Clinical trial NCT03407352: Response Inhibition to High Calorie Food Cues Among Adolescents Following Active and Sedentary Video Game Play
Official title: Response Inhibition to High Calorie Food Cues Among Adolescents Following Active and Sedentary Video Game Play Using a Go/No-Go Task: A Randomized Crossover Study
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Obesity
Keywords: Exercise
Video Games
Physical Activity
Children
Appetite
Lead sponsor: Brigham Young University
Sponsor class: OTHER
First posted: 2018-01-23
Enrollment: 65
Sex: ALL
Minimum age (years): 12
Maximum age (years): 15
Standard ages: CHILD
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Primary purpose: OTHER

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.

Detailed description: Design. A randomized crossover design with counterbalanced treatment conditions will be used to compare the amount of neural inhibition to high and low calorie food cues among adolescents between two conditions. These two conditions will include a 60-minute session of active video game play and a 60-minute session of sedentary video game play. The order of treatment conditions will be randomly assigned using a validated simple randomization technique. During each laboratory session EEG data will be recorded and then later analyzed to assess the individual event related potential (ERP) components following each of the two conditions. Participants will complete a go/no-go response inhibition viewing task with pictures of high calorie and low calorie foods displayed on a computer monitor. The response inhibition task will be administered immediately following the cessation of each video game condition.

Video gaming conditions. The sedentary video game play condition will require participants to play in a seated position for 60 minutes. The active video game play condition will require participants to complete 60 minutes of active video gaming at moderate intense levels (3.0-5.9 metabolic equivalents (METs)) using a popular dance simulation game called 'Dance Dance Revolution' (DDR by Konami Digital Entertainment, Redwood City, CA). DDR elicits moderate intense (3.0-5.9 METs) levels of physical activity. Sixty minutes of active video game play has been shown to produce moderate intense levels of physical activity. Current guidelines recommend adolescents engage daily in 60 minutes or more of moderate-to vigorous-intense physical activity.

Participants. Sixty two healthy adolescents (31 female and 31 male) will be recruited through advertisements and fliers in the local community. Participants will be adolescents between the ages of 12 and 15 years old.

Procedures Screening. Candidates for the study and their guardians will be sent an email containing a link to an online survey. The online survey will be a 'yes or no' questionnaire to ensure participants meet inclusion criteria. Candidates must answer each question properly in order to qualify for the study. As part of the online survey, participants will be asked to report any food allergies and complete a food preference questionnaire. The food preference questionnaire will be used to prepare the standardized meals for each participant. Qualifying candidates will be invited to participate in the study.

Orientation. Participants for the study will be asked to complete the assent and consent forms prior to the first lab session. Participants and their guardians will come to the Clinical Neuroscience lab at least one day prior to their first lab session to pick up their standardized meals. Each participant will be informed of the main purpose of the study and familiarized with the testing procedures. Participants will be instructed to avoid consuming caffeine and other stimulants on the testing day as well as avoiding vigorous-intense physical activity for the 24-hour period prior to testing. Participants will be given a copy of these testing instructions.

Standardized Meals. Participants will be given a standardized breakfast and lunch for each of the testing days. The energy needs for each participant will be estimated using the World Health Organization equations for predicting basal metabolic rates. This equation uses height (cm), weight (kg) and age (years) to predict basal metabolic rates and has been validated for accuracy and reliability. An activity factor of 1.6 for boys and 1.5 for girls will be used to estimate total daily energy requirements.

Meals will be standardized based on macronutrient content (60% carbohydrate, 20% fat, 20% protein), age, gender and physical activity level (light, moderate or high activity). Participants will be given 25% of their daily caloric requirements for breakfast and 25% for lunch. The same foods will be given on both test days and participants will be instructed to consume all the food for both meals. Meal adherence will be assessed at the beginning of each session. Any non-compliance (not eating all food and/or eating other foods) will require participants to redo that particular lab session on a different day when they have followed food protocols.

Meals are designed to represent a reasonable amount of food to provide participants with adequate food intake. Participants will be given both of their standardized meals prior to the first testing day along with instructions to consume only standardized meals on testing days, avoid caffeine consumption and vigorous-intense physical activity 24-hours prior to testing. Participants will be instructed to consume their breakfast and lunch at the same time of day and at least two hours before arriving for each testing day.

Video Gaming Sessions. Each participant will complete two separate lab sessions. Demographic and anthropometric information will be collected at the beginning of the first lab session. Participants will be asked to rate their hunger at the beginning of each lab session. Hoffman et al compared P3 and N2 components between fed (1 hour postprandial) and fasted (17 hour fast) states and found no significant differences in P3 or N2 component amplitude or latency among young adults. Testing procedures and protocols will be reviewed with each participant prior to initiation of each video gaming session. Participants will then be taken to the video game lab to complete a 60-minute video gaming session. At the beginning of the video game session, participants will be fitted with the K4b2 (Cosmed, Rome, Italy) metabolic system, Polar heart rate monitor (Polar, Helsinki, Finland) and GT3X activity monitor (Pensicola, FL) to measure energy expenditure. Metabolic information for each participant will be measured and recorded for analysis.

Immediately following each video gaming condition, participants will be brought into an adjacent room containing the EEG equipment where they will complete the response inhibition tasks. After the second video gaming session participants will be asked to classify each food image as either high- or low-calorie to ensure the participants were correctly identifying food images during the response inhibition tasks. Upon completion of the response inhibition task, participants will be led to a separate room to complete the Stroop-Color Word test.

