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Record W4389329118 · doi:10.5455/rmj.20210821031024

Effect of exergames by using Xbox 360 Kinect on cognition of older adults with mild cognitive impairment

2023· article· en· W4389329118 on OpenAlexaboutno aff
Hafsah Arshad, Hafsah Khattak, Kinza Anwar

Bibliographic record

VenueRawal Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitionVerbal fluency testCognitive impairmentPhysical therapyRandomized controlled trialIntervention (counseling)NeuropsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Objective: To determine the effects of exergames by using Xbox 360 Kinect on cognition in older adults with mild cognitive impairment (MCI). Methodology: This randomized control trial included 56 MCI older adults who were randomly divided into experimental group (n=28) and control group (n=28), using coin and toss method. The experimental group performed X-box 360 Kinect games while the control group received stretching and strength training exercises of upper and lower extremity. The intervention was of 30 min per day/5 days per week for six weeks. Mini-mental state examination, Montreal Cognitive Assessment, Trail making Test A and B, Verbal fluency (Semantic/ Phenomic) were used as outcome measures at baseline and after six weeks of intervention. Data were analyzed by SPSS 26. Results: Final analysis included 51 subjects, out of which 26 were in experimental group with mean age 62.85±5.56 yrs.,16 (61.5%) males and 10 (38.5%) females. In control, there were 25 subjects with mean age 63.24±5.12 yrs., 15 (60.0%) males and 10 (40.0%) females. Inter-group analysis showed significant improvement in outcomes measures after six weeks of intervention in experimental group (p<0.05). Conclusion: Exergames showed positive effects on cognitive domains in older adults with MCI.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.299
Teacher spread0.292 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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