New Computationally-Enabled Theoretical Models to Support Health BC&M Breakout group summary1 1) pertaining to the topic assigned to your group – identify

New Computationally-Enabled Theoretical Models to Support Health BC&M

Breakout group summary 1

1) pertaining to the topic assigned to your group – identify gaps that need to be bridged in order to improve the science of behavior change – all the way from fundamental science to commercialization – and 2) Develop an outline for the white paper of the sections that would be needed to write this up for a white paper for our funders and (peer reviewed) publications for broader audiences.

Topic: What about personal models versus general models? How can you imagine developing and evaluating a model that could be used in a broad population and then adapting that model dynamically to individuals..



Ilkka KorhonenJaakko AarnioEric HecklerPetra Wilson David AschRita KukafkaKatherina Martin AbelloHannu Nieminen Scribe: Heidi Lehtonen



• Challenge/barrier: What about personal models versus general models? How can you imagine developing and evaluating a model that could be used in a broad population and then adapting that model dynamically to individuals.



Gaps (1)

• Falsifiability: methods to falsify the theories – we need to be able to put models (personalised models) into testing of their validity to choose which models / approaches work and which not

• We don’t have a unified model of behaviours but several competing theories which all have a grain of truth. How well and to whom and when these models are good is not clearly known i.e. How to move from general unified model to personalised approach. Do we need a general model if we can identify personally optimal models? Or shall we flip: from personalised models to general models?



Gaps (2)

• Getting data to personalize the models: EHR – not sufficient. We need data related to behaviours, motivations, personality, values, preferences, barries, social networks, etc. This is needed for building strategies to personalise models but also to apply them in individual subjects.

• Adaptive interventions lead to adaptive (personalised) models (N=1 approach) = VISION. Gap = all the experimentation to learn how to do this, how to gather the data, and show that this works. Currently information is really scattered and not available.

• Behavioural change studies require prospective trials to get information about causality (N=1 trials are OK for this).



Gaps (3)

• Defining a virtual psychophysiological / behavioural model (profile) of a person is a key for personalization. Taxonomy for behavioural change is needed to do this.

• Optimisation (personalization) of a model needs a measure of goodness of the model – understanding what are the best dimensions to distinguish people (profile elements) in terms of behavioural change outcomes and what are the best measures to assess “goodness of fit” (“error function”)



Gaps (4)

• Business model issues are not so clear and have impact: if behavioural interventions are highly personalized what level of evidence is needed (and how that is gathered) for adaptive personalized interventions so that e.g. Insurance companies would reimburse them? “Education” of reimbursement authorities may be needed...

• Practical method problem – we need “Evidence based design cookbook” based on valid theories and good design principles (Designing behavioural interventions) for practitioners to apply personalisable models in their practical interventions – important also to speed up reimbursement decisions when spreading methods (no need to yet another “RCT” to prove the validity)



White paper outline

Documents

New Computationally-Enabled Theoretical Models to Support Health BC&M Breakout group summary1 1) pertaining to the topic assigned to your group – identify