Generating and sharing

large datasets: Moving out of our

measurement comfort

Rita Kukafka and Pamela M. Kato

October 16-17, 2012Bruxelles, Belgique

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

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

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

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)

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

Addressing Challenges

Need for psychometricians and experts 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

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)

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

Any other ideas??

Thank you!