Evidence map›Paper›PMID 39328904›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Artificial intelligence detection of cognitive impairment in older adults during walking.

Shuichi P Obuchi, Motonaga Kojima, Hiroyuki Suzuki, Juan C Garbalosa, Keigo Imamura, Kazushige Ihara, Hirohiko Hirano, Hiroyuki Sasai, Yoshinori Fujiwara, Hisashi Kawai

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. 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

10 authors.

Shuichi P ObuchiResearch Team for Human Care Tokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Motonaga KojimaDepartment of Physical Therapy University of Tokyo Health Sciences Tama-shi Tokyo Japan.
Hiroyuki SuzukiResearch Team for Social Participation and Community Health Tokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Juan C GarbalosaDepartment of Physical Therapy Quinnipiac University Hamden Connecticut USA.
Keigo ImamuraResearch Team for Human Care Tokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Kazushige IharaGraduate School of Medicine Hirosaki University Hirsaki-shi Aomori Japan.
Hirohiko HiranoResearch Team for Promoting Independence and Mental Health Tokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Hiroyuki SasaiResearch Team for Promoting Independence and Mental Health Tokyo Metropolitan Institute of Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Yoshinori FujiwaraTokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.
Hisashi KawaiResearch Team for Human Care Tokyo Metropolitan Institute for Geriatrics and Gerontology Itabashi-ku Tokyo Japan.ORCID https://orcid.org/0000-0003-3015-6041

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.

methodsThis cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.

resultsThe models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets. DISCUSSION: AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention. Highlights: Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.

Indexed as

accelerationangular velocityartificial intelligencegaitscreening

Identifiers

PMID39328904
PMCPMC11424983

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.