Digital Behavioural Interventions to Reduce Opioid Use in Chronic Non-Cancer Pain Patients: A Systematic Review
Notice bibliographique
Résumé
Abstract Introduction Opioid medications have become increasingly prevalent in the treatment of chronic non-cancer pain1. This increase in opioid usage has given rise to the ‘Opioid Crisis’ which refers to the significant increase in overdose, addiction, and deaths in the USA and Canada particularly2. Furthermore, the World Health Organisation has declared there to have been approximately 0.5 million deaths in relation to drug use worldwide, with more than 70% of those deaths related to opioids3. Advances in technology have paved the way for the use of digital behavioural interventions, such as mobile applications, virtual reality, and online therapies, to potentially mitigate opioid use in patients suffering from chronic non-cancer pain. These interventions aim to assist individuals in modifying health behaviours to promote overall well-being and may serve as a preventive measure against the detrimental effects of long-term opioid use. Aim This systematic review aimed to evaluate the current evidence regarding the effectiveness of digital behavioural interventions in reducing opioid use among chronic non-cancer pain patients. Methods A comprehensive search was conducted in three electronic databases (MEDLINE, EMBASE, and Web of Science) using specific search terms. Primary outcomes focused on opioid usage measurements, while secondary outcomes pertained to pain measurements. Eligible studies included adult patients with chronic non-cancer pain (excluding cancer/palliative patients) who received opioid prescriptions and engaged in digital behavioural interventions in any setting. Two researchers double-screened eligible studies based on titles and abstracts, excluding duplicates and those not meeting the eligibility criteria. Data extraction was performed, and study quality was assessed using the Cochrane Risk of Bias Tool (double-screened). Ethical approval was not required as this study was a systematic review. Results Out of 30,388 articles identified, six studies conducted in the USA met the inclusion criteria. Risk of bias assessment revealed one study with a low risk, two with some concerns, and the majority with a high risk of bias. The publication dates of the identified papers ranged from 2016 to 2021. There was an overall participant population of 605. The interventions included virtual reality, mobile health applications, web-based cognitive-behavioural therapy, guided audio-visual relaxation, and an electronic health toolkit. Two studies reported a specific reduction in opioid use, while three studies did not provide direct measurements for opioid use. However, these studies indicated a reduction in opioid analgesic usage by reporting either over-the-counter analgesic measures or a Current Opioid Misuse Measure score. Discussion/Conclusion This systematic review was conducted in response to the need for updated pain management strategies. The literature suggests that digital behavioural interventions hold promise for reducing opioid use among chronic non-cancer pain patients. However, due to limitations in the included studies, such as sample size, heterogeneity, and potential bias, further research is needed to fully understand their effectiveness. Future studies should focus on optimising the design and delivery of the interventions tailored to this patient population. Continued research in this area has the potential to address the societal burden of opioid misuse and improve patients' quality of life. References 1. Rosenblum A, Marsch LA, Joseph H, Portenoy RK. Opioids and the treatment of chronic pain: controversies, current status, and future directions. Experimental and Clinical Psychopharmacology 2008;16(5):405-16. 2. The Lancet Public Health. Opioid overdose crisis: time for a radical rethink. The Lancet Public Health. The Lancet Public Health; 2022;7(3):e195. 3. World Health Organization. Opioid overdose; 2021 [Date Accessed: 15/01/2023] [Available from: https://www.who.int/news-room/fact-sheets/detail/opioid-overdose]
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 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 ».