Next Article in Journal
Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging-Tractography in Resective Brain Surgery: Lesion Coverage Strategies and Patient Outcomes
Previous Article in Journal
Affective Prosody and Its Impact on the Neurology of Language, Depression, Memory and Emotions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 13
Issue 11
10.3390/brainsci13111573
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Brief Report

Feasibility of Virtual Shopping Budget-Management Training on Executive Functions in Healthy Young Adults: A Pilot Study

by
Si-An Lee
1,
Ji-Yea Kim
1 and
Jin-Hyuck Park
2,*
1
Department of ICT Convergence, The Graduate School, Soonchunhyang University, Asan 31538, Republic of Korea
2
Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan 31538, Republic of Korea
*
Author to whom correspondence should be addressed.
Brain Sci. 2023, 13(11), 1573; https://doi.org/10.3390/brainsci13111573
Submission received: 26 September 2023 / Revised: 6 November 2023 / Accepted: 8 November 2023 / Published: 9 November 2023
(This article belongs to the Topic Applications of Virtual Reality Technology in Rehabilitation)
Browse Figures
Review Reports Versions Notes

Abstract

:
To date, budget management in virtual shopping training has not been given much importance. The main objective of this study was to investigate the effects of virtual shopping budget-management training on executive functions and brain activation. Sixteen participants were randomly assigned to the experimental group that received virtual shopping budget-management training or the waitlist control group for a total of 16 sessions. To examine the effects of virtual shopping budget-management training on brain activation, HbO2 was measured in the prefrontal cortex via functional near-infrared spectroscopy (fNIRS) during the Trail Making Test Part B (TMT-B) and Stroop test. Mann–Whitney and Wilcoxon signed-rank tests were used to compare outcomes between and within the two groups. The virtual shopping budget-management training showed no significant difference in all outcomes between both groups (p > 0.05). No significant differences were observed in HbO2 levels during both TMT-B (p > 0.05) and the Stroop test (p > 0.05). However, in the pre-post comparisons, there was a significant difference in the TMT-B (p < 0.05) and Stroop test (p < 0.05) in the experimental group. In this study, although we did not find a distinct advantage in training, it confirmed its potential for clinical benefits in healthy young adults through training.

1. Introduction

Executive functions consist of high-level cognitive processes that enable individuals to function independently to facilitate new behavioral patterns and optimize approaches to unfamiliar environments, comprising sub-elements such as inhibitory control, working memory, cognitive flexibility, reasoning, problem-solving, and planning [1,2]. Additionally, instrumental activities of daily living (IADL) encompass a range of activities crucial for independent living [3]. The absence of appropriate executive control may result in a higher likelihood of encountering difficulties in performing IADL effectively [4]. Furthermore, because executive function impairment can have a significant impact on self-regulation [5], it can lead to problems with IADL that are more complex and require a higher level of personal autonomy for independent living, such as shopping [6]. Symptoms of executive dysfunction include slow initiation, inability to suppress automatic responses, poor problem-solving ability, and use of inappropriate rules [7], which can reduce independence in IADL [8]. Therefore, improving executive functions for independent IADL can be the goal of rehabilitation.
Conventional cognitive interventions aimed at enhancing executive functions primarily focus on improving sub-components of executive functions through demanding cognitive training with tabletop activities and computerized cognitive training programs, which limits the integration of overall executive functions [9]. Moreover, since their contents are far from real-world, it is difficult for their effects to be transferred to the subject’s daily life [10]. Therefore, cognitive training for executive functions should consider ecological validity, rather than focusing on sub-elements of executive functions [11].
Virtual reality (VR) is being widely applied to ensure ecologically validated cognitive training. It has the advantage of providing cognitive training in a safe environment, personalizing the situations, tasks, and difficulty of training, and providing feedback by tracking and analyzing the participant’s functional improvement [12]. Indeed, the ecological validity of VR-based cognitive training has been confirmed through a meta-analysis study [13], supporting the feasibility of VR for cognitive training.
Recently, virtual shopping training has been developed to assess and treat executive function impairment [14,15,16]. This training consists of having the subject remember shopping items, navigate the inside of the supermarket, and find and purchase them on the shopping list within their budget [14]. Spending and managing money within a budget are complex IADLs that heavily depend on intact executive functions [16]. However, in traditional VR-based shopping training, budget management typically involves the straightforward selection of products within a predetermined budget, rather than the creation of an optimal purchasing strategy. This approach might not fully engage executive functions [17]. Accordingly, virtual shopping budget-management training might yield superior outcomes through the optimization of executive functions compared to traditional VR-based shopping training.
On the other hand, previous studies have relied on paper-and-pencil-based executive function assessments to evaluate the effect of training [15,18]. These assessments, however, have limitations in objectively substantiating the basis for training due to their inability to observe clear changes in brain function [15]. Clear changes in brain function can be confirmed by observing brain areas related to executive functions through brain imaging techniques. One such brain area associated with executive functions is the dorsolateral prefrontal cortex (dlPFC). The dlPFC is conventionally acknowledged to play a crucial role in executive functions, which are high-level cognitive functions that support flexible goal-directed behaviors [19,20]. Brain imaging techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) have been used to measure changes in brain function, which could be evidence of cognitive training efficacy. Of brain imaging techniques, fNIRS non-invasively measures brain activity using changes in light absorption in the brain [21,22]. fNIRS has the advantages of portability, movement tolerability, and safety of use compared to other neuroimaging modalities [23].
Therefore, the primary objective of this study was to investigate the effects of virtual shopping budget-management training on executive functions and brain responses in healthy young adults as a pilot study before applying it to patients with executive dysfunction. This study hypothesized that virtual shopping budget-management training could be effective in improving executive functions and inducing changes in brain responses in healthy young adults.

