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Kyle S. Burger RD, MPH, PhD
Department of Nutrition
University of North Carolina, Chapel Hill
Weight regulation,
dietary restraint, and T1D
Prevention is contingent
on prediction
A multimodal approach
Objective measures
A multimodal approach
Implicit assessments
Explicit surveys &
perceptual ratings
A multimodal approach
It’s glass box approach too
statistical code,
paradigms, images
de-identified,
embargoed
each analysis
Commitment to Open
& Reproducible Science
Dietary Restraint
Today’s approach
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)
Dietary restraint
(Herman & Polivy, 1975; Laessle et al., 1989;
Wadden, Brownell, & Foster, 2002; Wilson, 2002)
Restraint: how
• Typically survey based
– Three Factor Eating
Questionnaire
– Dutch Eating Behavior
Questionnaire
– Restraint Scale
Restraint: reliability
Restraint survey’s tend to
show strong internal
consistency, as well as
test-retest reliability
(Herman & Polivy, 1975; Laessle et al., 1989;
Wadden, Brownell, & Foster, 2002; Wilson, 2002)
Restraint: validity
Survey’s validity based
on self-reported caloric
intake
(French, Jeffery, & Wing, 1994, Hill, Draper, & Stack, 1994
Laessle et al., 1989, Neumark-Sztainer, Jeffery, & French, 1997
van Strien et al., 1986)
Regulation of food cravings
Yokum & Stice 2013
Neural underpinnings
Burger & Stice 2011
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)
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)
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)
Dietary Disinhibition
Tendency to overeat in the
presence of palatable foods
• ‘Loss of control’ over eating
– Common in response to
distress
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)
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
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?
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
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
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
• 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
Burger ADA 6_2017

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Burger ADA 6_2017

  • 1. Kyle S. Burger RD, MPH, PhD Department of Nutrition University of North Carolina, Chapel Hill Weight regulation, dietary restraint, and T1D
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  • 11. Explicit surveys & perceptual ratings A multimodal approach
  • 12. It’s glass box approach too statistical code, paradigms, images de-identified, embargoed each analysis Commitment to Open & Reproducible Science
  • 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)
  • 15. Dietary restraint (Herman & Polivy, 1975; Laessle et al., 1989; Wadden, Brownell, & Foster, 2002; Wilson, 2002)
  • 16. Restraint: how • Typically survey based – Three Factor Eating Questionnaire – Dutch Eating Behavior Questionnaire – Restraint Scale
  • 17. Restraint: reliability Restraint survey’s tend to show strong internal consistency, as well as test-retest reliability (Herman & Polivy, 1975; Laessle et al., 1989; Wadden, Brownell, & Foster, 2002; Wilson, 2002)
  • 18. Restraint: validity Survey’s validity based on self-reported caloric intake (French, Jeffery, & Wing, 1994, Hill, Draper, & Stack, 1994 Laessle et al., 1989, Neumark-Sztainer, Jeffery, & French, 1997 van Strien et al., 1986)
  • 19. Regulation of food cravings Yokum & Stice 2013
  • 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

  1. Lab’s history: obesity to eating behavior to habits, decision making, reinforcement learning & habit formation (AI)
  2. T1D Clinical and behavioral intervetnion
  3. Increase in weight that parallel non-T1D
  4. Weight management seems pretty easy, handful of words Not that simple
  5. Dual outcomes Clinical (management) A little more behavioral