Generating and sharing large datasets: Moving out of our measurement comfort Rita Kukafka and Pamela M. Kato October 16-17, 2012 Bruxelles, Belgique.

Slides:



Advertisements
Similar presentations
Integrating the NASP Practice Model Into Presentations: Resource Slides Referencing the NASP Practice Model in professional development presentations helps.
Advertisements

The Teacher Work Sample
Developmentally Appropriate Practice
Child Development: Theory and Practice 1. Why is child development important? Evidence that social workers have limited training and knowledge about child.
Sharing Content and Experience in Smart Environments Johan Plomp, Juhani Heinila, Veikko Ikonen, Eija Kaasinen, Pasi Valkkynen 1.
Working with the Teachers’ Standards in the context of ITE. Some key issues for ITE Partnerships to explore.
National Human Resources for Health Observatory HRH Research Forum Dr. Ayat Abuagla.
4.1.5 System Management Background What is in System Management Resource control and scheduling Booting, reconfiguration, defining limits for resource.
Big Data and Predictive Analytics in Health Care Presented by: Mehadi Sayed President and CEO, Clinisys EMR Inc.
Maximizing Data and Data Services Monday, October 14, 2013 Location: Denver CO© 2013 Child Care Aware ® of America.
Information | Analytics | Expertise SOCIAL MEDIA INTELLIGENCE Practical Strategies for Using Social Media to Enhance Security AUGUST 2014 © 2014 IHS IHS.
Overview of Nursing Informatics
Technical Review Group (TRG)Agenda 27/04/06 TRG Remit Membership Operation ICT Strategy ICT Roadmap.
NURS 2210-Roles II Unit 1: Role of the RN in the Health Care System Nancy Pares, RN, MSN.
Multi-agents based wireless sensor telemedicine network for E-Health monitoring of HIV Aids Patients. By: Muturi Moses Kuria, SCI, University of Nairobi,
Copyright © hutchinson associates 2005 The Knowledge is in the Network Patti Anklam June Holley Valdis Krebs Using Network Analysis to Understand and Improve.
DED 101 Educational Psychology, Guidance And Counseling
Self-Concept, Self-Esteem, Self-Efficacy, and Resilience
Vivien Bonazzi Ph.D. Program Director: Computational Biology (NHGRI) Co Chair Software Methods & Systems (BD2K) Biomedical Big Data Initiative (BD2K)
DR EBTISSAM AL-MADI Consumer Informatics, nursing informatics, public health informatics.
Maximizing the Value of Your Investments With Advanced Campaign Management And Campaign Analysis Ad Campaigns.
Interview Team Selection Randall Birkwood. What it is: - A simple, clean process - Convenient resources for interview team - Ensures interview team is.
Career and Technology Foundations (CTF). How does CTF Benefit Students? CTF allows students to explore their interests and passions through meaningful,
1 Qualitative Evaluation Terms Coding/categorization The process of condensing qualitative data through the identification of common themes. Data Matrix.
1 MSP-Motivation Assessment Program (MSP-MAP) Tools for the Evaluation of Motivation-Related Outcomes of Math and Science Instruction Martin Maehr
V. Chandrasekar (CSU), Mike Daniels (NCAR), Sara Graves (UAH), Branko Kerkez (Michigan), Frank Vernon (USCD) Integrating Real-time Data into the EarthCube.
IMA CIM Overview. IMA Mission “Provide a knowledge-sharing platform for business professionals where proven Internet.
Integrated support for dealing with chronically ill patients Ole Martin Winnem SINTEF Telecom and Informatics.
Margaret J. Cox King’s College London
Interstate New Teacher Assessment and Support Consortium (INTASC)
Tie Into Practice Technology Integration Example: A Research Paper Website Jennifer Jarvis and Connie Keating.
Sue Huckson Program Manager National Institute of Clinical Studies Improving care for Mental Health patients in Emergency Departments.
Brunning – Chapter 10 Technological Contexts for Cognitive Growth Learning is influenced primarily by good instructional methods that takes advantage of.
