Mild Dementia Decision Support from AI-based Digital Biomarkers using Mobile Playful Exercises with High Adherence

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Conference Proceedings
Authors: Martin PszeidaLucas PalettaSilvia RusseggerThomas OrgelSandra DraxlerMarisa KoiniMartin BergerMaria FellnerStephan SpatSandra SchuesslerJulia ZuschneggBernhard StroblKarin PloderMaria M Hofmarcher Holzhacker

Abstract: Early detection of cognitive decline and monitoring of cognitive functioning in mild dementia are fundamental for timely adaptation of lifestyle and intervention strategies. The development of digital dementia biomarkers through playful exercises with high adherence rate was a key objective of the national project multimodAAL (no. FFG 875345). The results of a study on computer-based cognitive and physical training (CCPT) in persons diagnosed with mild Alzheimer’s disease (PwAD) are presented.Method: Tablet-PC-based intervention was applied within 6 months in Austria, engaging PwADs living at home by means of playful multimodal training and activation (n=11; female N=8, male N=3; age M=76.6 / SD=9.2 years, MMSE score M=21.50 / SD=4.41). PwADs interacted with a prototypical version of the BRAINMEE app that included a suite of cognitive exercises (puzzle, pairs, text gap filling) based on audiovisual information. The playful training app was introduced and assisted by mobile care professionals with weekly visits, however, PwDs played alone between these visits.Result: PwADs applied training with high adherence, finalizing M=72 (M=32) digital exercises per day within the first (last) month of the study. Duration of using exercise type ‘outsider’(p=.028*) and ‘quiz’ (p=.001**), averaged about 2 week figures, each provided statistically significant correlations (Spearman) with MMSE test scores, as well as ‘spot-the-difference’ (p=.003**) with Trail Making Test A, ‘outsider’ (p=.005**) with Auditory Verbal Learning Test (AVLT), respectively. A neural network (Support Vector Machine, linear kernel, 11-fold cross validation) using duration of use of ‘quiz’, ’outsider’ and ‘hearing’ (guessing animal sounds) as input data resulted in M=2.16 absolute error in MMSE score estimation on test data.Conclusion: The work outlined within the Austrian study on digital biomarker development indicates successful steps towards daily use of cognitive assessment using highly adherent playful training. The playful training app is applied in the European project MARA (no. FFG 886427) to enable continuous estimates of MCI’s mental state over time. The app was very well accepted by both PwADs and persons with MCI. It offers with its pervasive mental assessment tool a large potential for future long-term monitoring in dementia prevention, early detection as well as in numerous dementia care services.

Keywords: Decision Support, Mild Dementia, Digital Biomarkers, Mobile computing

DOI: 10.54941/ahfe1003970

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