2. Materials and Methods

2.1. Design

This study was a pilot study, and all participants were randomly assigned, with 8 participants in each, to either the experimental group or the wait-list control group using the Python computer language. The intervention consisted of 16 training sessions conducted twice a week over 8 weeks.

2.2. Participants

Sixteen healthy young adults participated in this study. Participants included in this study were individuals who had no visual or auditory impairments, were not undergoing behavioral interventions for cognitive enhancement, demonstrated comprehension of verbal instructions, and had no limitations in basic activities of daily living. Exclusion criteria comprised individuals who (1) had been diagnosed with psychiatric disorders, (2) had been diagnosed with neurological conditions, and (3) expressed an aversion to computer usage.

2.3. Intervention

The virtual shopping budget-management training was conducted twice a week for a total of 16 sessions, each lasting 15 min, over 8 weeks. The experimental group received the virtual shopping budget-management training program created with Scratch (MIT Media Lab, Cambridge, MA, USA). This program is an extension of conventional virtual shopping training programs, featuring added elements of budget-management training. The existing program was designed to simulate shopping in a supermarket. Participants navigated within a virtual supermarket, purchasing items from a shopping list. They had the freedom to navigate the virtual supermarket, selecting and purchasing items by touch. To make a payment, they were required to locate a cash desk, which then prompted the payment screen to appear. Following the payment, a statistical screen with feedback on their performance appeared [14]. In another program, participants performed the Adapted Four-Item Shopping Task. This task involved four shopping lists, with items positioned in two different aisles. Participants were provided with the lists, eliminating the need to memorize the items [24].
The virtual shopping budget-management training program introduced an added complexity by incorporating “scores” as variables assigned to product prices—lower prices resulted in lower scores, whereas higher prices yielded higher scores. Participants were asked to maximize their scores within a predetermined budget while retaining knowledge of their scores. This program places a particular emphasis on budget management within executive function training. It offers four levels of difficulty, challenging even healthy young adults, and utilizes various budget allocations to reduce learning effects (Figure 1).

2.4. Measurement

2.4.1. Primary Outcome

Participants’ executive functions were assessed using the Trail Making Test Part B (TMT-B), the Stroop test. TMT-B requires participants to connect numbers and letters alternately in a consecutive sequence [25]. This assessment is related to executive function and evaluates cognitive flexibility [26]. The time it took for participants to complete the TMT-B within 300 s was analyzed.
The Stroop test typically comprises 2–4 performance tasks. In this study, participants engaged in the “interference color naming” task, where they named the color of words written in ink different from the word’s meaning [27]. This assessment evaluates cognitive flexibility and working memory [28,29,30]. There were four colors: red, yellow, blue, and black. Participants were requested to read a total of 112 words as quickly as possible. The Stroop test results were analyzed in terms of the time taken to complete the task within a maximum of 300 s.

