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Evaluating the prevalence and nature of sleep disturbances in prostate cancer patients receiving androgen deprivation therapy using a combination of actigraphy and sleep questionnaires.

2021· article· en· W3167903547 on OpenAlexaff
Stephen Mangar, Shalini Mondal, Steve Edwards, Hashim U. Ahmed, Richard J. Wassersug, Alison Falconer, Ray K. Iles, Dagmara Dimitriou

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsActigraphyMedicinePittsburgh Sleep Quality IndexEpworth Sleepiness ScaleProstate cancerAndrogen deprivation therapyPhysical therapySleep diarySleep (system call)Sleep onsetSleep deprivationInternal medicineInsomniaCancerSleep qualityPolysomnographyCircadian rhythmPsychiatry

Abstract

fetched live from OpenAlex

e17046 Background: Sleep disturbances and cancer related fatigue are commonly associated. Prostate cancer patients may suffer from disturbed sleep as a result of their diagnosis and following treatment, especially with androgen deprivation therapy (ADT). Wrist actigraphy is a non-invasive objective method of sleep data collection. This feasibility study compares sleep data obtained by actigraphy with subjective data from sleep questionnaires in order to determine the nature and severity of sleep disturbances in patients with and without ADT use. Methods: A prospective cross-sectional pilot study was conducted on 74 patients with prostate cancer attending a regional oncology clinic. Two validated subjective sleep questionnaires namely the Pittsburgh Sleep Quality Index [PSQI] and the Epworth Sleepiness Scale [ESS] were used. Patients wore actigraphy watches for a minimum of five consecutive days. The parameters of interest included: actual sleep time, sleep efficiency, fragmentation index, daytime napping frequency and duration. The questionnaire and actigraphy data were compared between 20 patients receiving ADT and 41 who were treatment-naive. Results: The compliance rate for completed actigraphy was 85%. Complete data sets with actigraphy and questionnaires were available from 61 patients. Those already receiving ADT were on LHRH analogues for a median duration of 2.35 years. Poor sleep quality as self-identified by patients from the PSQI (cut-off > 5) was 49% in the treatment-naive group which increased to 70% for those on ADT. For daytime sleepiness as assessed by ESS (cut-off > 10) this was 16% and 20% respectively. Actigraphy showed that patients on ADT reported longer sleep duration (7.4 vs 6.5 hours, p = 0.02), higher levels of nocturnal wakings (51.1% vs 36.7%, p = 0.002), with greater daytime napping duration (80.7mins vs 53.0mins, p = 0.04), and frequency (8.6 vs 5.6, p = 0.02) compared to treatment-naive patients. Conclusions: Self-reported poor sleep quality is common in prostate cancer patients, which appears worse for those receiving ADT. In patients receiving ADT, data derived from actigraphy suggests that although they were sleeping for longer at night, the quality of sleep was poor which, in turn, may be responsible for an increase in the frequency and duration of daytime napping. Based on the current findings, we recommend the use of actigraphy to characterise patients’ sleep patterns and to assess if sleep treatment is needed. Actigraphic data may allow for direct comparisons of different hormonal agents on sleep whilst identifying those with specific sleep disorders amenable to therapeutic intervention.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.466
Teacher spread0.381 · 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 designObservational
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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