INTRODUCTION

Cognitive impairment affects 20% of individuals with Parkinson’s disease (PD) at diagnosis, with up to 46% developing dementia within 10 years [1]. Deficits in attention, executive function, working memory, and visuospatial skills are common and can impair daily life and increase caregiver burden [2].

Dopaminergic therapies have limited cognitive benefits, prompting interest in nonpharmacological approaches to management, such as cognitive training [3]. This approach involves guided practice with paper-based or computerized tasks that improve cognition and functional outcomes, with benefits lasting up to 18 months [4-6]. Digital cognitive training enables home-based, accessible, and consistent stimulation, particularly for those with comorbidities or motor limitations.

This approach has been tested in individuals with Alzheimer’s disease (AD) [7]. Previously, we validated the effectiveness of a digital cognitive training program—part of the SUPERBRAIN platform—in individuals who are at risk of dementia or in those with mild cognitive impairment [8,9].

This study examined whether a 12-week SUPERBRAIN intervention could similarly improve cognitive function in individuals with PD.

MATERIALS & METHODS

Study design

This multicenter, prospective, two-arm randomized controlled trial assessed the effects of 12 weeks of digital cognitive training on cognitive domains in individuals with PD and mild cognitive impairment (PD-MCI). Individuals in the intervention group used the SUPERBRAIN tablet-based program daily (25–30 min) for 12 weeks; those in the control group received standard care without cognitive training. Cognitive function was evaluated using the Seoul Neuropsychological Screening Battery (SNSB) [10] at baseline and post-intervention. All the participants provided written informed consent, and the study adhered to the Declaration of Helsinki and was approved by the Korea University Guro Hospital Institutional Review Board (IRB#: 2023GR0076).

Participants

Twenty-nine individuals with PD-MCI, which was diagnosed according to Movement Disorders Society Task Force Level-II criteria [11], were recruited from four clinics: Korea University Guro Hospital, Ewha Woman’s University Seoul Hospital, Samsung Medical Center, and Soonchunhyang University Seoul Hospital. The inclusion criteria were as follows: age 50–85 years, PD diagnosis according to the UK Brain Bank criteria [12] with dopamine transporter (DAT) deficits on DAT imaging, literacy, and cognitive impairment (≥1.0 standard deviation [SD] below the norm on ≥2 tests in one domain or ≥1 test in two domains) without significant functional decline. We used a more lenient cutoff of 1.0 SD to better capture early cognitive decline and to enhance the feasibility of identifying individuals who may benefit from early intervention. The exclusion criteria included secondary parkinsonism, severe sensory impairment, or use of unstable antiparkinsonian medication. Antiparkinsonian medication was continued without changes in the dose until the end of the study.

Randomization

Participants were randomized 2:1 (intervention vs. control) via block randomization. The allocation was concealed from outcome assessors, and the participants were instructed not to reveal group assignment. We allocated more participants to the intervention group (2:1 randomization) to obtain more robust data on the feasibility, acceptability, and safety of the digital cognitive training by collecting more information on the experimental treatment.

Outcomes

Global cognition was measured by the Digit Span Test (DST); domain-specific scores were derived from the SNSB [10] and converted to age- and education-adjusted z scores. Follow-up assessments were performed within 24 weeks post-intervention to evaluate sustained effects and minimize practice effects.

Intervention

The SUPERBRAIN platform included serious games targeting attention, working memory, language, calculation, visuospatial skills, and executive function, as well as delayed recall training for memory enhancement. Memory training involved the use of health- and knowledge-based material followed by short-delayed recall exercises (Supplementary Table 1 in the online-only Data Supplement). Sessions alternated between A (attention/working memory + visuospatial tasks) and B (language/calculation + executive function tasks). Delayed recall training was included in all sessions, as memory impairment is one of the hallmarks of PD-MCI and a key predictor of progression to dementia [2,13]. Such impairment is also linked to reduced quality of life [14], highlighting the need for early memory-focused interventions.

Statistical analysis

Within-group changes were assessed using paired t-tests. Between-group differences were analyzed via analysis of covariance (ANCOVA) (covariates: age, sex, education), with group as the fixed factor. The primary outcomes were adjusted group differences in cognitive change scores. ANCOVA results included F statistic, p value, and partial η². Robust linear models were used for sensitivity analyses, including both linear and quadratic age terms, to capture potential nonlinear effects. Analyses followed the intention-to-treat principle; significance was set to p<0.05. MATLAB (MathWorks R2020a) and SPSS 21 (IBM Corp.) were used for all the analyses and visualizations.

RESULTS

Baseline demographics

Six participants withdrew their consent prior to initiating the program, two experienced unrelated health issues, and four withdrew due to scheduling conflicts. Ultimately, 23 individuals with PD-MCI completed the study: 16 in the intervention group and seven in the control group. With the exception of differences in age distribution, demographic and clinical characteristics were comparable between groups (Table 1). Compliance, which was defined as the percentage of completed sessions out of the total, averaged 79.36%. No adverse events associated with SUPERBRAIN were reported.

