NCT03407352

Brief Summary

Response inhibition is a cognitive process that helps individuals suppress or avoid unwanted actions and inappropriate behavior. In regards to food, response inhibition allows a person to resist eating, ignore poor food choices and poor behavior related to food choices. Previous research suggests that physical activity may play a role in the regulation of response inhibition to food in adults. The purpose of this study is to compare the effects of an acute bout of active video gaming versus passive video gaming on response inhibition to high calorie and low calorie food images among adolescents. The proposed study is a randomized cross-over study with counterbalanced design. Participants will come to the lab two times. Each subject will serve as their own control. There will be two conditions and all participants will complete both conditions. One condition will be completed each time the participants come to the lab. The two conditions will include (a) 60 minutes of sedentary video game play and (b) 60 minutes of active video game play at moderate-intense levels. After each video game condition the participants will view pictures of high-calorie and low-calorie foods while being connected to an EEG machine. The EEG will measure the N2 and P3 event-related potentials, which will be used to index and analyze response inhibition. As participants view the pictures of high-calorie and low-calorie foods, they will be given a button pressing task where the press a button when they see pictures of the high-calorie or low-calorie foods. Immediately after each video game session participants will complete two cognitive measurement tasks. These tasks are (a) Stroop color-word task and (b) auditory verbal learning test. The Stroop color-word test requires the participants to read words typed in different colors on a sheet of paper and to say the name of the color of ink the words are printed in (ie to say "blue" when the word "red" is printed in blue ink). The auditory verbal learning test will require the researchers to read a list of 15 words and have participants repeat as many of the words back to the researcher as possible. This will be repeated 6 times. After the 5th time, there will be a 30 minute waiting period before the 6th trial where participants will be allowed to eat ad libitum pre-weighed foods while they wait to complete the last trial of the verbal learning test.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
65

participants targeted

Target at P50-P75 for not_applicable obesity

Timeline
Completed

Started Jun 2016

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

June 1, 2016

Completed
3 months until next milestone

First Submitted

Initial submission to the registry

August 26, 2016

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

September 1, 2017

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

October 1, 2017

Completed
4 months until next milestone

First Posted

Study publicly available on registry

January 23, 2018

Completed
Last Updated

February 21, 2019

Status Verified

February 1, 2019

Enrollment Period

1.3 years

First QC Date

August 26, 2016

Last Update Submit

February 19, 2019

Conditions

Keywords

ExerciseVideo GamesPhysical ActivityChildrenAppetite

Outcome Measures

Primary Outcomes (1)

  • Response inhibition

    Electrical activity of the brain will be recorded using EEG techniques using the Electrical Geodesics amplifier system (EGI: Electrical Geodesics Inc, Eugene OR) during the Go/No Go tasks. The EEG will be recorded continuously with a sampling rate of 250 Hz using a 24-bit analog-to-digital converter. The vertex sensor will be used as the reference electrode. Impedances will be kept below 50 kΩ according to EGI guidelines. A right posterior electrode approximately two inches behind the right mastoid will serve as the common ground. Electroencephalographic data will be segmented off-line and single trial epochs rejected if voltages exceed 100 µV, transitional (sample-to-sample) thresholds were greater than 100 µV or eye-channel amplitudes were above 70 µV. Data will be digitally re-referenced to an average reference then digitally low-pass filtered at 30Hz.

    Immediately within 15 minutes post intervention

Secondary Outcomes (3)

  • Memory

    Within 45 minutes post intervention

  • Hunger

    Before and immediately after the intervention

  • Executive Function

    Within 15 minutes post intervention

Other Outcomes (1)

  • Energy Expenditure

    This will be measured twice for 60 minutes during each gaming condition

Study Arms (2)

Passive Video Game Play

ACTIVE COMPARATOR

Participants will play video games in a seated position for 60 minutes.

Behavioral: Passive Video Game Play

Active Video Game Play

EXPERIMENTAL

Participants will play dance dance revolution (video game that requires lower body movement) for 60 minutes.

Behavioral: Active Video Game Play

Interventions

60 minutes of active video game play at a moderate intensity

Active Video Game Play

60 minutes of passive video game play

Passive Video Game Play

Eligibility Criteria

Age12 Years - 15 Years
Sexall
Healthy VolunteersYes
Age GroupsChild (0-17)

You may qualify if:

  • Participants must have normal or corrected to normal vision. Participants must be able to participate in moderate-intensity physical activity without restrictions as measured by a physical activity readiness questionnaire (PAR-Q).

You may not qualify if:

  • Participants will be excluded if they do not provide proper assent and written consent from guardians, have chronic or metabolic disease, have orthopedic impairments, have been diagnosed with an eating disorder (ie anorexia, bulimia or binge eating disorder), take medications that alter metabolism, appetite or neurological function, have a diagnosed learning disability, neurological disorder, brain injury or attention deficit/hyperactive disorder or have food allergies.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Health and Human Performance Research Center

Provo, Utah, 84602, United States

Location

Related Publications (35)

  • Biddiss E, Irwin J. Active video games to promote physical activity in children and youth: a systematic review. Arch Pediatr Adolesc Med. 2010 Jul;164(7):664-72. doi: 10.1001/archpediatrics.2010.104.

    PMID: 20603468BACKGROUND
  • Graves L, Stratton G, Ridgers ND, Cable NT. Energy expenditure in adolescents playing new generation computer games. Br J Sports Med. 2008 Jul;42(7):592-4.

    PMID: 18606832BACKGROUND
  • Graf DL, Pratt LV, Hester CN, Short KR. Playing active video games increases energy expenditure in children. Pediatrics. 2009 Aug;124(2):534-40. doi: 10.1542/peds.2008-2851. Epub 2009 Jul 13.

    PMID: 19596737BACKGROUND
  • Nijs IMT, Franken IHA, Muris P. Food cue-elicited brain potentials in obese individuals and external eaters. International Journal of Psychophysiology. 2008;69(3):228-228.

    BACKGROUND
  • Luck SJ. An introduction to the event-related potential technique. Cambridge, Massachusetts: Massachusetts Institute of Technology; 2005.

    BACKGROUND
  • Watson TD, Garvey KT. Neurocognitive correlates of processing food-related stimuli in a Go/No-go paradigm. Appetite. 2013 Dec;71:40-7. doi: 10.1016/j.appet.2013.07.007. Epub 2013 Jul 26.

    PMID: 23892319BACKGROUND
  • Nijs IM, Franken IH, Muris P. Food-related Stroop interference in obese and normal-weight individuals: behavioral and electrophysiological indices. Eat Behav. 2010 Dec;11(4):258-65. doi: 10.1016/j.eatbeh.2010.07.002. Epub 2010 Jul 22.

    PMID: 20850061BACKGROUND
  • Stockburger J, Schmalzle R, Flaisch T, Bublatzky F, Schupp HT. The impact of hunger on food cue processing: an event-related brain potential study. Neuroimage. 2009 Oct 1;47(4):1819-29. doi: 10.1016/j.neuroimage.2009.04.071. Epub 2009 May 4.

    PMID: 19409497BACKGROUND
  • Suresh K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan;4(1):8-11. doi: 10.4103/0974-1208.82352.

    PMID: 21772732BACKGROUND
  • Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A, Nixon PA, Pivarnik JM, Rowland T, Trost S, Trudeau F. Evidence based physical activity for school-age youth. J Pediatr. 2005 Jun;146(6):732-7. doi: 10.1016/j.jpeds.2005.01.055.

    PMID: 15973308BACKGROUND
  • Services USDoHaH. 2008 Physical Activity Guidelines for Americans. Washington, D.C.: Department of Health and Human Services; 2008:1-76.

