Exergaming Improves Executive Functions in Patients With Metabolic Syndrome

Introduction In recent years, the relationship between cognitive function and metabolic syndrome has been widely studied. Metabolic syndrome (MetS) has been shown to be associated with a decline in executive function due to multiple risk factors, including hypertension, dyslipidemia, impaired glucose homeostasis, and abdominal obesity. Executive functions include basic cognitive processes such as attentional control, cognitive inhibition, inhibitory control, working memory, and cognitive flexibility.

Cognitive neuroscience is using Stroop tasks to measure selective attention capacity and skills, as well as processing speed ability, indicating executive functions. Electroencephalographic (EEG) activity using event-related positioning technology P300 and N200 has been widely used to measure selective attention capacity and skills, and a behavioral performance such as reaction time (RT) is commonly used to measure processing speed ability. N200 negativity (200~350 ms post-stimulus) is an event-related potential (ERP), which indicates attentional capacity that is usually induced before motion response control and is related to the cognitive processes of stimulus recognition and differentiation. P300 positivity (300~600 ms post-stimulus) is another ERP, reflecting memory-related neural processing that is involved in categorizing incoming information and updating the context of the working memory (e.g., encoding, rehearsal, recognition, and retrieval).

It is well known that aerobic exercise training provides various beneficial clinical outcomes in metabolic disease patients. Its effects on cognitive function, especially executive function, also have been reported. Furthermore, recent studies reported that both aerobic and resistance exercise training facilitate overall electrophysiological effects (e.g., increased ERP P300 amplitudes) and behavior index (e.g., faster RT) in healthy elderly people. In addition, aerobic exercise has also been reported to improve cognitive processes in cortical cognitive control (P300 amplitude) in studies of chronic stroke patients.

Recently, exergaming (a combination of "exercise" and "gaming") has attracted much attention as a novel exercise method to improve cognitive function because it utilizes video games that require body movements while simultaneously presenting the user with a cognitively challenging environment. Along with its popular usage for leisure and entertainment, there is a growing interest in the application of exergaming to improve clinical outcomes. Recent studies using exergaming showed beneficial effects on cognitive and dual-task functions, which reduced falls in older adults as well as cardiovascular disease risks such as body fat, serum adipokine levels, and lipid profiles. Exergaming also promoted executive functions and cognitive processing speed in elderly and children. This growing evidence suggests that exergaming's have the benefit of improving cognitive and physical functions.

Although many previous studies have reported improvements in cognitive function following exergaming, it is not clear whether this benefit is due to an exercise effect or video game effect. In addition, all of these studies measured RT, instead of ERP using EEG, which limits to illuminate brain activities. Considering that EEG can measure electrical activities in various cortex areas in the brain, it is necessary to investigate ERP using EEG to evaluate executive function. Therefore, we examined the benefits of exergaming compared to normal exercise and investigated executive function by measuring RT as well as N200 and P300 in three cortex areas during Stroop tasks in patients with MetS.

Methods Participants A total of 22 MetS male and female patients aged between 50-80 years participated in this study. MetS was defined according to the modified NCEP Adult Treatment Panel III (NCEP-ATP III) definition for South Asians. Briefly, individuals with three or more of the following criteria were defined as MetS: central obesity (waist circumference ≥90 cm for men; ≥ 85 cm for women), fasting plasma glucose ≥100 mg/dL or current treatment for diabetes mellitus, systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or current treatment for hypertension, serum triglyceride ≥150 mg/dL, low HDL cholesterol (men<40 mg/dL; women<50 mg/dL). Subjects were asked to not exercise for 24 hours before the experiment. They were also instructed to eat usual meals and to finish meals 4 hours before the experiment, while avoiding alcohol 1 day before the experiment and caffeine during the 4 hours prior to the experiment. All subjects were required to complete a written informed consent approved by the Institutional Review Board of Kosin University College of Medicine.

The sample size was calculated using sample size calculation software (G*Power version 3.1.9.2 for Windows; http://www.gpower.hhu.de), with effect size 0.484, statistical power 0.8, and statistical level of significance 0.05. This effect size was calculated from the previous studies. As a result, the sample size for each group became 8, and we decided to recruit 11 patients for each group, with a potential 30% dropout rate.

Exercise Training Interventions Exercise training was conducted at the Kosin University Gospel Hospital U-healthcare Center. Each participant was instructed to immediately inform the supervisor if he or she experienced any unusual symptoms during exercise training and to consult a physician if needed. Subjects were excluded if they did not perform more than 80% of the exercise sessions.

