Opioid craving and opioid-related problems in the daily lives of patients with chronic pain who are prescribed long-term opioid therapy: Exploration using ambulatory assessment methods
Notice bibliographique
Résumé
Opioid medications are commonly prescribed to patients with chronic pain, but the use of opioids can be accompanied by problems such as opioid misuse and addiction. Evidence indicates that opioid craving (i.e., the desire and/or urge to use opioids) can increase patients' propensity to misuse opioids. Considerable research has been conducted on the determinants of opioid craving among illicit opioid users, but our understanding of factors contributing to craving among patients with chronic pain who are prescribed opioids has lagged behind. There is reason to believe that symptoms of physical dependence, pain, and psychological distress could contribute to opioid craving, but this needs to be further explored among patients with chronic pain. Little is also known on the contribution of biological factors to opioid craving in this population. Upregulation of the hypothalamic-pituitary-adrenal (HPA) axis has been linked to opioid craving among illicit opioid users, but the contribution of HPA axis activity to opioid craving among medical users of opioids has remained unexplored.The present thesis includes three empirical studies that relied on observational, micro-longitudinal study designs and the use of ambulatory assessment methods. Manuscript 1 explored the day-to-day association between symptoms of physical dependence (i.e., opioid withdrawal symptoms) and opioid craving over 14 days. Results indicated that day-to-day elevations in opioid withdrawal symptoms were associated with heightened opioid craving, and that this association was mediated by negative affect (NA) and catastrophic thinking. These findings suggest that opioid withdrawal symptoms might contribute to increases in negative affect and catastrophic thinking, which in turn might lead to heightened reports of opioid craving. Building upon these findings, Manuscript 2 relied on ecological momentary assessment (EMA) procedures for 10 consecutive days to examine the factors contributing to intra-day fluctuations in opioid craving. The factors contributing to daily opioid intake and opioid misuse were also examined. Findings revealed that intra-day elevations in opioid withdrawal symptoms, pain intensity and catastrophizing significantly contributed to opioid craving. Results also indicated that higher opioid craving levels were associated with greater opioid intake, even after accounting for patients’ opioid withdrawal symptoms, pain intensity, and psychological states. Higher reports of catastrophizing and opioid craving were associated with a greater likelihood of misusing opioids, but these associations were no longer significant when accounting for patients’ levels of pain and other daily variables. Finally, Manuscript 3 relied on EMA procedures involving a combination of self-reported diaries and saliva sampling to examine whether intra-individual variations in HPA axis (i.e., cortisol) activity contributed to patients’ reports of opioid craving. The interactive effects of HPA axis activity and psychological states on opioid craving were also examined. In this study, we found that the association between negative affect and opioid craving was stronger during concurrent elevations in cortisol. These results suggest that upregulation in HPA axis activity might amplify the effects of psychological states, such as NA, on opioid craving. Taken together, findings from the present thesis provide valuable new insights into the factors that contribute to opioid craving, opioid intake, and opioid-related problems among patients with chronic pain who are prescribed long-term opioid therapy. The present thesis might also stimulate further research on the psychological and biological determinants of opioid craving in this population. Importantly, findings from the present thesis could inform the nature of interventions aimed at preventing or reducing opioid-related harms among patients with chronic pain
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».