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Published on: December 11, 2015
Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development
Meaghan McKenna1, John Torous2, Eden Rozenblit2
1Department of Neurology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, United States, 1 617 667 2622.
Background:
The diagnosis and monitoring of Alzheimer disease (AD) currently rely on clinician-administered, in-person, and cross-sectional pen-and-paper cognitive assessments. While clinically validated, these measures are time-intensive, infrequently administered, and limited in their ability to detect early, subtle, or short-term cognitive changes. Thus, more frequent, ecologically valid assessments are critical to improving sensitivity to early cognitive impairment and disease progression.
Objective:
This study aims to develop and pilot a smartphone-based assessment battery that combines active cognitive assessments with passive smartphone sensor data (eg, steps, sleep) and survey data to identify and longitudinally characterize cognitive impairment associated with AD.
Methods:
We developed a suite of digitized versions of standard cognitive tests alongside novel, game-based cognitive tests within the mindLAMP platform. Uniquely, these tests integrate into the platform's mobile survey and digital phenotyping capabilities to produce a comprehensive assessment tool capable of simultaneously tracking self-reported, behavioral, and cognitive symptoms in real time. These tools were unified within the Smartphone Monitoring Assessment in Real Time-Alzheimer's framework. Across a 6-month pilot study involving individuals with mild cognitive impairment or mild AD, we will examine the feasibility, acceptability, and longitudinal adherence to these assessments. We will compare digital cognitive and passive data streams against standard clinical assessments to evaluate their usefulness in detecting cognitive impairment and change over time.
Results:
Recruitment began in April of 2025. As of February 2026, 13 participants with mild cognitive impairment or AD (mean age 72.8, SD 6.5 y, 8 male) and 12 controls (mean age 71.6, SD 7.8 y, 6 male) have been enrolled; recruitment is ongoing. Preliminary analyses on participant compliance, passive data, and variations in game scores are in progress. Data analysis is expected to be completed by mid-2026, and we anticipate results to be published in 2027. This study is funded by the a2 Pilot Awards, a subaward of funding given to the Trustees of the University of Pennsylvania under the a2 Collective, beginning in April 2024.
Conclusions:
Smartphone-based cognitive assessments, when combined with digital phenotyping, offer a scalable and ecologically valid approach to detecting and monitoring AD in real-world settings. This framework has the potential to enhance early detection, enable continuous monitoring, and support future machine learning-based automated identification of cognitive impairment, ultimately facilitating earlier and more personalized care.
