
Army Technology (http://www.army-technology.com/) Frost and Sullivan BCC Research Gartner Lux Research Defense Innovation Marketplace (DoD): Human Systems (http://defenseinnovationmarketplace.mil/) National Defense Industry Association (NDIA): (http://www.ndia.org/Pages/default.aspx) Human Performance Resource Center http://hprc-online.org/ Human Performance, Training, and Biosystems Directorate (http://www.acq.osd.mil/rd/hptb/index.html )
U.S. Defense Department Research, Development, Test and Evaluation (RDT&E) reports were sourced as follows:
DARPA: http://www.darpa.mil/attachments/DARPAFY17PresidentsBudgetRequest.pdf U. S. Army: http://asafm.army.mil/search.aspx U. S. Air Force: http://www.saffm.hq.af.mil/Budget U. S. Navy: http://www.secnav.navy.mil/fmc/fmb/Pages/Fiscal-Year-2017.aspx
12.3 Analysis Bibliographic references were imported into VantagePoint software for merging, cleaning and analysis. VantagePoint facilitates the creation of various groupings, statistical analyses, matrices, graphs, and cross-correlations to analyze the data and profile the activities of the major players. Other analytical tools were used to generate graphs based on statistical operations performed in VantagePoint. Tableau software was used to generate the bubble graph matrices and TouchGraph Navigator was used to create the cluster and collaboration maps.
Page 81 of 85
Scientometric Study on Education, Training, and Exercise November 2016
12.3.1 Top Terms Cluster Maps The maps for this project were created using the TouchGraph Navigator application. TouchGraph’s clustering algorithm clusters terms together based on statistical similarity to each other (i.e. word co- occurrences) and dissimilarity with other clusters. Generally, a cluster illustrates a self-contained group of concepts that is independent from (though still connected to) the rest of the graph.
In the cluster maps, the size of the nodes represents the relative number of publications associated with each node and the lines in between nodes show the correlation coefficient (multiplied by 100) between two nodes. For greater visual clarity, the maps have been filtered to ≥ 20% correlations. Complete maps and details are included in Attachment 1 (Tableau workbook) to this report.
12.3.2 Subject Groups and Sub-Dataset Comparisons In each of the main datasets, subject groups were created by combining similar terms into non-exclusive groups in order to capture content on the most frequently cited solutions. In the Physical Education and Fitness dataset, 37 groups were created. For Learning-Conducive technologies, 41 groups were used.
Publication counts and dataset percentages for each of the datasets are shown below in Figures 29 (Physical Education and Fitness) and 30 (Learning-Conducive Technologies). These graphics also show the corresponding counts and percentages for the military subsets of the respective datasets. Columns are sorted according to the largest subject groups in each of the respective datasets. The graphics are also reproduced in Attachment 1 (Tableau workbook) to this report.
Figure 29. Physical Education & Fitness Dataset: Global Dataset & Military Subset, No. Publications & Share of Subject Groups
Page 82 of 85
Scientometric Study on Education, Training, and Exercise November 2016
Figure 30. Learning-Conducive Technologies Dataset: Global Dataset & Military Subset: No. Publications & Share of Subject Groups
12.3.3 R&D Momentum Indicator To ascertain the normalized growth rates and compare values according to their standard deviation for each of the behavioural biometrics subject groups, publication rates and the angle (slope) of their increase or decline over time were plotted using linear regression. Average slope degrees and standard deviation were then calculated and standardized, to produce Z-scores.
Standardized scores can be used to reduce “noise” and identify topical areas with the greatest growth rates (velocity) in the dataset, as well as the subjects showing sub-standard rates.
The Momentum Indicator (4-quadrant graph shown in Figures 11 and 25) is designed to identify rapidly rising subjects with relatively few publications. The challenge of identifying such subjects lies with the publication volume as a confounding factor, since their rapid growth and evolution is dwarfed by the high volume of established subjects. Specifically, the notion of “emerging” consists not only of a sharply rising trend line but also of a small footprint in the domain of interest. A relatively small footprint is the reason emerging subjects are often overlooked until their disruptive impacts become obvious.
Page 83 of 85
Scientometric Study on Education, Training, and Exercise November 2016
In the Momentum indicator, the two parameters correspond to (1) growth rate, which is the slope of a subject’s trend line (right-left axis), and (2) volume, which is the cumulated total number of publications (vertical axis).
Once growth rate and volume are separated, a two-dimensional coordinate can be used to plot a group of subjects. To do so, the two parameters have to be normalized with z-scores. The normalization process converts two sets of values in different units into the same measure by means of standard deviation, which also standardizes the variations for each of the two parameters. The four-quadrant visualization provides a structured view of the relative position of these subjects within the group.
Page 84 of 85
DOCUMENT CONTROL DATA (Security classification of title, body of abstract and indexing annotation must be entered when the overall document is classified) 1. ORIGINATOR (The name and address of the organization preparing the 2. SECURITY CLASSIFICATION document. Organizations for whom the document was prepared, e.g. Centre (Overall security classification of the document sponsoring a contractor's report, or tasking agency, are entered in section 8.) including special warning terms if applicable.)
NRC-Knowledge and Information Technology Services (KITS) UNCLASSIFIED 1200 Montreal Rd., Ottawa, ON K1A 0R6
3. TITLE Scientometric Study: Education, Training and Exercise
4. AUTHORS Brady, Brenda
5. DATE OF PUBLICATION 6a. NO. OF PAGES 6b. NO. OF REFS (Total cited in document.)
November 2016 85 270
7. DESCRIPTIVE NOTES (The category of the document, e.g. technical report, technical note or memorandum. If appropriate, enter the type of report, e.g. interim, progress, summary, annual or final. Give the inclusive dates when a specific reporting period is covered.)
8. SPONSORING ACTIVITY (The name of the department project office or laboratory sponsoring the research and development – include address.)
Defence R&D Canada Ottawa, Ontario Canada
9a. PROJECT OR GRANT NO. (If appropriate, the applicable 9b. CONTRACT NO. (If appropriate, the applicable number under research and development project or grant number under which which the document was written.) the document was written. Please specify whether project or grant.)
10a. ORIGINATOR'S DOCUMENT NUMBER (The official document 10b. OTHER DOCUMENT NO(s). (Any other numbers which may be number by which the document is identified by the originating assigned this document either by the originator or by the activity. This number must be unique to this document.) sponsor.)
11. DOCUMENT AVAILABILITY (Any limitations on further dissemination of the document, other than those imposed by security classification.)
Unlimited
12. DOCUMENT ANNOUNCEMENT (Any limitation to the bibliographic announcement of this document. This will normally correspond to the Document Availability (11). However, where further distribution (beyond the audience specified in (11) is possible, a wider announcement audience may be selected.))
Unlimited