Non-invasive, Wearable Multi-parameter System for the Early Prediction of Cognitive Decline and Dementia in Older Adults

Empa, Swiss Federal Laboratories for Materials Science and Technology

Status

Unknown

Conditions

Mild Cognitive Impairment

Healthy Aging

Treatments

Other: Cognitive-motor training

Study type

Interventional

Funder types

Other

Identifiers

NCT04262674

5213.00187

Details and patient eligibility

About

This project develops an innovative screening system and prediction model to detect preclinical symptoms of cognitive impairment and predict the potential development of mild cognitive impairments and dementia in older adults. The earliest possible detection of preclinical symptoms is prerequisite to improve the efficacy of subsequent preventative non-pharmacological, life-style and exercise related, personalized treatment interventions.

Full description

BACKGROUND: Early detection of preclinical symptoms and prediction of potential development of mild cognitive impairment (MCI) and Alzheimer's disease (AD) could improve non-pharmacologic, life-style and exercise related preventative interventions' efficacy and slow-down disease progression. To achieve this goal, discriminating the earliest preclinical stage of MCI/AD from healthy state would be necessary. However, this is still challenging and current clinical methods are not feasible for preventative screening in larger populations of older adults, as they involve invasive sampling of molecular blood or cerebrospinal fluid biomarkers, as well as expensive brain imaging and extensive neuropsychological testing. Recently, several non-invasive alternative measures, including electroencephalography (EEG), gait analysis, heart rate variability (HRV), and core body temperature (Tc), were shown to be associated with preclinical symptoms of MCI/AD and to predict disease progression.

AIM: The investigators aim to combine these measures in a novel non-invasive multi-parameter prediction model, which better reflects multimodal symptomatology compared to currently used methods and, therefore, allows discriminating healthy persons from MCI state with adequate sensitivity (i.e. >80%).

METHODS: A cohort of 85 older adults, ≥65 years of age, including healthy persons and patients with MCI, will be recruited. Assessments will be performed at baseline, after 2 months (within these two 2 months one group will follow a cognitive-motor training intervention, while the other serves as passive control), and at 12-month follow-up. Assessments include EEG, gait analysis, HRV, and Tc at rest and during walking, and will be compared to reference measures of MCI status, including neuropsychological tests, to develop the prediction model and evaluate its sensitivity.

Enrollment

82 patients

Sex

All

Ages

65+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

participants have to be older than 65 years of age
cognitively healthy or diagnosed with MCI
able to walk at least 8 minutes for gait analysis, with or without walking aids
live independently or in a retirement home (classified 0, 1, or 2 within the Swiss classification system for health-care requirements BESA-levels [German abbreviation for: Bewohner-Einstufungs- und Abrechnungs-System; level 0 meaning the person does not need care or treatment; level 1 to 2 meaning, the person only needs little care or treatment])
sign informed consent

Exclusion criteria

previously diagnosed dementia, e.g. Alzheimer's disease
recent head injury
judgment by the participant's primary care physician will be required in the case of acute or instable chronic diseases (e.g. stroke, diabetes) and rapidly progressing or terminal illnesses

Additional exclusion criteria for the subgroup of 15 participants who would agree to take the telemetric gastrointestinal temperature pill:

history of operations and/or disease related to the gastrointestinal tract within last 5 years
implanted medical device
planned MRI examination
nausea, vomiting, constipation or abdominal pain within 1 months prior to the day of planned measurement