1. Development Plans for a JITAI to Prevent Eating Lapses
Bonnie Spring
Northwestern University
NSF International Workshop on Dynamic Modeling of Health Behavior Change and Maintenance, Sept 8-9, 2015, London, UK
NSF Workshop on Dynamic Modeling of Health Behavior Change, University College – London, September 2016
This information was initially at the bottom of the introduction slide, but it became redundant with the addition of information requested by Dr. Spruijt-Metz- BM

2. Overview Conceptualization of the behavioral target
(Because you can’t just not eat)
Theoretical framework
Intervention development approach
Idealized Intervention Framework

3. Conceptualizing Behavioral Target(s)
Primary clinical outcome: overweight → normal weight
Intermediate outcome (presumed causal, mediating):
too frequent → infrequent/no between meal snacking behavior
Alternative intermediate outcomes:
Meals: frequency, regularity, composition (energy, nutrient, satiating value)
Snack composition (energy dense, nutrient poor,
low satiation)
Eating lapse (Evan Forman) at unplanned time or amount greater than planned)
Eliminate unplanned eating episodes unintended time)

4. Theoretical Framework
Self-regulation (self-control) theory
Baumeister: self-control as limited resource depleted by use
But sufficient incentive, positive mood prevent depletion effect
Inzlicht: process model - waning self-control reflects diminished motivation for self-regulatory goal and heightened priority for “want to” vs. “have to” goals
Hoffman: dual systems theory: fatigue (?hunger?) weakens dominance of reflective system, heightens influence of impulsive system

5. Intervention Development Approach (i)
Machine learning to detect eating episodes using worn camera and dual smart wristbands with annotation to capture ground truth for eating
Temporal variation around planned mealtimes to set acceptable range of detection accuracy
Event-triggered and random EMA to capture
Eating a meal or a snack
Motivational priority of not snacking
Positive & negative affect
Hunger & fatigue
Presence of tempting food cue

6. Intervention Development Approach (ii)
Validate detection of eating episodes using Confirm/Refute protocol to 90% accuracy (sensitivity & specificity)
Validate prediction of snacking eating episodes (using skipped meal, motivation, affect, hunger, fatigue, and cue presence) to 90% accuracy
Develop brief personalized motivational intervention to bolster prioritization given to snacking self-regulation
Microrandomize high vs. medium vs. low risk of snacking intervals to motivational intervention vs. distraction intervention vs. no intervention to verify decision rules for JITAI

7. Idealized Intervention Approach
Pull: continuously pull info from band and phone to classify intervals as low vs. moderate vs. high risk of snacking
Push
distraction intervention when snacking risk is high
motivational intervention when risk is intermediate
?nothing or amusing anecdote when risk is low