Cognitive decline affects millions of older adults worldwide and has a profound impact on individuals, families, and healthcare systems. Mild Cognitive Impairment (MCI) is often an early stage of Alzheimer's disease (AD), a condition for which there is currently no cure. Identifying individuals at risk at the earliest possible stage remains a major challenge. Traditional diagnostic approaches, such as laboratory biomarkers, neuroimaging, and neuropsychological testing, are usually performed at a single point in time and may fail to detect subtle or early changes in brain function and daily behavior.
Recent advances in wearable technology, such as smartwatches and smart rings, allow continuous and noninvasive monitoring of physiological and behavioral patterns in daily life. These devices can capture data related to physical activity, sleep, heart rate, and other parameters that may change before clear cognitive symptoms become evident. When combined with clinical, laboratory, neuropsychological, neuroimaging, and electroencephalographic (EEG) information, these data may help identify early signs of cognitive decline.
The objective of this study is to develop and validate models capable of detecting early indicators of MCI and early-stage Alzheimer's disease by integrating multiple sources of data, including clinical assessments, blood tests, neuropsychological evaluations, brain imaging, EEG recordings, and continuous data obtained from wearable devices.
This is an observational, analytical, single-center, prospective cohort study that will include 150 participants of both sexes, aged 65 years or older. Participants will be recruited from the Dementia Outpatient Clinic of Getúlio Vargas University Hospital (HUGV), through referrals from external neurologists, or via study dissemination on social media. To achieve the target sample size, up to 250 individuals may be approached using a non-probabilistic, convenience-based recruitment strategy. After providing informed consent, participants will undergo a comprehensive medical evaluation, standardized and validated neuropsychological testing, laboratory and imaging examinations, and EEG recording. Participants will also receive training to use wearable devices for continuous monitoring in their daily routines. A control group of older adults without cognitive impairment will be included for comparison.
All collected data will be securely stored in a centralized database and used to develop and validate analytical models aimed at identifying patterns associated with cognitive decline. The results of this study may support earlier identification of individuals at risk for MCI and Alzheimer's disease, help guide timely interventions, and potentially delay disease progression and early institutionalization, contributing to improved quality of life for older adults and their families.
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Participants will be identified primarily at the Dementia Clinic of the Araújo Lima Outpatient Clinic while waiting to see a neurologist. A clinical screening will be conducted by trained nurses to verify the presence of characteristics compatible with the eligibility criteria according to CRF02 Eligibility. In addition to these patients, individuals referred by physicians not affiliated with HUGV or who directly seek out the Clinical Research Center (CPC/HUGV) after learning about the study through social media, institutional publications, or news reports may also undergo the same pre-screening. Patients who meet the inclusion criteria will initially be invited to a presentation on the objectives, procedures, risks, and benefits of participating in the project. If they agree to participate voluntarily in the project, they will be presented with the Informed Consent Form (ICF) for subsequent signature, in accordance with current ethical guidelines.
Cohort
Prospective
Not listed
Accuracy of a Predictive Model for Identification of Mild Cognitive Impairment and Early Alzheimer's Disease
Evaluation of the accuracy, defined as the proportion of correctly classified participant, of a predictive model developed to identify Mild Cognitive Impairment and early-stage Alzheimer's Disease based on data collected from wearable technologies and digital questionnaires, using established clinical and standardized neuropsychological diagnoses as the reference standard.
Association Between Speech and Voice Features From Smartphone Audio and Clinical Group
Evaluation of the relationship between speech and voice features extracted from smartphone-based audio recordings including speech rate articulation pause patterns pitch variability and voice quality measures and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with cognitive and functional scores.
Association Between Wearable-Derived Sleep Metrics and Clinical Group
Evaluation of the association between wearable-derived sleep parameters including total sleep time, sleep efficiency, sleep latency, wake after sleep onset, and sleep stage distribution and clinical group classification cognitively unimpaired control, mild cognitive impairment, and early Alzheimer's disease, as well as correlations between sleep metrics and standardized cognitive and functional assessment scores.
Association Between Wearable-Derived Physical Activity Metrics and Clinical Group
Assessment of the relationship between wearable-derived physical activity metrics including daily step count, activity intensity levels, sedentary time, and energy expenditure and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with cognitive and functional performance measures.
Association Between Wearable-Derived Heart Rate and Heart Rate Variability Metrics and Clinical Group
Analysis of the association between wearable-derived heart rate and heart rate variability parameters including resting heart rate and time-domain and frequency-domain HRV indices and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, as well as correlations with cognitive and functional scores
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Getúlio Vargas University Hospital
Lead sponsor
Universidade Federal do Amazonas
Collaborator
Samsung Eletrônica da Amazônia Ltda
Collaborator
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