    BACKGROUND
  • Tan B, Aziz AR, Chua K, Teh KC. Aerobic demands of the dance simulation game. Int J Sports Med. 2002 Feb;23(2):125-9. doi: 10.1055/s-2002-20132.

    PMID: 11842360BACKGROUND
  • Nieman DC, Austin MD, Benezra L, Pearce S, McInnis T, Unick J, Gross SJ. Validation of Cosmed's FitMate in measuring oxygen consumption and estimating resting metabolic rate. Res Sports Med. 2006 Apr-Jun;14(2):89-96. doi: 10.1080/15438620600651512.

    PMID: 16869134BACKGROUND
  • Vandarakis D, Salacinski AJ, Broeder CE. A comparison of COSMED metabolic systems for the determination of resting metabolic rate. Res Sports Med. 2013;21(2):187-94. doi: 10.1080/15438627.2012.757226.

    PMID: 23541105BACKGROUND
  • Eisenmann JC, Brisko N, Shadrick D, Welsh S. Comparative analysis of the Cosmed Quark b2 and K4b2 gas analysis systems during submaximal exercise. J Sports Med Phys Fitness. 2003 Jun;43(2):150-5.

    PMID: 12853896BACKGROUND
  • Lee JM, Bassett DR Jr, Thompson DL, Fitzhugh EC. Validation of the Cosmed Fitmate for prediction of maximal oxygen consumption. J Strength Cond Res. 2011 Sep;25(9):2573-9. doi: 10.1519/JSC.0b013e3181fc5c48.

    PMID: 21869633BACKGROUND
  • Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011 Sep;14(5):411-6. doi: 10.1016/j.jsams.2011.04.003. Epub 2011 May 25.

    PMID: 21616714BACKGROUND
  • Staudenmayer J, He S, Hickey A, Sasaki J, Freedson P. Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. J Appl Physiol (1985). 2015 Aug 15;119(4):396-403. doi: 10.1152/japplphysiol.00026.2015. Epub 2015 Jun 25.

    PMID: 26112238BACKGROUND
  • Scott JJ, Morgan PJ, Plotnikoff RC, Lubans DR. Reliability and validity of a single-item physical activity measure for adolescents. J Paediatr Child Health. 2015 Aug;51(8):787-93. doi: 10.1111/jpc.12836. Epub 2015 Feb 3.

    PMID: 25643749BACKGROUND
  • Blechert J, Meule A, Busch NA, Ohla K. Food-pics: an image database for experimental research on eating and appetite. Front Psychol. 2014 Jun 24;5:617. doi: 10.3389/fpsyg.2014.00617. eCollection 2014.

    PMID: 25009514BACKGROUND
  • Fearnbach SN, Silvert L, Keller KL, Genin PM, Morio B, Pereira B, Duclos M, Boirie Y, Thivel D. Reduced neural response to food cues following exercise is accompanied by decreased energy intake in obese adolescents. Int J Obes (Lond). 2016 Jan;40(1):77-83. doi: 10.1038/ijo.2015.215. Epub 2015 Oct 9.

    PMID: 26449418BACKGROUND
  • Franzen MD, Tishelman AC, Sharp BH, Friedman AG. An investigation of the test-retest reliability of the Stroop Color-Word Test across two intervals. Arch Clin Neuropsychol. 1987;2(3):265-72.

    PMID: 14589618BACKGROUND
  • Jensen AR, Rohwer WD Jr. The Stroop color-word test: a review. Acta Psychol (Amst). 1966;25(1):36-93. doi: 10.1016/0001-6918(66)90004-7. No abstract available.

    PMID: 5328883BACKGROUND
  • Golden CJF, SM. The stroop color and word test. A manual for clinical and experimental uses. Wood Dale, IL: Stoetling, Co.; 2002.

    BACKGROUND
  • Organization WH. Estimates of energy and protein requirements of adults and children. Energy and Protein Requirements (Geneva: World Health Organization, 1985) pp. 1985:71-112.

