MétaCan
Menu
Retour à la cohorte
Enregistrement W2061928116 · doi:10.5665/sleep.3908

Boosting Access to Insomnia Treatment for Cancer Patients

2014· letter· en· W2061928116 sur OpenAlexaff
Judith Davidson

Notice bibliographique

RevueSLEEP · 2014
Typeletter
Langueen
DomainePsychology
ThématiqueSleep and related disorders
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésBoosting (machine learning)InsomniaMedicineSleep (system call)PsychiatryArtificial intelligenceComputer science

Résumé

récupéré en direct d'OpenAlex

Dr. Davidson has indicated no financial conflicts of interest. “Build it into the cancer care system.” That is what we heard from patients. “It” is the recognition and treatment of insomnia. The patients were people with cancer and sleep difficulty who provided ideas for enhancing access to effective insomnia interventions.1 Now efforts are directed at doing just that: looking at ways to build it into the cancer care system. Exactly how to do that is the focus of necessary research, given that clinicians with training in behavioral sleep medicine are hard to find anywhere, let alone in cancer centers. At least 30% of oncology patients report insomnia, one of the most frequent symptoms along with pain and fatigue. There is evidence that cognitive behavioral therapy for insomnia (CBT-I) works well in the context of cancer.2,3 The question now is one of access. How do we build it into the cancer care system, providing access to so many patients, while retaining its effectiveness? The article by Savard and colleagues4 in this issue of SLEEP takes a close and careful look at one potential way, via animated video segments that patients can watch at home in DVD format. They used a three-arm randomized controlled trial (RCT) to compare a video-based intervention to individual face-to-face CBT-I, and to no treatment. Each arm had approximately 80 participants who had received radiation therapy for breast cancer within the previous 18 months. This study was done with the attention to detail and reporting that are features of high-quality RCTs. The sample size is large for this type of intervention and the steps of recruitment, screening and randomization are clearly laid out in the RCT flow chart. Split-plot mixed model, intent-to-treat, analyses were used. The trial by Savard et al. produced some very useful data. It showed that whereas face-to-face CBT-I was generally superior to the video-based CBT-I, the latter was superior to no treatment. The video-based intervention was associated with medium to large effect sizes (0.50 to 1.40, depending on the sleep diary variable) and an insomnia remission rate of 44% (defined as the proportion scoring < 8 on the post-treatment Insomnia Severity Index). Thus, the video-based CBT-I intervention worked quite well, considering it involved much less time for both the clinician and the patient than the regular face-to-face treatment. Given the low access to face-to-face treatment, should this type of video-based intervention be provided in cancer clinics? We know from this study that, for breast cancer patients, it is superior to no treatment, and it is probably less expensive (after production) than face-to-face treatment—although the costs were not a focus of the trial.4 The feasibility of offering video CBT-I in the cancer care system now needs investigation. The recruitment information provided by Savard and colleagues points to obstacles to the speedy identification of cancer patients who are ready for any type of CBT-I. To gather 242 participants, the researchers approached 1,817 patients, about half of whom had insomnia symptoms, over 3.5 years. The main reasons given by patients for non-participation were no sleep complaint (514 patients), lack of interest (194), and a perception that the study requirements, including travel to the research center, were too burdensome (333). Not only do we need an efficient way of identifying cancer patients with insomnia who are ready for CBT-I, but we need a way of determining which patients are best-matched to a video-based treatment, and which to other forms of CBT-I including the face-to-face version. Savard et al. suggest that the video-based intervention may be useful at the entry level of a stepped-care approach, to be followed if needed, by a professionally administered treatment. However, in the real world, I wonder whether cancer patients who are still not sleeping well after the video-based intervention would actually be open to using the same approach again, provided by a professional, even if it were readily available. A more feasible entry level might be abbreviated sleep instructions based on the principles of CBT-I, delivered by the oncology nurse who already assesses and follows the patient's cancer-related symptoms. The next level then could be the effective video-based intervention by Savard et al.4 This would make CBT-I easily accessible within cancer centers. Face-to-face CBT-I with a clinician trained in behavioral sleep medicine, if available, could be reserved for complex cases. Whether it is access to insomnia treatment for cancer patients, for primary care patients, for military personnel, for people with chronic pain or psychiatric disorders, novel methods are being investigated to expand availability of CBT-I. We have moved from research on in-person CBT-I to research on various delivery systems, for example, telephone,5 telehealth,6 online,7,8 and video.4 These modalities provide opportunities for reaching many more people with insomnia than traditional methods. They also require new partnerships and new ways of working for the clinical research team. Collaborations with professional scriptwriters and animation experts,9 developers, programmers, engineers, and having sophisticated equipment for delivery are now part and parcel of providing CBT-I. The new modalities are also accompanied by cost considerations for production and maintenance, and the task of determining the source of funding for interventions that no longer involve in-person therapy. Scientist-practitioners in the field of insomnia are moving away from the clinician's chair to take a seat in the director's chair. This shift to more accessible, but less personal insomnia treatment means that more patients will have access to CBT-I, but we can't expect outcomes to necessarily match face-to-face successes. The video-based intervention offered by Savard and colleagues lends itself particularly well to the cancer context, where many patients experience extreme fatigue. It is simpler and probably less tiring than other more interactive technologies, and certainly easier than attending several in-person visits to a clinician. Watching brief video clips at home seems an elegant solution to a prevalent problem. We just need to find ways to efficiently identify those cancer patients who will benefit from this treatment modality and are ready to begin CBT-I.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,056

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,025
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,002
Communication savante0,0030,003
Science ouverte0,0010,003
Intégrité de la recherche0,0490,028
Charge utile insuffisante (le modèle a refusé de juger)0,0170,005

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.

Tête enseignante Opus0,042
Tête enseignante GPT0,349
Écart entre enseignants0,307 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2014
Routes d'admission1
Résumé présentnon

Explorer davantage

Même revueSLEEPMême sujetSleep and related disordersTravaux en français237 207