Comparing Email Versus Text Messaging as Delivery Platforms for Supporting Patients With Major Depressive Disorder: Noninferiority Randomized Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND The prevalence of major depressive disorder (MDD) poses significant global health challenges, with available treatments often insufficient in achieving remission for many patients. Digital health technologies, such as SMS text messaging–based cognitive behavioral therapy, offer accessible alternatives but may not reach all individuals. Email communication presents a secure avenue for health communication, yet its effectiveness compared to SMS text messaging in providing mental health support for patients with MDD remains uncertain. OBJECTIVE This study aims to compare the efficacy of email versus SMS text messaging as delivery platforms for supporting patients with MDD, addressing a critical gap in understanding optimal digital interventions for mental health care. METHODS A randomized noninferiority pilot trial was conducted, comparing outcomes for patients receiving 6-week daily supportive messages via email with those receiving messages via SMS text message. This duration corresponds to a minimum of 180 days of message delivery. The supportive messages maintained consistent length and structure across both delivery methods. Participants (N=66) were recruited from the Access 24/7 clinic in Edmonton, Alberta, among those who were diagnosed with MDD. The outcomes were measured at baseline and 6 months after enrollment using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the World Health Organization Well-Being Index (WHO-5). RESULTS Most of the participants were females (n=43, 65%), aged between 26 and 40 years (n=34, 55%), had high school education (n=35, 58%), employed (n=33, 50%), and single (n=24, 36%). Again, most participants had had no history of any major physical illness (n=56, 85%) and (n=61, 92%) responded “No” to having a history of admission for treatment of mood disorders. There was no statistically significant difference in the mean changes in PHQ-9, GAD-7, and WHO-5 scores between the email and SMS text messaging groups (mean difference, 95% CI: –1.90, 95% CI –6.53 to 2.74; 5.78, 95% CI –1.94 to 13.50; and 11.85, 95% CI –3.81 to 27.51), respectively. Both supportive modalities showed potential in reducing depressive symptoms and improving quality of life. CONCLUSIONS The study’s findings suggest that both email and SMS text messaging interventions have equivalent effectiveness in reducing depression symptoms among individuals with MDD. As digital technology continues to evolve, harnessing the power of multiple digital platforms for mental health interventions can significantly contribute to bridging the existing treatment gaps and improving the overall well-being of individuals with depressive conditions. Further research is needed with a larger sample size to confirm and expand upon these findings. CLINICALTRIAL ClinicalTrials.gov NCT04638231; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552095/
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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 ».