A Mobile Ecological Momentary Intervention for Reducing Experiential Avoidance in the Context of Rumination: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Rumination is a transdiagnostic process present in several psychological disorders, involving repetitive negative thinking that individuals may perceive as difficult to control. While the roles of numerous mechanisms underlying rumination have been supported, experiential avoidance (EA) still lacks empirical backing, despite a strong theoretical foundation, partly due to difficulties in examining EA in an ecologically valid context. One promising approach to addressing this challenge is through reducing EA using mobile health (mHealth) and ecological momentary intervention (EMI), and assessing any subsequent decrease in rumination's deleterious outcomes. OBJECTIVE: This paper outlines the protocol for a randomized controlled trial using a novel mHealth EMI to address EA in the context of rumination. The app was developed by a multidisciplinary team, incorporating feedback from potential end users. METHODS: Consenting individuals (target N=60) who meet the inclusion criteria (self-reporting problems with repetitive negative thinking) will be randomly assigned to 1 of 4 conditions: (1) intervention with therapist support and daily sampling, (2) intervention without support and with daily sampling, (3) partial intervention (emotion validation EMI only) with daily sampling, or (4) control (daily sampling only). The intervention consists of a series of modules delivered over 4 weeks, with assessments conducted before and after intervention, and again at 1-month follow-up (plus an additional 3-month follow-up for intervention participants). Data will be collected both through online self-report assessments and via the app itself. The potential of the EMI to modify the maladaptive feature of repetitive negative thinking will be assessed using mixed-design ANOVA, while the links between avoidance-mood and rumination-mood, in terms of the moderating effect of trial condition, will be evaluated using multilevel models. These will be assessed as primary outcomes. Secondary outcomes are the effects of a supporting therapist on postintervention outcomes in the intervention groups, and time spent using the app as a measure of engagement (analyzed using the mixed-design analysis of covariance). Compliance will be defined as completing both of the first 2 weeks of intervention content in full, and 5 out of the 6 exercises from weeks 3 and 4. Additionally, the study will control for both the amount of time spent in the app and the length of responses to open-ended questions. RESULTS: Recruitment and enrollment for the trial are expected to begin in May 2025 and be completed by July 2025. Data collection will conclude once the target sample size for each of the 4 conditions has been reached. The main results of the trial are expected to be submitted for publication in October 2025. CONCLUSIONS: The outcomes of this research trial will not only provide insights into the clinical capabilities of the app, including its usability and acceptability in real-world contexts (and its potential future viability as a scalable product), but will also offer valuable theoretical insights into the role of EA in maladaptive rumination. TRIAL REGISTRATION: ClinicalTrials.gov NCT06570694; https://clinicaltrials.gov/ct2/show/NCT06570694. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/66067.
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 | 0,024 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,004 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,011 |
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 ».