A comprehensive analysis of digital health-focused Living Labs: innovative approaches to dementia.

The increasing prevalence of dementia demands innovative solutions; however, existing technological products often lack tailored support for individuals living with this condition. The Living Lab approach, as a collaborative innovation method, holds promise in addressing this issue by actively involving end-users in the design and development of solutions adapted to their needs. Despite this potential, the approach still faces challenges due to its lack of recognition as a research methodology and its absence of tailored guidelines, particularly in dementia care, prompting inquiries into its effectiveness. This narrative review aims to fill this gap by identifying and analysing digital health Living Labs focusing on dementia solutions. Additionally, it proposes guidelines for enhancing their operations, ensuring sustainability, scalability, and greater impact on dementia care. Fifteen Living Labs were identified and analyzed. Based on trends, best practices, and literature, the guidelines emphasize user engagement, interdisciplinary collaboration, technological infrastructure, regulatory compliance, transparent innovation processes, impact measurement, sustainability, scalability, dissemination, and financial management. Implementing these guidelines can enhance the effectiveness and long-term impact of Living Labs in dementia care, fostering new collaborations globally.

Do informant-reported subjective cognitive complaints predict progression to mild cognitive impairment and dementia better than self-reported complaints in old adults? A meta-analytical study.

BACKGROUND: Subjective cognitive complaints (SCCs) are considered a risk factor for objective cognitive decline and conversion to dementia. The aim of this study was to determine whether self-reported or informant-reported SCCs best predict progression to mild cognitive impairment (MCI) and/or dementia. METHODS: We reviewed prospective longitudinal studies of Cognitively Unimpaired (CU) older adults with self-reported and informant-reported SCCs at baseline, assessed by questions or questionnaires that considered the transition to MCI and/or dementia. A random-effects meta-analysis was performed to obtain pooled estimates and 95% CIs. RESULTS: Both self-reported and informant-reported SCCs are associated with an elevated risk of transition from CU to MCI and/or dementia. The association appears stronger and more robust for informant-reported data [1.38, with a 95% CI of 1.16 -1.64, p < 0.001] than for self-reported data [1.27 (95% CI 1.06 - 1.534, p = 0.011]. CONCLUSIONS: Our results suggest that corroborated information from one informant could provide important details for distinguishing between normal aging and clinical states.

Relevance of Complaint Severity in Predicting the Progression of Subjective Cognitive Decline and Mild Cognitive Impairment: A Machine Learning Approach.

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.