Digital health · 2025
Identifying digital phenotypes of risk for Alzheimer's disease and related dementia among Hispanic/Latino persons living in the United States: A protocol for a prospective quantitative study.
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Moore Raeanne C, Nakahira Shay, Gallo Linda C, Demos Alexander P, Sundermann E E, Galasko Douglas, Delano-Wood Lisa, Perez Martha, Betancourt Luis Ricardo, Churchill Emma G, Portillo Kassandra, Rocha Perla, Mendoza Paulina, Scandalis Lina, Daly Rebecca, Marquine María J
Аннотация
OBJECTIVE: Hispanic/Latino/a/x (hereafter Latino) persons living in the U.S. are at increased risk for Alzheimer's disease and related dementias (ADRD) compared to non-Latino Whites. Early detection of preclinical changes is crucial. The SALUD-Tech study aims to identify digital behavioral markers-"digital signatures"-of ADRD risk in diverse middle-aged and older Latinos using passive data from smartphones and smartwatches.
METHODS: Participants include Latino adults aged 50-70 years living in southern California, with varying degrees of ADRD risk as defined by the presence of mild cognitive impairment and cardiovascular disease risk. Data collection began in April 2022 and will continue through 2026. Participants complete comprehensive laboratory assessments (neurobehavioral, medical, sociocultural, and psychiatric assessments). High-frequency data on sensors, keyboard dynamics, and phone use activity are collected for 30 days following the baseline visit. A subset of study participants completes 18- and 36-month longitudinal assessments; these participants are selected based on risk profiles and retention likelihood. All data are securely encrypted, de-identified, and collected respecting participant privacy and consent in accordance with ethical standards. Data analysis involves integrating multimodal data streams using machine learning to identify behavioral patterns associated with early cognitive decline.
RESULTS: We anticipate 300 participants will be enrolled in the study. Study results will be published in peer-reviewed scientific journals.
DISCUSSION: Early detection of ADRD risk using smartphone and wearable data could help reduce disparities by providing a low-cost, accessible tool. Ultimately, this approach may be integrated into clinical care to enable earlier interventions and reduce healthcare costs.
METHODS: Participants include Latino adults aged 50-70 years living in southern California, with varying degrees of ADRD risk as defined by the presence of mild cognitive impairment and cardiovascular disease risk. Data collection began in April 2022 and will continue through 2026. Participants complete comprehensive laboratory assessments (neurobehavioral, medical, sociocultural, and psychiatric assessments). High-frequency data on sensors, keyboard dynamics, and phone use activity are collected for 30 days following the baseline visit. A subset of study participants completes 18- and 36-month longitudinal assessments; these participants are selected based on risk profiles and retention likelihood. All data are securely encrypted, de-identified, and collected respecting participant privacy and consent in accordance with ethical standards. Data analysis involves integrating multimodal data streams using machine learning to identify behavioral patterns associated with early cognitive decline.
RESULTS: We anticipate 300 participants will be enrolled in the study. Study results will be published in peer-reviewed scientific journals.
DISCUSSION: Early detection of ADRD risk using smartphone and wearable data could help reduce disparities by providing a low-cost, accessible tool. Ultimately, this approach may be integrated into clinical care to enable earlier interventions and reduce healthcare costs.