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Record W4382314706 · doi:10.5195/ijt.2023.6560

The Effect of Telerehabilitation on Physical Fitness and Depression/Anxiety in Post-COVID-19 Patients: A Randomized Controlled Trial

2023· article· en· W4382314706 on OpenAlexaff
Paloma Lopes de Araújo Furtado, Maria do Socorro Brasileiro-Santos, Brenda Lopes Cavalcanti de Mello, Alex Andrade Araújo, Maria Alessandra Sipriano da Silva, Jennifer Arielly Suassuna, Gabriella Brasileiro-Santos, Renata de Lima Martins, Amilton da Cruz Santos

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTelerehabilitationAnxietyDepression (economics)Physical therapyCoronavirus disease 2019 (COVID-19)Randomized controlled trialIntervention (counseling)Physical fitnessMedicinePhysical medicine and rehabilitationRehabilitationPsychologyTelemedicinePsychiatryHealth careInternal medicineDisease

Abstract

fetched live from OpenAlex

Aim: The aim of this research was to evaluate the impact of a telerehabilitation program on physical fitness, muscle strength, and levels of depression and anxiety in post-COVID-19 patients. Methods: Thirty-two individuals recovered from COVID-19 (48.20±12.82 years) were allocated into either a telerehabilitation (TG n=16) or control (CG n=16) group. Physical fitness, handgrip strength, depression and anxiety levels were assessed before and after an 8-week intervention. Results: There was a significant improvement in muscle strength in both groups. Physical fitness significantly increased compared to the CG at the end of the intervention. Levels of anxiety and depression significantly decreased after the intervention when compared to the CG. Conclusion: Eight weeks of functional telerehabilitation training is a viable and efficient way to rehabilitate patients affected by COVID-19, as it improved physical conditioning and mental health.

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)0.006
metaresearch head score (Gemma)0.125
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.123
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.328
Teacher spread0.323 · 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.

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

Citations7
Published2023
Admission routes1
Has abstractyes

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