Adaptive testing for smartphone-based cognitive screening: Reducing patient burden while maintaining classification accuracy.

IF 4.4 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2026-08-19 eCollection Date: 2026-07-01 DOI:10.1002/dad2.70455

Stephanie Ruth Young, Julia Yoshino Benavente, Elizabeth McManus Dworak, Michael S Wolf, Cindy J Nowinski

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Abstract

Introduction: Remote cognitive screening for primary care must balance accuracy with patient burden. We evaluated whether adaptive task administration could reduce testing while preserving classification accuracy.

Methods: Using data from older adults (N = 277; 100 mild cognitive impairment [MCI]; 177 normal cognitive aging) who self-administered MyCog Mobile, we developed a stepped protocol in which the final task is skipped when preceding tasks classify the patient with confidence. Leave-one-out cross-validation (LOO-CV) assessed agreement with the full-battery classification and accuracy relative to the reference diagnosis.

Results: Thirty-three percent of participants were classified at the initial decision point, and only 17% required the full battery. LOO-CV showed a negligible change in area under the curve (ΔAUC = 0.008; bootstrapped 95% confidence interval [-0.004, 0.021]) using the adaptive battery.

Discussion: Approximately one third of older adults can be accurately classified as with MCI using only two MyCog Mobile tasks under an adaptive approach. Prospective validation, in which the adaptive battery is administered in real time, is warranted.

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基于智能手机的认知筛查的自适应测试：在保持分类准确性的同时减少患者负担。

初级保健远程认知筛查必须平衡准确性和患者负担。我们评估了自适应任务管理是否可以在保持分类准确性的同时减少测试。方法：使用自我使用MyCog Mobile的老年人（N = 277； 100轻度认知障碍[MCI]； 177正常认知衰老）的数据，我们制定了一个阶梯式方案，在该方案中，当前面的任务对患者进行自信分类时，跳过最后的任务。留一交叉验证（LOO-CV）评估了与全电池分类的一致性和相对于参考诊断的准确性。结果：33%的参与者在最初的决策点被分类，只有17%的人需要充满电池。使用自适应电池时，lo - cv曲线下面积的变化可以忽略不计（ΔAUC = 0.008；自举95%置信区间[-0.004,0.021]）。讨论：大约三分之一的老年人可以准确地归类为轻度认知障碍，仅使用两种适应性方法下的MyCog Mobile任务。前瞻性验证是必要的，其中自适应电池是实时管理的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Medicine-Psychiatry and Mental Health

CiteScore

7.80

自引率

7.50%

发文量

101

审稿时长

8 weeks

期刊介绍： Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.