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Enregistrement W4390078745 · doi:10.1017/s1355617723001340

52 Depressive Symptoms and Subjective Cognitive Decline in Individuals with COVID-19

2023· article· en· W4390078745 sur OpenAlexaffabout
Eva Friedman, Petra Legaspi, Katie C Benitah, Samantha J. Feldman, Theone Paterson, Kristina M. Gicas

Notice bibliographique

RevueJournal of the International Neuropsychological Society · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensUniversity of VictoriaYork University
Organismes subventionnairesnon disponible
Mots-clésCognitive declineDepression (economics)CognitionDepressive symptomsClinical psychologyMedicineNeuropsychologyCoronavirus disease 2019 (COVID-19)PsychologyModerationPsychiatryGerontologyDementiaDiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

Objective: Many individuals with COVID-19 develop mild to moderate physical symptoms that can last days to months. In addition to physical symptoms, individuals with COVID-19 have reported depressive symptoms and cognitive decline, posing a long-term threat to mental health and functional outcomes. Few studies have examined the presence of co-occurring depression and subjective cognitive decline in individuals who tested positive for COVID-19. The current study examined whether having COVID-19 is subsequently associated with greater depressive symptoms and subjective cognitive decline when compared to healthy individuals. Our study also examined differential associations between symptoms of depression and subjective cognitive decline between individuals who have and have never had COVID-19. Participants and Methods: Adults (N = 104; mean age = 37 years, 69% female) were recruited online from Ontario and British Columbia, Canada. Participants were categorized into two groups: (1) persons who tested positive for COVID-19 at least three months prior, had been symptomatic, and had not been ventilated (N = 50); and (2) persons who have never been suspected of having COVID-19 (N = 54). The Center for Epidemiological Studies Depression Scale (CES-D) and the Subjective Cognitive Decline Questionnaire (SCD-Q) were administered to both groups as part of a larger clinical neuropsychological evaluation. Two separate linear regression analyses were conducted to examine the association of COVID-19 with depressive symptoms and subjective cognitive decline. A moderation analysis was performed to examine whether depressive symptoms were associated with subjective cognitive decline and the extent to which this differed by group (COVID-19 and controls). Participants’ age, self-reported sex, and history of depression were included as covariates. Results: The first regression model explained 17.2% of the variance in CES-D scores. It was found that the COVID-19 group had significantly higher CES-D scores (ß = .20, p = .03). The second regression model explained 35.9% of the variance in SCD-Q scores. Similar to the previous model, it was found that the COVID-19 group had significantly higher SCD-Q scores compared to healthy controls (ß = .22 p = .01). Lastly, the moderation model indicated that higher CES-D scores were associated with higher SCD-Q scores (ß = .43, p < .01), but there was no statistically significant group X CES-D score interaction. Conclusions: These findings suggest that individuals who previously experienced a mild to moderate symptomatic COVID-19 infection report greater depressive symptom severity as well as greater subjective cognitive decline. Additionally, while more severe depressive symptoms predicted greater subjective cognitive decline in our sample, the magnitude of this association did not vary between those with and without a previous COVID-19 infection. While the underlying neurobiological and social mechanisms of cognitive difficulties and depressive symptoms in persons who have had COVID-19 have yet to be fully elucidated, our findings highlight treatment for depression and cognitive rehabilitation as potentially useful intervention targets for the post COVID-19 condition.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,354
Écart entre enseignants0,330 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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