Date: 8th – 11th May, 2017
Venue: University of Technology Sydney, Ultimo NSW
Instructors: Nathaniel Osgood, Mohammad Hashemian
Acquisition of evidence-based understanding of human health Behaviour and exposure to environments forms a central focus of health research, and a critical prerequisite for effective health policy. The use of mobile devices to study health behaviour via crosslinked sensor data and on-device self-reporting and crowdsourcing has been demonstrated to provide important insights that traditional techniques cannot. However the design, delivery and analysis of mobile data studies require skills rarely developed in training in the health sciences.
This course introduces public health researchers and practitioners to tools, practical skills and the conceptual background required to collect and analyse mobile data on health behaviour, and assists participants in getting started in applying such techniques to studies and applications of specific interest to them. This course will include hands-on work with novel and standard tools and techniques.
The course includes both a classroom curriculum (featuring much hands-on work) and hands-on learning designed to help participants craft and test out study designs, survey instruments, and sensor-based data collection mechanisms for their specific data collection priorities. Both portions of the course will make heavy use of the Ethica smartphone and wearable-based data collection system (the latest generation version of the longstanding iEpi epidemiological data collection system).
This course is targeted at professionals from a variety of health fields including health researchers, health service delivery, public health workers, health decision makers, and any health professionals or modellers seeking empirical behavioural data.
This component of the course will further leverage the extensive experience of the instructors by having them provide ongoing advice, guidance, tips and hands-on assistance as participants build, explore, test, and refine their own study designs, survey and crowdsourcing instruments, sensor data collection mechanisms addressing their surveillance needs. Guided by instructors, participants will have the opportunity to design a prototype data collection experiment, and to acquire, visualize and analyse the collected data using current tools and techniques.
FEES AND CAPACITY
- Student: AUD $330 (includes GST)
- Non-student: AUD $660 (includes GST)
Strictly limited to 20 participants.
Refunds will be provided for delegate registration cancelled in writing and received by April 30th, 2017. A cancellation penalty of 25% +GST will apply. After May 1st, 2017 all registration fees become non-refundable.
Substitutions can be made at any time, but will require advance written notice. Please direct your correspondence by email to email@example.com.
VENUE AND LOCATION
Computer Lab CB10.07.104, UTS Facilities Hire, Level 7, Building 10, 235 Jones, Ultimo NSW 2007.
Venue map PDF, 201.57 KB (opens in a new window)
Nathaniel Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan. His research is focused on providing cross-linked simulation, ubiquitous sensing, and machine learning tools to inform understanding of population health trends and health policy trade-offs. His applications work has addressed challenges in the communicable, zoonotic, environmental, and chronic disease areas. Dr Osgood is further the co-creator of two novel mobile sensor-based epidemiological monitoring systems, most recently the Google Android- and iPhone-based Ethica Health mobile epidemiological monitoring systems. He has additionally contributed innovations to improve dynamic modelling quality and efficiency, introduced novel techniques hybridizing multiple simulation approaches and simulation models with decision analysis tools, and which leverage such models using data gathered from wireless epidemiological monitoring systems. Dr Osgood has led many international courses in simulation modelling and health around the world, and his online videos on the subject attract thousands of views per month. Prior to joining the U of S faculty, he graduated from MIT with a PhD in Computer Science in 1999, served as a Senior Lecturer at MIT and worked for a number of years in a variety of academic, consulting and industry positions.
Mohammad Hashemian is the CEO and founder of Ethica Data, a spinoff company from Computational Epidemiology and Public Health Informatics Lab in University of Saskatchewan. Mohammad has background in Computer Science and Software Engineering, and has worked in software industry for 5 years. He has been part of the Ethica Data's founding team, and has been operating as the CEO for the past 2 years, directing the design and development effort to create Ethica research platform. He has been involved as an investigator in more than ten health-related research projects over the past two years, advising research teams across US and Canada on design, deployment, and analysis phase of the projects.
Lectures and step-by-step hands-on tutorials will be provided on conceptual foundations, mechanics & best practices. Topics are anticipated to include the following, with details of coverage of these and additional topics depending on participant interests expressed via pre-study surveys:
- Behavioural and physiological sensing via smartphones and paired devices (smartwatches, weight scales, etc.)
- On-device questionnaires, crowdsourcing mechanisms
- Case studies from diverse health areas
- Effective study design
- Recruitment, including discussion of recruitment needs in diverse population types
- Smartphones as surveillance, smartphones as interventions
- Securing community buy-in and support
- Privacy and confidentiality
- Ensuring operation within ethical research guidelines, and working with Institutional Review Boards/Research Ethics Boards
- Ensuring security and confidentiality
- Support for ongoing and retroactive participant opt-out
- Addressing privacy concerns via retaining data in escrow for contingent use
- Design of effective survey instruments
- Size, frequency and participant burden trade-offs
- Using contextually triggered instruments: Opportunities, strengths and risks
- Supporting, Eligibility, entry, ecological momentary assessments (EMAs), study completion and opt-out questionnaires
- Capturing skip patterns and conditional questions in survey instruments
- Using per-question completion timing information
- Multipage vs. single page questionnaires
- Enabling multimedia responses (photos, audio)
- Supporting informed consent, both remote and in-person
- Participant incentives
- Participant access to own data
- Operating studies with and without incentives
- Nonmonetary incentives
- Community-based sharing of data
- Recruiting networks: Study design, practical and ethical considerations
- How much data is enough?
- Different needs in inpatient and population surveillance
- Budgeting a study: Cost economics of running smartphone-based studies
- The data backhaul (WiFi vs. Cell data networks): Impacts on reporting and monitoring timeliness, financial impact on study, trade-offs across populations.
- Study management and operation
- Working with participant-owned and study-provided mobile devices, including special needs with low-socioeconomic status populations
- Monitoring adherence/involvement
- Database structure and retrieval
- Cross-leveraging smartphone-collected data with traditional and other electronic data sources
- Data Analysis
- Models for sense-making: Hierarchies of data analysis needs (the data analysis pipeline)
- Routine reporting via website-based analytics
- Using cross-linked data from multiple smartphone and federated measurement modalities
- Data filtering, pruning and conditioning
- Dealing with missing data
- Use of smartphone-collected data with biostatistical analysis (e.g., survival, recurrent event, multiple regression, and other analyses)
- Machine learning-based classification & inference
- Understanding intervention effects across multiple causal pathways
- Integration of data with dynamic models
- Geospatial Behaviour and GIS
- Prospects for use of data with Behavioural and choice modelling
- Visualization (Tableau, R and other tools)
- Tools for large-scale data analysis: R, Anaconda, Spark
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