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Record W3188663638 · doi:10.1093/sleep/zsab202

A step in the right direction: making cognitive-behavioral therapy for insomnia more accessible to people diagnosed with cancer

2021· editorial· en· W3188663638 on OpenAlexaff
Sheila N. Garland

Bibliographic record

VenueSLEEP · 2021
Typeeditorial
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInsomniaCognitionMedicineCognitive behavioral therapyCognitive behavioral therapy for insomniaCancerPsychologyPsychiatryPhysical medicine and rehabilitationPhysical therapyClinical psychologyPsychotherapistInternal medicine

Abstract

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Insomnia is a prevalent and persistent condition that affects roughly half of all people diagnosed with cancer. As such, demand for insomnia treatment far exceeds available resources. A dearth of providers trained in sleep interventions and inadequate access to services continues to plague the field of behavioral sleep medicine [1], but for those whose insomnia began or was worsened by a cancer diagnosis, treatment is even more elusive [2]. Stepped care has been proposed as a possible service delivery model for increasing access to scarce Cognitive Behavioral Therapy for Insomnia (CBT-I) services [3]. In this issue, Savard et al. used a noninferiority design to test whether the short- and long-term effects of a stepped care approach to deliver CBT-I (StepCBT-I) was not significantly worse than professionally administered treatment (StanCBT-I) in a cancer setting [4]. Their CBT-I protocol was a 6-week intervention combining stimulus control, sleep restriction, cognitive restructuring, and sleep hygiene education. They found that StepCBT-I was not significantly inferior to StanCBT-I with overall reductions in Insomnia Severity Index (ISI) scores of −8.16 and −9.24, respectively, and that these improvements were durable up to 1 year. Improvements were also observed on measures of anxiety, depression, fatigue, and quality of life. These reductions are consistent with the overall data for the efficacy of CBT-I 5, further strengthening the evidence for the use of CBT-I with those impacted by cancer. Clinical guidelines recommend screening and treatment of insomnia in cancer settings [5–7], but individual providers and organizations often lack clear treatment pathways based on this information. An important feature of the model tested by Savard et al. was that initial treatment was tailored based on symptom severity (also known as a stratified stepped care model). In the Savard et al. trial, those who were randomized to the StanCBT-I group (n = 59) received 6 weekly 50-minute face-to-face sessions of CBT-I, whereas those who were randomized to the StepCBT-I (n = 118) group were further stratified by insomnia severity. In Step 1, those with less severe symptoms (as indicated by an ISI score of ≧8 but <15) were provided with a web-based CBT-I program called Insomnet, while those with more severe insomnia symptoms (as indicated by an ISI score of ≧15) were provided with face-to-face treatment. Compared with a pure stepped care model where everyone starts out with the least intensive intervention and then moves upwards, this stratification allows patients to receive more timely delivery of the appropriate intensity of intervention. Another unique feature of the Savard et al. trial was that the treatment algorithm was able to adapt to the needs of the patient in cases where there was an incomplete therapeutic response to the lower-level intervention. In Step 2, only those who were unremitted (defined as an ISI score of ≧8) were given up to three face-to-face booster sessions dedicated to individual problem solving and motivational interviewing to address difficulties with implementation of specific aspects of treatment. The availability and utilization of booster sessions in the StepCBT-I group has clinical applications to more efficiently utilize provider time. After Step 1, 72% of StanCBT-I and 58% of StepCBT-I patients were remitted. Of those individuals assigned to the StepCBT-I group, 66% of patients who began with face-to-face sessions experienced remission, compared with 51% of patients who received the web-based treatment. The acceptance of booster sessions also appeared to differ by group. Of those individuals who received web-based CBT-I as their first step, 27% accepted one or more booster sessions, compared with only 8% of those who received face-to-face CBT-I as their first step. This suggests that face-to-face therapy will remain necessary for a proportion of patients who need more individualized treatment because of challenges with motivation and/or more complex clinical presentations. Accordingly, the preponderance of evidence still suggests that face-to-face intervention produces outcomes that exceed those from web-based or digital treatment [8–10]. As such, entry-level interventions such as the program tested by Savard et al. should not be used to replace face-to-face service provision by skilled practitioners. Rather, less-intensive interventions, when applied appropriately, can make treatment more accessible and more efficiently utilize scarce resources. Only one other study has assessed the use of a stepped care model to treat insomnia in cancer survivors [11]. In Step 1 of this study, participants with an ISI score of ≧12 received a single sleep education session. If participants