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Record W2061928116 · doi:10.5665/sleep.3908

Boosting Access to Insomnia Treatment for Cancer Patients

2014· letter· en· W2061928116 on OpenAlexaff
Judith Davidson

Bibliographic record

VenueSLEEP · 2014
Typeletter
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsBoosting (machine learning)InsomniaMedicineSleep (system call)PsychiatryArtificial intelligenceComputer science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0490.028
Insufficient payload (model declined to judge)0.0170.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.349
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2014
Admission routes1
Has abstractno

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