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Sleep physiology features from wearable trackers can predict next-day physical fatigue in healthy adults with moderate accuracy.

In a feasibility study of 134 participants (42 healthy, 39 NDD, 53 IMID) across 3,062 nights, machine learning models using sleep tracker data achieved an AUC of 0.75 for predicting next-day physical fatigue in healthy adults, with respiratory rate and REM sleep duration as key predictors. Predictive performance was lower in neurodegenerative disease (AUC=0.62) and immune-mediated inflammatory disease cohorts, indicating limited generalizability to chronic disease populations.

Last updated: Jul 7, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score34/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.

observational

Zhai B, Chen L, Ma X, Pinaud C, Chatterjee M, Kortelainen JM, Rehman RZU, Ahmaniemi T, Avey S, Guan Y, Macrae V, Hinchliffe C, Del Din S, Manyakov NV, Göder R, Romijnders R, Maetzler W, Reilmann R, Aufenberg S, Schubert R, van der Woude CJ, Zhang D, Ng WF · Frontiers in digital health (2026)

Participants: N/A
Duration: 3,062 nights (average ~23 nights per participant)
Intervention: Use of consumer- and research-grade sleep trackers (BedSensor, ZKONE, DREEM 2) for overnight sleep monitoring
Outcome: Next-day physical fatigue, mental fatigue, and daytime sleepiness measured via patient-reported outcomes
Effect Size: AUC=0.75 for physical fatigue in healthy adults; AUC=0.62 in NDD; AUC=0.66 for mental fatigue in healthy adults; AUC=0.66 for daytime sleepiness in NDD
Population: 134 participants (42 healthy adults, 39 with neurodegenerative diseases, 53 with immune-mediated inflammatory diseases) across four European centres

Result:

PubMed: 42388291DOI: 10.3389/fdgth.2026.1752629

Mechanism Graph

Sleep trackers record physiological and sleep-architecture features (e.g., respiratory rate, REM duration, REM latency, deep sleep)
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Machine learning models are trained on these features to predict next-day fatigue and sleepiness
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Models use leave-one-subject-out cross-validation to assess predictive performance
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Result: Respiratory rate and REM sleep duration are key predictors of physical fatigue in healthy adults

Limitations

  • ⚠Outcome binarisation using a global threshold may conflate between-person disease-group differences with within-person symptom variation
  • ⚠Predictive performance in chronic disease cohorts is limited (AUC ≤ 0.66), suggesting need for larger multimodal studies
  • ⚠Study is exploratory and not designed for confirmatory hypothesis testing

Frequently Asked Questions

What sleep features best predicted next-day fatigue in healthy adults?▼

Respiratory rate and REM sleep duration were the key drivers of physical fatigue prediction in healthy adults.

How accurate were the models for predicting fatigue in neurodegenerative diseases?▼

The AUC for physical fatigue in NDD was 0.62 under enriched training, with REM latency and deep sleep as key features, indicating limited predictive accuracy.

What types of sleep trackers were used in this study?▼

Three sleep trackers were used: BedSensor, ZKONE, and DREEM 2, with a polysomnography sub-study (n=28) to validate their performance.

Can these findings be applied to clinical practice?▼

No, the findings are exploratory and preliminary. The study underscores the need for larger, multimodal studies to establish disease-specific digital fatigue endpoints before clinical application.

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References

  1. 1.Zhai B, Chen L, Ma X, Pinaud C, Chatterjee M, Kortelainen JM, Rehman RZU, Ahmaniemi T, Avey S, Guan Y, Macrae V, Hinchliffe C, Del Din S, Manyakov NV, Göder R, Romijnders R, Maetzler W, Reilmann R, Aufenberg S, Schubert R, van der Woude CJ, Zhang D, Ng WF. "Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.." Frontiers in digital health, 2026. PMID: 42388291 DOI: 10.3389/fdgth.2026.1752629
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