MétaCan
Menu
Retour à la cohorte
Enregistrement W2532713515 · doi:10.2196/mhealth.5863

Text Message-Based Intervention Targeting Alcohol Consumption Among University Students: Findings From a Formative Development Study

2016· article· en· W2532713515 sur OpenAlexvenueno aff
Kristin Thomas, Catharina Linderoth, Marcus Bendtsen, Preben Bendtsen, Ulrika Müssener

Notice bibliographique

RevueJMIR mhealth and uhealth · 2016
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesFolkhälsomyndighetenPublic Health Agency
Mots-clésShort Message ServiceFormative assessmentFocus groupPsychological interventionIntervention (counseling)Descriptive statisticsmHealthPsychologyText messagingQualitative researchBehavior changeMedical educationData collectionComputer scienceApplied psychologyMedicineWorld Wide WebNursingSocial psychologyMathematics education

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Drinking of alcohol among university students is a global phenomenon; heavy episodic drinking is accepted despite several potential negative consequences. There is emerging evidence that short message service (SMS) text messaging interventions are effective to promote behavior change among students. However, it is still unclear how effectiveness can be optimized through intervention design or how user interest and adherence can be maximized. OBJECTIVE: The objective of this study was to develop an SMS text message-based intervention targeting alcohol drinking among university students using formative research. METHODS: A formative research design was used including an iterative revision process based on input from end users and experts. Data were collected via seven focus groups with students and a panel evaluation involving students (n=15) and experts (n=5). Student participants were recruited from five universities in Sweden. A semistructured interview guide was used in the focus groups and included questions on alcohol culture, message content, and intervention format. The panel evaluation asked participants to rate to what degree preliminary messages were understandable, usable, and had a good tone on a scale from 1 (very low degree) to 4 (very high degree). Participants could also write their own comments for each message. Qualitative data were analyzed using qualitative descriptive analysis. Quantitative data were analyzed using descriptive statistics. The SMS text messages and the intervention format were revised continuously in parallel with data collection. A behavior change technique (BCT) analysis was conducted on the final version of the program. RESULTS: Overall, students were positive toward the SMS text message intervention. Messages that were neutral, motivated, clear, and tangible engaged students. Students expressed that they preferred short, concise messages and confirmed that a 6-week intervention was an appropriate duration. However, there was limited consensus regarding SMS text message frequency, personalization of messages, and timing. Overall, messages scored high on understanding (mean 3.86, SD 0.43), usability (mean 3.70, SD 0.61), and tone (mean 3.78, SD 0.53). Participants added comments to 67 of 70 messages, including suggestions for change in wording, order of messages, and feedback on why a message was unclear or needed major revision. Comments also included positive feedback that confirmed the value of the messages. Twenty-three BCTs aimed at addressing self-regulatory skills, for example, were identified in the final program. CONCLUSIONS: The formative research design was valuable and resulted in significant changes to the intervention. All the original SMS text messages were changed and new messages were added. Overall, the findings showed that students were positive toward receiving support through SMS text message and that neutral, motivated, clear, and tangible messages promoted engagement. However, limited consensus was found on the timing, frequency, and tailoring of messages.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,073
Tête enseignante GPT0,439
Écart entre enseignants0,367 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations48
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJMIR mhealth and uhealthMême sujetMobile Health and mHealth ApplicationsTravaux en français237 207