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Record W4389340164 · doi:10.1007/s12603-023-2044-4

Intensive Weight-Loss Lifestyle Intervention Using Mediterranean Diet and COVID-19 Risk in Older Adults: Secondary Analysis of PREDIMED-Plus Trial

2023· article· en· W4389340164 on OpenAlexaff
Sangeetha Shyam, Jesús García‐Gavilán, Indira Paz‐Graniel, José J. Gaforio, M.Á. Martínez-González, Dolores Corella, J. Alfredo Martínéz, Ángel M. Alonso‐Gómez, Julia Wärnberǵ, Jesús Vioqué, Dora Romaguera, José López‐Miranda, Ramón Estruch, Francisco J. Tinahones, José Lapetra, J. Luís Serra‐Majem, Aurora Bueno‐Cavanillas, Josep A. Tur, Vicente Martín, Xavier Pintó, Pilar Matía-Martín, Josép Vidal, M. del Mar Alcarria, Lidia Daimiel, Emilio Ros, Fernando Fernández‐Aranda, Stephanie Nishi, Oscar Garcia-Regata, R. Perez Araluce, Maximiliano Asensio, Olga Castañer, Antoni Sureda, Alejandro Oncina-Cánovas, Cristina Bouzas, M. Ángeles Zulet, Elena Rayó, Rosa Casas, Sandra Martín‐Peláez, Lucas Tojal‐Sierra, M. Rosa Bernal‐López, Silvia Carlos, José V. Sorlí, Albert Goday, Patricia J. Peña‐Orihuela, Ana Pastor-Morel, Sonia Eguaras, María Dolores Zomeño, Miguel Delgado‐Rodríguez, Nancy Babió, Montserrat Fitó, Jordi Salas‐Salvadó

Bibliographic record

VenueThe journal of nutrition health & aging · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineOverweightMediterranean dietWeight lossRandomized controlled trialObesityIncidence (geometry)GerontologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We tested the effects of a weight-loss intervention encouraging energy-reduced MedDiet and physical activity (PA) in comparison to ad libitum MedDiet on COVID-19 incidence in older adults. DESIGN: Secondary analysis of PREDIMED-Plus, a prospective, ongoing, multicentre randomized controlled trial. SETTING: Community-dwelling, free-living participants in PREDIMED-Plus trial. PARTICIPANTS: 6,874 Spanish older adults (55-75 years, 49% women) with overweight/obesity and metabolic syndrome. INTERVENTION: Participants were randomised to Intervention (IG) or Control (CG) Group. IG received intensive behavioural intervention for weight loss with an energy-reduced MedDiet intervention and PA promotion. CG was encouraged to consume ad libitum MedDiet without PA recommendations. MEASUREMENTS: COVID-19 was ascertained by an independent Event Committee until December 31, 2021. COX regression models compared the effect of PREDIMED-Plus interventions on COVID-19 risk. RESULTS: Overall, 653 COVID-19 incident cases were documented (IG:317; CG:336) over a median (IQR) follow-up of 5.8 (1.3) years (inclusive of 4.0 (1.2) years before community transmission of COVID-19) in both groups. A significantly lowered risk of COVID-19 incidence was not evident in IG, compared to CG (fully-adjusted HR (95% CI): 0.96 (0.81,1.12)). CONCLUSIONS: There was no evidence to show that an intensive weight-loss intervention encouraging energy-reduced MedDiet and PA significantly lowered COVID-19 risk in older adults with overweight/obesity and metabolic syndrome in comparison to ad libitum MedDiet. Recommendations to improve adherence to MedDiet provided with or without lifestyle modification suggestions for weight loss may have similar effects in protecting against COVID-19 risk in older adults with high cardiovascular risks.

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)0.002
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.437
Teacher spread0.365 · 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

Citations7
Published2023
Admission routes1
Has abstractno

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