1. Just-In-Time, Adaptive Intervention framework for lifelong healthy dietary habits Donna Spruijt-Metz, MFA PhD Research Professor, Psychology Director, USC mHealth Collaboratory University of Southern California dmetz@usc.edu @metzlab @ NSF International Workshop on Dynamic Modeling of Health Behavior Change and Maintenance, Sept 8-9, 2015, London, UK

2. What, when, where and how Skyler lives within a system of embedded systems (family, workplace, community, etc.) Given how little we know of the dynamics of these systems in real time, choose 1 (family) Use current behavioral evidence – Using food as reward or self medication – Stress & stressful interactions – Modeling – Availability & Theories of choice – Self-regulation/self-control strength model (Muraven & Baumesiter Psyc Bull 2000) – Some form of family Systems Theory (McHale, Amato, Booth 2014) To inform development of a learning sensor system to collect data describe eating behaviors in time and context Wearable & deployable sensors that have pull as well as push capabilities. NSF SCH 1521722, Spruijt-Metz, Stancovic, Lach & De LaHaye

3. What, when, where and how Using data accrued, develop dynamic models of selected and discovered behaviors to understand behavior (interactions, loops) in system in real time – Conceptually seeded with current behavioral theories & behavioral evidence – ‘Cycle out’ constructs when they no longer appear to contribute to the model, – Cycle in new ones as they are ‘discovered’ in an iterative fashion – This is a transdisciplinary effort Use these models to inform choices on what, when and where to intervene on these models Base “how” on theory and iterative user-centered design. NSF SCH 1521722, Spruijt-Metz, Stancovic, Lach & De LaHaye

4. Legend Family Members C1 = Child 1 C2 = Child 2 A1 = Adult 1 A2 = Adult 2 Eating Event Categories Hunger No Hunger Meal EE-1 EE-2 Snack EE-3 EE-4 A1 StressC1 Stress C1 Self Regulation C1: EE-4 A1 angry tone C1 speed of Eating Social Facilitation C2: EE-3 Duration C1: EE-4 A1 mimic C1 A1 speed of eating A1: EE-4 A1 self-regulation + + - + + + + + - + C2 mimic C1 + + + + + - + FED System Dynamic Model Self-Regulation of Eating in real-time & in context NSF SCH 1521722, Spruijt-Metz, Stancovic, Lach & De LaHaye

5. Current knowledge on behavior & theory breaks here: Multilevel & Multisystem Momentary Within a day Over a week Months Years Time System Within- Individual Family Self-Control strength Ability to self-regulate mood Self-Control depletion Self-Control replenishing Develop healthy eating habits Modeling eating sweets Stressful interactions Availability of sweets System of dinner table stressors Parent Child- feeding practices Spruijt-Metz, Hekler, Saranummi, Intille, Korhonen, Nilsen, Rivera, Spring, Michie, Asch, Sanna, Salcedo, Kukafka, Pavel, Trans Beh Med 2015

6. Not just when in need: Learning algorithms Meaningful moments Receptivity 1 Availability 2 Opportune moments 3 – In need and/or vulnerable – Receptive and/or available – Motivated and/or able 1 Nahum-Shani, Hekler, Spruijt-Metz, Health Psyc in press 2 Sharmin, Ali, Rahman, Bari, Hossain, Kumar, UbiComp ’14 3 Poppinga, Heuten, Boll, Pervasive Computing 2014

7. Baseline data: What and how long? Combining sensing & Self-report Momentary level Eating episodes (with whom, quality of interactions, where, when, speed, duration, stress, external cues/temptation – if possible, what) Antecedents (physical activity, proximity to others, satiety, hunger, mood, access) Outcomes (stress relief, self-control depletion) Dynamic models to determine Meaningful moments How momentary behavior, emotion, cognition and environment interact And how they interact with past behavior, emotion, cognition

8. Baseline data: What and how long? Combining sensing & Self-report Periodically Impulsivity Chronic stressors Pantry inventory & shopping habits Whatever else choices of theory/behavior dictate These get fed into the multilevel models.

9. Framework: prompting, pulling and pushing information, decision rules Prompts via phone, via deployable and wearable sensors, via network members – at meaningful moments Models will inform choices of data to acquire, sensors, platforms, types of messages, when/where to intervene, decision rules Models will need to be run in an ongoing fashion (decision rule needed here too) in order to continue adaptation ‘on the fly’ – the intervention targets and messaging will evolve as person changes. One can’t model everything all the time. Choices will need to be made to achieve some kind of parsimony