2.4.2. Secondary Outcome

Also, participants’ activation in the dlPFC area was assessed through fNIRS (Octamon, Artinis, The Netherlands), measuring hemodynamic responses within the dlPFC area. The fNIRS comprised a total of 8 channels, utilizing 760 nm and 850 nm infrared to detect changes in the cortical concentration of HbO2 and HHb [31,32]. Participants wore this device while performing the TMT-B and Stroop test. Hemodynamic response measurements continued until the completion of all assessments. Resting periods of indefinite duration were provided between each assessment until stability in activity was achieved. In this study, the mean values of HbO2 in the left and right dlPFC areas were utilized to measure hemodynamic responses. All data were sampled with a frequency of 10 Hz with Oxysoft version 3.0.52.

2.5. Statistical Analysis

All outcomes were analyzed using the SPSS 22.0 version (SPSS Inc., Chicago, IL, USA). To assess the normality of the sample, the Chi-square test was employed. The Mann–Whitney U test was used to analyze the pre- and post-intervention results between groups, while the Wilcoxon signed rank test was used for pre- and post-intervention analysis within the group. A statistical significance was set as p < 0.05.

3. Results

The flow of the study is illustrated in Figure 2. A total of 16 participants were randomly assigned, forming the experimental group with 8 participants and the control group with 8 participants. Following the pre-test, one participant in the control group dropped out. The results were analyzed using data from 8 participants in the experimental group and 7 participants in the control group.
Table 1 presents demographic characteristics, showing no significant differences between the two groups (p’s > 0.05) (Table 1).

3.1. Primary Outcome

In the pre-intervention, there were no significant differences between groups in both TMT-B (U = 20.000, p > 0.05) and the Stroop test (U = 24.000, p > 0.05) (Table 2). In the post-intervention, there were no significant differences in TMT-B (U = 24.000, p > 0.05) and the Stroop test (U = 24.000, p > 0.05) (Table 3). However, when comparing changes within each group, significant differences were found in the experimental group for both the TMT-B (Z = −2.38, p < 0.05) and Stroop test (Z = −2.38, p < 0.05). In the control group, a significant difference was observed in the Stroop test (Z = −2.197, p < 0.05) (Table 4).

3.2. Secondary Outcome

There were no significant differences between and within groups for HbO2 for the TMT-B (p’s > 0.05) and Stroop test (p’s > 0.05) (Table 2, Table 3 and Table 4).

