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Improving Sleep Quality for Shift Workers With the Easleep Brain-Machine

Improving Sleep Quality for Shift Workers With the Easleep Brain-Machine

Client: A 24/7 operations team (healthcare & logistics staff)
Product Used:
Easleep Brain-Machine

Challenge

Shift workers operating across rotating day and night schedules were experiencing chronic sleep disruption, fatigue, and reduced recovery.

Irregular working hours made it difficult to:

  • Fall asleep consistently after shifts
  • Achieve deep, restorative sleep
  • Maintain energy and focus during working hours

Many workers relied on short-term solutions such as caffeine or sleep aids, which failed to address the underlying disruption to natural sleep cycles.

The organisation required a non-invasive, drug-free solution that could actively support sleep quality — not just monitor it.

Easleep product card
Easleep app card

Solution

The Easleep Brain-Machine was introduced as a brain-responsive sleep system, designed to help shift workers adapt to irregular sleep schedules.

Using EEG sensors and closed-loop CES (cranial electrotherapy stimulation), the device:

  • Monitored brainwave activity across sleep stages
  • Identified when users were struggling to enter or maintain sleep
  • Delivered gentle, real-time stimulation to guide the brain toward deeper sleep states

Workers used the device alongside the Easleep app, which provided:

  • Personalised sleep programs tailored to rotating shifts
  • Adaptive audio and sleep-inducing soundscapes
  • Detailed sleep analysis and recovery insights over time

Unlike traditional sleep tools, Easleep created a closed-loop system — continuously adjusting based on real-time brain activity rather than relying on fixed routines.

“Easleep enables shift workers to retrain their sleep patterns using real-time brainwave feedback and adaptive stimulation, supporting deeper, more consistent rest despite irregular schedules.”

Neuro-AI Australia

Outcome

After eight weeks of consistent use, the organisation observed meaningful improvements:

  • Workers fell asleep faster following late or overnight shifts
  • Reduced night-time waking and more stable sleep cycles
  • Improved recovery between shifts
  • Increased alertness and energy during working hours

The ability to both measure and influence sleep in real time helped workers transition from inconsistent rest to a more structured and reliable recovery pattern.

The program has since been expanded across additional teams, forming part of the organisation’s broader fatigue management strategy.

Workers reported improved ability to fall asleep after irregular shifts
69%
Reduction in sleep disruptions and night-time waking
31%