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Sleep physiology features from wearable trackers predict next-day physical fatigue in healthy adults with moderate discriminative capacity

The study evaluated the feasibility of using consumer- and research-grade sleep trackers to predict next-day fatigue and sleepiness in individuals with neurodegenerative and immune-mediated inflammatory diseases. In healthy adults, machine learning models achieved an AUC of 0.75 for predicting next-day physical fatigue, with respiratory rate and REM sleep duration as key features. Predictive performance was lower in chronic disease cohorts (AUC 0.62 for physical fatigue in NDD).

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

Wearable sleep trackers capture physiological and sleep-architecture features (e.g., respiratory rate, REM sleep 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 are evaluated using leave-one-subject-out cross-validation
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Result: Preliminary discriminative capacity for next-day physical fatigue, particularly 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 remains limited, underscoring the need for larger, multimodal studies
  • ⚠Findings are exploratory and not confirmatory

Frequently Asked Questions

What sleep features were most predictive of next-day fatigue?▼

Respiratory rate and REM sleep duration were the key drivers for predicting physical fatigue in healthy adults. In neurodegenerative disease, REM latency and deep sleep were important features.

How accurate were the sleep trackers compared to polysomnography?▼

The sleep trackers showed moderate agreement with polysomnography in a sub-study of 28 participants.

Can these findings be applied to people with chronic diseases?▼

Predictive performance was limited in neurodegenerative and immune-mediated inflammatory disease cohorts (AUC ≤ 0.66), so the findings are not yet clinically applicable to these populations.

What is the main limitation of this study?▼

The binarisation of fatigue outcomes using a global threshold may mix between-person differences with within-person variation, and the study is exploratory without definitive clinical recommendations.

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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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