were still symptomatic 1 month later, they were offered a group treatment program consisting of three sessions. Participants were classified as “responders” if their ISI score improved by ≧6 points and as “remitted” if their post-treatment ISI score was <12. Close to half (45%) of participants responded to Step 1, but they were more likely to have less chronic and less severe insomnia compared with the 87% response rate in those people who proceeded to Step 2. Interestingly, 53% of those who did not respond to Step 1, chose not to move on to Step 2, suggesting that an ineffective first step for those with more chronic and severe insomnia may influence willingness to engage with a more intensive treatment at a future date. Other evaluations of stepped care programs for psychological disorders have also reported significant drop-out at the initial step when the patient did not respond to the low intensity intervention [12]. As a result, there have been calls to include consideration of patient treatment preference when determining the optimal initial step in the stepped care model. Despite CBT-I being the recommended treatment for insomnia in people diagnosed with cancer [7, 13], dissemination and implementation of CBT-I in cancer treatment centers remains subpar. In a survey of 25 adult survivorship programs at National Cancer Institute-designated cancer centers, not a single cancer center reported that at least 50% of their patients were receiving optimal treatment for their insomnia [14]. More than half of the centers surveyed screened less than 25% of people diagnosed with cancer for sleep-related issues, and 72% of centers lacked on-site access to a provider specializing in the treatment of sleep disorders [14]. When problems were identified in these centers, medications were the most common treatment option, whereas only 13% of cancer centers referred their patients for CBT-I [14]. As a result of this knowledge to action gap, cancer survivors with insomnia are underdiagnosed, mismanaged, and left to suffer the long-term effects of a highly treatable condition. Recent efforts have tried to understand the discrepancy between the evidence for CBT-I and its implementation into real-world clinical practice. Barriers to the implementation of guideline-based practice for insomnia in cancer survivors have been identified at the patient, clinician, and institutional/societal levels [15]. Some barriers include (1) lack of education about the importance of sleep health; (2) lack of awareness of the evidence for nonpharmacological treatment; (3) financial constraints (e.g., lack of insurance coverage); (4) access and practical issues (e.g., shortage of qualified clinicians); and (5) existing attitudes and beliefs (e.g., medication is the only treatment available) [15]. Other settings have had more success with implementation of CBT-I by identifying and engaging sleep champions, eliciting support from leaders, integrating services within existing clinical care, and having well-defined referral pathways [16]. As a clinician and researcher in sleep and cancer, it is frustrating that an effective treatment for insomnia, which can be implemented in creative and flexible ways, is not being universally utilized in organizations with the explicit purpose of helping people recover from, or live well with, cancer. Insomnia in those diagnosed with cancer is far from benign and can have immediate-, short-, and long-term consequences. In the immediate and short term, insomnia can worsen levels of pain, fatigue, anxiety, and depression [17] and increase risk of infections [18]. In the long term, insomnia significantly contributes to poor quality of life up to 10 years postdiagnosis [19], leads to increases in healthcare expenditures and work absenteeism [20], and may contribute to poorer treatment response and greater overall mortality [21]. The study by Savard et al. is a much-needed demonstration that a stepped care approach to treating insomnia in cancer survivors is effective and can make efficient use of limited resources. Considering the recent American Academy of Sleep Medicine declaration that insufficient sleep and untreated sleep disorders are detrimental for health and well-being [22], cancer-treatment organizations need to “step up” and ensure that patients can avail of evidence-based interventions to improve their sleep, cancer experience, and quality (and possibly quantity) of life. Financial disclosure statement: All authors declare no competing interests. Nonfinancial disclosure statement: All authors declare no competing interests.

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)0.013
metaresearch head score (Gemma)0.042
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.031
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.042
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.002
Science and technology studies0.0050.003
Scholarly communication0.0090.005
Open science0.0050.002
Research integrity0.0310.034
Insufficient payload (model declined to judge)0.0120.007

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 head0.021
GPT teacher head0.373
Teacher spread0.352 · 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
GenreEditorial

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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Citations5
Published2021
Admission routes1
Has abstractno

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