Building the case for actionable ethics in digital health research supported by artificial intelligence

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research

Scintillation reduction for combined Gaussian-vortex beam propagating through turbulent atmosphere

We numerically examine the spatial evolution of the structure of coherent and partially coherent laser beams (PCBs), including the optical vortices, propagating in turbulent atmospheres. The influence of beam fragmentation and wandering relative to the axis of propagation (z-axis) on the value of the scintillation index (SI) of the signal at the detector is analyzed. A method for significantly reducing the SI, by averaging the signal at the detector over a set of PCBs, is described. This novel method is to generate the PCBs by combining two laser beams - Gaussian and vortex beams, with different frequencies (the difference between these two frequencies being significantly smaller than the frequencies themselves). In this case, the SI is effectively suppressed without any high-frequency modulators.Comment: 13 pages, 8 figure

Digital tools for youth mental health.

No abstract available

Expected returns and expected erowth in rents of commercial real estate

Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate - that is, the rent-price ratio in commercial real estate - captures fluctuations in expected returns for apartments, retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on o±ces are also time-varying. As implied by the present value relation, cap rates marginally forecast o±ce rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data- generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market, Treasury bills, as well as commercial real estate

Toward impactful collaborations on computing and mental health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions

Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data

Reduction of laser intensity scintillations in turbulent atmospheres using time averaging of a partially coherent beam

We demonstrate experimentally and numerically that the application of a partially coherent beam (PCB) in combination with time averaging leads to a significant reduction in the scintillation index. We use a simplified experimental approach in which the atmospheric turbulence is simulated by a phase diffuser. The role of the speckle size, the amplitude of the phase modulation, and the strength of the atmospheric turbulence are examined. We obtain good agreement between our numerical simulations and our experimental results. This study provides a useful foundation for future applications of PCB-based methods of scintillation reduction in physical atmospheres.Comment: 18 pages, 14 figure

Recent advances in understanding schizophrenia

Schizophrenia is a highly disabling disorder whose causes remain to be better understood, and treatments have to be improved. However, several recent advances have been made in diagnosis, etiopathology, and treatment. Whereas reliability of diagnosis has improved with operational criteria, including Diagnostic and Statistical Manual of Mental Disorders, (DSM) Fifth Edition, validity of the disease boundaries remains unclear because of substantive overlaps with other psychotic disorders. Recent emphasis on dimensional approaches and translational bio-behavioral research domain criteria may eventually help move toward a neuroscience-based definition of schizophrenia. The etiology of schizophrenia is now thought to be multifactorial, with multiple small-effect and fewer large-effect susceptibility genes interacting with several environmental factors. These factors may lead to developmentally mediated alterations in neuroplasticity, manifesting in a cascade of neurotransmitter and circuit dysfunctions and impaired connectivity with an onset around early adolescence. Such etiopathological understanding has motivated a renewed search for novel pharmacological as well as psychotherapeutic targets. Addressing the core features of the illness, such as cognitive deficits and negative symptoms, and developing hypothesis-driven early interventions and preventive strategies are high-priority goals for the field. Schizophrenia is a severe, chronic mental disorder and is among the most disabling disorders in all of medicine. It is estimated by the National Institute of Mental Health (NIMH) that 2.4 million people over the age of 18 in the US suffer from schizophrenia. This illness typically begins in adolescence and derails the formative goals of school, family, and work, leading to considerable suffering and disability and reduced life expectancy by about 20 years. Treatment outcomes are variable, and some people are successfully treated and reintegrated (i.e. go back to work). Despite the effort of many experts in the field, however, schizophrenia remains a chronic relapsing and remitting disorder associated with significant impairments in social and vocational functioning and a shortened lifespan. Comprehensive treatment entails a multi-modal approach, including psychopharmacology, psychosocial interventions, and assistance with housing and financial sustenance. Research to date suggests a network of genetic, neural, behavioral, and environmental factors to be responsible for its development and course. This article aims to summarize and explain recent advancements in research on schizophrenia, to suggest how these recent discoveries may lead to a better understanding and possible further development of effective therapies, and to highlight the paradigm shifts that have taken place in our understanding of the diagnosis, etiopathology, and treatment