Response Inhibition Task. Immediately following both the active and passive video game conditions, participants will complete two go/no-go active viewing tasks. The go/no-go task will include pictures of high-calorie and low-calorie foods. Pictures for the viewing tasks will be selected based on findings from Blechert et al who recently reported normative ratings of 568 high- and low-calorie food images. The first viewing task will use a total of 150 pictures, 100 of these will include high-calorie pictures and 50 low-calorie pictures; the low-calorie images will be the target stimuli. The second viewing task will include 100 low-calorie pictures and 50 high-calorie pictures; the high-calorie images will be the target stimuli. Each of the viewing tasks will be shown in a different random order for each participant. Images will be shown for 500 milliseconds (ms) with an inter-trial interval of 1300 ms between images. Following the second response inhibition task, participants will be asked to subjectively rate the pictures according to pleasantness and arousal by using a picture rating scale from 1-9 (with 1 being the lowest and 9 the highest). To determine participant understanding of low vs high calorie foods, participants will be asked to complete a short task in which they will categorize each food picture as either low calorie or high calorie.

Go/No-Go Paradigm. For the high-calorie portion of the go/no-go task, participants will be instructed to press a button with their right index finger when they see images of low-calorie foods, which will be the target stimuli. The low calorie foods will be the target stimulus and the participants must commit their response by pressing the appropriate button. This portion of the task will be called "go". The "no-go" portion of the task will require the participants to withhold pressing the button when images of high-calorie foods are presented. The second go/no-go task will use the opposite instructions of the first task. Participants will be instructed to "go" or press a button when pictures of high-calorie foods are presented and to withhold, or "no-go", when the images of the low-calorie foods are presented, the high-calorie foods will be the target stimuli. Images for both tasks will be presented random order using 150 pictures displayed for 500 ms with an inter-trial interval of 1300 ms. Participants will be instructed to remain as still as possible and to minimize their eye blinks to reduce the amount of artifacts (disruptions or noise) while EEG data is recorded.

To further examine the effects of moderate intense physical activity on response inhibition, participants will be given several food choices while completing an auditory verbal learning test, the Rey auditory verbal learning test (AVLT). The Rey AVLT will be administered according to the protocol validated by Vakil et al which is described previously. After the 6th trial of the Rey AVLT, participants will be given 30 minutes before the final portions of the learning test to consume high and low energy dense foods ad-libitum. During the 30 minute delay period, foods which have been pre-measured and weighed to the nearest 0.1 gram beforehand will be given to each participant as they wait for the delay period of the Rey AVLT. High calorie foods will include high energy dense food items. Low calorie foods will include low energy dense food items, such as fruit slices and vegetables. The test will include 8 trials and allow appropriate time for participants to consume pre-measured foods. Completion of the Rey AVTL test will conclude the entire session.

After completion of the first video gaming session, participants will return after 7 days to complete the second video gaming session. Participants will be contacted 24 hours before their second video gaming session to remind participants of the upcoming testing session. Participants who complete both video gaming sessions and the accompanying tests will receive $40 dollars for completing the study.

Power Analysis. The number of participants was estimated based on a meaningful shift in N2 amplitudes. Since exercise has been shown to have a moderate effect on neural responses to food cues among adults, an estimated mean difference of 0.50 microvolts was used. A mean standard error of 0.63 microvolts was also used in this analysis based on previous research which helped establish a test-retest reliability of N2 amplitudes using EEG techniques.197 Thus, based on a mean N2 difference of 0.5 microvolts, a mean standard error of 0.63 microvolts and an alpha of 0.05, a sample of 55 participants is needed to have 90% power. In addition, based on previous experience measuring ERPs we expect to lose 10% of the EEG measurements due to poor or incomplete data. To account for this loss we will recruit an extra 7 participants, for a total of 62 total participants.

Statistical Analysis. Analysis will be completed using SAS statistical software (version 9.4; SAS Institute, Inc. Cary NC). Participant data will be reported as means and standard deviations. To evaluate the main and interaction effects within each subject, a 2-condition (active and passive) x 2-neural inhibition (Go and No/Go) x 2-calorie (high calorie and low calorie) repeated measures ANOVA will be used. An alpha level of 0.05 will be used for this analysis. Post hoc t-tests will be performed if significant effects are observed to identify significant differences. Bonferroni adjustments will be conducted for multiple comparisons.

Eligibility criteria: Inclusion Criteria:

* 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).

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Response inhibition
timeFrame: Immediately within 15 minutes post intervention
Secondary outcomes:
measure: Memory
timeFrame: Within 45 minutes post intervention
measure: Hunger
timeFrame: Before and immediately after the intervention
measure: Executive Function
timeFrame: Within 15 minutes post intervention
Other outcomes:
measure: Energy Expenditure
timeFrame: This will be measured twice for 60 minutes during each gaming condition
Locations:
city: Provo
country: United States
facility: Health and Human Performance Research Center
state: Utah
Study officials:
affiliation: Brigham Young University
name: Bruce W Bailey, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: Luck SJ. An introduction to the event-related potential technique. Cambridge, Massachusetts: Massachusetts Institute of Technology; 2005.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: Services USDoHaH. 2008 Physical Activity Guidelines for Americans. Washington, D.C.: Department of Health and Human Services; 2008:1-76.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: Golden CJF, SM. The stroop color and word test. A manual for clinical and experimental uses. Wood Dale, IL: Stoetling, Co.; 2002.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.