Abstract
Computerized cognitive training allows real-time tracking of performance metrics that may serve as digital biomarkers. This study investigated the value of a novel in-game digital biomarker, RTACC (Reaction Time-Accuracy Correlation), the correlation between reaction time and accuracy, using data from 130 participants with mild cognitive impairment enrolled in the intervention arm of the SUPERBRAIN-MEET randomized controlled trial. Participants underwent a 24-week multi-domain intervention, consisting of computerized cognitive training, physical exercise, nutritional education, vascular/metabolic risk management, and motivation enhancement. RTACC was derived from task-level RT and accuracy and examined in relation to cognitive and biomarker outcomes. Linear regression analysis revealed a significant association between RTACC and changes in Repeatable Battery for the Assessment of Neuropsychological Status scores from baseline to 24 weeks (beta coefficient = -11.90 ± 3.78, T = − 3.14, P = 0.002). RTACC also showed a marginal effect on changes in brain-derived neurotrophic factor levels (beta coefficient = − 3.13 ± 1.64, P = 0.057). Logistic regression analysis demonstrated that RTACC combined with clinical information identified good responders with an area under the receiver operating characteristic curve of 0.73 (95% CI: 0.62–0.84). These findings suggest that this in-game digital biomarker (RTACC) may help identify individuals likely to benefit from multi-domain intervention.
Data availability
The data used in this study were obtained from the SUPERBRAIN-MEET trial. Due to privacy and ethical restrictions, the datasets are not publicly available. Access to the dataset is available from the corresponding author of SUPEPBRAIN-MEET tiral on reasonable request.
References
Albert, M. S. et al. The diagnosis of mild cognitive impairment due to alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Focus 11 (1), 96–106 (2013).
Tahami Monfared, A. A. et al. Estimating transition probabilities across the alzheimer’s disease continuum using a nationally representative Real-World database in the united States. Neurol. Therapy. 12 (4), 1235–1255 (2023).
Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the lancet commission. Lancet 396 (10248), 413–446 (2020).
Ngandu, T. et al. A 2-year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet 385 (9984), 2255–2263 (2015).
Kivipelto, M. et al. A global approach to risk reduction and prevention of dementia. Alzheimer’s Dement. 16 (7), 1078–1094 (2020).
Moon, S. Y. et al. Facility-based and home-based multidomain interventions including cognitive training, exercise, diet, vascular risk management, and motivation for older adults: A randomized controlled feasibility trial. Aging 13 (12), 15898 (2021).
Cho, S. H. et al. SoUth Korean study to prevent cognitive impairment and protect BRAIN health through multidomain interventions via facE-to-facE and video communication platforms in mild cognitive impairment (SUPERBRAIN-MEET): protocol for a multicenter randomized controlled trial. Dement. Neurocognitive Disorders. 23 (1), 30 (2024).
Moon, S. Y. et al. South Korean study to prevent cognitive impairment and protect brain health through multidomain interventions via face-to‐face and video communication platforms in mild cognitive impairment (SUPERBRAIN‐MEET): A randomized controlled trial. Alzheimer’s Dement. 21 (2), e14517 (2025).
Stamatis, C. A. et al. Real-time cognitive performance metrics derived from a digital therapeutic for inattention predict ADHD-related clinical outcomes: replication across three independent trials of AKL-T01. Translational Psychiatry. 14 (1), 328 (2024).
Zhang, P. et al. Initial performance predicts improvements in computerized cognitive training: evidence from a selective attention task. PsyCh J. 10 (5), 742–750 (2021).
Walhovd, K. B. et al. Within-session verbal learning slope is predictive of lifespan delayed recall, hippocampal volume, and memory training benefit, and is heritable. Sci. Rep. 10 (1), 21158 (2020).
Myers, C. E., Interian, A. & Moustafa, A. A. A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences. Front. Psychol. 13, 1039172 (2022).
Retzler, J. et al. Using drift diffusion modeling to understand inattentive behavior in preterm and term-born children. Neuropsychology 34 (1), 77–87 (2020).
Alfers, T. et al. Assessing the speed–accuracy tradeoff in psychological testing using experimental manipulations. Educ. Psychol. Meas. 85 (2), 357–383 (2025).
Johnson, M. A., Simonian, N. & Reggente, N. Lightening the Mind with audiovisual stimulation as an accessible alternative to breath-focused meditation for mood and cognitive enhancement. Sci. Rep. 14 (1), 25553 (2024).
Chalmers, J. et al. World Health Organization-International Society of Hypertensive Guidelines for the management of hypertension. Guidelines subcommittee of the World Health Organization. Clinical and Experimental Hypertension. 21(5–6), 1009–1060 (1999). (1999).
Association, A. D. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 26 (1), S5–S20 (2003).
Deary, I. J., Johnson, W. & Starr, J. M. Are processing speed tasks biomarkers of cognitive aging? Psychol. Aging. 25 (1), 219 (2010).
Rousseeuw, P. J. & Hubert, M. Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Min. Knowl. Discovery. 1 (1), 73–79 (2011).