Effects of cognitive training

After 12 weeks, the intervention group showed significant improvements in the Seoul Verbal Learning Test (SVLT) delayed recall (t=-3.99, p=0.001; Cohen’s d=0.99) and Controlled Oral Word Association Test (COWAT) (animal, t=-2.24, p=0.04, Cohen’s d=0.56; phonemic, t=-2.75, p=0.014, Cohen’s d=0.68). In contrast, the control group showed no significant improvement in cognition (Supplementary Figure 1 and Supplementary Table 2 in the online-only Data Supplement).

ANCOVA revealed a significant difference between groups in the change in SVLT delayed recall scores between the intervention and control groups (F=7.15, p=0.015, partial η²=0.28), indicating a large effect size (Figure 1 and Supplementary Table 3 in the online-only Data Supplement).

Sensitivity analysis confirmed a significant intervention effect on SVLT delayed recall (β=1.43, p=0.006). Adjusting for linear and quadratic age terms yielded similar results (β=1.46, p=0.005), with age terms not significant.

DISCUSSION

This study evaluated the feasibility and clinical effects of a 12-week digital cognitive training program (SUPERBRAIN) in individuals with PD-MCI. Adherence was 79.36%, with 93.75% of participants completing the program; this result was comparable to previous reports (75%–92.7%) [15], supporting the feasibility of this intervention. However, adherence was slightly lower than that in our AD-MCI cohort [8], likely due to PD-related motor symptoms that were not addressed by the original AD-focused design (one-touch interface for older adults). Future adaptations should improve the usability for individuals with PD.

Significant improvements in COWAT and SVLT delayed recall scores occurred only in the intervention group, but longitudinal analysis revealed a sustained effect only for SVLT delayed recall, which is a core memory measure. Unlike prior PD-MCI studies emphasizing fronto-executive gains [5], this memory-specific improvement likely reflects the program’s AD-oriented, memory-focused structure. While PD often involves fronto-executive dysfunction, memory impairment is also common and may result from retrieval deficits that are associated with dopaminergic frontostriatal disruption [16]. Additionally, episodic memory encoding/storage deficits, which are linked to cholinergic dysfunction in the posterior parietal and superior temporal regions [17-19], are associated with dementia risk. Thus, interventions targeting memory, such as SUPERBRAIN, may hold particular value for individuals with PD-MCI.

The limitations of this study include the small sample size, especially in the control group, which potentially limited the study’s power and generalizability. The lenient cognitive impairment cutoff increased the detection of early decline but may introduce diagnostic heterogeneity. Future studies should use larger cohorts and stricter criteria. We also omitted immediate postintervention testing to assess durable effects and reduce participant burden and potential practice-related biases [20], but future work should include both immediate and delayed assessments. Baseline differences in age between groups could have influenced responsiveness; although the results were robust to sensitivity analyses, future trials should ensure balanced randomization. Finally, a positive control (e.g., dummy device) would help isolate intervention-specific effects.

In conclusion, SUPERBRAIN appears to be feasible and potentially beneficial for enhancing memory in individuals with PD-MCI. Further research with larger, methodologically rigorous trials is warranted to confirm its role as a supportive strategy for managing cognitive impairment in individuals with PD.

Supplementary Materials

Notes

Conflicts of Interest

Dongje Lee, Hang-Rai Kim, Seung Yeon Kim, and Yeji Hwang are employees of Rowan, the developer of the digital cognitive training program SUPERBRAIN. The remaining authors declare no commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Statement

This research was supported by the “National Institute of Health” Research Project (2022-ER1005-00) and the Korea University Guro Hospital (KOREA RESEARCH-DRIVEN HOSPITAL) grant (No. O2208241).

Acknowledgments

None

Author Contributions

Conceptualization: Seong-Beom Koh, Sung Hoon Kang. Data curation: Yu Jeong Park, Yisuh Ahn, Daeho Lee, Jungyeun Lee, Ji Young Yun, Jin Whan Cho, Kyum-Yil Kwon. Formal analysis: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang. Funding acquisition: Sung Hoon Kang. Investigation: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang. Methodology: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang. Project administration: Seong-Beom Koh, Sung Hoon Kang. Resources: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang. Software: Dongje Lee, Hang-Rai Kim. Supervision: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang, Su Jin Chung, Seung Yeon Kim, Yeji Hwang. Validation: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang, Su Jin Chung, Seung Yeon Kim, Yeji Hwang. Visualization: Dongje Lee, Hang-Rai Kim. Writing—original draft: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang. Writing—review & editing: Dongje Lee, Hang-Rai Kim, Seong-Beom Koh, Sung Hoon Kang.

Figure 1.

Violin plot of SVLT delayed recall score changes by group. Changes of SVLT delayed recall score were adjusted for the effect of age, sex, and years of education using a linear regression model. SVLT, Seoul Verbal Learning Test.

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Figure & Data

References

Citations

Citations to this article as recorded by  

• Integrated bio-cooperative robotic platform for virtual cognitive training in Parkinson's disease: design and methodology of the OPERA project
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  Frontiers in Neurology.2026;[Epub]     CrossRef

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