Training Response Inhibition to Obesogenic Foods

NCT04659837 | Completed

• 1 other identifier: R21CA191859
• Study Type: interventional
• Target: 125
• Locations: 1 country
• Sites: 1

Brief Summary

Dietary choices, and in particular, excess calorie intake leading to obesity, are strong, but reversible risk factors for cancer. For example, foods high in added sugars are low-nutrient, high calorie foods that increase the risk of cancer by promoting weight gain. As such, the reduction of sweets is consistent with American Institute for Cancer Research and the American Cancer Society dietary recommendations. Behavioral interventions to alter diet have limited long-term efficacy, most likely because eating decisions are governed by automatic neurocognitive processes that are not addressed in conventional interventions. In particular, the ability to refrain from consuming unhealthy, but widely available, palatable foods, is increasingly understood to depend on inhibitory control, i.e., the ability to cut off action tendencies that are put in motion by innate drives towards rewarding behaviors. Recent work by our team and others have demonstrated that computer-based inhibitory control trainings result in short-term, specific changes in behavior, such as reducing intake of salty snack food, chocolate, and alcoholic beverages. An automatized, home computer-based inhibitory control training offers the potential of an inexpensive and highly disseminable method of lowering cancer risk across wide swaths of the population. As such, we aim to evaluate the feasibility, acceptability, mechanism of action, effectiveness and persistence of a home computer-based inhibitory control training. In particular, we hypothesize that a high-repetition training in inhibitory control will result in increased adherence to a low-sugar diet, and that effects will be mediated through improved inhibitory control. We further hypothesize that gamefying the training will improve efficacy because it will boost compliance with the daily trainings. We also hypothesize that the training will be most effective for those starting of with impaired inhibitory control, as well as those with strongest desire for palatable foods and those with strongest explicit health goals. Lastly, we aim to examine the impact of inhibitory control training on secondary outcomes, including on overall caloric intake, and on short-term weight loss. To achieve these aims, the proposed study will recruit 100 overweight and obese individuals who currently eat high-sugar diets, and who wish to improve their diets. Participants will be assigned a reduced-sugar diet for 8 weeks. After a baseline period, participants will be randomized, via a 2 x 2 factorial design, to receive 6 weeks of either inhibitory control training or a sham training, and either a gamified or non-gameified training. The 6-week intervention will consist of 15 minutes per day of home computer- based inhibitory control training, and will be followed by a 1-week booster and then 1-week follow-up period. Dietary adherence will be measured via a food frequency questionnaire and via automated 24-hour food recall. Neurocognitive variables will be assessed pre and post-training in order to test trainings' mechanism of action, and moderation will be assessed through baseline trait measures of explicit health goals, implicit attitudes towards appetitive stimuli, body mass index, and responsivity to food cues.

Conditions

Inhibitory Control Training; Gameifaction

Outcome Measures

Primary Outcomes (3)

• Weight

  8 weeks

• Sweets consumption

  8 weeks

• Inhibitory control in response to high-sugar foods

  Inhibitory control in response to high-sugar foods is measured via performance (i.e., speed of inhibiting prepotent responses to high-sugar food stimuli) on the daily inhibitory control training (i.e., Go/No-Go) task.

  8 weeks

Secondary Outcomes (2)

• Compliance with daily inhibitory control training prescription

  8 weeks

• Enjoyment of the daily training task

  8 weeks

Study Arms (2)

Inhibitory Control Training

ACTIVE COMPARATOR

Active Inhibitory Control Training vs. Sham Inhibitory Control Training

Behavioral: No-Added-Sugar Diet

Gameification

OTHER

Gameified elements added vs. No gameified elements added

Behavioral: No-Added-Sugar Diet

Interventions

No-Added-Sugar Diet BEHAVIORAL

All participants received a no-added-sugar dietary intervention and followed the diet during the course of the study.

Gameification; Inhibitory Control Training

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• BMI 25-50
• Baseline consumption of ≥ 3 servings/day of high-sugar foods

You may not qualify if:

• Medical or psychiatric conditions that could interfere with the ability to comply with diet recommendations,
• pregnancy (or planning to become pregnant in the next 12 months) or current breastfeeding,
• a history of bariatric surgery,
• weight loss of five percent or more within the last six months
• beginning or changing a dosage of a weight-affecting medication within the last three months

Sponsors & Collaborators

• Drexel University: lead

Study Sites (1)

Drexel University

Philadelphia, Pennsylvania, 19104, United States

Location

Related Publications (1)

• Forman EM, Manasse SM, Dallal DH, Crochiere RJ, Loyka CM, Butryn ML, Juarascio AS, Houben K. Computerized neurocognitive training for improving dietary health and facilitating weight loss. J Behav Med. 2019 Dec;42(6):1029-1040. doi: 10.1007/s10865-019-00024-5. Epub 2019 Mar 19.

  PMID: 30891657 RESULT

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: TREATMENT
• Intervention Model: FACTORIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 21, 2020
• First Posted: December 9, 2020
• Study Start: June 1, 2016
• Primary Completion: August 7, 2019
• Study Completion: December 1, 2019
• Last Updated: December 9, 2020

Record last verified: 2019-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)