Effects of Text4Hope-Addiction Support Program on Cravings and Mental Health Symptoms: Results of a Longitudinal Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Drug misuse is complex, and various treatment modalities are emerging. Providing supportive text messages to individuals with substance use disorder offers the prospect of managing and improving symptoms of drug misuse and associated comorbidities. OBJECTIVE: This study evaluated the impact of the daily supportive text message program (Text4Hope-Addiction Support) in mitigating cravings and mental health symptoms in subscribers and quantify user satisfaction with the Text4Hope-Addiction Support program. METHODS: Subscribers to the Text4Hope-Addiction Support program received daily supportive text messages for 3 months; the messages were crafted based on addiction counseling and cognitive behavioral therapy principles. Participants completed an anonymous web-based questionnaire to assess cravings, anxiety, and depressive symptoms using the Brief Substance Craving Scale (BSCS), Generalized Anxiety Disorder-7 (GAD-7) scale, and Patient Health Questionnaire-9 (PHQ-9) scale at enrollment (baseline), after 6 weeks, and after 3 months. Likert scale satisfaction responses were used to assess various aspects of the Text4Hope-Addiction program. RESULTS: In total, 408 people subscribed to the program, and 110 of 408 (26.9%) subscribers completed the surveys at least at one time point. There were significant differences between the mean baseline and 3-month BSCS scores P=.01 (-2.17, 95% CI -0.62 to 3.72), PHQ-9 scores, P=.004 (-5.08, 95% CI -1.65 to -8.51), and GAD-7 scores, P=.02 (-3.02, 95% CI -0.48 to -5.56). Participants who received the supportive text messages reported a reduced desire to use drugs and a longer time interval between substance use, which are reflected in 41.1% and 32.5% decrease, respectively, from baseline score. Approximately 89% (23/26) of the participants agreed that Text4Hope-Addiction program helped them cope with addiction-related stress, and 81% (21/25) of the participants reported that the messages assisted them in dealing with anxiety. Overall, 69% (18/26) of the participants agreed that it helped them cope with depression related to addiction; 85% (22/26) of the participants felt connected to a support system; 77% (20/26) of the participants were hopeful of their ability to manage addiction issues; and 73% (19/26) of the participants felt that their overall mental well-being was improved. Most of the participants agreed that the interventions were always positive and affirmative (19/26, 73%), and succinct (17/26, 65%). Furthermore, 88% (21/24) of the participants always read the messages; 83% (20/24) of the participants took positive or beneficial actions after reading; and no participant took a negative action after reading the messages. In addition, most participants agreed to recommend other diverse technology-based services as an adjunctive treatment for their mental and physical health disorders. CONCLUSIONS: Subscribers of Text4Hope-Addiction Support program experienced improved mental health and addiction symptoms. Addiction care practitioners and policy makers can implement supportive text-based strategies to complement conventional treatments for addiction, given that mobile devices are widely used.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 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 ».