Beating Cancer-Related Fatigue With the Untire Mobile App: Protocol for a Waiting List Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Many cancer patients and survivors worldwide experience disabling fatigue as the main side effect of their illness and the treatments involved. Face-to-face therapy is effective in treating cancer-related fatigue (CRF), but it is also resource-intensive. Offering a self-management program via a mobile phone app (ie, the Untire app), based on elements of effective face-to-face treatments, might increase the number of patients receiving adequate support for fatigue and decrease care costs. OBJECTIVE: The aim of this protocol is to describe a randomized controlled trial (RCT) to assess the effectiveness of the Untire app in reducing fatigue in cancer patients and survivors after 12 weeks of app use as compared with a waiting list control group. Substudies nested within this trial include questions concerning the reach and costs of online recruitment and uptake and usage of the Untire app. METHODS: The Untire app study is a waiting list RCT targeting cancer patients and survivors who experience moderate to severe fatigue via social media (Facebook and Instagram) across 4 English-speaking countries (Australia, Canada, the United Kingdom, and the United States). The Untire app includes psychoeducation and exercises concerning energy conservation, activity management, optimizing restful sleep, mindfulness-based stress reduction, psychosocial support, cognitive behavioral therapy, and physical activity. After randomization, participants in the intervention group could access the Untire app immediately, whereas control participants had no access to the Untire app until the primary follow-up assessment at 12 weeks. Participants completed questionnaires at baseline before randomization and after 4, 8, 12, and 24 weeks. The study outcomes are fatigue (primary) and quality of life (QoL; secondary). Potential moderators and mediators of the hypothesized treatment effect on levels of fatigue and QoL were also assessed. Link clicks and app activation are used to assess reach and uptake, respectively. Log data are used to explore the characteristics of app use. Sample size calculations for the primary outcome showed that we needed to include 164 participants with complete 12-week measures both in the intervention and the control groups. The intention-to-treat approach is used in the primary analyses, which refers to analyzing all participants regardless of their app use. RESULTS: Participants were recruited from March to October 2018. The last participant completed the 24-week assessment in March 2019. CONCLUSIONS: This mobile health (mHealth) RCT recruited participants online in multiple countries to examine the uptake and effectiveness of the Untire self-management app to reduce CRF. Many advantages of mHealth apps are assumed, such as the immediate access to the app, the low thresholds to seek support, and the absence of contact with care professionals that will reduce costs. If found effective, this app can easily be offered worldwide to patients experiencing CRF. TRIAL REGISTRATION: Netherlands Trial Register NL6642; https://www.trialregister.nl/trial/6642. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15969.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».