Evidence map›Paper›PMID 40830390›Full record

ArticleScientific reports2025

Optimizing mobile cognitive assessment reduces administration time while maintaining screening accuracy in older adults.

Hyun Jeong Ko, Jin Sung Kim, Whani Kim, Byung Hun Yun, So Yoon Park, Dong Han Kim, Ui Jun Kwon, Sang Kwon Lim, Bo Ri Kim, Jee Hang Lee and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hyun Jeong Ko *Department of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Jin Sung Kim *Department of Human-Centred AI, Sangmyung University, Seoul, Republic of Korea.
Whani KimDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Byung Hun YunDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
So Yoon ParkDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Dong Han KimDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Ui Jun KwonDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Sang Kwon LimDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.
Bo Ri KimEwha Medical Research Institute, Ewha Womans University, Seoul, Republic of Korea.
Jee Hang LeeDepartment of Human-Centred AI, Sangmyung University, Seoul, Republic of Korea. jeehang@smu.ac.kr.
Geon Ha KimDepartment of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, Seoul, Republic of Korea. geonha@ewha.ac.kr.
Jin Woo KimDepartment of Digital Therapeutics Research and Development, HAII Corp, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growing dementia prevalence underscores the need for efficient screening methods, but lengthy digital assessments often cause fatigue among older adults. To address this, we developed and validated the Digital Assessment of Cognitive Impairment (DACI), a brief mobile application designed to accurately identify cognitive impairment (CI). Initially, 304 older adults (272 healthy, 32 cognitively impaired) completed both a pencil-and-paper Cognitive Impairment Screening Test (CIST) and a full-length DACI. The best-performing CatBoost model achieved an area under the curve (AUC) of 0.813, with sensitivity of 0.903, requiring an average completion time of 321 s. Subsequent feature selection identified two essential subtests for a compact DACI. This compact version was then validated with an additional 297 participants (227 healthy, 70 cognitively impaired), achieving improved diagnostic performance (AUC = 0.871) in only 91 s. DACI effectively distinguishes cognitively impaired older adults with minimal test duration, potentially reducing fatigue and improving adherence. By providing rapid, accurate remote assessment without additional equipment, DACI may significantly enhance accessibility of cognitive screening among older adults. Future studies are warranted to validate DACI's feasibility and performance in unsupervised home settings.

Indexed as

Cognitive DysfunctionMass ScreeningMobile ApplicationsAgedAged, 80 and overCognitionDementiaFemaleHumansMaleMiddle AgedNeuropsychological TestsCognitive impairmentDigital healthMobile deviceOptimal test designRemote cognitive assessment

Identifiers

PMID40830390
PMCPMC12365245

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.