JMIR Publications

ABSTRACT

Background:

Diagnosis and monitoring of Alzheimer’s 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 improve sensitivity to early cognitive impairment and disease progression.

Objective:

This study aims to develop and evaluate a smartphone-based assessment battery that combines active cognitive assessments with passive smartphone sensor data (e.g., 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 able to simultaneously track self-reported, behavioral, and cognitive symptoms in real time. These tools were unified within the Smartphone Monitoring Assessment in Real Time–Alzheimer’s (SMART-A) framework. Across a six-month observational 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 relative sensitivity and specificity for detecting cognitive impairment and change over time.

Results:

This paper reports on the design and implementation of the SMART-A framework, including the integration of new interactive cognitive tasks, surveys, and passive sensor data. Planned analyses will assess which ecological digital biomarkers most effectively capture cognitive impairment and disease progression.

Conclusions:

Smartphone-based cognitive assessments, when combined with digital phenotyping, offer a scalable and ecologically valid approach to detecting and monitoring Alzheimer’s disease 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.