All subjects were randomly divided into two groups: exergaming and treadmill exercise group. Subjects had 2 weeks of adaptation and then carried out 12 weeks of exercise training: 60 min/day, 60-80% of heart rate reserved (HRR), 3 days/week. Each exercise session consisted of 10 minutes of warm-up, 40 minutes of main exercise, and 10 minutes of warm-down.

The exergaming group (EXG) performed exercise using Exerheart® devices (D&J Humancare, Busan, South Korea) composed of a running/jumping mat [730(W) × 730(D) × 130(H)] and a tablet PC on a stand (can be adjusted to any height between 70 and 155 cm) (Supplemental Figure 1A). Exerheart® is an exergaming developed for in-situ running along with the video game called "Alchemist's Treasure" (D&J Humancare, Busan, South Korea). To play this game, the subject has to run or jump on a spot on the mat to move a virtual avatar on the screen of the tablet PC to the front, back, left, and right along with music (Supplemental Video 1 for online). The subject can control the speed of avatar movement by running or jumping speed on the mat. The treadmill exercise group (TEG) performed exercise using commercial treadmills (MOTUS, Gyeonggi-do, South Korea). Each subject walked or ran on the treadmill at a comfortable speed.

For both EXG and TEG, all subjects' heart rates (HR) during exercise were monitored using HR monitors (polar RS400sd, Madison Height, Michigan, USA) to confirm that the value was within the target HR range. The Karvonen formula (1957) was used to calculate HR reserve (HRR, estimated maximal HR- resting HR) and target HR during exercise [(HRR × given percentage of training intensity) + resting HR)].

Stroop Test For assessment of executive function, a computer-based version of the Stroop task was administered with Telescan software (LAXTHA, Daejeon, South Korea). During the task, subjects were presented with a color word appearing in the same color on congruent trials (e.g. "blue" printed in blue) and in a different color on incongruent trials (e.g. "blue" printed in green). To provide similar visual content, blue, green, and yellow were chosen as stimuli.

Subjects performed Stroop task twice, pre- and post-exercise training. Subjects sat 1 meter from the screen, and when the color words appeared on the screen, they clicked the left keyboard for the congruent test and the right keyboard for the incongruent test. Subjects were instructed to respond as quickly and accurately as possible. The rate of measurement targeted for 50%. Each color word (vertical viewing angle: 2 °) was presented for 200 ms, and the response was allowed within 1500 ms. The inter-stimulus interval varied randomly between 1500 and 2500 ms.

Electroencephalographic (EEG) Measurements EEG activity was recorded during the modified Stroop task using a computerized polygraph system- Type A: A total of 31-channel Poly G-A (LAXTHA, Daejeon, South Korea). Ag-AgCl electrodes (LAXTHA, Daejeon, South Korea) were placed on frontal (Fz), central (Cz), and parietal (Pz) cortex areas, according to the International 10-20 system. Midline locations referenced to link earlobe electrodes. Horizontal and vertical electrooculograms (EOGs) were monitored by electrodes placed above and below the left eye and at the outer canthus of both eyes, respectively. The impedance of all electrodes was maintained below 10 kΩ. The bandpass filter of the amplifier was 0.1-100 Hz, the sampling rate was 1000 Hz, and a notch filter was at 60 Hz.

The N200 component was defined as the largest positive peak occurring between 200-350 ms post-stimulus, and the P300 component was defined as the largest positive peak occurring between 300~600 ms post-stimulus. N200 and P300 amplitudes were measured as the difference between the mean pre-stimulus baseline and maximum peak amplitude. Telescan's built-in high pass IIR filter was used for filtering. Waveforms were digitally smoothed with a low-pass filter using a half-power cut-off of 10 Hz prior to analysis.

Statistical Analysis Due to the small sample size, we used nonparametric statistics for data analysis. We used the Wilcoxon signed-rank test to examine the changes of each dependent variable after intervention within each group. The Mann-Whitney U test was used to make comparisons of the delta values between training groups (Δ‐EXG vs Δ‐TEG). The effect size of partial eta-squared (η2) was reported for significant effects, where the alpha level for all of the tests was set at 0.05. Data were expressed as mean ± standard deviation of mean. All statistical tests were processed using the software SPSS 24 version.