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Record W4404383907 · doi:10.21275/sr22719112755

Comparing the Efficacy of Dual Task Training, Strength Training and Aerobic Exercises on Higher Mental Functions in Geriatric Population

2022· article· en· W4404383907 on OpenAlexaboutno aff
Baljeet Kaur

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2022
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAerobic exerciseTraining (meteorology)Task (project management)Dual (grammatical number)Strength trainingPhysical medicine and rehabilitationPhysical therapyPsychologyMedicinePopulationEngineering

Abstract

fetched live from OpenAlex

Background and purpose: Higher mental functions are one of the important determinants of well-being in elderly. The purpose of this study was to investigate the effect of dual task training, strength training and Aerobictraining, on higher mental functions in geriatric population. And also to compare which was the most effective training among the three. Methods: Sixty elderly, aged 65-85 years, were randomly assigned into three groups: Aerobic Training (AT), Strength Training (ST), Dual Task Training Group (DT). Mini Mental Status Examination & Montreal Cognitive Assessment were recorded for all participants and the data was recorded before and after nine weeks of training. Training involved three sessions per week. Result: The results suggested that, at post training, the mean MMSE score of DT group was found significantly (p<0.05 or p<0.01) different and higher as compared to both AT(p=0.001) and ST (p=0.029), as well as the post test scores of MoCA of both ST (p=0.034) and DT (p<0.001) was found significantly (p<0.05 or p<0.001) different and higher as compared to AT. Conclusion: Study found all the three training (Aerobic, Strength and Dual task) effective in the management of cognition in elderly, but Dual task training was found to be more effective than both Aerobic and Strength training.

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)0.004
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 score0.530
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.144
GPT teacher head0.445
Teacher spread0.302 · 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
Published2022
Admission routes1
Has abstractyes

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