Text Messaging Intervention for Young Smokers Experiencing Homelessness: Lessons Learned From a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Smoking rates are significantly higher among young people experiencing homelessness than in the general population. Despite a willingness to quit, homeless youth have little success in doing so on their own, and existing cessation resources tailored to this population are lacking. Homeless youth generally enjoy the camaraderie and peer support that group-based programs offer, but continuous in-person support during a quit attempt can be prohibitively expensive. OBJECTIVE: This study aimed to assess the feasibility and acceptability of an automated text messaging intervention (TMI) as an adjunct to group-based cessation counseling and provision of nicotine patches to help homeless youth quit smoking. This paper outlines the lessons learned from the implementation of the TMI intervention. METHODS: Homeless youth smokers aged 18 to 25 years who were interested in quitting (n=77) were recruited from drop-in centers serving homeless youth in the Los Angeles area. In this pilot randomized controlled trial, all participants received a group-based cessation counseling session and nicotine patches, with 52% (40/77) randomly assigned to receive 6 weeks of text messages to provide additional support for their quit attempt. Participants received text messages on their own phone rather than receiving a study-issued phone for the TMI. We analyzed baseline and follow-up survey data as well as back-end data from the messaging platform to gauge the acceptability and feasibility of the TMI among the 40 participants who received it. RESULTS: Participants had widespread (smart)phone ownership-16.4% (36/219) were ineligible for study participation because they did not have a phone that could receive text messages. Participants experienced interruptions in their phone use (eg, 44% [16/36] changed phone numbers during the follow-up period) but reported being able to receive the majority of messages. These survey results were corroborated by back-end data (from the program used to administer the TMI) showing a message delivery rate of about 95%. Participant feedback points to the importance of carefully crafting text messages, which led to high (typically above 70%) approval of most text messaging components of the intervention. Qualitative feedback indicated that participants enjoyed the group counseling session that preceded the TMI and suggested including more such group elements into the intervention. CONCLUSIONS: The TMI was well accepted and feasible to support smoking cessation among homeless youth. Given high rates of smartphone ownership, the next generation of phone-based smoking cessation interventions for this population should consider using approaches beyond text messages and focus on finding ways to develop effective approaches to include group interaction using remote implementation. Given overall resource constraints and in particular the exigencies of the currently ongoing COVID-19 epidemic, phone-based interventions are a promising approach to support homeless youth, a population urgently in need of effective smoking cessation interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03874585; https://clinicaltrials.gov/ct2/show/NCT03874585. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13722-020-00187-6.
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 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 | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».