Benefits, Recruitment, Dropout, and Acceptability of the Strength Back Digital Health Intervention for Patients Undergoing Spinal Surgery: Nonrandomized, Qualitative, and Quantitative Pilot Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Patients undergoing spinal surgery report high levels of insecurity, pain, stress, and anxiety before and after surgery. Unfortunately, there is no guarantee that surgery will resolve all issues; postsurgical recovery often entails moderate to severe postoperative pain, and some patients undergoing spinal surgery do not experience (long-term) pain relief after surgery. Therefore, focusing on sustainable coping skills and resilience is crucial for these patients. A digital health intervention based on acceptance and commitment therapy (ACT) and positive psychology (PP) was developed to enhance psychological flexibility and well-being and reduce postsurgical pain. OBJECTIVE: The objective of this study was 3-fold: to explore the potential benefits for patients undergoing spinal surgery of the digital ACT and PP intervention Strength Back (research question [RQ] 1), explore the feasibility of a future randomized controlled trial in terms of recruitment and dropout (RQ 2), and assess the acceptability of Strength Back by patients undergoing spinal surgery (RQ 3). METHODS: We used a nonrandomized experimental design with an intervention group (n=17) and a control group (n=20). To explore the potential benefits of the intervention, participants in both groups filled out questionnaires before and after surgery. These questionnaires included measurements of pain intensity (Numeric Pain Rating Scale), pain interference (Multidimensional Pain Inventory), anxiety and depression (Hospital Anxiety and Depression Scale), valued living (Engaged Living Scale), psychological flexibility (Psychological Inflexibility in Pain Scale), and mental well-being (Mental Health Continuum-Short Form). Semistructured interviews combined with log data and scores on the Twente Engagement With eHealth Technologies Scale were used to assess the acceptability of the intervention. RESULTS: A significant improvement over time in emotional (V=99; P=.03) and overall (V=55; P=.004) well-being (Mental Health Continuum-Short Form) was observed only in the intervention group. In addition, the intervention group showed a significantly larger decline in pain intensity (Numeric Pain Rating Scale) than did the control group (U=75; P=.003). Of the available weekly modules on average 80% (12/15) was completed by patients undergoing spinal fusion and 67% (6/9) was completed by patients undergoing decompression surgery. A total of 68% (17/25) of the participants used the intervention until the final interview. Most participants (15/17, 88%) in the intervention group would recommend the intervention to future patients. CONCLUSIONS: This pilot feasibility study showed that combining ACT and PP in a digital health intervention is promising for patients undergoing spinal surgery as the content was accepted by most of the participants and (larger) improvements in pain intensity and well-being were observed in the intervention group. A digital intervention for patients undergoing (spinal) surgery can use teachable moments, when patients are open to learning more about the surgery and rehabilitation afterward. A larger randomized controlled trial is now warranted.
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 | 0,056 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».