Brief, Web-Based Interventions to Motivate Smokers With Schizophrenia: Randomized Trial
Notice bibliographique
Résumé
Background In-person motivational interventions increase engagement with evidence-based cessation treatments among smokers with schizophrenia, but access to such interventions can be limited because of workforce shortages and competing demands in mental health clinics. The use of digital technology to deliver interventions can increase access, but cognitive impairments in schizophrenia may impede the use of standard digital interventions. We developed an interactive, multimedia, digital motivational decision support system for smokers with schizophrenia (Let’s Talk About Smoking). We also digitalized a standard educational pamphlet from the National Cancer Institute (NCI Education). Both were tailored to reduce cognitive load during use. Objective We conducted a randomized trial of Let’s Talk About Smoking versus NCI Education to test whether the interactive motivational intervention was more effective and more appealing than the static educational intervention for increasing use of smoking cessation treatment, quit attempts, and abstinence among smokers with schizophrenia, accounting for the level of cognitive functioning. Methods Adult smokers with schizophrenia (n=162) were enrolled in the study from 2014 to 2015, randomly assigned to intervention condition, and assessed in person at 3- and 6-month follow-ups. Interventions were delivered on a laptop computer in a single session. All participants had access to standard, community-delivered cessation treatments during follow-up. Multivariate models were used to evaluate outcomes. Results Treatment initiation outcomes were not different between intervention conditions (27/84 [32%] for Let’s Talk About Smoking vs 36/78 [46%] for NCI Education; odds ratio [OR] 0.71 [95% CI 0.37-1.33]); 38.9% (63/162) of participants initiated treatment. Older age (OR 1.03 [95% CI 1.00-1.07]; P=.05), higher education (OR 1.21 [95% CI 1.04-1.41]; P=.03), and fewer positive symptoms (OR 0.87 [95% CI 0.80-0.96]; P=.01) predicted cessation treatment initiation, whereas level of cognition did not. The mean satisfaction and usability index score was higher for Let’s Talk About Smoking versus NCI Education (8.9 [SD 1.3] vs 8.3 [SD 2.1]; t120.7=2.0; P=.045). Quit attempts (25/84, 30% vs 36/78, 46%; estimate [Est]=−0.093, SE 0.48; P=.85) and abstinence (1/84, 1% vs 6/78, 7%; χ21=3.4; P=.07) were not significantly different between intervention conditions. Cognitive functioning at baseline (Est=1.47, SE 0.47; P=.002) and use of any behavioral or medication cessation treatment (Est=1.43, SE 0.47; P=.003) predicted quit attempts with self-reported abstinence over the 6-month follow-up. Conclusions The interactive, multimedia intervention was not more effective than the static, text-based intervention among smokers with schizophrenia. Both tailored digital interventions resulted in levels of treatment engagement and quit attempts that were similar to findings from previous studies of in-person interventions, confirming the potential role of digital interventions to educate and motivate smokers with schizophrenia to use cessation treatment and to quit smoking. These findings indicate that additional cessation treatment is needed after brief education or motivational interventions, and that cessation treatment should be adjusted for people with cognitive impairment. Trial Registration ClinicalTrials.gov NCT02086162; https://clinicaltrials.gov/show/NCT02086162.
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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) | 0,001 | 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) | 0,001 | 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 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 ».