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Record W4407768161 · doi:10.1188/25.onf.e15-e34

Sleep Hygiene Education, ReadiWatch™ Actigraphy, and Telehealth Cognitive Behavioral Training for Insomnia for People With Prostate Cancer

2025· article· en· W4407768161 on OpenAlexaff
Jamie Myers, Rebekah E Humphrey-Sewell, Lauren A. Fowler, Daniel F. English, Rachael Stickler, Dedrick Hooper, Jaromme Kim, Jianghua He, Mary Penne Mays, Catherine Siengsukon, Elizabeth Wulff‐Burchfield, Xinglei Shen, Jennifer Heins, William B. Parker, Sally L. Maliski

Bibliographic record

VenueOncology nursing forum · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsFowler Kennedy Sport Medicine Clinic
FundersNational Cancer Institute
KeywordsMedicineActigraphyInsomniaSleep hygieneProstate cancerTelehealthSleep (system call)Cognitive behavioral therapy for insomniaCognitionPhysical therapyCancerCognitive behavioral therapyPsychiatryInternal medicineTelemedicineSleep qualityHealth care

Abstract

fetched live from OpenAlex

OBJECTIVES: To test the feasibility of sleep hygiene education and longitudinal wrist actigraph sleep metrics measurement alone versus in combination with telehealth-delivered cognitive behavioral therapy for insomnia (teleCBT-I) for people with prostate cancer (PC) receiving androgen deprivation therapy (ADT). SAMPLE & SETTING: 45 men with PC receiving ADT were recruited from a midwestern comprehensive cancer center. METHODS & VARIABLES: Participants were provided with wrist actigraphs, their individual sleep metrics data, and sleep hygiene education. Half the sample was randomized to a four-week teleCBT-I intervention. Outcomes were collected at baseline, one month, and two months. Exit interviews were conducted to glean participants' feedback about the study. RESULTS: Feasibility was demonstrated. Physical function, sleep efficiency, fatigue, and health-related quality of life improved for participants receiving teleCBT-I. IMPLICATIONS FOR NURSING: Assessment of sleep disturbance, access to sleep hygiene education, and teleCBT-I may benefit people with PC receiving ADT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.390
Teacher spread0.366 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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