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Enregistrement W4401422446 · doi:10.1111/jgs.19123

Medication beliefs and depression in Black individuals with diabetes and mild cognitive impairment

2024· article· en· W4401422446 sur OpenAlexaboutno aff
Barry W. Rovner, Robin J. Casten

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Aging
Mots-clésMedicineDepression (economics)Cognitive impairmentDiabetes mellitusPsychiatryCognitionGerontologyClinical psychology

Résumé

récupéré en direct d'OpenAlex

Depression and impaired cognition occur frequently in older patients with diabetes, and may influence beliefs about diabetes medications.1, 2 These beliefs, when not shared by clinicians, may engender mistrust and reduce medication adherence. Thus, the relationship between depression, cognition, and medication beliefs is important, especially because treating depression may improve cognition and reverse depressive symptoms (e.g., pessimism, loss of interest, and hopelessness) that compromise medication adherence. In this study, we examined relationships between beliefs about diabetes medications, depression, and cognition in older Black individuals with type 2 diabetes and mild cognitive impairment (MCI). The results may guide ways to optimize diabetes treatment in this high-risk population. This was a cross-sectional analysis of baseline data (N = 289) from two clinical trials testing the efficacy of two different behavioral interventions in older Black primary care patients with type 2 diabetes and MCI to improve glycemic control. Previous publications describe the two studies (Study 1; N = 144 and Study 2; N = 145).3, 4 Institutional Review Board (IRB) approval was obtained, and all participants provided informed consent. Baseline data in both studies included age, sex, and education; hemoglobin A1c level; Patient Health Questionnaire-9 (PHQ-9); participants with PHQ-9 scores ≥10 were considered to have clinically significant depression5; and Beliefs About Medicines Questionnaire (BMQ), which rates medication beliefs from 1 ("strongly disagree") to 5 ("strongly agree").6 Beliefs that were agreed to or strongly agreed to were considered present. To assess cognition, we used the Mini-Mental Status Examination (MMSE)7 in Study 1, and the Montreal Cognitive Assessment (MoCA)8 in Study 2. Statistical tests included one-way analysis of variance (ANOVA) for continuous data and chi-squares for categorical variables. Among 289 participants, 89 (30.8%) met criteria for clinically significant depression. Depressed and nondepressed participants were similar in age, sex, education, and hemoglobin A1c levels (Table 1). In the Study 1 sample, depressed and nondepressed participants had comparable MMSE scores; in the Study 2 sample, depressed participants had lower MoCA scores than nondepressed participants (Table 1). Depressed participants were significantly more likely to endorse negative medication beliefs than nondepressed participants (Figure 1). Many participants held negative beliefs about physicians' medication prescribing (i.e., "doctors use too many medicines"; "doctors place too much trust on medicines"; and "if doctors had more time with patients, they would prescribe fewer medicines") but rates were higher in depressed than nondepressed participants. We found that negative beliefs about diabetes medications were related to depression in older Black patients with type 2 diabetes and MCI. Although depression may induce negative health beliefs (e.g., worry, disruption, and misunderstanding), both depressed and nondepressed participants held many negative medication beliefs, particularly concerning physicians. These findings are important because depression, health beliefs, medication adherence, glycemic control, and diabetes complications are interrelated.9, 10 In this study, all participants had impaired cognition, which can compromise insight and shape health beliefs. Medication beliefs, however, appeared more related to depression than cognition, notwithstanding the somewhat lower MoCA scores in depressed versus nondepressed participants in Study 2. This study has a number of limitations, including uncertain generalizability, absence of data on medication beliefs among individuals with normal cognition, and uncertainty about whether treating depression can modify health beliefs. Despite these limitations, this study suggests that discussing medication beliefs with patients may identify those at risk of medication nonadherence, depression, and impaired cognition. Moreover, such discussions can provide an opportunity to promote positive attitudes about treatment and trust in physicians, and thereby optimize care for older Black individuals, in whom rates of diabetes, depression, and impaired cognition are high. Concept and design: Both authors. Acquisition, analysis, or interpretation of data: Both authors. Drafting of the manuscript: Barry W. Rovner, Robin J. Casten. Statistical analysis: Robin J. Casten. Obtained funding: Barry W. Rovner. The authors have no conflicts of interest to disclose. The sponsors had no role in the study design, data collection, in the analysis and interpretation of data, in the writing of this manuscript, or the decision to submit this manuscript for publication. This study was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; grant R01 DK102609-01) and the National Institute on Aging (R01AG065467).

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)0,001
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 enseignants0,023
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
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 Opus0,013
Tête enseignante GPT0,329
Écart entre enseignants0,316 · 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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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