Designing and implementing of the NQF Tempus Project N° TEMPUS-2008-SE-SMHES ( )
1 PI 34 and RtI Connecting the Dots Linda Helf Teacher, Manitowoc Public School District Chairperson, Professional Standards Council for Teachers.
Education at UCSF School of Medicine Spring 2012 Catherine Lucey MD.
HIM Breaking Into Informatics Mari Pirie-St. Pierre, MS, RHIA.
MIS – 3030 Business Technologies Social Media & Conversation Big Data.
ITU Workshop: “Radio-Activity Safety and Security Threats Protection and Telemedical Support for Irradiated People” Telebiometrics: Enhancing Telepresence.
Socio-cultural and ethical aspects Anne G. Ekeland Berlin,
Join the Conversation: Active Listening on Social Media By Lauren Cleland New Media Specialist, Explore Georgia #TeamGaSocial.
Future Learning Landscapes Yvan Peter – Université Lille 1 Serge Garlatti – Telecom Bretagne.
DVC Essay #2. The Essay  Read the following six California Standards for Teachers.  Discuss each standard and the elements that follow them  Choose.
By Joseph Torres. Teachers are communicators who must express with clarity what they expect of students and how they will support their learning. Teachers.
Graduate studies - Master of Pharmacy (MPharm) 1 st and 2 nd cycle integrated, 5 yrs, 10 semesters, 300 ECTS-credits 1 Integrated master's degrees qualifications.
The Role of Conditional Release Technologies and Intelligent Tutors in Graduate Management Education Owen P. Hall, Jr., P.E., Ph.D. Michael L. Williams,
Third Sector Evaluation: Challenges and Opportunities Presentation to the Public Legal Education in Canada National Conference on “Making an Impact” 26.
Introducing Unit Specifications and Unit Assessment Support Packs Philosophy National 5.
Qualitative Research January 19, Selecting A Topic Trying to be original while balancing need to be realistic—so you can master a reasonable amount.
Developer TECH REFRESH 15 Junho 2015 #pttechrefres h Understand your end-users and your app with Application Insights.
Nursing Informatics NI.
CASIE MYP Workshop June 21-23, 2011 International Mindedness: From Outside to Inside the Classroom.
Integrated Knowledge System on Climate Change Adaptation Conceptual & Technological Framework OneWorld South Asia December 2008.
H2020 FOCUS ON EDUCATION Creat-it Conference
CISC 849 : Applications in Fintech Namami Shukla Dept of Computer & Information Sciences University of Delaware iCARE : A Framework for Big Data Based.
Preparing learners for 21 st Century Living.  Task: Spend 2-3 minutes creating a bulleted list of key features of Project Based Learning.
Building Schools for the Future Transforming the Learning Landscape in Birmingham.
The case for scientific literacy? so pretty i never knew mars had a sun.
Five Year Forward View: Personal Health Budgets and Integrated Personal Commissioning Jess Harris January 2016.
Career and Technology Foundations (CTF) Welcome to this introductory session to CTF. Today we will specifically address: What is CTF? What does a CTF classroom.
Big Data Quality Challenges for the Internet of Things (IoT) Vassilis Christophides INRIA Paris (MUSE team)
INFERENCE FOR BIG DATA Mike Daniels The University of Texas at Austin Department of Statistics & Data Sciences Department of Integrative Biology.
D RAFT OF F RAMEWORK OF C OLLABORATION A CTIVITIES “SEAEDUNET 2.0: D IGITAL -A GE T EACHING AND L EARNING M ODEL ”
Digital Health Solutions for Vulnerable Populations: Addressing the Needs of Vulnerable Populations through Digital Innovation June
The Methods: Use of an Analytic Rubric to Evaluate Dissemination Strategies for Evidence-Based Interventions Jennifer Berktold Joseph Sonnefeld Presented.
Journal Club Notes.
Technical Capabilities
United Nations Statistics Division
Health Disparities and Case Management
Big DATA.
Presentation transcript:

Generating and sharing large datasets: Moving out of our measurement comfort Rita Kukafka and Pamela M. Kato October 16-17, 2012 Bruxelles, Belgique

