Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic impacted how people accessed health services and likely how they managed chronic conditions such as type 2 diabetes (T2D). Social media forums present a source of qualitative data to understand how adaptation might have occurred from the perspective of the patient. OBJECTIVE: Our objective is to understand how the care-seeking behaviors and attitudes of people living with T2D were impacted during the early part of the pandemic by conducting a scoping literature review. A secondary objective is to compare the findings of the scoping review to those presented on a popular social media platform Reddit. METHODS: A scoping review was conducted in 2021. Inclusion criteria were population with T2D, studies are patient-centered, and study objectives are centered around health behaviors, disease management, or mental health outcomes during the COVID-19 pandemic. Exclusion criteria were populations with other noncommunicable diseases, examining COVID-19 as a comorbidity to T2D, clinical treatments for COVID-19 among people living with T2D, genetic expressions of COVID-19 among people living with T2D, gray literature, or studies not published in English. Bias was mitigated by reviewing uncertainties with other authors. Data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Data from the Reddit forums related to T2D from March 2020 to early March 2021 were downloaded, and support vector machines were used to classify if a post was published in the context of the pandemic. Latent Dirichlet allocation topic modeling was performed to gather topics of discussion specific to the COVID-19 pandemic. RESULTS: A total of 26 studies conducted between February and September 2020, consisting of 13,673 participants, were included in this scoping literature review. The studies were qualitative and relied mostly on qualitative data from surveys or questionnaires. Themes found from the literature review were "poorer glycemic control," "increased consumption of unhealthy foods," "decreased physical activity," "inability to access medical appointments," and "increased stress and anxiety." Findings from latent Dirichlet allocation topic modeling of Reddit forums were "Coping With Poor Mental Health," "Accessing Doctor & Medications and Controlling Blood Glucose," "Changing Food Habits During Pandemic," "Impact of Stress on Blood Glucose Levels," "Changing Status of Employment & Insurance," and "Risk of COVID Complications." CONCLUSIONS: Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D, which were found to be comparable to the findings of the literature review. The study was limited by only having 1 reviewer for the literature review, but biases were mitigated by consulting authors when there were uncertainties. Qualitative analysis of Reddit forms can supplement traditional qualitative studies of the behaviors of people living with T2D.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,045 | 0,164 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».