    BACKGROUND
  • Dietz WH, Bandini LG, Schoeller DA. Estimates of metabolic rate in obese and nonobese adolescents. J Pediatr. 1991 Jan;118(1):146-9. doi: 10.1016/s0022-3476(05)81870-0.

    PMID: 1986084BACKGROUND
  • Finan K, Larson DE, Goran MI. Cross-validation of prediction equations for resting energy expenditure in young, healthy children. J Am Diet Assoc. 1997 Feb;97(2):140-5. doi: 10.1016/S0002-8223(97)00039-4.

    PMID: 9020240BACKGROUND
  • Rodriguez G, Moreno LA, Sarria A, Fleta J, Bueno M. Resting energy expenditure in children and adolescents: agreement between calorimetry and prediction equations. Clin Nutr. 2002 Jun;21(3):255-60. doi: 10.1054/clnu.2001.0531.

    PMID: 12127936BACKGROUND
  • Bruce AS, Holsen LM, Chambers RJ, Martin LE, Brooks WM, Zarcone JR, Butler MG, Savage CR. Obese children show hyperactivation to food pictures in brain networks linked to motivation, reward and cognitive control. Int J Obes (Lond). 2010 Oct;34(10):1494-500. doi: 10.1038/ijo.2010.84. Epub 2010 May 4.

    PMID: 20440296BACKGROUND
  • LaBar KS, Gitelman DR, Parrish TB, Kim YH, Nobre AC, Mesulam MM. Hunger selectively modulates corticolimbic activation to food stimuli in humans. Behav Neurosci. 2001 Apr;115(2):493-500. doi: 10.1037/0735-7044.115.2.493.

    PMID: 11345973BACKGROUND
  • Hanlon B, Larson MJ, Bailey BW, LeCheminant JD. Neural response to pictures of food after exercise in normal-weight and obese women. Med Sci Sports Exerc. 2012 Oct;44(10):1864-70. doi: 10.1249/MSS.0b013e31825cade5.

  • Stubbs RJ, Hughes DA, Johnstone AM, Rowley E, Reid C, Elia M, Stratton R, Delargy H, King N, Blundell JE. The use of visual analogue scales to assess motivation to eat in human subjects: a review of their reliability and validity with an evaluation of new hand-held computerized systems for temporal tracking of appetite ratings. Br J Nutr. 2000 Oct;84(4):405-15. doi: 10.1017/s0007114500001719.

  • Vakil E, Greenstein Y, Blachstein H. Normative data for composite scores for children and adults derived from the Rey Auditory Verbal Learning Test. Clin Neuropsychol. 2010 May;24(4):662-77. doi: 10.1080/13854040903493522. Epub 2010 Feb 11.

  • Hoffman LD, Polich J. EEG, ERPs and food consumption. Biol Psychol. 1998 Jun;48(2):139-51. doi: 10.1016/s0301-0511(98)00010-6.

  • Clayson PE, Larson MJ. Psychometric properties of conflict monitoring and conflict adaptation indices: response time and conflict N2 event-related potentials. Psychophysiology. 2013 Dec;50(12):1209-19. doi: 10.1111/psyp.12138. Epub 2013 Aug 29.

MeSH Terms

Conditions

ObesityMotor Activity

Condition Hierarchy (Ancestors)

OverweightOvernutritionNutrition DisordersNutritional and Metabolic DiseasesBody WeightSigns and SymptomsPathological Conditions, Signs and SymptomsBehavior

Study Officials

  • Bruce W Bailey, PhD

    Brigham Young University

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
OTHER
Intervention Model
CROSSOVER
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 26, 2016

First Posted

January 23, 2018

Study Start

June 1, 2016

Primary Completion

September 1, 2017

Study Completion

October 1, 2017

Last Updated

February 21, 2019

Record last verified: 2019-02

Data Sharing

IPD Sharing
Will not share

Locations