Purpose
Acquisition of evidencebased understanding of human health behavior 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 behavior via crosslinked sensor data and on-device self-reporting and crowdsourcing have been demonstrated to provide important insights that traditional techniques cannot. However, design, delivery and analysis of mobile data studies requires 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 analyze mobile data on health behavior, 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 wearablebased data collection system (the latest generation version of the longstanding iEpi epidemiological data collection system).
Intended Audience
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.
Kinesthetic Learning
This component of the course will further leverage the extensive experience of the instructor and teaching assistants (TAs) 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 and interdisciplinary team of TAs, participants will have the opportunity to design a prototype data collection experiment, and to acquire, visualize and analyze the collected data using current tools and techniques.
Fees and Capacity
Early Bird Registration (until March 30th, 2017):
- Students: $600.00 + $30.00 GST = $630.00 CAD
- Faculty/Post-Doctoral: $1,200.00 + $60.00 GST = $1,260.00 CAD
- Corporate and Private Sector: $2,000.00 + $100.00 GST = $2,100.00 CAD
Registration (after March 31st, 2017):
- Students: $900.00 + $45.00 GST = $945.00 CAD
- Faculty/Post-Doctoral: $1,500.00 + $75.00 GST = $1,575.00 CAD
- Corporate and Private Sector: $2,300.00 + $115.00 GST = $2,415.00 CAD
Note that due to the practical nature of the event, the registration is limited to only 33 participants.
Refund Policy
Refunds will be provided for delegate registration cancelled in writing and received by April 30th, 2017. A cancellation penalty of $75.00 CAD plus 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 conference.events@usask.ca
.Instructors
We will update this list with guest speakers as their attendence is confirmed.
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Nathaniel Osgood
Nathaniel Osgood is an Associate 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 sensorbased 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 modeling 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 modeling 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.
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Kevin Stanley
Kevin Stanley is an Associate Professor at the University of Saskatchewan in the Department of Computer Science, specializing in research on mobile sensing systems, particularly on smartphones. As one of the primary architects of the Ethica health data system, Dr. Stanley has significant expertise in the collection and analysis of mHealth data. Dr. Stanley’s current research interests include data analytics of sensed data streams, particularly human behavioural data collected from smart phones.
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Mohammad Hashemian
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 is bene involved as an investigator in more than ten health-related research projects for the past two year, advising research teams across US and Canada on design, deployment, and analysis phase of the projects.
Classroom Teaching
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 optout
- Addressing privacy concerns via retaining data in escrow for contingent use
- Design of effective survey instruments
- Size, frequency and participant burden tradeoffs
- Using contextually triggered instruments: Opportunities, strengths and risks
- Supporting, Eligibility, entry, ecological momentary assessments (EMAs), study completion and optout questionnaires
- Capturing skip patterns and conditional questions in survey instruments
- Using perquestion 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 smartphonebased studies
- The data backhaul (WiFi vs. Cell data networks): Impacts on reporting and monitoring timeliness, financial impact on study, tradeoffs across populations.
- Study management and operation
- Working with participant-owned and study-provided mobile devices, including special needs with low-socioeconomic status populations
- Retention
- 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 behavior and GIS
- Prospects for use of data with behavioral and choice modeling
- Visualization (Tableau, R and other tools)
- Tools for large-scale data analysis: R, Anaconda, Spark
Event Location
Thorvaldson Building,
Spinks Addition
University of Saskatchewan,Saskatoon, SK, Canada