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Record W3091493886 · doi:10.5817/sts2020-1-2

Influence of long-term fasting and intermittent fasting on the cognitive abilities

2020· article· en· W3091493886 on OpenAlexaboutno aff
Marie Crhová, Kateřina Kapounková

Bibliographic record

VenueStudia sportiva · 2020
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
Fundersnot available
KeywordsIntermittent fastingCognitionTerm (time)MedicineVerbal memoryWeight lossPsychologyAudiologyDevelopmental psychologyInternal medicinePsychiatryObesity

Abstract

fetched live from OpenAlex

Fasting as skipping or abstaining from eating or drinking for a certain time is known mainly due to religion. In addition to religious reasons, we can also fast for weight loss or detoxication. We have decided to examine the impact of fasting on the human organism more closely, especially on the cognitive functions, such as short-term memory, attention, concentration, language skills, abstract reasoning, etc. The research completed 16 participants (M+F; 25.8y±2.7; 179.5cm±11.6; 74.6kg±15.1). There were divided into 2 groups (long-term fasting (LTF) and intermittent fasting (IF)). For measurement cognitive function we used Montreal Cognitive Assessment (MoCA), which was completed by all participants in the study before and after the fasting period. The research completed 16 participants. The total score of MoCA decreased in both group after the fasting period, more in the IF group (-1.1 points), but not statistically significant. Values for short-term memory evaluation decreased in both groups, also in the IF group more (-0.9 points), there was a large effect size. Verbal production values decreased in both group and these changes were statistically significant with small effect size. Our results suggest that long-term fasting and intermittent fasting may reduce genitive abilities. Especially short-term memory can be influenced by intermittent fasting. Both fasting methods decreased the level of verbal production.

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)0.002
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 score0.020
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.039
GPT teacher head0.308
Teacher spread0.269 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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