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Record W4401421357 · doi:10.32920/26520673.v1

How Did the COVID-19 Lockdown Pandemic Affect the Depression Symptomatology in Mediterranean Older Adults with Metabolic Syndrome?

2024· preprint· en· W4401421357 on OpenAlexaff
Indira Paz‐Graniel, Nancy Babió, Stephanie Nishi, Miguel Ángel González Martínez, Dolores Corella, Montserrat Fitó, J. Alfredo Martínéz, Ángel M. Alonso‐Gómez, Julia Wärnberǵ, Jesús Vioqué, Dora Romaguera, José López‐Miranda, Ramón Estruch, Francisco J. Tinahones, José Manuel Santos‐Lozano, J. Luís Serra‐Majem, Aurora Bueno‐Cavanillas, Josep A. Tur, Vicente Martín, Xavier Pintó, Miguel Delgado‐Rodríguez, Pilar Matía‐Martín, Josép Vidal, Cristina Calderon-Sanchez, Lidia Daimiel, Emilio Ros, Fernando Fernández‐Aranda, Estefanía Toledo, Cristina Valle‐Hita, José V. Sorlí, Camille Lassale, Antonio García-Ríos, Alejandro Oncina-Cánovas, Francisco Javier Barón-López, M. Ángeles Zulet, Elena Rayó, Rosa Casasús, Esther Thomas-Carazo, Lucas Tojal‐Sierra, Miguel Damas-Fuentes, Miguel Ruiz‐Canela, Sara De las Heras-Delgado, Rebeca Fernández-Carrión, Olga Castañer, Patricia J. Peña‐Orihuela, Sandra González‐Palacios, Pilar Buil‐Cosiales, Albert Goday, Jordi Salas‐Salvadó

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsSt. Michael's Hospital
FundersInstituto de Salud Carlos IIIEuropean Regional Development FundCentro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y NutriciónJunta de AndalucíaGeneralitat de CatalunyaCentres de Recerca de Catalunya
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Affect (linguistics)Depression (economics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMediterranean climateMetabolic syndromePsychologyMedicineGerontologyPsychiatryVirologyHistoryDiseaseEconomicsInternal medicineOutbreakObesityInfectious disease (medical specialty)Communication

Abstract

fetched live from OpenAlex

Background and Aims. To control the COVID-19 spread, in March 2020, a forced home lockdown was established in Spain. In the present study, we aimed to assess the effect of mobility and social COVID-19-established restrictions on depressive symptomatology in older adults with metabolic syndrome. We hypothesize that severe restrictions might have resulted in detrimental changes in depressive symptomatology. Methods. 2,312 PREDIMED-Plus study participants (men = 53.9%; mean age = 64.9 ± 4.8 years) who completed a COVID-19 lockdown questionnaire to assess the severity of restrictions/lockdown and the validated Spanish version of the Beck Depression Inventory-II (BDI-II) during the three established phases concerning the COVID-19 lockdown in Spain (prelockdown, lockdown, and postlockdown) were included in this longitudinal analysis. Participants were categorized according to high or low lockdown severity. Analyses of covariance were performed to assess changes in depressive symptomatology across lockdown phases. Results. No significant differences in participant depression symptomatology changes were observed between lockdown severity categories (low/high) at the studied phases. During the lockdown phase, participants showed a decrease in BDI-II score compared to the prelockdown phase (mean (95% CI), -0.48 (-0.24, -0.72), P < 0.001); a nonsignificantly larger decrease was observed in participants allocated in the low-lockdown category (low: -0.59 (-0.95, -0.23), high: -0.43 (-0.67, -0.19)). Similar decreases in depression symptomatology were found for the physical environment dimension. The post- and prelockdown phase BDI-II scores were roughly similar. Conclusions. The COVID-19 pandemic lockdown was associated with a decrease in depressive symptomatology that returned to prelockdown levels after the lockdown. The degree of lockdown was not associated with depressive symptomatology. The potential preventive role of the physical environment and social interactions on mental disorders during forced home lockdown should be further studied. This trial is registered with ISRCTN89898870. Retrospectively registered on 24 July 2014.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.340
Teacher spread0.315 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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