000131782 001__ 131782 000131782 005__ 20241125101124.0 000131782 0247_ $$2doi$$a10.1186/s12911-023-02294-1 000131782 0248_ $$2sideral$$a137024 000131782 037__ $$aART-2023-137024 000131782 041__ $$aeng 000131782 100__ $$aVarela-Aldás, José Luis 000131782 245__ $$aMemory rehabilitation during the COVID-19 pandemic 000131782 260__ $$c2023 000131782 5060_ $$aAccess copy available to the general public$$fUnrestricted 000131782 5203_ $$aBackground Loss of cognitive and executive functions is a problem that affects people of all ages. That is why it is important to perform exercises for memory training and prevent early cognitive deterioration. The aim of this work was to compare the cognitive performance of the participants after an intervention by using two mnemonic techniques to exercise memory functions (paired-associate learning and method of loci). Methods A longitudinal study was conducted with 21 healthy participants aged 18 to 55 years over a 2-month period. To assess the impact of this proposal, the NEUROPSI brief battery cognitive assessment test was applied before and after the intervention. In each session, a previous cognitive training was carried out using the paired-associate learning technique, to later perform a task based on the loci method, all from a smart device-based application. The accuracy response and reaction times were automatically collected in the app. Results After the intervention, a statistically significant improvement was obtained in the neuropsychological assessment (NEUROPSI neuropsychological battery) reflected by the Wilcoxon paired signed-rank test (P < .05). Conclusion The task based on the method of loci also reflected the well-known age-related effects common to memory assessment tasks. Episodic memory training using the method of loci can be successfully implemented using a smart device app. A stage-based methodological design allows to acquire mnemic skills gradually, obtaining a significant cognitive improvement in a short period of time. 000131782 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000131782 590__ $$a3.3$$b2023 000131782 592__ $$a1.002$$b2023 000131782 591__ $$aMEDICAL INFORMATICS$$b19 / 44 = 0.432$$c2023$$dQ2$$eT2 000131782 593__ $$aComputer Science Applications$$c2023$$dQ1 000131782 593__ $$aHealth Policy$$c2023$$dQ1 000131782 593__ $$aHealth Informatics$$c2023$$dQ1 000131782 594__ $$a7.2$$b2023 000131782 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000131782 700__ $$aBuele, Jorge 000131782 700__ $$aPérez, Doris 000131782 700__ $$0(orcid)0000-0002-9408-1280$$aPalacios-Navarro, Guillermo$$uUniversidad de Zaragoza 000131782 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000131782 773__ $$g23, 195 (2023), 1-14$$pBMC Medical Informatics and Decision Making$$tBMC Medical Informatics and Decision Making$$x1472-6947 000131782 8564_ $$s2103908$$uhttps://zaguan.unizar.es/record/131782/files/texto_completo.pdf$$yVersión publicada 000131782 8564_ $$s2544815$$uhttps://zaguan.unizar.es/record/131782/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000131782 909CO $$ooai:zaguan.unizar.es:131782$$particulos$$pdriver 000131782 951__ $$a2024-11-22-11:57:16 000131782 980__ $$aARTICLE