Remote Foot Temperature Monitoring Among Veterans: Large Observational Study of Noncompliance and Its Correlates
Notice bibliographique
Résumé
Background: In-home remote foot temperature monitoring (RTM) holds promise as a method to reduce foot ulceration in high-risk patients with diabetes. Few studies have evaluated adherence to this method or evaluated the factors associated with noncompliance. Objective: The aims of this study were to estimate noncompliance in patients who were enrolled in RTM nationwide across Department of Veterans Affairs (VA) and to evaluate characteristics associated with noncompliance. Methods: We conducted an observational study including 1137 patients in the VA who were enrolled in RTM between January 2019 and June 2021, with follow-up through October 2021. Patient information was obtained from the VA's electronic health record and RTM use was obtained from the company. Noncompliance was defined as using the mat <2 days per week for ≥4 of the 12 months of follow-up. Using a multivariable model, we calculated odds ratios (ORs) and 95% CIs for associations between various factors and noncompliance and compared using Akaike information criterion statistics, a measure of model fit. Results: The sample was predominantly male (n=1125, 98.94%) ; 21.1% (n=230) were Black and 75.7% (n=825) were White. Overall, 37.6% (428/1137) of patients were classified as noncompliant. In the multivariable model, an intermediate area deprivation index was statistically significantly and inversely associated with noncompliance (area deprivation index 50-74 vs 1-24; OR 0.56, 95% CI 0.35-0.89); factors significantly and positively associated with noncompliance included recent history of osteomyelitis (OR 1.44, 95% CI 1.06-1.97), Gagne comorbidity index score ≥4 (vs ≤0; OR 1.81, 95% CI 1.15-2.83), telehealth encounters (28+ vs <6; OR 1.70, 95% CI 1.02-2.84), hemoglobin A1c≥10 (vs <5.7; OR 2.67, 95% CI 1.27-5.58), and current smoking (OR 2.06, 95% CI 1.32-3.20). Based on Akaike information criterion differences, the strongest factors associated with noncompliance were behavioral factors (poor glucose control [as measured by hemoglobin A1c] and smoking), and to a lesser extent, factors such as a recent history of osteomyelitis and an elevated Gagne comorbidity index, indicating a high comorbidity burden. Conclusions: To reduce the risk of ulcer recurrence and amputation, proactively providing additional support for self-monitoring to patients with characteristics identified in this study (poor glucose control, current smoking, high comorbidity burden) may be helpful. Furthermore, research is needed to better understand barriers to use, and whether the addition of design features, reminders, or incentives may reduce noncompliance and the risk of foot ulcers.
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 | 0,000 | 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 ».