A Mobile-Based Intervention for Glycemic Control in Patients With Type 2 Diabetes: Retrospective, Propensity Score-Matched Cohort Study
Notice bibliographique
Résumé
Background Mobile-based interventions appear to be promising in ameliorating huge burdens experienced by patients with type 2 diabetes. However, it is unclear how effective mobile-based interventions are in glycemic management of patients with type 2 diabetes based on real-world evidence. Objective This study aimed to evaluate the effectiveness of a mobile-based intervention on glycemic control in patients with type 2 diabetes based on real-world population data. Methods This retrospective, propensity score-matched cohort study analyzed longitudinal data from a clinical electronic health database. The study population included 37,913 patients with type 2 diabetes at cohort entry between October 1, 2016, and July 31, 2018. A total of 2400 patients were matched 1:1, using propensity score matching, into the usual care and mobile health (mHealth) groups. The primary outcomes of glycemic control included control rates of glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and postprandial 2-hour blood glucose (P2BG). Mean values and variation trends of difference with 95% CI were the secondary outcomes. The general linear model was used to calculate repeated-measures analyses of variance to examine the differences between the two groups. Subgroup and sensitivity analyses were performed. Results Of the 2400 patients included in the analysis, 1440 (60.00%) were male and the mean age was 52.24 years (SD 11.56). At baseline, the control rates of HbA1c, FBG, and P2BG in the mHealth and usual care groups were 45.75% versus 47.00% (P=.57), 38.03% versus 32.76% (P=.07), and 47.32% versus 47.89% (P=.83), respectively. At the 3-, 6-, 9-, and 12-month follow-ups, the mHealth group reported higher control rates of HbA1c than did the usual care group: 69.97% versus 46.06% (P<.001), 71.89% versus 61.24% (P=.004), 75.38% versus 53.44% (P<.001), and 72.31% versus 46.70% (P<.001), respectively. At the four follow-up sessions, the control rates of FBG in the mHealth and usual care groups were statistically different: 59.24% versus 34.21% (P<.001), 56.61% versus 35.14% (P<.001), 59.54% versus 34.99% (P<.001), and 59.77% versus 32.83% (P<.001), respectively. At the four follow-up sessions, the control rates of P2BG in the mHealth group were statistically higher than in the usual care group: 79.72% versus 48.75% (P<.001), 80.20% versus 57.45% (P<.001), 81.97% versus 54.07% (P<.001), and 76.19% versus 54.21% (P=.001), respectively. At the four follow-up sessions, the percentages of HbA1c reduction in the mHealth group were 8.66% (95% CI 6.69-10.63), 10.60% (95% CI 8.66-12.54), 10.64% (95% CI 8.70-12.58), and 8.11% (95% CI 6.08-10.14), respectively. At the four follow-up sessions, the percentages of P2BG reduction in the mHealth group were 8.44% (95% CI 7.41-10.73), 17.77% (95% CI 14.98-20.23), 16.23% (95% CI 13.05-19.35), and 16.91% (95% CI 13.17-19.84), respectively. Starting from the sixth month, the mean HbA1c and P2BG values in the two groups increased slightly. Conclusions This mobile-based intervention delivered by a multidisciplinary team can better improve glycemic control rates of patients with type 2 diabetes than usual care. These effects were best sustained within the first 6 months. Starting from the sixth month, intensive management needs to be conducted to maintain long-term effectiveness of the mobile-based intervention.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».