Prevention of dementia using mobile phone applications (PRODEMOS): a multinational, randomised, controlled effectiveness-implementation trial.

BACKGROUND: The expected increase of dementia prevalence in the coming decades will mainly be in low-income and middle-income countries and in people with low socioeconomic status in high-income countries. This study aims to reduce dementia risk factors in underserved populations at high-risk using a coach-supported mobile health (mHealth) intervention. METHODS: This open-label, blinded endpoint, hybrid effectiveness-implementation randomised controlled trial (RCT) investigated whether a coach-supported mHealth intervention can reduce dementia risk in people aged 55-75 years of low socioeconomic status in the UK or from the general population in China with at least two dementia risk factors. The primary effectiveness outcome was change in cardiovascular risk factors, ageing, and incidence of dementia (CAIDE) risk score from baseline to after 12-18 months of intervention. Implementation outcomes were coverage, adoption, sustainability, appropriateness, acceptability, fidelity, feasibility, and costs assessed using a mixed-methods approach. All participants with complete data on the primary outcome, without imputation of missing outcomes were included in the analysis (intention-to-treat principle). This trial is registered with ISRCTN, ISRCTN15986016, and is completed. FINDINGS: Between Jan 15, 2021, and April 18, 2023, 1488 people (601 male and 887 female) were randomly assigned (734 to intervention and 754 to control), with 1229 (83%) of 1488 available for analysis of the primary effectiveness outcome. After a mean follow-up of 16 months (SD 2·5), the mean CAIDE score improved 0·16 points in the intervention group versus 0·01 in the control group (mean difference -0·16, 95% CI -0·29 to -0·03). 1533 (10%) invited individuals responded; of the intervention participants, 593 (81%) of 734 adopted the intervention and 367 (50%) of 734 continued active participation throughout the study. Perceived appropriateness (85%), acceptability (81%), and fidelity (79%) were good, with fair overall feasibility (53% of intervention participants and 58% of coaches), at low cost. No differences in adverse events between study arms were found. INTERPRETATION: A coach-supported mHealth intervention is modestly effective in reducing dementia risk factors in those with low socioeconomic status in the UK and any socioeconomic status in China. Implementation is challenging in these populations, but those reached actively participated. Whether this intervention will result in less cognitive decline and dementia requires a larger RCT with long follow-up. FUNDING: EU Horizon 2020 Research and Innovation Programme and the National Key R&D Programmes of China. TRANSLATION: For the Mandarin translation of the abstract see Supplementary Materials section.

Amyloid-PET imaging predicts functional decline in clinically normal individuals.

BACKGROUND: There is good evidence that elevated amyloid-ß (Aß) positron emission tomography (PET) signal is associated with cognitive decline in clinically normal (CN) individuals. However, it is less well established whether there is an association between the Aß burden and decline in daily living activities in this population. Moreover, Aß-PET Centiloids (CL) thresholds that can optimally predict functional decline have not yet been established. METHODS: Cross-sectional and longitudinal analyses over a mean three-year timeframe were performed on the European amyloid-PET imaging AMYPAD-PNHS dataset that phenotypes 1260 individuals, including 1032 CN individuals and 228 participants with questionable functional impairment. Amyloid-PET was assessed continuously on the Centiloid (CL) scale and using Aß groups (CL < 12 = Aß-, 12 ≤ CL ≤ 50 = Aß-intermediate/Aß± , CL > 50 = Aß+). Functional abilities were longitudinally assessed using the Clinical Dementia Rating (Global-CDR, CDR-SOB) and the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q). The Global-CDR was available for the 1260 participants at baseline, while baseline CDR-SOB and A-IADL-Q scores and longitudinal functional data were available for different subsamples that had similar characteristics to those of the entire sample. RESULTS: Participants included 765 Aß- (61%, Mdnage = 66.0, IQRage = 61.0-71.0; 59% women), 301 Aß± (24%; Mdnage = 69.0, IQRage = 64.0-75.0; 53% women) and 194 Aß+ individuals (15%, Mdnage = 73.0, IQRage = 68.0-78.0; 53% women). Cross-sectionally, CL values were associated with CDR outcomes. Longitudinally, baseline CL values predicted prospective changes in the CDR-SOB (bCL*Time = 0.001/CL/year, 95% CI [0.0005,0.0024], p = .003) and A-IADL-Q (bCL*Time = -0.010/CL/year, 95% CI [-0.016,-0.004], p = .002) scores in initially CN participants. Increased clinical progression (Global-CDR > 0) was mainly observed in Aß+ CN individuals (HRAß+ vs Aß- = 2.55, 95% CI [1.16,5.60], p = .020). Optimal thresholds for predicting decline were found at 41 CL using the CDR-SOB (bAß+ vs Aß- = 0.137/year, 95% CI [0.069,0.206], p < .001) and 28 CL using the A-IADL-Q (bAß+ vs Aß- = -0.693/year, 95% CI [-1.179,-0.208], p = .005). CONCLUSIONS: Amyloid-PET quantification supports the identification of CN individuals at risk of functional decline. TRIAL REGISTRATION: The AMYPAD PNHS is registered at www.clinicaltrialsregister.eu with the EudraCT Number: 2018-002277-22.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.