4. Discussion

This study was designed to investigate the effects of virtual reality shopping budget-management training on executive functions and prefrontal brain activity. To achieve this, the experimental group participated in 16 training sessions, and significant differences in executive functions were observed after the 16 sessions of training. These results are consistent with previous research findings, demonstrating the effectiveness of virtual shopping training in enhancing executive functions [15,16].
Executive functions can commonly be categorized into three components: updating and monitoring of information (‘updating’), inhibition of prepotent impulses (‘inhibition’), and mental set shifting (‘shifting’) [33,34]. Specifically, the ability to update information about prices, inhibit the impulse to buy, and think flexibly to select the most appropriate items within a budget is crucial for reasonable consumption [35], indicating the importance of executive functions in shopping contexts. Previous research has revealed that individuals with executive function deficits, such as those with MCI or stroke, take longer to shop, exceed their budgets more frequently, and require more assistance compared to healthy individuals [14,16]. This supports the notion that shopping could serve as an effective training method for enhancing executive functions.
One significant difference between the existing shopping training and the current study is the emphasis on budget management within the shopping tasks. Existing shopping training was relatively straightforward, primarily involving the task of selecting items within a budget constraint. This simplicity facilitated the implementation of a strategy focused on purchasing the cheapest items, which might not have effectively enhanced executive functions. In contrast, the virtual shopping budget-management training introduced variability in prices across different brands, with higher prices yielding more points. To achieve higher scores within the given budget, participants had to engage in more complex mental calculations of adding and subtracting items. Consequently, this training presented a more intricate task compared to existing ones, requiring efforts to choose more suitable items within the budget. As the task complexity increased, this training closely resembled the actual shopping environment, encouraging participants to exhibit more rational consumption behavior.
Another difference is our attempt to investigate the effects of the training along with fNIRS, focusing on activation in the PFC. Neuronal activation is crucial in neurorehabilitation, as it can lead to structural and functional changes in the brain through neuroplasticity [36,37]. In this study, we measured HbO2 levels in the dlPFC, which is responsible for executive functions [19], and compared pre- and post-intervention differences. Although statistically significant results did not emerge, this effort is significant as the first attempt to demonstrate that virtual shopping budget-management training induces brain activation.
In this study, we confirmed improved executive functions when conducting virtual shopping budget-management training in healthy young adults. This suggests that virtual shopping budget-management training could be effective in enhancing executive functions not only in clinical groups such as MCI or stroke patients but also in healthy individuals. By focusing on budget management in shopping training and by adjusting the difficulty level by dividing it into four stages, we can provide suitable training for a diverse range of participants, including healthy adults. However, there was no difference between the experimental and control groups. This could be attributed to the assessment tool being too easy to evaluate healthy young adults, potentially resulting in a ceiling effect [38,39]. Furthermore, we conducted 16 sessions of training, which might not have been a sufficient training period to examine changes in executive functions in healthy young adults. In a previous study, it has been demonstrated that studies with more than 20 sessions of training have resulted in significant changes [40,41], whereas those with fewer training sessions have not yielded significant results [42,43,44].
This study emphasizes the importance of advanced measurement techniques, such as neuroimaging, in evaluating cognitive training. Beyond statistical significance, it is essential to measure objective evidence, such as neural efficiency, to confirm intervention effects, surpassing the limitations of traditional assessments influenced by external factors like mood states [45,46]. Additionally, despite the small sample size and non-significant results in primary analyses, our research can contribute to the development of effective virtual reality education programs for shopping budget management in the future. Hence, our study can serve as a foundational step towards creating practical virtual reality education for budget management during shopping. Furthermore, these findings can also benefit rehabilitation and elderly care programs. Training in a virtual environment enhances cognitive abilities and daily functioning, with potential for broader societal impact [47].
This study has several limitations. Firstly, the training was conducted on healthy young adults, despite the aim of enhancing executive functions. Nevertheless, the observed improvement in executive functions in this population suggests the potential applicability of virtual shopping budget-management training to various groups. Secondly, the limited sample size in this study hinders the generalization of the research findings. Thirdly, while improvements in executive functions were observed through paper-and-pencil assessments, there were no significant results in HbO2 levels in the prefrontal cortex, which contrasts with previous research findings [48]. Individual differences in transfer outcomes are well-documented and it can be problematic, particularly with relatively small sample sizes typically gathered in brain imaging studies [49,50]. Fourthly, despite the presence of various sub-elements in executive functions, this study only measured some aspects using the TMT-B and Stroop test. Future research should utilize a variety of executive function assessment tools for additional analysis, particularly considering a larger sample with executive function impairments.

5. Conclusions

In conclusion, 8 weeks of virtual shopping budget-management training does not result in improvements in executive functions or changes in brain activation when compared to the control group in healthy young adults. However, within-group comparisons revealed improvements in executive functions. The findings of this study demonstrate the potential utility of virtual shopping budget-management training in enhancing executive functions in healthy young adults. Future research should include a larger sample size and a longer intervention, particularly targeting individuals with executive function impairments.

