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Record W2549619879 · doi:10.1159/000452309

Instrumented Trail-Making Task to Differentiate Persons with No Cognitive Impairment, Amnestic Mild Cognitive Impairment, and Alzheimer Disease: A Proof of Concept Study

2016· article· en· W2549619879 on OpenAlexaboutno aff
He Zhou, Marwan N. Sabbagh, R Wyman, Carolyn Liebsack, Mark E. Kunik, Bijan Najafi

Bibliographic record

VenueGerontology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute on Aging
KeywordsCognitionPsychologyPhysical medicine and rehabilitationTrail Making TestCognitive impairmentAudiologyMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Objective and time-effective tools are needed to identify motor-cognitive impairment and facilitate early intervention. OBJECTIVE: We examined the feasibility, accuracy, and reliability of an instrumented trail-making task (iTMT) using a wearable sensor to identify motor-cognitive impairment among older adults. METHODS: Thirty subjects (age = 82.2 + 6.1 years, body mass index = 25.7 + 4.8, female = 43.3%) in 3 age-matched groups, 11 healthy, 10 with amnestic mild cognitive impairment (aMCI), and 9 with Alzheimer disease (AD), were recruited. Subjects completed iTMT, using a wearable sensor attached to the leg, which translates the motion of the ankle into a human-machine interface. iTMT tests included reaching to 5 indexed circles on a computer screen by moving the ankle-joint while standing. iTMT was quantified by the time required to reach all circles in the correct sequence. Three iTMT tests were designed, including numbers (1-5) positioned in a fixed (iTMTfixed) or random (iTMTrandom) order, or numbers (1-3) and letters (A and B) positioned in random order (iTMTnumber-letter). Each test was repeated twice to examine test-retest reliability. In addition, the conventional trail-making task (TMT A and B), Montreal Cognitive Assessment (MoCA), and dual-task cost (DTC: gait-speed difference between walking alone and walking while counting backward) were used as references. Re sults: Good-to-excellent reliability was achieved for all iTMT tests (intraclass correlation [ICC] = 0.742-0.836). Between-group difference was more pronounced, when using iTMTnumber-letter, with average completion time of 26.3 ± 12.4, 37.8 ± 14.1, and 61.8 ± 34.1 s, respectively, for healthy, aMCI, and AD groups (p = 0.006). Pairwise comparison suggested strong effect sizes between AD and healthy (Cohen's d = 1.384, p = 0.001) and between aMCI and AD (d = 0.923, p = 0.028). Significant correlation was observed when comparing iTMTnumber-letter with MoCA (r = -0.598, p = 0.001), TMT A (r = 0.519, p = 0.006), TMT B (r = 0.666, p < 0.001), and DTC (r = 0.713, p < 0.001). CONCLUSION: This study demonstrated proof of concept of a simple, safe, and practical iTMT system with promising results to identify cognitive and dual-task ability impairment among older adults, including those with aMCI and AD. Future studies need to confirm these observations in larger samples, as well as iTMT's ability to track motor-cognitive decline over time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.364
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2016
Admission routes1
Has abstractyes

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