Background: The presence of subjective cognitive complaints (SCCs) is a core criterion for diagnosis of subjective cognitive decline (SCD); however, no standard procedure for distinguishing normative and non-normative SCCs has yet been established. Objective: To determine whether differentiation of participants with SCD according to SCC severity improves the validity of the prediction of progression in SCD and MCI and to explore validity metrics for two extreme thresholds of the distribution in scores in a questionnaire on SCCs. Methods: Two hundred and fifty-three older adults with SCCs participating in the Compostela Aging Study (CompAS) were classified as MCI or SCD at baseline. The participants underwent two follow-up assessments and were classified as cognitively stable or worsened. Severity of SCCs (low and high) in SCD was established by using two different percentiles of the questionnaire score distribution as cut-off points. The validity of these cut-off points for predicting progression using socio-demographic, health, and neuropsychological variables was tested by machine learning (ML) analysis. Results: Severity of SCCs in SCD established considering the 5th percentile as a cut-off point proved to be the best metric for predicting progression. The variables with the main role in conforming the predictive algorithm were those related to memory, cognitive reserve, general health, and the stability of diagnosis over time. Conclusion: Moderate to high complainers showed an increased probability of progression in cognitive decline, suggesting the clinical relevance of standard procedures to determine SCC severity. Our findings highlight the important role of the multimodal ML approach in predicting progression.

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Background: Cognitive training has been found to be effective in preventing and delaying cognitive decline in MCI and early dementia, and gains could be enhanced with transcranial electrical stimulation (tDCS). Cognitive-training applications (app) allow remote interventions, optimize the cost-benefit ratio, and a continuous monitoring. Most of apps are web-based applications but specific narrative cognitive-training video-games are scarce. Our RCT aims to compare memory changes after training using a web-based app (i.e., NeuronUp) and a narrative video-game (i.e., 'Following the traces of time') combining with tDCS or Sham (placebo). Method: Fifty-three participants with SCDs and MCI were randomized assigned to the five experimental groups (i.e., Active-control=11; NeuronUP-tDCS=14; NeuronUP-Sham=9; Videogame-tDCS=9; Videogame-Sham=10). NeuronUP groups sequentially performed 24 computerized activities (in 8 activities/session cycles) with increasing in complexity according actual performance. Videogame groups resolved puzzles with three difficulty levels integrated into a meaningful narrative plot allowing them to advance in the story. NeuronUP and Videogame mainly implemented memory and executive function training. The active-control group attended specific classes for older people (i.e., computing and mildfulness/philosophy). All interventions extended along 20 hours (5 weekly sessions of 120 min for 2 weeks) and participants simultaneously received 20' of tDCS/Sham in the last 6 sessions. Pre-Post assessments were accomplished to tests changes in measures of immediate verbal recall (Lists A and B of the RAVLT), and prospective memory (Forms 1 and 2 of the Event-Related Task; MPMT).Results: Repeat measures ANOVA showed that Immediate recall (Figure 1) significantly improved in post-intervention, F(1,48)=12.56, p=.001, ηp 2 =.207, but Group*Measurement interaction was not significant. Group factor differences only pointed out significant improvement in the tDCS-NeuronUP group compared to the Sham-Videogame group.

BackgroundThe validity of Subjective Cognitive Complaints (SCCs) from dyadic patterns to predict progression to dementia is not yet clear. Some studies suggest that the validity of informant report in predicting dementia increases as cognitive function and awareness of symptoms decline. Our aim was to compare validity of informant and participant reports, and its agreement, to predict progression to MCI and conversion to dementia.MethodA total of 226 participants from the CompAS study were longitudinally assessed (intervals 18–24 months). The sample consisted of SCD (198) and MCI (28) participants. SCCs from participants and informants were assessed at baseline, 1st and 2nd follow‐ups using the QAM questionnaire. Informant‐participant total score agreement (disagreement, over‐ and under‐estimation, were identically considered) were calculated. Logistic regressions were separately performed to distinguish SCD stables and participants progressing either from SCD to MCI/dementia or from MCI to dementia using SCCs reports from participants, informants and agreement as predictive variables.ResultsInformant report at baseline significantly predicted conversion to dementia from baseline to the 3rd follow‐up (β = .173; SE = .048; p<.001; OR = 1.189, CI = 1.082‐1.306), similarly to that observed for informant reports at 1st follow‐up to predict conversion from 1st to the 3rd follow‐up (β = .225; SE = .050; p = .001; OR = 1.252; CI = 1.135‐1.381), and at 2nd follow‐up to predict conversion from 2nd to the 3rd follow‐up (β = .271; SE = .083; p<.001; OR = 1.313; CI = 1.110–1.552) (see Table 1).Informant report at baseline also successfully predicted the progression to MCI from baseline to the 3rd follow‐up (β = .139; SE = .057; p = .017; OR = 1.49; CI = 1.025‐1.288), similarly to that observed for informant reports at 1st (β = .124; SE = .056; p = .026; OR = 1.132; CI = 1.015‐1.264), and the 2nd follow‐ups (β = .195; SE = .073; p = .008; OR = 1.216; CI = 1.053‐1.404) (see Table 2).Conversion to dementia (β = .113; SE = .048; p = .018; OR = 1.119; CI = 1.020‐1.229) and progression to MCI (β = .138; SE = .054; p = .011; OR = 1.148; CI = 1.032‐1.277) using Self‐report were only significantly predicted using 1st follow‐up reports.Agreement did not significantly predict the progression to MCI or dementia at any of measurement points.ConclusionsInformant report successfully predicted progression and conversion at any transition point. Self‐reports were only predictive at 1st follow‐up. Agreement did not significantly predict progression and conversion.

Objectives: To analyze the impacts of the restrictions implemented in LTCF during the COVID-19 pandemic on the psychological and functional status of older adults.Design: A retrospective multicentre study. We hypothesize that the negative effects of the restrictions will lead to a higher rate of decline between the measures taken immediately before and after the lockdown than between the two measures taken before the lockdown.Setting and participants: 365 participants recruited in four Spanish LTCFs in Galicia and Valencia.Methods: Impacts of restrictions on cognitive (MMSE), affective (GDS) and functional status (Barthel index, Tinetti) were analyzed by Linear Mixed Models with random intercepts, random slopes, and personal and contextual factors as covariates.Results: Social measures covaried significantly with the cognitive and functional status but did not predict longitudinal change. MMSE, Barthel index and Tinetti scores decreased significantly across pre-and post-lockdown measurement times, but only the Tinetti scores showed a specific impact of the restrictions.Conclusions and Implications: Only performance-based functional measures showed the real impact of restrictions. The findings highlight the importance of having data from several pre-lockdown measurements to enable identification of changes that can be causally attributed to the restrictions. The findings also support the resilience of older adults in mitigating the effect of the restrictions.