Author Contributions

Conceptualization, S.-A.L., J.-Y.K. and J.-H.P.; methodology, J.-H.P.; software, S.-A.L.; formal analysis, S.-A.L.; investigation, S.-A.L.; writing—original draft preparation, S.-A.L.; writing—review and editing, J.-Y.K.; supervision, J.-H.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the Soonchunhyang University Research Fund and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by Ministry of Education (No. 2021R1I1A3041487).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Soonchunhyang (protocol code 202302-SB-013 approved 16 March 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The group data presented in this study are available upon request from the corresponding author. The individual data are not publicly available due to privacy and confidentiality.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Gilbert, S.J.; Burgess, P.W. Executive function. Curr. Biol. 2008, 18, R110–R114. [Google Scholar] [CrossRef] [PubMed]
  2. Diamond, A. Executive functions. Annu. Rev. Psychol. 2013, 64, 135–168. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, S.-W.; Chippendale, T. Factors associated with IADL independence: Implications for OT practice. Scand. J. Occup. Ther. 2017, 24, 109–115. [Google Scholar] [CrossRef] [PubMed]
  4. Bell-McGinty, S.; Podell, K.; Franzen, M.; Baird, A.D.; Williams, M.J. Standard measures of executive function in predicting instrumental activities of daily living in older adults. Int. J. Geriatr. Psychiatry 2002, 17, 828–834. [Google Scholar] [CrossRef] [PubMed]
  5. Hanks, R.A.; Rapport, L.J.; Millis, S.R.; Deshpande, S.A. Measures of executive functioning as predictors of functional ability and social integration in a rehabilitation sample. Arch. Phys. Med. Rehabil. 1999, 80, 1030–1037. [Google Scholar] [CrossRef] [PubMed]
  6. Millán-Calenti, J.C.; Tubío, J.; Pita-Fernández, S.; González-Abraldes, I.; Lorenzo, T.; Fernández-Arruty, T.; Maseda, A. Prevalence of functional disability in activities of daily living (ADL), instrumental activities of daily living (IADL) and associated factors, as predictors of morbidity and mortality. Arch. Gerontol. Geriatr. 2010, 50, 306–310. [Google Scholar] [CrossRef] [PubMed]
  7. Godefroy, O. Frontal syndrome and disorders of executive functions. J. Neurol. 2003, 250, 1–6. [Google Scholar] [CrossRef] [PubMed]
  8. Cahn, D.A.; Sullivan, E.V.; Shear, P.K.; Pfefferbaum, A.; Heit, G.; Silverberg, G. Differential contributions of cognitive and motor component processes to physical and instrumental activities of daily living in Parkinson’s disease. Arch. Clin. Neuropsychol. 1998, 13, 575–583. [Google Scholar] [CrossRef]
  9. Park, J.-H. Effects of cognitive-physical dual-task training on executive function and activity in the prefrontal cortex of older adults with mild cognitive impairment. Brain Neurorehabilit. 2021, 14, e23. [Google Scholar] [CrossRef]
  10. Kizony, R.; Josman, N.; Katz, N.; Rand, D.; Weiss, P. Virtual reality and the rehabilitation of executive functions: An annotated bibliography. Isr. J. Occup. Ther. 2008, 17, E47–E61. [Google Scholar]
  11. Josman, N.; Katz, N. Relationships of categorization on tests and daily tasks in patients with schizophrenia, post-stroke patients and healthy controls. Psychiatry Res. 2006, 141, 15–28. [Google Scholar] [CrossRef] [PubMed]
  12. Mesa-Gresa, P.; Gil-Gómez, H.; Lozano-Quilis, J.-A.; Gil-Gómez, J.-A. Effectiveness of virtual reality for children and adolescents with autism spectrum disorder: An evidence-based systematic review. Sensors 2018, 18, 2486. [Google Scholar] [CrossRef] [PubMed]
  13. Son, C.; Park, J.-H. Ecological Effects of VR-Based Cognitive Training on ADL and IADL in MCI and AD patients: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 15875. [Google Scholar] [CrossRef] [PubMed]
