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I-Jung Liu,1 Wen-Te Liu,2– 7 Rachel Chien,1 Ying-Ying Chen,1,8 Yen-Ling Chen,9 Yi-Chih Lin,6,10 Yi-Chun Kuan,6,11– 13 Kang-Yun Lee,2,3,14,15 Tzu-Tao Chen,2,3,14,15 Arnab Majumdar,16 Jiunn-Horng Kang,7,9,17,18 Wayne Lai,19,20 Cheng-Yu Tsai1,3,4,6,7,14,21 1Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 2Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 3Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei, 23561, Taiwan; 4School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 5Advanced Technology Lab, Wistron Corporation, Taipei, 11469, Taiwan; 6Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei, 23561, Taiwan; 7TMU Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 8Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, 106319, Taiwan; 9College of Biomedical Engineering, Taipei Medical University, Taipei, 11031, Taiwan; 10Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei, 23561, Taiwan; 11Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei, 23561, Taiwan; 12Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 13Taipei Neuroscience Institute, Taipei Medical University, Taipei, 11031, Taiwan; 14TMU Research Center for Thoracic Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 15Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan; 16Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ, UK; 17Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, 11031, Taiwan; 18Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, 11031, Taiwan; 19TranQ Medical and Technology Inc, Kelowna, BC V1Y 5A8, Canada; 20Division of Neurology, Department of Medicine, the University of British Columbia, Vancouver, BC V6T 2B5, Canada; 21School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, 11031, TaiwanCorrespondence: Cheng-Yu Tsai, School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan, Tel +886-2-2736-1661, Fax +886-2-2739-1143, Email momo_cyt@tmu.edu.twObjective: Continuous positive airway pressure (CPAP) therapy is recognized as first-line treatment for obstructive sleep apnea (OSA), but tolerance to pressure adjustments may differ with age. In this study, we examined age-related differences in physiological and neurophysiological responses following CPAP pressure adjustment.Methods: In this retrospective study, we analyzed baseline polysomnography (PSG) and CPAP titration data from 40 individuals, including 20 younger (< 65 years) and 20 older adults (≥ 65 years) matched at the group level. Time-specific analyses were conducted using 10-min windows following pressure adjustments. Group comparisons across predefined pressure categories of CPAP (4– 5, 6– 7, and ≥ 8 cmH2O) and age groups were performed using one-way analysis of variance (ANOVA) or Kruskal–Wallis tests, as appropriate, with post-hoc analyses. Single and multiple linear regression analyses were conducted to assess associations between CPAP pressure categories and physiological responses, using the 4– 5 cmH2O group as the reference and adjusting for prior pressure change history and sleep-stage distribution.Results: Both age groups demonstrated improvements in sleep architecture and sleep disorder indices during CPAP titration. Among older individuals, analyses of 10-min periods following pressure adjustments showed a higher arousal frequency, increased elevated standard deviation (SD) of normal-to-normal (NN) intervals (SDNN) and low-frequency (LF)/high-frequency (HF) ratios, and reduced slow-wave peak-to-peak amplitudes and slopes compared to younger counterparts. Regression analyses further indicated associations between pressure categories, HRV features, and slow-wave characteristics in the elderly group.Conclusion: These findings highlight potential age-related differences in short-term responses to pressure adjustments for CPAP. Future prospective studies are needed to validate and enhance the generalizability and robustness of these findings.Keywords: continuous positive airway pressure titration, arousal event, slow-wave characteristic, heart rate variability, aging, sleep medicine