A Wearable Technology Delivering a Web-Based Diabetes Prevention Program to People at High Risk of Type 2 Diabetes: Randomized Controlled Trial
Notice bibliographique
Résumé
Background Intensive lifestyle interventions are effective in reducing the risk of type 2 diabetes, but the implementation of learnings from landmark studies is expensive and time consuming. The availability of digital lifestyle interventions is increasing, but evidence of their effectiveness is limited. Objective This randomized controlled trial (RCT) aimed to test the feasibility of a web-based diabetes prevention program (DPP) with step-dependent feedback messages versus a standard web-based DPP in people with prediabetes. Methods We employed a two-arm, parallel, single-blind RCT for people at high risk of developing diabetes. Patients with a hemoglobin A1c (HbA1c) level of 39-47 mmol/mol were recruited from 21 general practices in London. The intervention integrated a smartphone app delivering a web-based DPP course with SMS texts incorporating motivational interviewing techniques and step-dependent feedback messages delivered via a wearable device over 12 months. The control group received the wearable technology and access to the web-based DDP but not the SMS texts. As this was a feasibility study, the primary aim was to estimate potential sample size at different stages of the study, including the size of the target study population and the proportion of participants who consented, were randomized, and completed follow-up. We also measured the main outcomes for a full-scale RCT, namely, change in weight and physical activity at 6- and 12-month follow-ups, and secondary outcomes, including changes in the HbA1c level, blood pressure, waist circumference, waist-to-hip ratio, and lipid levels. Results We enrolled 200 participants: 98 were randomized to the intervention and 102 were randomized to the control group. The follow-up rate was higher in the control group (87/102, 85.3%) than in the intervention group (69/98, 70%) at 12 months. There was no treatment effect on weight at 6 months (mean difference 0.15; 95% CI −0.93 to 1.23) or 12 months (mean difference 0.07 kg; 95% CI −1.29 to 1.44) or for physical activity levels at 6 months (mean difference −382.90 steps; 95% CI −860.65 to 94.85) or 12 months (mean difference 92.64 steps; 95% CI −380.92 to 566.20). We did not observe a treatment effect on the secondary outcomes measured at the 6-month or 12-month follow-up. For the intervention group, the mean weight was 92.33 (SD 15.67) kg at baseline, 91.34 (SD 16.04) kg at 6 months, and 89.41 (SD 14.93) kg at 12 months. For the control group, the mean weight was 92.59 (SD 17.43) kg at baseline, 91.71 (SD 16.48) kg at 6 months, and 91.10 (SD 15.82) kg at 12 months. In the intervention group, the mean physical activity was 7308.40 (SD 4911.93) steps at baseline, 5008.76 (SD 2733.22) steps at 6 months, and 4814.66 (SD 3419.65) steps at 12 months. In the control group, the mean physical activity was 7599.28 (SD 3881.04) steps at baseline, 6148.83 (SD 3433.77) steps at 6 months, and 5006.30 (SD 3681.1) steps at 12 months. Conclusions This study demonstrates that it is feasible to successfully recruit and retain patients in an RCT of a web-based DPP. Trial Registration ClinicalTrials.gov NCT02919397; http://clinicaltrials.gov/ct2/show/NCT02919397
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| 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 | 0,001 | 0,001 |
| 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 ».