Automated algorithms identify IN and OUT of bedtimes more consistently than patient-reported methods in adults with advanced cancer
Automated accelerometry algorithms identified IN and OUT bedtimes with 93-100% consistency compared to 48-83% for patient-reported methods (sleep diaries and event markers). All methods showed significant correlations (p < 0.001), but choice of method influenced sleep onset latency, percent sleep, and dichotomy index values.
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Automated algorithms identify IN and OUT of bedtimes more consistently than patient-reported methods in adults with advanced cancer The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 32/100 (low).
The Claim
Automated algorithms identify IN and OUT of bedtimes more consistently than patient-reported methods in adults with advanced cancer
This conclusion is most relevant to: 72 adults with advanced cancer.
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Identifying IN and OUT of bedtimes in patients with advanced cancer using self-reported methods, accelerometry and automated algorithms: Implications for measures of sleep and circadian rhythmicity. (Chronobiology international, 2026) —
How It Works
The proposed biological pathway:
- ▸Automated algorithms process accelerometry data continuously
- ▸Algorithms detect transitions in activity patterns to infer bedtimes
- ▸Patient-reported methods rely on subjective recall or manual event marking
- ▸Automated methods yield higher consistency but may still under- or over-estimate timings
Who Might Benefit
Evidence fit by population:
- ▸72 adults with advanced cancer
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Both automated and patient-reported methods showed under- and over-estimation of timings
- ▸Study duration was only 72 hours, limiting generalizability to longer-term monitoring
- ▸Sample was limited to advanced cancer patients, may not apply to other populations
Frequently Asked Questions
What methods were compared for identifying bedtimes?▼
Patient sleep diaries, wrist accelerometry event markers, and automated accelerometry algorithms were compared.
How consistent were automated algorithms versus patient reports?▼
Automated algorithms achieved 93-100% consistency, while patient-reported methods ranged from 48-83%.
Did the choice of method affect sleep parameter calculations?▼
Yes, it influenced sleep onset latency, percent sleep, and dichotomy index values.
What population was studied?▼
72 adults with advanced cancer in a 12-month observational study.
References
- 1.Gouldthorpe C, Davies A. “Identifying IN and OUT of bedtimes in patients with advanced cancer using self-reported methods, accelerometry and automated algorithms: Implications for measures of sleep and circadian rhythmicity..” Chronobiology international, 2026. PMID: 42482442 DOI: 10.1080/07420528.2026.2703831