Liu, L. et al. Dose–response relationship between computerized cognitive training and cognitive improvement. NPJ Digit. Med. 7 (1), 214 (2024).
Singh, A. et al. Deep learning-based predictions of older adults’ adherence to cognitive training to support training efficacy. Front. Psychol. 13, 980778 (2022).
Juras, L., Hromatko, I. & Vranic, A. Parietal alpha and theta power predict cognitive training gains in middle-aged adults. Front. Aging Neurosci. 17, 1530147 (2025).
Krebs, C. et al. Application of eye tracking in puzzle games for adjunct cognitive markers: pilot observational study in older adults. JMIR Serious Games 9(1), e24151 (2021).
Chittka, L., Skorupski, P. & Raine, N. E. Speed–accuracy tradeoffs in animal decision making. Trends Ecol. Evol. 24 (7), 400–407 (2009).
Ratcliff, R., Thapar, A. & McKoon, G. The effects of aging on reaction time in a signal detection task. Psychol. Aging. 16 (2), 323 (2001).
Peters, J. & D’Esposito, M. The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controls. PLoS Comput. Biol. 16(4), e1007615 (2020).
Zhang, J. & Rowe, J. B. Dissociable mechanisms of speed–accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model. Front. NeuroSci. 8, 69 (2014).
Ratcliff, R., Thapar, A. & McKoon, G. Aging, practice, and perceptual tasks: A diffusion model analysis. Psychol. Aging. 21 (2), 353 (2006).
Logan, G. D. Toward an instance theory of automatization. Psychol. Rev. 95 (4), 492–527 (1988).
Everard, G. et al. A self-adaptive serious game to improve motor learning among older adults in immersive virtual reality: Short-term longitudinal pre-post study on retention and transfer. JMIR Aging. 8, e64004 (2025).
FDA–NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and Other Tools) Resource (Food and Drug Administration (US), 2016).
Bekinschtein, P., Cammarota, M. & Medina, J. H. BDNF and memory processing. Neuropharmacology 76, 677–683 (2014).
Nicastri, C. M. et al. BDNF mediates improvement in cognitive performance after computerized cognitive training in healthy older adults. Alzheimer’s Dementia: Translational Res. Clin. Interventions 8(1), e12337 (2022).
Damirchi, A., Hosseini, F. & Babaei, P. Mental training enhances cognitive function and BDNF more than either physical or combined training in elderly women with MCI: A small-scale study. American Journal of Alzheimer’s Disease & Other Dementias. 33(1), 20–29 (2018).
Roheger, M., Kessler, J. & Kalbe, E. Structured cognitive training yields best results in healthy older adults, and their ApoE4 state and baseline cognitive level predict training benefits. Cogn. Behav. Neurol. 32 (2), 76–86 (2019).
Qian, M. et al. Predictors of improvement after cognitive training in mild cognitive impairment: insights from the cognitive training and neuroplasticity in mild cognitive impairment trial. Alzheimer Disease Assoc. Disorders. 38 (3), 227–234 (2024).
Funding
This research was supported by grants from the National Research Council of Science and Technology (NST) Aging Convergence Research Center (CRC22014-600) and Institute of Information and Communications Technology Planning and Evaluation (IITP) (2022-0-00448/RS-2022-II220448) funded by the Ministry of Science and ICT, Republic of Korea, and from the Korea Dementia Research Project through the Korea Dementia Research Center (KDRC) funded by the Ministry of Health & Welfare and the Ministry of Science and ICT, Republic of Korea (RS-2021-KH112062 and RS-2021-KH112636).
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H.R.K., J.H.P., H.S.K., and H.R.N. conceptualized and designed the study. H.R.K. J.H.P., H.S.K., and H.R.N. performed the data analysis and interpretation. J.H.P. drafted the manuscript. H.R.K. J.H.P., H.S.K., H.R.N., and S.H.C. revised the manuscript. S.H.C., J.H.J., S.Y.M., Y.K.P., C.H.H., H.R.N., H.S.K., and S.H.C. contributed to the clinical data collection and patient diagnosis; they provided clinical advice throughout the study. All authors reviewed and approved the manuscript’s final version.
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H.R.K and J.H.P. are employees of Rowan Inc. but were not involved in the original SUPERBRAIN-MEET trial; their role in the present study was limited to secondary data analysis. S.H.C., J.H.J., S.Y.M., Y.K.P., C.H.H., and H.R.N. were investigators in the original trial and are shareholders of Rowan Inc. All clinical assessments and data collection were conducted independently by investigators at the participating hospitals. Employees of Rowan Inc. were not involved in participant recruitment, clinical assessments, data management, or outcome evaluation. J.H.J. and S.H.C. have served as consultants for PeopleBio Co. Ltd. C.H.H. has received research funding from Eisai Korea Inc., and S.H.C. is the head of Exercowork. The remaining authors declare no competing interests.
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Park, J.H., Kim, H.S., Choi, S.H. et al. Developing digital biomarker for predicting cognitive response to multi-domain intervention. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37123-8
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DOI: https://doi.org/10.1038/s41598-026-37123-8
