14. Dietary restraint
Intentional and sustained restriction of
caloric intake for the purposes of weight loss
or weight maintenance
(Herman & Polivy, 1975; Laessle et al., 1989;
Wadden, Brownell, & Foster, 2002; Wilson, 2002)
21. Restraint: intake validity
Relation to meal intake
in lab and natural
environment:
~17 studies: dietary
restraint scales are not
related to short- or
longer-term caloric intake
(e.g., Jansen et al., 2003; Martin et al., 2005;
Rolls et al., 1997; Sysko et al., 2005)
22. Restraint: intake validity
Doubly-labeled water (DLW)
estimates of energy intake:
• No relation 14 day energy intake
• Elevated restraint scores greatest
bias of under-(self)reporting of
intake
DLW
(Bandini et al., 1990; Bingham et al., 1995;
Bathon 2000)
23. Restraint: weight validity
Restraint predicting
weight loss:
• People with elevated
restraint scores tend
show greater weight
gain than.
• How can this be?
(French et al., 1994; Stice et al., 2000;
Stice et al., 2005)
24. Dietary Disinhibition
Tendency to overeat in the
presence of palatable foods
• ‘Loss of control’ over eating
– Common in response to
distress
25. Restraint Theory
Reliance on cognitive control, i.e., dietary
restraint, leaves people vulnerable to
disinhibited eating if cognitive control is
disrupted, may increase reward value of food
(Polivy & Herman 1985)
26. Alternate thought
Restrained individuals are limiting
desired intake; however they
consume more than energy
needs
• Supported by:
–fMRI data showing increased
prefrontal response
– biased relation of DLW & self-
reported intake
27. T1D & Restraint Theory
Can we apply the model of dietary
restraint to better understand weight
regulation in individuals with T1D?
Can we use T1D to better validate
dietary restraint measures?
28. Current T1D management tools
Restraint Theory – T1D management
analogs:
– Dietary restraint ~ Carbohydrate restriction
– Disinhibition ~ Carbohydrate intake in
response to lows
T1D management imposes eating habits that
are agonistic to hunger & satiation cues
29. Emerging T1D management tools
Emergence & utilization of
automated closed loop
systems will remove the
‘imposed’ restraint theory
• Increased freedom in intake
• Likely altered relation with internal
hunger/fullness cues
Aberrant eating behaviors, theoretically
contribute to weight gain
30. Discussion points
• Non-zero-sum investigation
• Improving prediction of weight gain (encoding)
– Qualitative, Perceptual, and Objective data
• Related constructs e.g., impulsivity
– Machine learning for decoding: reverse
inference
– Dual outcome recommendations
Improved prediction begets improved prevention
31. • Anna Kahkoska
• Grace Shearrer, PhD
• Jenny Gilbert
• Lily Jones
• You could be here*
NIBLunc.org
kyle_burger@unc.edu
*Currently taking applications
for funded doctoral positions
entering Fall 2018
Github/niblUNC
OpenfMRI.org
Open Science Framework
@NIBL_unc
act1on working group
Editor's Notes
Lab’s history: obesity to eating behavior to habits, decision making, reinforcement learning & habit formation (AI)
T1D Clinical and behavioral intervetnion
Increase in weight that parallel non-T1D
Weight management seems pretty easy, handful of words
Not that simple
Dual outcomes
Clinical (management)
A little more behavioral