Lifestyle and incident dementia: A COSMIC individual participant data meta­analysis.

INTRODUCTION: The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis. RESULTS: A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. HIGHLIGHTS: A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.

Increasing knowledge on dementia risk reduction in the general population: Results of a public awareness campaign.

Strategies to reduce dementia risk are needed to minimize the burden of this growing public health concern. Most individuals are not aware that dementia risk reduction is possible, let alone how this could be achieved. Health education, such as public awareness campaigns on the topic of dementia risk reduction, can meet this need. A public health campaign (including social media and offering an online individual risk assessment tool) was carried out over a 7-month period in Flanders, Belgium. Impact was assessed in two independent online surveys, before (n = 1003) and after the campaign (n = 1008), in representative samples of adults aged 40-75 years. Questions regarding personal needs, wishes and barriers were also included. After the campaign, more individuals (10.3%) were aware that dementia risk reduction is possible than before the campaign, and more individuals correctly identified 10 out of 12 surveyed modifiable dementia risk and protective factors. However, no differences were observed in low-educated individuals. Further, specific differences in potential needs, wishes and barriers for future campaigns or interventions were observed between demographic strata. The majority of the respondents (89%) indicated that they would welcome more information on improving their brain-health. More than half (54%) also believed that they lacked the necessary knowledge to make brain-healthy behavior changes. In conclusion, effective public awareness campaigns on the topic of dementia risk reduction are feasible and timely, given the state of the evidence. Special efforts need to be made to develop effective campaigns, tailored towards low-educated individuals.

Multidomain Dementia Risk Reduction in Primary Care is Feasible: A Proof-of-concept study.

Background: Dementia risk reduction is a public health priority, but interventions that can be easily implemented in routine care are scarce. Objective: To evaluate the feasibility of integrating dementia risk reduction in regular consultations in primary care and the added value of a dedicated smartphone app ('MyBraincoach'). Methods: 188 participants (40-60 years), with modifiable dementia risk factors were included from ten Dutch general practices in a cluster-randomized trial (NL9773, 06/10/2021). Practices were randomly allocated (1 : 1) to provide a risk-reduction consultation only or to additionally provide the app. During the consultation, participants learned about dementia risk reduction and how to improve their risk profile. The app group received daily microteaching-notifications about their personally relevant risk factors. Feasibility was evaluated after 3 months using questionnaires assessing knowledge on dementia risk reduction and health behavior change. The primary outcome was change in the validated "LIfestyle for BRAin health" (LIBRA) score. In-depth interviews were conducted with participants and primary care providers (PCPs). Results: The interventions were positively perceived, with 72.0% finding the consultation informative and 69.2% considering the app useful. Drop-out was low (6.9%). LIBRA improved similarly in both groups, as did Mediterranean diet adherence and body mass index. Knowledge of dementia risk reduction increased, but more in the app group. Interviews provided insight in participants' and PCPs' needs and wishes. Conclusions: Integrating dementia risk reduction in primary care, supported by a smartphone app, is a viable approach towards dementia risk reduction. Larger trials are needed to establish (cost-)effectiveness.

The Reliability and Clinical Validation of Automatically-Derived Verbal Memory Features of the Verbal Learning Test in Early Diagnostics of Cognitive Impairment.

BACKGROUND: Previous research has shown that verbal memory accurately measures cognitive decline in the early phases of neurocognitive impairment. Automatic speech recognition from the verbal learning task (VLT) can potentially be used to differentiate between people with and without cognitive impairment. OBJECTIVE: Investigate whether automatic speech recognition (ASR) of the VLT is reliable and able to differentiate between subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS: The VLT was recorded and processed via a mobile application. Following, verbal memory features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to distinguish between participants with SCD versus MCI/dementia. RESULTS: The ICC for inter-rater reliability between the clinical and automatically derived features was 0.87 for the total immediate recall and 0.94 for the delayed recall. The full model including the total immediate recall, delayed recall, recognition count, and the novel verbal memory features had an AUC of 0.79 for distinguishing between participants with SCD versus MCI/dementia. The ten best differentiating VLT features correlated low to moderate with other cognitive tests such as logical memory tasks, semantic verbal fluency, and executive functioning. CONCLUSIONS: The VLT with automatically derived verbal memory features showed in general high agreement with the clinical scoring and distinguished well between SCD and MCI/dementia participants. This might be of added value in screening for cognitive impairment.

The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting.

OBJECTIVE: To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored. METHOD: We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants. RESULTS: The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning. CONCLUSION: The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice.

Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study.

BACKGROUND: In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. OBJECTIVE: This is a comparative performance study of commercial image recognition platforms. METHODS: A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. RESULTS: Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. CONCLUSIONS: Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods.