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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Mild Cognitive Impairment; Alzheimer's Disease; Alzheimer's Disease Dementia

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: Robson Luís Oliveira de Amorim, PhD; Phone Number: +55 92 99403-4101; Email: robsonamorim@ufam.edu.br
• Study Contact Backup: Name: Eliana Brasil Alves, Master of Health Sciences; Phone Number: +55 92 99603-2236; Email: eliana.brasil@ufam.edu.br

Study Locations

• Brazil
  • Amazonas
    • Manaus, Amazonas, Brazil, 69020-170
      • Getúlio Vargas University Hospital
      • Contact: Plinío José Cavalcante Monteiro; Phone Number: +55 (92) 3305-4813; Email: plinio.monteiro@ebserh.gov.br

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

Inclusion criteria Age 65 years or older; Clinical suspicion of MCI, early AD Patients who are conscious, oriented, and able to respond to questionnaires; Ability and willingness to use wearable devices for 30 days; Signature of the Informed Consent Form (ICF)

Non-inclusion criteria Advanced dementia Severe uncontrolled psychiatric disorder Severe visual or hearing impairment that prevents communication Severe physical or cognitive inability to use wearables Pacemaker incompatible with the devices

Exclusion criteria Unconfirmed diagnosis of MCI or early AD; Voluntary withdrawal at any stage of the study; Death during the research period; Inappropriate use or non-adherence to the use of wearable devices Refusal or inability to fully perform the requested tests and exams.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Control Group; Individuals without significant abnormalities on imaging exams, biomarkers, or neuropsychological assessment.
Mild Cognitive Impairment (MCI); Participants with abnormalities in cognitive questionnaire scores and/or laboratory biomarkers, but without significant functional impairment.
Early-stage Alzheimer's disease (eAD); Individuals with altered laboratory and/or imaging results compatible with the initial phase of neurodegeneration.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Up to 30 days of continuous wearable monitoring

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Up to 30 days
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.	Up to 30 days
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.	Up to 30 days
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	Up to 30 days
Difference in Timed Up and Go Performance Across Clinical Groups	Comparison of Timed Up and Go test performance across clinical groups control, mild cognitive impairment, and early Alzheimer's disease and evaluation of correlations between TUG performance and wearable-derived physical activity and sleep metrics.	Baseline assessment Day 1 (up to 30 days)
Association Between Facial Expression Features From Smartphone Video and Clinical Group	Assessment of the association between facial expression features extracted from smartphone-based video recordings including facial action units emotional expressivity and movement dynamics and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease.	Up to 30 days
Association Between Smartphone-Derived Eye Movement Features and Clinical Group	Assessment of the association between eye movement and ocular tracking features derived from smartphone-based assessments including saccade metrics fixation stability and pursuit performance and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease.	Up to 30 days
Association Between Electroencephalography (EEG) Classification and Clinical Group	Evaluation of the association between electroencephalography classification normal versus abnormal and type of abnormality and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, as well as correlations between EEG findings and wearable-derived sleep and physical activity metrics.	Up to 30 days
Association Between Non-Invasive Intracranial Compliance Metrics and Clinical Group	Assessment of the relationship between non-invasive intracranial compliance metrics obtained using the Brain4Care device including P2 P1 ratio and waveform morphology and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with wearable-derived sleep and physical activity metrics.	Up to 30 days
Association Between Carotid and Vertebral Doppler Findings and Clinical Group	Evaluation of the association between carotid and vertebral Doppler ultrasound findings including intima-media thickness and presence or degree of arterial stenosis and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease.	Up to 30 days
Feasibility and Adherence to Continuous Wearable Monitoring	Assessment of feasibility and participant adherence to continuous wearable monitoring, including the percentage of days with valid wearable data, average daily wear time, and study dropout rate.	Up to 30 days
Association Between Cognitive and Neuropsychological Performance and Clinical Group	Evaluation of the relationship between global and domain-specific cognitive and neuropsychological performance, including screening measures and standardized neuropsychological tests assessing memory, executive function, attention, language, processing speed, and visuospatial abilities, and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease.	Up to 30 days
Association Between Functional, Neuropsychiatric, and Behavioral Measures and Clinical Group	Assessment of the association between functional capacity, neuropsychiatric symptoms, and behavioral measures, including instrumental activities of daily living, anxiety, and depressive symptoms, and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with cognitive and digital biomarkers.	Up to 30 days
Association Between Blood-Based Biomarkers and Clinical Group	Evaluation of the relationship between blood-based biomarkers, including routine laboratory parameters and plasma biomarkers of Alzheimer's disease pathology, and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with cognitive, functional, and neuroimaging measures.	Up to 30 days
Association Between Neurophysiological Measures and Clinical Group	Assessment of the association between neurophysiological features, including electroencephalographic patterns and noninvasive intracranial compliance waveform metrics, and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease, including correlations with cognitive and neuroimaging outcomes.	Up to 30 days
Association Between Neuroimaging Features and Clinical Group	Evaluation of the relationship between structural neuroimaging and cerebrovascular features, including cortical and medial temporal atrophy, white matter disease, microbleeds, infarcts, volumetric and cortical thickness measures, and clinical group classification control, mild cognitive impairment, and early Alzheimer's disease.	Up to 30 days

Collaborators and Investigators

• Sponsor: Getúlio Vargas University Hospital
• Collaborators: Universidade Federal do Amazonas; Samsung Eletrônica da Amazônia Ltda
• Investigators: Study Director: Marly Guimarães Fernandes Costa, Federal University of Amazonas; Principal Investigator: Robson Luís Oliveira de Amorim, Getúlio Vargas University Hospital; Study Chair: Caio Eduardo Rodrigues Falcão, Getúlio Vargas University Hospital; Study Director: Eliana Brasil Alves, Getúlio Vargas University Hospital; Study Director: Cícero Ferreira Fernandes Costa Filho, Federal University of Amazonas

Study record dates

Study Major Dates

• Study Start (Estimated): March 1, 2026
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: January 29, 2026
• First Submitted That Met QC Criteria: January 29, 2026
• First Posted (Actual): February 5, 2026

Study Record Updates

• Last Update Posted (Actual): February 5, 2026
• Last Update Submitted That Met QC Criteria: January 29, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: Alzheimer's disease; Mild Cognitive Impairment; Machine Learning; Wearable Devices; Alzheimer's Disease Dementia
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Mental Disorders; Neurocognitive Disorders; Cognition Disorders; Dementia; Tauopathies; Neurodegenerative Diseases; Cognitive Dysfunction; Alzheimer Disease
• Other Study ID Numbers: 93783525.5.0000.5020; Convênio No 02/2025 (Other Grant/Funding Number: SAMSUNG ELETRÔNICA DA AMAZONIA)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No