Use of Wearables for Identifying Factors Associated With Mild Cognitive Impairment and Early-Stage Alzheimer's Disease

Overview

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.

Mild Cognitive Impairment (MCI) and early-stage Alzheimer's disease (AD) represent critical stages along the continuum of neurodegenerative cognitive disorders. Understanding these conditions is essential for enabling early diagnosis, implementing targeted therapeutic strategies, and developing comprehensive care plans aimed at improving patient and caregiver quality of life and, when possible, delaying neurodegenerative progression. The global burden of dementia is rapidly increasing and poses an urgent public health challenge. Projections estimate that approximately 115 million people worldwide will be living with dementia by 2050. In the United States alone, the prevalence of MCI is expected to exceed 21 million individuals, with nearly 14 million cases of Alzheimer's disease by 2060. These figures highlight the pressing need for advances in translational neuroscience, particularly in early diagnostic strategies and preventive, personalized approaches to care. In this context, wearable technologies have emerged as promising tools for continuous, noninvasive monitoring of physiological and behavioral parameters in real-world settings. Devices such as smartwatches and smart rings enable longitudinal collection of data related to heart rate variability, sleep architecture, physical activity, and circadian rhythms-factors increasingly associated with cognitive decline. Emerging evidence suggests that changes in these parameters may precede or accompany early cognitive impairment. When integrated with advanced artificial intelligence (AI) methods, these data may reveal subtle patterns indicative of early neurodegenerative processes that precede overt clinical symptoms. Combining wearable-derived data with neuroimaging and electroencephalography (EEG) has the potential to generate more robust diagnostic models. While wearables capture behavioral and physiological dynamics, magnetic resonance imaging (MRI) provides detailed information on brain structure and function, and EEG enables analysis of neural oscillations and connectivity patterns linked to early cognitive impairment. The integration of these multimodal data streams represents a complex methodological challenge, requiring advanced computational frameworks capable of handling heterogeneous, high-dimensional datasets. Advances in artificial intelligence and machine learning enable the integration of multimodal data and the identification of complex patterns not detectable through traditional analytical approaches. Multimodal data fusion strategies that combine wearable-derived physiological and behavioral features with neuropsychological, neuroimaging, and EEG-derived variables may enhance diagnostic performance and support individualized risk stratification. This is an observational, analytical, single-center, prospective cohort study designed to integrate multimodal clinical and digital data for the development and validation of AI-based models aimed at early detection of MCI and early-stage Alzheimer's disease. The study is conducted at Getúlio Vargas University Hospital (HUGV), a tertiary academic center and regional reference for high-complexity care, in collaboration with the Center for Research and Development in Electronic and Information Technology (CETELI/UFAM), which provides expertise in intelligent systems, data infrastructure, and AI model development. Machine learning and deep learning approaches are applied to identify patterns associated with cognitive decline, support early detection of MCI and early AD, and enable risk stratification. Model development and validation prioritize robustness, interpretability, and potential clinical applicability. This study aims to support the development of scalable, accessible, and noninvasive AI-based tools for early detection of cognitive impairment. By leveraging continuous wearable monitoring and multimodal data integration, the proposed approach may contribute to earlier diagnosis, improved risk stratification, and more timely intervention strategies for individuals at risk of Alzheimer's disease.