Introduction: Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote “digital phenotyping” of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones. Methods: Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed. Results: The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; p = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (p = 0.04), primarily with negative symptoms of schizophrenia. Conclusions: Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

Introduction: Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote “digital phenotyping” of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones. Methods: Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed. Results: The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; p = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (p = 0.04), primarily with negative symptoms of schizophrenia. Conclusions: Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

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This is a mixed methods data collection.<br> <br> The primary purpose of this study was to examine patterns of participation in post-compulsory science and science-related programmes. The data held at the UK Data Archive comprises a set of semi-structured interviews (13 individual interviews and one focus group interview) conducted with undergraduates in various disciplines, and a quantitative survey of the higher education (HE) experience of undergraduate scientists and the reasons they give for choosing, or not choosing, to follow a career in the Science, Technology, Engineering and Mathematics (STEM) field. The research project sought to address three broad research questions:<ul><li>What are the long-term patterns of participation in post-compulsory science and science-related courses in the UK?</li><li>To what extent does the undergraduate experience influence students’ decisions to pursue a career in science?</li><li>What happens to science graduates once they leave HE?</li></ul>These three questions constitute the study’s three main phases. Phase 1 considered the period up to the start of HE and looked at patterns of participation in the pure sciences at A-level and at applications and admissions to university programmes. Phase 2 included the survey of undergraduate science and non-science students and Phase 3 looked at what happens to science graduates once they leave HE.<br> <br> The project also made use of pre-existing data: aggregate data was retrieved from examination boards and government and publicly accessible datasets retrieved from the UK University and Colleges Admissions Service (UCAS).<br> <br> Further information may be found on the ESRC <a href="http://www.esrc.ac.uk/my-esrc/grants/RES-000-22-2005/read/" title="Who is studying science? An analysis of patterns in the recruitment, training and employment of scientists">Who is studying science? An analysis of patterns in the recruitment, training and employment of scientists</a> award webpage. <br> <br>

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