Can Lo-Fi Music Facilitate Sleep? A Preliminary Study of Pre-sleep Music Intervention
Poster No:
776
Submission Type:
Abstract Submission
Authors:
Yuchi Huang1, Jia-Rou Lin1, Wen-Chi Chiu1, Chia-Hui Wu2, Hsin-Chien Lee1, Changwei Wu3
Institutions:
1Taipei Medical University, Taipei, Taiwan, 2Fu Jen Catholic University, Taipei, Taiwan, 3Taipei Medical University, New Taipei, Taiwan
First Author:
Co-Author(s):
Introduction:
Sleep deficiency proliferates in the world (Chen, B. et al., 2024). Though sedative medications are commonly used in Taiwan, side effects like drowsiness and addiction urge the needs for non-pharmacological treatments. Music has been believed to have facilitation effects on sleep (Pan, B. Y., 2017), and white-noise was shown to reduce sleep onset latency (SOL) by creating a consistent auditory environment that facilitates faster sleep initiation (Messineo, L. et al., 2017). Recently, the lo-fi music gained popularity for its simplicity and relaxing qualities, but its effectiveness in aiding sleep remains untested. Here we used polysomnography (PSG) to evaluate the effect of lo-fi in SOL reduction, with white noise stimuli as a control.
Methods:
We recruited seven young adults (4 females, 3 males; aged 24.43 ± 5.35 years) to experience two sleep sessions given pre-sleep auditory stimuli, passive listening to lo-fi or white noise for 30 minutes before sleep, respectively, with at least a one-day interval between sessions. Lo-fi music tracks, selected using sedative criteria (Lingham & Theorell, 2009), were compiled into a 30-minute Spotify playlist and delivered via tablet and headphones. Prior to the experiment, participants completed sleep diaries and Pittsburgh Sleep Quality Index (PSQI). Experiment schedules were aligned with participants sleep routines with control of room temperature and dim light. Pre-sleep anxiety and arousal were assessed using the State version of the State-Trait Anxiety Inventory (STAI-S) and the Pre-Sleep Arousal Scale (PSAS). PSG was monitored across auditory stimuli and sleep, and the EEG power was assessed one-minute resting state before auditory stimuli (Pre) and after stimulation (Post). Sleep scoring was carried out using YASA Python code based on C3 channel (Hanna & Flöel, 2023), and the SOL was subsequently calculated. The R software was used to assess the two-way repeated-measure ANOVA with significance level of 0.05, and Spearman correlation analysis was carried out between the EEG power changes (Alpha and Theta bands) and the SOL.
Results:
In the lo-fi condition, five participants showed no clinical signs of insomnia, one exhibited sub-threshold insomnia, and one showed moderate insomnia, based on ISI. For white noise group, four participants showed no clinical signs of insomnia, two exhibited sub-threshold insomnia, and one showed moderate insomnia. Moreover, five out of seven participants scored above five on PSQI, indicating poor sleep quality. Paired comparison of STAI-S and PSAS scores between the lo-fi and white-noise groups revealed no significant differences (STAI-S: p = 0.87; PSAS: p = 0.43). Figure 1A shows Alpha power results without significant main effects of Music(F(1,6) = 0.03, p = 0.87) and Time (F(1,6) = 3.55, p = 0.11), nor interactions (F(1,6) = 1.25, p = 0.31). Similarly, the results of Theta power (Fig. 2A) indicated non-significant main effects for Music (F(1,6) = 0.85, p = 0.39) and Time (F(1,6) = 3.84, p = 0.10), nor interaction effect (F(1,6) = 3.61, p = 0.11). Moderate positive Spearman correlations were observed in the lo-fi group between SOL and Alpha power changes (ρ = 0.43). In contrast, the white-noise group showed moderate negative correlations (ρ = -0.61), though these results were not statistically significant (Fig. 1B). For Theta power, the lo-fi group demonstrated positive correlations with SOL (ρ = 0.55), while the white-noise group displayed modest positive correlations (ρ = 0.09) without statistical significance in either condition. (Fig. 2B)
Conclusions:
Our findings were insignificant due to low sample size, so we cannot differentiate the effect of lo-fi and white noise by now. Even though, both Alpha and Theta power reduced after auditory stimuli, and the opposite correlations across stimuli imply differential effects of lo-fi and white noise on sleep. Future research with more sample sizes is ongoing to unveil the potential effects of different auditory stimuli in sleep promotion.
Higher Cognitive Functions:
Music 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Electroencephaolography (EEG)
NORMAL HUMAN
Sleep
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
2. Pan, B. Y. (2017). The use of music therapy/music to address sleep quality and sleep disorders: a systematic literature review (Doctoral dissertation, Concordia University).
3. Messineo, L. et al. (2017). Broadband Sound Administration Improves Sleep Onset Latency in Healthy Subjects in a Model of Transient Insomnia. Front Neurol 8, 718. 10.3389/fneur.2017.00718.
4. Hanna, J., & Flöel, A. (2023). An accessible and versatile deep learning-based sleep stage classifier. Frontiers in neuroinformatics, 17, 1086634.
5. Lingham, J., and Theorell, T. (2009). Self-selected “favourite” stimulative and sedative music listening – how does familiar and preferred music listening affect the body? Nord J Music Ther 18, 150–166. 10.1080/08098130903062363.