ArticleCancers2023
Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 15 citations in OpenAlex.
- Review
- The clinical relevance of sleep disturbance ("insomnia") in patients with advanced cancer receiving palliative care: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Using actigraphy to evaluate physical activity in patients with pleural effusion: a pilot study.Journal of thoracic disease · 2026Article
- Daily locomotor activity declines with tumor growth and disease progression in glioblastoma.JCI insight · 2026Article
- Circadian rest-activity rhythm disorders in advanced cancer: assessment, diagnosis and clinical correlates.BMJ supportive & palliative care · 2025Observational
- Artificial intelligence-based approaches for advance care planning: a scoping review.BMC palliative care · 2025Article
- Optimizing postoperative recovery in colorectal cancer patients through integrated psychological and nutritional interventions.Journal of cancer survivorship : research and practice · 2025Article
- Tracking Daily Variations in Rest-Wake to Guide Personalized Timing of Temozolomide for High-Grade Glioma Patients.Journal of biological rhythms · 2025Article
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Sleep Composition of Patients With Colorectal Cancer and Their Sleep-Partner Caregivers: Physical Health Correlates of Sleep Diary and Actigraphy Measurements.Psycho-oncology · 2024Article
- Vital Signs in Palliative Care: A Scoping Review.Cancers · 2023Review
- Article
- Article
- Circadian rhythm disorders in patients with advanced cancer: a scoping review.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician’s estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.