0456 Reducing Insomnia in Nightshift Workers with Light Therapy: Personalization Using Consumer-Based Wearable Technology

Abstract Introduction In nightshift workers, insomnia is frequently caused by circadian misalignment which can be alleviated with targeted light interventions. However, generic “light at night” interventions do not account for the widely variable circadian phases among night shift workers. To address this, we used consumer-based wearables (Apple Watch [AW]) to predict circadian phase and inform personalized light therapy. We previously demonstrated that AW-informed light-dark schedules produce greater phase shifts; here, we extend this approach by examining its impact on insomnia symptoms. We hypothesized the AW-informed light therapy would result in greater reduction of insomnia symptoms following treatment vs the generic (i.e., non-personalized) light therapy. Methods Participants (N=85) were randomly assigned to either the AW-informed or generic light schedules. Insomnia severity for both daytime (ISI-D) and nighttime (ISI-N) sleep periods were measured before and after treatment. AW accelerometer and heart rate data over two weeks were processed through a biomathematical model of the human circadian system. Those in the AW-informed group received light schedules based on their circadian phase predictions. Participants in the control group followed a generic light schedule (light from 18:00 and 21:00; light avoidance from 04:00 and 10:00). Both groups implemented schedules with light boxes and blue-blocker glasses. Changes in insomnia severity associated with the treatment was quantified using a difference score (Post-treatment – Pre-treatment), with negative numbers indicating improved insomnia. Results The AW-informed group showed improved ISI-D (-1.9 ± 5.8 SD) compared to exacerbated insomnia in the control group (+1.7 ± 5.5 SD), t(84) = -2.43, p = .017. No significant differences were found for ISI-N (AW-informed: -1.0 ± 4.9 SD, generic: -0.6 ± 5.4 SD). Conclusion These findings support the use of AW as a consumer-based wearable to conduct personalized light treatments for nightshift workers. Accessible and effective circadian treatments are key to improving the safety of nightshift workers. Future research should examine facilitators and barriers of real-world implementation of personalized light interventions. Support (if any) Support for this study was provided from the NIH R01HL160870 and the AASM (245-SR-21) awarded to Dr. Philip Cheng.