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Record W4225388289 · doi:10.3389/fneur.2022.855226

Dual-Task Abilities During Activities Representative of Daily Life in Community-Dwelling Stroke Survivors: A Pilot Study

2022· article· en· W4225388289 on OpenAlexafffund
Anne Deblock-Bellamy, Anouk Lamontagne, Bradford J. McFadyen, Marie‐Christine Ouellet, Andréanne K. Blanchette

Bibliographic record

VenueFrontiers in Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des LaurentidesCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in RehabilitationUniversité LavalMcGill UniversityJewish Rehabilitation HospitalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersCentre for Interdisciplinary Research in RehabilitationInstitut de Réadaptation en Déficience Physique de QuébecUniversité Laval
KeywordsCognitionPhysical medicine and rehabilitationTask (project management)Activities of daily livingPsychologyRecallStroke (engine)Physical therapyMedicineCognitive psychology

Abstract

fetched live from OpenAlex

Background In addition to several physical skills, being able to walk in the community, walking independently and safely in the community requires the ability to divide attention between walking and other tasks performed simultaneously. The aims of the present pilot study were to measure cognitive-locomotor dual-task (DT) abilities during activities representative of daily living in stroke survivors and to compare them with age- and gender-matched healthy individuals. Methods To assess DT abilities, all participants walked along a virtual shopping mall corridor and memorized a 5-item shopping list. Two levels of task complexity were used for the walking task (with or without virtual agents to avoid) and the cognitive task to recall a list of items (with or without a modification at mid-course). The assessment was conducted using an omnidirectional platform and a virtual reality (VR) headset. Locomotor and cognitive DT costs (DTC) were calculated as the percent change from single-task (ST) performance. Walking speed and minimal distance between the participant and the virtual agents were used to characterize locomotor performance. Cognitive performance was assessed by the number of correctly recalled items. One-sample Wilcoxon tests were used to determine the presence of DTCs and Mann-Whitney tests were performed to compare DTCs between the 2 groups. Results Twelve community-dwelling stroke survivors [60.50 years old (25-75 th percentiles: 53.50–65.75); 5 women; 13.41 months post-stroke (5.34–48.90)] and 12 age- and gender- matched healthy individuals were recruited. Significant cognitive or mutual (cognitive and locomotor) interferences were observed in participants with stroke in all DT conditions, except the simplest (no virtual agents, no modifications to the list). For the control group, significant mutual interferences were only observed during the most complex DT condition. A group difference was detected in cognitive DTCs during the most complex DT condition (virtual agents and list modifications; p = 0.02). Stroke survivors had greater cognitive DTCs than the control group. Conclusions Using an ecological perspective contributes to understanding behavior of stroke survivors in daily activities. Virtual scenarios appear to be an interesting avenue for a more comprehensive understanding of DT abilities during activities representative of daily living in stroke survivors. The usability and feasibility of such an approach will have to be studied before considering implementation in rehabilitation settings.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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".

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Citations18
Published2022
Admission routes2
Has abstractyes

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