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A randomized trial of the effects of an aquatic exercise program on depression, anxiety levels, and functional capacity of people who suffered an ischemic stroke

2018· article· en· W2617440322 on OpenAlexaff
Felipe J. Aidar, Dihogo Gama de Matos, Philip D. Chilibeck, Raphael Fabrício de Souza, André Luíz Gomes Carneiro, Víctor Machado Reis

Bibliographic record

VenueThe Journal of Sports Medicine and Physical Fitness · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAnxietyDepression (economics)Randomized controlled trialPhysical therapyStroke (engine)MedicineBeck Depression InventoryRehabilitationState-Trait Anxiety InventoryTrait anxietyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Aquatic exercise programs are used in rehabilitation and might help to reduce disability after stroke. This was a randomized intervention trial to assess the influence of an aquatic exercise program on people suffering from depression and anxiety after ischemic stroke. METHODS: Participants were randomized to an experimental group (EG) composed of 19 individuals (51.8±8.5 years; ten males and nine females), and a control group (CG) composed of 17 people (52.7±6.7 years; nine males and eight females). The aquatic exercise program consisted of two sessions per week, each lasting between 45 and 60 minutes and divided into 5 to 10 minutes exercise sections during 12 weeks. The State-Trait Anxiety Inventory was used to determine anxiety levels while the Beck Depression Inventory was used as a self-assessment of depression. RESULTS: EG improved measures of depression, anxiety trait and anxiety state between pre- and post-treatment, with no changes in CG. EG improved in all tests related to functional capacity compared to CG. CONCLUSIONS: The practice of aquatic exercises promotes improvements in the levels of depression and anxiety in people who suffered an ischemic stroke.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.285
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 designRandomized trial
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

Citations55
Published2018
Admission routes1
Has abstractyes

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