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Effects of Retro-Walking Training on Kinesiophobia and Cognition in Geriatric Population: A Quasi - Experimental Study

2024· article· en· W4401426624 on OpenAlexaboutno aff
Suruchi, Sathya Guruprasad, Feba Roy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical medicine and rehabilitationCognitionTraining (meteorology)Cognitive trainingPsychologyComputer sciencePhysical therapyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Introduction: Aging brings challenges such as Kinesio phobia and cognitive decline, significantly impacting quality of life and fall risk in older adults. Retro-walking, a novel exercise modality, has shown promise in improving balance and motor control. Objective: This study aimed to assess the effects of retro-walking training on kinesiophobia and cognition in geriatric subjects. Methodology: A quasi-experimental design was employed with 60 geriatric participants aged 65-75 years. Participants were divided into control and experimental groups. The experimental group underwent retro-walking training thrice weekly for 6 weeks, complemented by home exercises. Kinesio phobia was assessed using the Tampa Scale of Kinesiophobia (TSK), and cognition was evaluated using the Montreal Cognitive Assessment (MoCA) scale. Result: Significant improvements were observed in both TSK (pre: 50.6 ± 8.6, post: 27.6 ± 6.9, p < 0.001) and MoCA scores (pre: 25.0 ± 3.1, post: 27.7 ± 1.8, p < 0.001) post- intervention. The experimental group showed a marked reduction in kinesiophobia and enhanced cognitive function compared to the control group. Conclusion: Retro-walking training demonstrated beneficial effects on reducing kinesiophobia and enhancing cognition in geriatric subjects. These findings suggest that retro-walking can be an effective intervention to improve functional outcomes and mitigate fall risks in the elderly population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.457
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.481
Teacher spread0.405 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

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