Why this is important Takes advantage of technological capabilities to capture and store and analyze large amounts of health behavior data Takes advantage of technological capabilities to capture and store and analyze large amounts of health behavior data From sensors, mobile technology, etc. From sensors, mobile technology, etc. Cloud computing Cloud computing Capture and store a multitude of data streams to represent simultaneously contextual factors, as well as individual level factors Capture and store a multitude of data streams to represent simultaneously contextual factors, as well as individual level factors Behavior change interventions can be adaptive in response to emerging patterns and contexts Behavior change interventions can be adaptive in response to emerging patterns and contexts

Examples Ecological Momentary Assessment Data Ecological Momentary Assessment Data Data automatically connected via blood glucose monitors, blood pressure monitors, scales Data automatically connected via blood glucose monitors, blood pressure monitors, scales Web data collected daily Web data collected daily Data collected semiannually in extended longitudinal studies Data collected semiannually in extended longitudinal studies Thank you, Runze Li:

Statistical Analysis Challenges Complex data structure Complex data structure Data collected at irregular time points within and between subjects Data collected at irregular time points within and between subjects Covariates can vary over time (negative affect) and/or be constant (gender) Covariates can vary over time (negative affect) and/or be constant (gender) Ordinary linear statistical approaches are not appropriate Ordinary linear statistical approaches are not appropriate

Practical Challenges Expertise Expertise Inadequate knowledge to plan data collection and ability to analyze the data Inadequate knowledge to plan data collection and ability to analyze the data Not knowing where to find appropriate expertise (not knowing you need to work with one) Not knowing where to find appropriate expertise (not knowing you need to work with one)

Research Challenges Causality Causality correlational, non-experimental, post-hoc analyses, atheoretical correlational, non-experimental, post-hoc analyses, atheoretical Reliability and validity Reliability and validity Were data collected in the same way at each site? Were data collected in the same way at each site? Is the data clean or noisy? How can we tell? Is the data clean or noisy? How can we tell? Some principles may be ignored Some principles may be ignored such as choosing a representative sample such as choosing a representative sample Selecting data that is driven by behavior change theory and models Rita-What is meant by “data” Selecting data that is driven by behavior change theory and models Rita-What is meant by “data” Need theory and specialists in behavior change to contextualize and offer insights into data Need theory and specialists in behavior change to contextualize and offer insights into data Integration across heterogeneous data resources Integration across heterogeneous data resources logistical as well as analytical challenges logistical as well as analytical challenges

Addressing Challenges Need for psychometricians and experts in analyzing complex data Need for psychometricians and experts in analyzing complex data Need for collaboration across disciplines and distances Need for collaboration across disciplines and distances Need the right metrics to measure outcomes Need the right metrics to measure outcomes Focusing on what matters to the end users Focusing on what matters to the end users patient oriented outcomes patient oriented outcomes Usability issues Usability issues

Exploring Opportunities I Expertise Expertise Sharing experts and expertise Sharing experts and expertise Promoting the role that behavioral scientists play Promoting the role that behavioral scientists play Directory of experts??? Where? Directory of experts??? Where? Use framework/manual for non-experts Use framework/manual for non-experts End Users End Users Sharing with end users – require models that can be opened up for inspection so that the user can see how the data collected has represented his or her progress and misconceptions Sharing with end users – require models that can be opened up for inspection so that the user can see how the data collected has represented his or her progress and misconceptions Listening to the end user (patient/consumer) and meeting their needs (what do patients value and want) Listening to the end user (patient/consumer) and meeting their needs (what do patients value and want)

Exploring Opportunities II Linking data, people, technologies Linking data, people, technologies Cloud capabilities Cloud capabilities Creating a community site where standards are debated, agreed on, shared between researchers (behavioral scientists, statisticians, etc.), end-users, care providers and technology specialists Creating a community site where standards are debated, agreed on, shared between researchers (behavioral scientists, statisticians, etc.), end-users, care providers and technology specialists Use of communication technologies (video conferencing, Google docs) Use of communication technologies (video conferencing, Google docs) Ensuring interoperability of technologies across platforms and devices Ensuring interoperability of technologies across platforms and devices

Any other ideas?? Thank you! Thank you!