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Record W4390795450 · doi:10.1186/s12906-023-04284-2

Utilizing fNIRS to investigate the impact of Baduanjin training on attentional function in post-stroke cognitive impairment patients: a study protocol for a randomized controlled trial

2024· article· en· W4390795450 on OpenAlexaboutno aff
Xingchen Zhou, Yiwen Wan, Zhengxian Xu, Cancan Yu, Ziyi Wu, Zesen Zhuang, Rui Xia, Hongyu Wang, Shangjie Chen

Bibliographic record

VenueBMC Complementary Medicine and Therapies · 2024
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsnot available
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceSanming Project of Medicine in ShenzhenNational Natural Science Foundation of China
KeywordsStroke (engine)CognitionMontreal Cognitive AssessmentRandomized controlled trialCognitive trainingPhysical medicine and rehabilitationPhysical therapyRehabilitationPsychologyCognitive declineLateralization of brain functionActivities of daily livingEffects of sleep deprivation on cognitive performanceMedicineCognitive impairmentAudiologyDementiaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a prevalent complication among stroke survivors. It not only directly impacts patients' cognitive abilities but also hampers their capacity to regain independence in daily activities, consequently diminishing their quality of life. Among the various cognitive deficits following stroke, impaired attention is the most frequently observed, influencing not only daily functioning but also higher cognitive functions like working memory, executive functioning, and language.Emerging evidence indicates that Baduanjin, a traditional Chinese exercise, may have a positive impact on enhancing attention in older adults with mild cognitive impairment and stroke survivors. However, the precise mechanisms behind this effect remain unclear. In this study, we employed Baduanjin training as an intervention to address attention decline in post-stroke cognitive impairment patients and to delve into the potential mechanisms through which Baduanjin training may enhance attention in individuals with PSCI. METHODS: In this prospective randomized controlled trial, we plan to recruit 72 participants diagnosed with post-stroke cognitive impairment (PSCI). These participants will be randomly assigned in a 1:1:1 ratio to one of three groups: Baduanjin training(left hemisphere stroke and right hemisphere stroke) and conventional treatment.The conventional treatment group will receive standard rehabilitation sessions. In addition to conventional treatment, participants in the octogenarian training groups will undergo octogenarian training sessions lasting 40 min, five times a week, over a total period of 12 weeks.The primary outcome measures will include the Montreal Cognitive Assessment (MoCA) scale and the Attentional Lateralization Index. These assessments will be conducted by a trained evaluator before the start of the intervention and at weeks 6 and 12 after the intervention begins.Secondary outcome measures will be assessed using the baseline Mandarin version of the Oxford Cognitive Screening (OCS-P) scale, the simplified Fugl-Meyer Motor Function Assessment (FMA) scale, the Pittsburgh Rehabilitation Participation (PRPS) scale, and the Activities of Daily Living (ADL) scale before and after the intervention, respectively. DISCUSSION: This trial aims to examine the impact of Baduanjin training on attentional lateralization among patients with post-stroke cognitive impairment (PSCI). Functional brain imaging utilizing near-infrared spectroscopy will be employed to investigate how Baduanjin exercise influences the structural and functional connectivity of distinct brain regions or brain networks. TRIAL REGISTRATION: Chictr.org.cn, ID: ChiCTR2300076533 . Registered on 11 October 2023.

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)0.019
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.025
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.014
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0250.004

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 head0.083
GPT teacher head0.390
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations7
Published2024
Admission routes1
Has abstractyes

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