  14. Zygouris, S.; Giakoumis, D.; Votis, K.; Doumpoulakis, S.; Ntovas, K.; Segkouli, S.; Karagiannidis, C.; Tzovaras, D.; Tsolaki, M. Can a virtual reality cognitive training application fulfill a dual role? Using the virtual supermarket cognitive training application as a screening tool for mild cognitive impairment. J. Alzheimer’s Dis. 2015, 44, 1333–1347. [Google Scholar] [CrossRef] [PubMed]
  15. Park, J.-H. Does the virtual shopping training improve executive function and instrumental activities of daily living of patients with mild cognitive impairment? Asian J. Psychiatry 2022, 69, 102977. [Google Scholar] [CrossRef] [PubMed]
  16. Nir-Hadad, S.Y.; Weiss, P.L.; Waizman, A.; Schwartz, N.; Kizony, R. A virtual shopping task for the assessment of executive functions: Validity for people with stroke. Neuropsychol. Rehabil. 2017, 27, 808–833. [Google Scholar] [CrossRef] [PubMed]
  17. Drever, A.I.; Odders-White, E.; Kalish, C.W.; Else-Quest, N.M.; Hoagland, E.M.; Nelms, E.N. Foundations of Financial Well-Being: Insights into the Role of Executive Function, Financial Socialization, and Experience-Based Learning in Childhood and Youth. J. Consum. Aff. 2015, 49, 13–38. [Google Scholar] [CrossRef]
  18. Jacoby, M.; Averbuch, S.; Sacher, Y.; Katz, N.; Weiss, P.L.; Kizony, R. Effectiveness of executive functions training within a virtual supermarket for adults with traumatic brain injury: A pilot study. IEEE Trans. Neural Syst. Rehabil. Eng. 2013, 21, 182–190. [Google Scholar] [CrossRef]
  19. Nieder, A. Prefrontal cortex and the evolution of symbolic reference. Curr. Opin. Neurobiol. 2009, 19, 99–108. [Google Scholar] [CrossRef]
  20. Panikratova, Y.R.; Vlasova, R.M.; Akhutina, T.V.; Korneev, A.A.; Sinitsyn, V.E.; Pechenkova, E.V. Functional connectivity of the dorsolateral prefrontal cortex contributes to different components of executive functions. Int. J. Psychophysiol. 2020, 151, 70–79. [Google Scholar] [CrossRef]
  21. Hoshi, Y.; Tamura, M. Near-infrared optical detection of sequential brain activation in the prefrontal cortex during mental tasks. NeuroImage 1997, 5, 292–297. [Google Scholar] [CrossRef] [PubMed]
  22. Tak, S.; Ye, J.C. Statistical analysis of fNIRS data: A comprehensive review. NeuroImage 2014, 85, 72–91. [Google Scholar] [CrossRef] [PubMed]
  23. Pinti, P.; Tachtsidis, I.; Hamilton, A.; Hirsch, J.; Aichelburg, C.; Gilbert, S.; Burgess, P.W. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann. N. Y. Acad. Sci. 2020, 1464, 5–29. [Google Scholar] [CrossRef] [PubMed]
  24. Rand, D.; Katz, N.; Weiss, P.L. Evaluation of virtual shopping in the VMall: Comparison of post-stroke participants to healthy control groups. Disabil. Rehabil. 2007, 29, 1710–1719. [Google Scholar] [CrossRef] [PubMed]
  25. Zayat, E.; Rempfer, M.; Gajewski, B.; Brown, C.E. Patterns of association between performance in a natural environment and measures of executive function in people with schizophrenia. Psychiatry Res. 2011, 187, 1–5. [Google Scholar] [CrossRef] [PubMed]
  26. Kortte, K.B.; Horner, M.D.; Windham, W.K. The trail making test, part B: Cognitive flexibility or ability to maintain set? Appl. Neuropsychol. 2002, 9, 106–109. [Google Scholar] [CrossRef] [PubMed]
  27. Kim, T.Y.; Kim, S.; Sohn, J.E.; Lee, E.A.; Yoo, B.G.; Lee, S.C.; Hong, T.Y.; Kim, M.J. Development of the Korean Stroop Test and Study of the Validity and the Reliability. J. Korean Geriatr. Soc. 2004, 8, 233–240. [Google Scholar]
  28. Jensen, A.R.; Rohwer, W.D. The stroop color-word test: A review. Acta Psychol. 1966, 25, 36–93. [Google Scholar] [CrossRef]
  29. Kane, M.J.; Engle, R.W. Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. J. Exp. Psychol. Gen. 2003, 132, 47–70. [Google Scholar] [CrossRef]
  30. Scarpina, F.; Tagini, S. The Stroop Color and Word Test. Front. Psychol. 2017, 8, 557. [Google Scholar] [CrossRef]
