# Tracking autobiographical thoughts: a smartphone-based approach to identifying cognitive correlates of Alzheimer's disease biomarkers and risk factors in clinically normal older adults

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2023 · $840,230

## Abstract

While the earliest phase of Alzheimer’s disease (AD) pathology is often described as “clinically silent”, prior work
raises the possibility that early AD is associated with detectable alterations in autobiographical thought – a class
of cognition encompassing memories, plans, and other mental simulations related to our personal lives. Here
we introduce two multi-modal studies that investigate whether cognitive markers of early AD neuropathology can
be detected by deploying smartphone applications (apps) to track autobiographical thoughts in everyday life.
Using two smartphone apps developed by our team to naturalistically assess cognition, the proposed studies
will a) examine the sensitivity of real-world autobiographical thoughts to AD plasma and brain biomarkers in
clinically normal older adults, b) reveal the predictive and scalable potential of measuring autobiographical
thoughts in older adults for a host of longitudinal AD biomarker and associated health outcomes, and c) shed
light on neurocognitive autobiographical thought characteristics that may separate normal from abnormal
cognitive and brain aging. MPIs Dr. Grilli and Dr. Andrews-Hanna have formed a team of researchers with
expertise in smartphone-based assessment of cognition, autobiographical thought, functional magnetic
resonance imaging, healthy and pathological aging, and longitudinal analysis of large datasets. Leveraging our
team’s interdisciplinary expertise, we will execute an innovative two-pronged project harnessing in-lab, at-home,
and online assessment methods that will evaluate the relationships of AD biomarkers and aging to the
autobiographical thoughts of 1,225+ adults, with a subset completing additional in-lab experimental cognitive
tests, neuroimaging, plasma biomarker assays, and longitudinal follow-up. In Aim 1, we will test the hypothesis
that among clinically normal older adults, smartphone measures of autobiographical thoughts are sensitive to
plasma AD biomarkers, and resting state functional connectivity in the default network, a brain network targeted
by early AD. Aim 2 tests the hypothesis that these smartphone measures predict future plasma biomarker
accumulation among older adults who were clinically normal at enrollment, as well as future resting state
functional connectivity of the default network, and daily psychosocial / instrumental decline. Aim 3 deploys one
of our smartphone apps to a large remote, clinically normal, and genotyped cohort, providing an opportunity to
evaluate questions about effects of age and genetic risk for AD on autobiographical thoughts at an
unprecedented scale. Across the aims, we also examine how smartphone measures of autobiographical
thoughts compare to in-lab cognitive tests, and if they improve sensitivity to aging and AD risk. To our knowledge,
this project will be the first to use smartphones to track autobiographical thoughts as a means to identify cognitive
correlates of AD biomarkers, despite theoretical tenets and ...

## Key facts

- **NIH application ID:** 10680538
- **Project number:** 5R01AG068098-02
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Jessica Renee Andrews-Hanna
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $840,230
- **Award type:** 5
- **Project period:** 2022-08-15 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10680538

## Citation

> US National Institutes of Health, RePORTER application 10680538, Tracking autobiographical thoughts: a smartphone-based approach to identifying cognitive correlates of Alzheimer's disease biomarkers and risk factors in clinically normal older adults (5R01AG068098-02). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10680538. Licensed CC0.

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