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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.

Last updated: Jul 31, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score32/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. 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.

observational

Gouldthorpe C, Davies A · Chronobiology international (2026)

Participants: N/A
Duration: 72 hours per participant (12-month study period)
Intervention: Comparison of automated accelerometry algorithms versus patient-reported methods (sleep diaries, wrist accelerometry event markers) for identifying IN and OUT bedtimes
Outcome: Consistency of bedtime identification, agreement between methods, and impact on sleep parameters (sleep onset latency, percent sleep, dichotomy index)
Effect Size: N/A
Population: 72 adults with advanced cancer

Result:

PubMed: 42482442DOI: 10.1080/07420528.2026.2703831

Mechanism Graph

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
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Automated methods yield higher consistency but may still under- or over-estimate timings

Limitations

  • ⚠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.

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References

  1. 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
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.