Efficacy and Accuracy of an AI-Driven Sleep Earbud for Chronic Insomnia

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-driven Neuromodulation Music (NeuroRhythm): Acoustic stimulation delivered via the ANKER soundcore Sleep A40 Pro earbuds. This experimental condition utilizes the proprietary NeuroRhythm closed-loop algorithm to dynamically adjust music parameters (including timbre, tempo, white noise, and binaural beats) in real time. The acoustic adjustments are driven by the participant's live biological feedback (heart rate variability and motion data) captured by the earbud's embedded in-ear photoplethysmography (PPG) sensor.

Standard Music (Sham Control): Acoustic stimulation delivered via the identical ANKER soundcore Sleep A40 Pro earbuds. This control condition plays standard, non-adaptive relaxation music pre-selected to match the participant's baseline audio preferences. The acoustic properties remain completely static throughout the night and do not adjust or respond to any real-time biometric or physiological feedback from the user.

Study summary

This study evaluates a new smart sleep earbud designed to help adults suffering from chronic insomnia. The device uses artificial intelligence (AI) to track a user's real-time heart rate and movement through the ear canal, automatically adjusting soothing music parameters to help the user fall asleep faster and achieve deeper sleep. Participants will spend three consecutive nights in a hospital sleep laboratory. The first night serves as a baseline screening using medical-grade sleep tracking (polysomnography) to rule out other hidden sleep conditions like sleep apnea. On the second and third nights, participants will test two different audio options in a randomized order: the AI-driven adaptive music and standard, non-adjusting music. Researchers will compare the earbud's internal sensor data against the hospital's clinical equipment to verify the earbud's tracking accuracy, and participants will complete brief touch-screen brain function tests each morning. Following the lab phase, participants will continue using the earbuds in their natural home environment for two weeks before a final check-up. The goal is to determine if personalized, AI-adjusted sound therapy can effectively treat insomnia symptoms and if a consumer ear-worn device can monitor sleep architecture as accurately as a clinical hospital system.

Eligibility

Primary outcome measure(s)

• Change from Baseline in Polysomnography (PSG)-Measured Sleep Onset Latency (SOL) — Measured on Laboratory Night 1 (Day 2 morning) and Laboratory Night 2 (Day 3 morning).
  The objective time, in minutes, from turning the lights off to the appearance of the first continuous epoch of sleep, as recorded by the mobile PSG system.
• Change from Baseline in Percentage of Slow Wave Sleep (N3 Stage) — Measured on Laboratory Night 1 (Day 2 morning) and Laboratory Night 2 (Day 3 morning).
  The percentage of total sleep time spent in the N3 deep sleep stage (slow-wave sleep), derived from the objective PSG recordings scored according to AASM standards.
• Change from Baseline in Insomnia Severity Index (ISI) Score — Baseline (Day 1), Post-Laboratory (Day 3), and Endpoint (Day 14).
  The ISI is a 7-item self-report instrument assessing the nature, severity, and impact of insomnia. The total score ranges from 0 to 28, where 0-7 indicates no clinically significant insomnia and 22-28 indicates severe clinical insomnia. A reduction in score represents an improvement in insomnia severity.

Trial sites (2)

More The Chinese University of Hong Kong, Shenzhen trials in China

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