Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia: protocol for the HOPE-S observational study.

INTRODUCTION: The course of schizophrenia illness is characterised by recurrent relapses which are associated with adverse clinical outcomes such as treatment-resistance, functional and cognitive decline. Early identification is essential and relapse prevention remains a primary treatment goal for long-term management of schizophrenia. With the ubiquity of devices such as smartphones, objective digital biomarkers can be harnessed and may offer alternative means for symptom monitoring and relapse prediction. The acceptability of digital sensors (smartphone and wrist-wearable device) and the association between the captured digital data with clinical and health outcomes in individuals with schizophrenia will be examined. METHODS AND ANALYSIS: In this study, we aim to recruit 100 individuals with schizophrenia spectrum disorders who are recently discharged from the Institute of Mental Health (IMH), Singapore. Participants are followed up for 6 months, where digital, clinical, cognitive and functioning data are collected while health utilisation data are obtained at the 6 month and 1 year timepoint from study enrolment. Associations between digital, clinical and health outcomes data will be examined. A data-driven machine learning approach will be used to develop prediction algorithms to detect clinically significant outcomes. Study findings will inform the design, data collection procedures and protocol of future interventional randomised controlled trial, testing the effectiveness of digital phenotyping in clinical management of individuals with schizophrenia spectrum disorders. ETHICS AND DISSEMINATION: Ethics approval has been granted by the National Healthcare Group (NHG) Domain Specific Review Board (DSRB Reference no.: 2019/00720). The results will be published in peer-reviewed journals and presented at conferences. TRIAL REGISTRATION NUMBER: NCT04230590.

HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.

The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and early experiences with a comprehensive digital phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). HOPES is based on the open-source Beiwe platform but adds a wider range of data collection, including the integration of wearable devices and further sensor collection from smartphones. Requirements were partly derived from a concurrent clinical trial for schizophrenia that required the development of significant capabilities in HOPES for security, privacy, ease of use, and scalability, based on a careful combination of public cloud and on-premises operation. We describe new data pipelines to clean, process, present, and analyze data. This includes a set of dashboards customized to the needs of research study operations and clinical care. A test use case for HOPES was described by analyzing the digital behavior of 22 participants during the SARS-CoV-2 pandemic.

Temperature dependence dynamical permeability characterization of magnetic thin film using near-field microwave microscopy.

A temperature dependence characterization system of microwave permeability of magnetic thin film up to 5 GHz in the temperature range from room temperature up to 423 K is designed and fabricated as a prototype measurement fixture. It is based on the near field microwave microscopy technique (NFMM). The scaling coefficient of the fixture can be determined by (i) calibrating the NFMM with a standard sample whose permeability is known; (ii) by calibrating the NFMM with an established dynamic permeability measurement technique such as shorted microstrip transmission line perturbation method; (iii) adjusting the real part of the complex permeability at low frequency to fit the value of initial permeability. The algorithms for calculating the complex permeability of magnetic thin films are analyzed. A 100 nm thick FeTaN thin film deposited on Si substrate by sputtering method is characterized using the fixture. The room temperature permeability results of the FeTaN film agree well with results obtained from the established short-circuited microstrip perturbation method. Temperature dependence permeability results fit well with the Landau-Lifshitz-Gilbert equation. The temperature dependence of the static magnetic anisotropy H(K)(sta), the dynamic magnetic anisotropy H(K)(dyn), the rotational anisotropy H(rot), together with the effective damping coefficient α(eff), ferromagnetic resonance f(FMR), and frequency linewidth Δf of the thin film are investigated. These temperature dependent magnetic properties of the magnetic thin film are important to the high frequency applications of magnetic devices at high temperatures.

High frequency dielectric properties distribution of BiFeO3 thin film using near-field microwave microscopy.

A near-field scanning microwave microscopy (NSMM) is applied to investigate the local perpendicular dielectric information of single-phase multiferroic BiFeO(3) thin film and single crystal LaAlO(3) material. Our NSMM is composed of a vector network analyzer and a simple open-ended coaxial probe, which is quite different from the commercial probe with a lambda/4 coaxial resonator. The local permittivity is calculated quantitatively according to resonance frequency shift under the quasistatic microwave perturbation theory. We make use of the magnitude of reflection loss S(11) to construct an image reflecting the distribution of dielectric constant of a material. A homogeneous permittivity is observed in LaAlO(3) material and the inhomogeneous permittivity epsilon=215-250 for BiFeO(3) film is depicted from the change of feedback signal S(11) over an area of 100x100 microm(2).