  31. de Belli, V.; Orcioli-Silva, D.; Beretta, V.S.; Vitório, R.; Zampier, V.C.; Nóbrega-Sousa, P.; da Conceição, N.R.; Gobbi, L.T.B. Prefrontal cortical activity during preferred and fast walking in young and older adults: An fNIRS Study. Neuroscience 2021, 473, 81–89. [Google Scholar] [CrossRef] [PubMed]
  32. Nóbrega-Sousa, P.; Gobbi, L.T.B.; Orcioli-Silva, D.; Conceição, N.R.d.; Beretta, V.S.; Vitório, R. Prefrontal cortex activity during walking: Effects of aging and associations with gait and executive function. Neurorehabilit. Neural Repair 2020, 34, 915–924. [Google Scholar] [CrossRef] [PubMed]
  33. Baumeister, R.F.; Schmeichel, B.J.; Vohs, K.D. Self-regulation and the executive function: The self as controlling agent. Soc. Psychol. Handb. Basic Princ. 2007, 2, 516–539. [Google Scholar]
  34. Miyake, A.; Friedman, N.P.; Emerson, M.J.; Witzki, A.H.; Howerter, A.; Wager, T.D. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cogn. Psychol. 2000, 41, 49–100. [Google Scholar] [CrossRef] [PubMed]
  35. Baumeister, R.F.; Sparks, E.A.; Stillman, T.F.; Vohs, K.D. Free will in consumer behavior: Self-control, ego depletion, and choice. J. Consum. Psychol. 2008, 18, 4–13. [Google Scholar] [CrossRef]
  36. Demarin, V.; Morović, S. Neuroplasticity. Period. Biol. 2014, 116, 209–211. [Google Scholar]
  37. Puderbaugh, M.; Emmady, P.D. Neuroplasticity. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2023. [Google Scholar]
  38. Judson, E. Learning about bones at a science museum: Examining the alternate hypotheses of ceiling effect and prior knowledge. Instr. Sci. 2012, 40, 957–973. [Google Scholar] [CrossRef]
  39. Montero-Odasso, M.; Almeida, Q.J.; Bherer, L.; Burhan, A.M.; Camicioli, R.; Doyon, J.; Fraser, S.; Muir-Hunter, S.; Li, K.Z.; Liu-Ambrose, T. Consensus on shared measures of mobility and cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA). J. Gerontol. Ser. A 2019, 74, 897–909. [Google Scholar] [CrossRef]
  40. Nouchi, R.; Kawata, N.Y.d.S.; Saito, T.; Himmelmeier, R.M.; Nakamura, R.; Nouchi, H.; Kawashima, R. Dorsolateral prefrontal cortex activity during a brain training game predicts cognitive improvements after four weeks’ brain training game intervention: Evidence from a randomized controlled trial. Brain Sci. 2020, 10, 560. [Google Scholar] [CrossRef]
  41. Brehmer, Y.; Westerberg, H.; Bäckman, L. Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Front. Hum. Neurosci. 2012, 6, 63. [Google Scholar] [CrossRef]
  42. Dorbath, L.; Hasselhorn, M.; Titz, C. Effects of education on executive functioning and its trainability. Educ. Gerontol. 2013, 39, 314–325. [Google Scholar] [CrossRef]
  43. Gordon, S.; Todder, D.; Deutsch, I.; Garbi, D.; Alkobi, O.; Shriki, O.; Shkedy-Rabani, A.; Shahar, N.; Meiran, N. Effects of neurofeedback and working memory-combined training on executive functions in healthy young adults. Psychol. Res. 2020, 84, 1586–1609. [Google Scholar] [CrossRef]
  44. Enge, S.; Behnke, A.; Fleischhauer, M.; Küttler, L.; Kliegel, M.; Strobel, A. No evidence for true training and transfer effects after inhibitory control training in young healthy adults. J. Exp. Psychol. Learn. Mem. Cogn. 2014, 40, 987. [Google Scholar] [CrossRef] [PubMed]
  45. Chepenik, L.G.; Cornew, L.A.; Farah, M.J. The influence of sad mood on cognition. Emotion 2007, 7, 802. [Google Scholar] [CrossRef] [PubMed]
  46. Jeun, Y.J.; Nam, Y.; Lee, S.A.; Park, J.-H. Effects of Personalized Cognitive Training with the Machine Learning Algorithm on Neural Efficiency in Healthy Younger Adults. Int. J. Environ. Res. Public Health 2022, 19, 13044. [Google Scholar] [CrossRef] [PubMed]
  47. Bauer, A.C.M.; Andringa, G. The potential of immersive virtual reality for cognitive training in elderly. Gerontology 2020, 66, 614–623. [Google Scholar] [CrossRef] [PubMed]
  48. Nguyen, L.; Murphy, K.; Andrews, G. Cognitive and neural plasticity in old age: A systematic review of evidence from executive functions cognitive training. Ageing Res. Rev. 2019, 53, 100912. [Google Scholar] [CrossRef] [PubMed]
