Low Carb Program Health App Within a Hospital-Based Obesity Setting: Observational Service Evaluation
Notice bibliographique
Résumé
Background Obesity underlies much chronic disease. Digitalization of obesity management provides an opportunity to innovate our traditional model of health care delivery within this setting, and to transform its scalability potentially to the population level. Objective The objective was to assess the feasibility and effectiveness of the Low Carb Program app for weight loss, applied within our hospital-based (tier 3) obesity service. Due to the disrupting effects of the COVID-19 pandemic on our obesity service, we compared the clinical outcomes from the Low Carb Program app applied in the context of remote patient appointments over the telephone with the prepandemic traditional standard of care. Methods We invited patients who attended our hospital-based obesity service to engage with the Low Carb Program smartphone app. We combined this approach with remote delivery (over the telephone) of obesity management from medical and psychology members of our obesity team during the COVID-19 pandemic. Outcome variables included changes in body weight and changes in HbA1c as a marker of glycemic control. We compared data from the Low Carb Program group with a retrospective control group (n=126) that had received traditional face-to-face obesity management from our team without concomitant use of the Low Carb Program app in the pre–COVID-19 era. T test comparisons were employed, with P<.05 considered significant. Results The mean weight of participants (n=105) was 130.2 kg, with 59% (n=62) females and a mean age of 48.8 years. Most participants (90/105, 86%) completed the Low Carb Program app registration process and engaged with the Low Carb Program app program; at follow-up, most participants (88/105, 84%) had actively engaged with the Low Carb Program app within the prior 30 days. The majority of participants (58/105, 55%) self-reported outcomes within the app. Mean duration of clinical follow-up for recruited participants who received the app was 7.4 months. Paired data were available for 48 participants for body weight and 41 participants for HbA1c. Paired sample t test analysis revealed a statistically significant mean loss of body weight of 2.7 kg (P=.001) and improvement in HbA1c of 3.3 mmol/mol (P=.01). The mean weight of control group patients (n=126) was 137.1 kg, with 74% (93/126) females and a mean age of 44.4 years. The mean follow-up for this group was 6 months. Data comparisons between the app user group and the pre–COVID-19 retrospective control group revealed equivalence for loss of body weight and change in HbA1c between the two groups. Conclusions We provide evidence to support the feasibility of implementing the Low Carb Program app combined with remote management; this is the first proof of concept for digitalized management within a hospital-based (tier 3) obesity service. We demonstrate the potential clinical efficacy of the approach in terms of improvements in body weight and glycemic control.
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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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».