Specialized Smartphone Intervention Apps: Review of 2014 to 2018 NIH Funded Grants.

BACKGROUND: The widespread adoption of smartphones provides researchers with expanded opportunities for developing, testing and implementing interventions. National Institutes of Health (NIH) funds competitive, investigator-initiated grant applications. Funded grants represent the state of the science and therefore are expected to anticipate the progression of research in the near future. OBJECTIVE: The objective of this paper is to provide an analysis of the kinds of smartphone-based intervention apps funded in NIH research grants during the five-year period between 2014 and 2018. METHODS: We queried NIH Reporter to identify candidate funded grants that addressed mHealth and the use of smartphones. From 1524 potential grants, we identified 397 that met the requisites of including an intervention app. Each grant's abstract was analyzed to understand the focus of intervention. The year of funding, type of activity (eg, R01, R34, and so on) and funding were noted. RESULTS: We identified 13 categories of strategies employed in funded smartphone intervention apps. Most grants included either one (35.0%) or two (39.0%) intervention approaches. These included artificial intelligence (57 apps), bionic adaptation (33 apps), cognitive and behavioral therapies (68 apps), contingency management (24 apps), education and information (85 apps), enhanced motivation (50 apps), facilitating, reminding and referring (60 apps), gaming and gamification (52 apps), mindfulness training (18 apps), monitoring and feedback (192 apps), norm setting (7 apps), skills training (85 apps) and social support and social networking (59 apps). The most frequently observed grant types included Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) grants (40.8%) and Research Project Grants (R01s) (26.2%). The number of grants funded increased through the five-year period from 60 in 2014 to 112 in 2018. CONCLUSIONS: Smartphone intervention apps are increasingly competitive for NIH funding. They reflect a wide diversity of approaches that have significant potential for use in applied settings.

Technological Transformation.

Technology has enabled bionics and artificial intelligence, each of which can have important applications in health care. As we continue to substitute body parts with machinery, however, we might wonder, "What makes us human?" This drawing interrogates the relationship between humanity and embodiment, specifically in neck and facial musculature and brain structures.

An integrated CMOS bio-potential amplifier with a feed-forward DC cancellation topology.

This paper describes a novel technique to realize an integrated CMOS bio-potential amplifier with a feedforward DC cancellation topology. The amplifier is designed to provide substantial DC cancellation even while amplifying very low frequency signals. More than 80 dB offset rejection ratio is achieved without any external capacitors. The cancellation scheme is robust against process and temperature variations. The amplifier is fabricated through MOSIS AMI 1.5 microm technology (0.05 mm2 area). Measurement results show a gain of 43.5 dB in the pass band (<1 mHz-5 KHz), an input referred noise of 3.66 microVrms, and a current consumption of 22 microA.

Recent studies in the design of a self-paced brain interface with low false positive rate.

The findings of two recent studies that aim at developing a self-paced brain interface (BI) system with low false positive rates are discussed. The first study examines the use of information extracted from different neurological phenomena and the second study examines the wavelet coefficients extracted from a single neurological phenomenon. The analysis of the data of two subjects shows that both are successful at yielding low false positive rates. These studies also show that for each subject, a unique set of features and EEG channels lead to superior performance.