Evidence map›Paper›PMID 39862156›Full record

ArticleAge and ageing2025

A mobile interactive cognitive self-assessment scale for screening cognitive impairment due to Alzheimer's disease.

Kexin Xie, Juan Huang, Ting Chen, Dan Li, Tianxinyu Xia, Min Chu, Yue Cui, Mei Tang, Dantao Peng, Jingtong Wang and 8 more

Abstract readValidation Study
In one paragraph

Article in Age and ageing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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

18 authors.

Kexin XieDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Juan HuangDepartment of Neurology, FuXing Hospital, Capital Medical University, Beijing, China.
Ting ChenInstitute for Smart Ageing, Beijing Academy of Science and Technology, Beijing, China.
Dan LiDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Tianxinyu XiaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Min ChuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yue CuiDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Mei TangDepartment of Geriatric Medicine, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China.
Dantao PengDepartment of Neurology, China-Japan Friendship Hospital, Ministry of Health, Beijing, China.
Jingtong WangDepartment of Gerontology, Peking University People's Hospital, Beijing, China.
Jianling LiuHome Care Center for Senior People, Beijing Yangfangdian Hospital, Beijing, China.
Xiaojuan ChenBeijing Active Ageing and Smart Service Technology Co. Ltd., Beijing, China.
Weiwei CuiBeijing Active Ageing and Smart Service Technology Co. Ltd., Beijing, China.
Li LiuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yingtao WangDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Jianbing LiuInstitute for Smart Ageing, Beijing Academy of Science and Technology, Beijing, China.
Fang LiDepartment of Neurology, FuXing Hospital, Capital Medical University, Beijing, China.
Liyong WuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.

Funding

Beijing Municipal Science and Technology Committee D171100008217005National Natural Science Foundation of China 82271464
6 · The paper itself

Abstract

backgroundA mobile cognition scale for community screening in cognitive impairment with rigorous validation is in paucity. We aimed to develop a digital scale that overcame low education for community screening for mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and AD.

methodsA mobile cognitive self-assessment scale (CogSAS) was designed through the Delphi process, which is feasible for the older population with low education. In Phase 1, 518 clinically diagnosed participants were subjected to optimise the items. In Phase 2, the scale was validated in 358 participants with cognitively unimpaired and 396 participants of clinically diagnosed MCI and dementia for reliability, validity and diagnostic accuracy. In Phase 3, specificity and sensitivity were tested for biologically diagnosed participants of 38 with cognitively unimpaired and 45 with MCI and dementia due to AD according to the amyloid, tau, neurodegeneration classification system.

resultsThe CogSAS was a three-task mobile scale testing memory and executive function. In Phase 2, the internal consistency was 0.81, and the test-retest reliability was 0.82. The construct validity was 0.74, and the criterion validity was 0.77. The sensitivity and specificity for discriminating clinically diagnosed participants with MCI and dementia from cognitively unimpaired were 0.90 and 0.67, respectively. For discriminating biologically diagnosed MCI and dementia due to AD from cognitively unimpaired, the sensitivity and specificity were 1.00 and 0.78, respectively.

conclusionsThe CogSAS has good reliability, validity and feasibility, showing a high sensitivity and specificity both in the community and the clinic, identifying biologically diagnosed MCI and dementia due to AD.

Indexed as

Alzheimer DiseaseCognitionCognitive DysfunctionMobile ApplicationsNeuropsychological TestsAgedAged, 80 and overDelphi TechniqueExecutive FunctionFeasibility StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsAlzheimer’s diseasecognitive impairmentdiagnostic accuracymobile cognitive assessment scaleolder peoplescreening measurevalidation

Identifiers

PMID39862156
PMCPMC11761740

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.