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.

People with Visual Impairments (PVI) experience greater difficulties with daily tasks, such as supermarket shopping. Identifying and purchasing an item proves challenging for PVI. Using a user-centered design process, we understand the difficulties PVI encounter in their daily routines. Consequently, the previous FingerReader model was elevated to a new level. In contrast, FingerReader2.0 incorporates a highly integrated hardware design, as it is standalone, wearable, and not tethered to a computer. Software-wise, the prototype utilizes a deep learning system, relying on a hybrid, an on-board and a cloud-based model. The advanced design significantly extends the range of mobile assistive technology, particularly for shopping purposes. This paper presents the findings from interviews, several iterative studies, and a field study in supermarkets to demonstrate the FingerReader2.0's enhanced capabilities for those with varied levels of visual impairment.

IntroductionThe 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 analysisIn 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 disseminationEthics 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 numberNCT04230590.

No abstract

No abstract