  49. Reddy, P.; Shewokis, P.A.; Izzetoglu, K. Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity. Brain Inform. 2022, 9, 9. [Google Scholar] [CrossRef]
  50. Salmi, J.; Soveri, A.; Salmela, V.; Alho, K.; Leppämäki, S.; Tani, P.; Koski, A.; Jaeggi, S.M.; Laine, M. Working memory training restores aberrant brain activity in adult attention-deficit hyperactivity disorder. Hum. Brain Mapp. 2020, 41, 4876–4891. [Google Scholar] [CrossRef]
Brainsci 13 01573 g001
Figure 1. The virtual shopping budget-management training program. (A) Full view of the aisles. (B) A view of some of the shelves.
Figure 1. The virtual shopping budget-management training program. (A) Full view of the aisles. (B) A view of some of the shelves.
Brainsci 13 01573 g001
Brainsci 13 01573 g002
Figure 2. Flowchart of the study.
Figure 2. Flowchart of the study.
Brainsci 13 01573 g002
Table 1. Demographic characteristics of the groups.
Table 1. Demographic characteristics of the groups.
Demographic CharacteristicsExperimental Group
(n = 8)		Control Group
(n = 8)		 χ 2
Sex: Male0 (0 %)3 (42.8 %)0.055
Sex: Female8 (100 %)4 (57.1 %)
Age (years)20.38 ± 0.9221 ± 1.600.771
Table 2. Comparison of executive functions between both groups (pre-intervention).
Table 2. Comparison of executive functions between both groups (pre-intervention).
Experimental Group
(n = 8)		Control Group
(n = 7)		Up
TMT-B (s)19.01 ± 5.2517.79 ± 6.7520.0000.397
Stroop Test (s)87.86 ± 18.9490.43 ± 19.6424.0000.694
fNIRS
  TMT-B2.53 ± 1.122.75 ± 2.1624.0000.694
  Stroop Test2.62 ± 1.584.37 ± 4.3321.0000.463
TMT-B: Trail Making Test Part B.
Table 3. Comparison of executive functions between both groups (post-intervention).
Table 3. Comparison of executive functions between both groups (post-intervention).
Experimental Group
(n = 8)		Control Group
(n = 7)		Up
TMT-B (s)15.64 ± 4.8715.73 ± 2.5624.0000.643
Stroop Test (s)77.62 ± 12.4781.31 ± 16.3224.0000.643
fNIRS
  TMT-B2.30 ± 0.992.33 ± 1.7628.0000.908
  Stroop Test2.40 ± 1.252.47 ± 1.9625.0000.728
TMT-B: Trail Making Test Part B.
Table 4. Comparison of executive functions within each group (pre and post-intervention).
Table 4. Comparison of executive functions within each group (pre and post-intervention).
Experimental Group
(n = 8)		Control Group
(n = 7)
Pre-TestPost-TestZpPre-TestPost-TestZp
TMT-B (s)19.01 ± 5.2515.64 ± 4.87−2.3800.017 *17.79 ± 6.7515.73 ± 2.56−0.6760.499
Stroop Test (s)87.86 ± 18.9477.62 ± 12.47−2.3800.017 *90.43 ± 19.6481.31 ± 16.32−2.1970.028 *
fNIRS
  TMT-B2.53 ± 1.122.30 ± 0.99−0.7000.4842.75 ± 2.162.33 ± 1.76−0.5070.612
  Stroop Test2.62 ± 1.582.40 ± 1.25−0.2800.7794.37 ± 4.332.47 ± 1.96−1.3520.176
* p < 0.05, TMT-B: Trail Making Test Part B.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lee, S.-A.; Kim, J.-Y.; Park, J.-H. Feasibility of Virtual Shopping Budget-Management Training on Executive Functions in Healthy Young Adults: A Pilot Study. Brain Sci. 2023, 13, 1573. https://doi.org/10.3390/brainsci13111573

AMA Style

Lee S-A, Kim J-Y, Park J-H. Feasibility of Virtual Shopping Budget-Management Training on Executive Functions in Healthy Young Adults: A Pilot Study. Brain Sciences. 2023; 13(11):1573. https://doi.org/10.3390/brainsci13111573

Chicago/Turabian Style

Lee, Si-An, Ji-Yea Kim, and Jin-Hyuck Park. 2023. "Feasibility of Virtual Shopping Budget-Management Training on Executive Functions in Healthy Young Adults: A Pilot Study" Brain Sciences 13, no. 11: 1573. https://doi.org/10.3390/brainsci13111573

APA Style

Lee, S.-A., Kim, J.-Y., & Park, J.-H. (2023). Feasibility of Virtual Shopping Budget-Management Training on Executive Functions in Healthy Young Adults: A Pilot Study. Brain Sciences, 13(11), 1573. https://doi.org/10.3390/brainsci13111573

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop