Evaluating a Smartphone App (MeT4VeT) to Support the Mental Health of UK Armed Forces Veterans: Feasibility Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Previous research demonstrates that less than 50% of military veterans experiencing mental health difficulties seek formal support. Veterans often struggle to identify problems as mental health difficulties. In addition, they may fail to recognize the need for support before reaching a crisis point and face difficulties navigating care pathways to access support. OBJECTIVE: A feasibility trial was conducted to assess a novel digital smartphone app (Mental Health Toolkit for Veterans Project [MeT4VeT]) for UK Armed Forces (UKAF) veterans experiencing mental health difficulties. The trial aimed to explore the feasibility and acceptability of trial procedures for a later randomized controlled trial (RCT) and to assess the acceptability of the MeT4VeT app. METHODS: Participants were recruited at UK military medical centers, by advertising on social media, and through veteran third-sector organizations between February and November 2021, and assessed for eligibility (male, owned a smartphone, served at least 2 years in the UKAF, left the UKAF within the last 2 years, not undertaking formal mental health treatment). Eligible participants were assigned, on a 1:1 ratio, to either the intervention group (full app) or a control group (noninteractive app with signposting information). Three key objectives were determined a priori to assess the practicality of running an RCT including an assessment of recruitment and retention, evaluation of the technical app delivery and measurement processes, and acceptability and usability of the intervention. RESULTS: In total, 791 individuals completed the participant information sheet, of which 261 (33%) were ineligible, 377 (48%) declined or were unable to be contacted for consent, and 103 (13%) did not download the app or complete the baseline measures. Of this, 50 participants completed baseline measures and were randomly assigned to the intervention group (n=24) or the control group (n=26). The trial was effective at enabling both the technical delivery of the intervention and collection of outcome measures, with improvements in mental health demonstrated for the intervention group from baseline to the 3-month follow-up. Recruitment and retention challenges were highlighted with only 50 out of the 530 eligible participants enrolled in the trial. The acceptability and usability of the MeT4VeT app were generally supported, and it was reported to be a useful, accessible way for veterans to monitor and manage their mental health. CONCLUSIONS: The results highlighted that further work is needed to refine recruitment processes and maintain engagement with the app. Following this, an RCT can be considered to robustly assess the ability of the app to positively affect mental health outcomes indicated within this trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT05993676; https://clinicaltrials.gov/ct2/show/NCT05993676.
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 | 0,010 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,003 |
| 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 ».