Blood Glucose Monitoring and Sharing Amongst People With Diabetes and Their Facilitators: Cross-sectional Study of Methods and Practices
Notice bibliographique
Résumé
BACKGROUND: The last two decades have witnessed a burgeoning rise in the prevalence of diabetes globally. It has already reached epidemic proportions in Saudi Arabia, with reported high risk among women. As a result, diabetes monitoring and self-management programs are being highly prioritized for diabetes control and management. OBJECTIVE: To investigate measuring and sharing practices of the self-monitoring of blood glucose (SMBG) among patients with type 1 or 2 diabetes using insulin. METHODS: A cross-sectional study was conducted on a sample of 203 patients attending primary care clinics at a tertiary care center. The questionnaire assessed the measuring, recording, and sharing of SMBG practices of patients having diabetes with their physicians. The methods used for recording and sharing were categorized into paper-based and electronic-based. In addition, the determinants of the different methods used and frequency of sharing were analyzed. RESULTS: The overall monitoring prevalence was 95% (193/203), and 57% (117/203) of participants shared the SMBG results. Among the 193 individuals that performed self-monitoring, 138 (72%) performed daily monitoring, and 147 (76%) recorded their blood sugar levels. Almost 55% (81/147) used paper-based materials like notebooks and paper for recording, while the rest (66/147, 45%) used digital devices like laptops and smartphones. A shift towards the use of digital devices and smart applications was observed in patients below 50 years of age. The digitally recorded blood glucose measurements were being shared thrice more often than the recordings made on paper or in notebooks (OR [odds ratio] 2.8; P=.01). Patients >50 years of age (OR 2.3; P=.02), with lesser formal education, married (OR 4.2; P<.001), with smaller family size (OR 2.6; P=.01), having type 2 diabetes (OR 4.1; P<.001) and any comorbid conditions (OR 2.6; P=.01) were associated with higher odds of using paper-based sharing methods. Only the female gender and type 2 diabetes were associated with increased frequency of sharing, while uncontrolled diabetes, the presence of other comorbidities, and duration of diabetes did not show any influence. CONCLUSIONS: Good monitoring and optimal sharing practices were found. Sharing using electronic devices can be emphasized. Diabetes self-management programs can incorporate the use of digital technology in training sessions. Digital literacy and its applications in health care may enhance SMBG practices resulting in better diabetes control.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».