Generating and sharing large datasets: Moving out of our measurement comfort

1. Generatingand sharing large datasets: Moving out of our measurement comfort RitaKukafkaand Pamela M. Kato October 16-17, 2012 Bruxelles, Belgique

2. Why this is important • Takes advantage of technological capabilities to capture and store and analyze large amounts of health behavior data • From sensors, mobile technology, etc. • Cloud computing • 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

3. Examples • Ecological Momentary Assessment Data • Data automatically connected via blood glucose monitors, blood pressure monitors, scales • Web data collected daily • Data collected semiannually in extended longitudinal studies Thank you, Runze Li: http://methodology.psu.edu/media/2012_SRNT/Li_SRNT.pdf

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

6. Practical Challenges • Expertise • 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)

7. Research Challenges • Causality • correlational, non-experimental, post-hoc analyses, atheoretical • Reliability and validity • Were data collected in the same way at each site? • Is the data clean or noisy? How can we tell? • Some principles may be ignored • such as choosing a representative sample • 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 • Integration across heterogeneous data resources • logistical as well as analytical challenges

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

9. Exploring Opportunities I • Expertise • Sharing experts and expertise • Promoting the role that behavioral scientists play • Directory of experts??? Where? • Use framework/manual for non-experts • 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 • Listening to the end user (patient/consumer) and meeting their needs (what do patients value and want)

10. Exploring Opportunities II • Linking data, people, technologies • 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 • Use of communication technologies (video conferencing, Google docs) • Ensuring interoperability of technologies across platforms and devices

11. Any other ideas?? • Thank you!