Saiyue Deng1, Quan Wang2, Jingjing Fan3, Xiaoyun Yang3, Junhua Mei4, Jiajia Lu5, Guohua Chen4, Yuan Yang1, Wenhua Liu6, Runsen Wang7, Yujia Han7, Rong Sheng7, Wei Wang1, Li Ba1, Fengfei Ding1,8. 1. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China. 2. School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, People's Republic of China. 3. Cardiac Unit, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China. 4. Department of Neurology, Wuhan No.1 Hospital, Wuhan, 430022, People's Republic of China. 5. Cardiac Unit, Wuhan No.1 Hospital, Wuhan, 430022, People's Republic of China. 6. Department of Clinical Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, People's Republic of China. 7. Huawei Technologies Co, Shenzhen, People's Republic of China. 8. Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
Abstract
Purpose: Heart rate variability (HRV) indices have been used as stress indicators. Rare studies investigated the associations of circadian rhythms of the HRV indices with the stress, mood, and sleep conditions in populations under stress. Methods: In total 257 female participants (203 shift workers and 54 non-shift workers) were included. All the participants completed a structured questionnaire to assess the stress, mood, and sleep conditions and performed 24-hour Holter electrocardiogram monitoring on the day away from shifts. Using epochs of 1-min or 5-min beat-to-beat intervals, the HRV indices (SDNN, RMSSD, LF, HF, LF/HF, and LFnu, SD1, SD2, SD1/SD2) were plotted as a function of time and fitted into cosine periodic curves, respectively. Three mathematical parameters based on the cosine periodic curves were extracted, MESOR (M, overall averages of the cosine curve), amplitude (A, amplitude of the peak of the cosine curve), and acrophase (θ, latency to the peak) to quantify the circadian rhythms of the HRV indices. Multivariable linear regression models were used to reveal the associations of these parameters with the clinical assessments of stress, mood, or sleep conditions, as well as with the 24-h averages of the HRV indices. Results: The parameters M and A of SDNN, RMSSD, LF, and HF, and θ of LF/HF and LFnu significantly differ between shift and non-shift workers. The parameter θ of LF/HF positively correlates with the severity of stress and anxiety. The parameter A of LF/HF and LFnu also positively correlates with daytime sleepiness and sleep fragmentation. In addition, the parameters M and A instead of θ of SDNN, RMSSD, LF, LF/HF, and LFnu significantly correlate with the 24-h averages of HRV indices. Conclusion: The circadian rhythms of the HRV indices over 24 hours can, to some extent, predict the severity of stress, emotion and sleep conditions in female populations under stress.
Purpose: Heart rate variability (HRV) indices have been used as stress indicators. Rare studies investigated the associations of circadian rhythms of the HRV indices with the stress, mood, and sleep conditions in populations under stress. Methods: In total 257 female participants (203 shift workers and 54 non-shift workers) were included. All the participants completed a structured questionnaire to assess the stress, mood, and sleep conditions and performed 24-hour Holter electrocardiogram monitoring on the day away from shifts. Using epochs of 1-min or 5-min beat-to-beat intervals, the HRV indices (SDNN, RMSSD, LF, HF, LF/HF, and LFnu, SD1, SD2, SD1/SD2) were plotted as a function of time and fitted into cosine periodic curves, respectively. Three mathematical parameters based on the cosine periodic curves were extracted, MESOR (M, overall averages of the cosine curve), amplitude (A, amplitude of the peak of the cosine curve), and acrophase (θ, latency to the peak) to quantify the circadian rhythms of the HRV indices. Multivariable linear regression models were used to reveal the associations of these parameters with the clinical assessments of stress, mood, or sleep conditions, as well as with the 24-h averages of the HRV indices. Results: The parameters M and A of SDNN, RMSSD, LF, and HF, and θ of LF/HF and LFnu significantly differ between shift and non-shift workers. The parameter θ of LF/HF positively correlates with the severity of stress and anxiety. The parameter A of LF/HF and LFnu also positively correlates with daytime sleepiness and sleep fragmentation. In addition, the parameters M and A instead of θ of SDNN, RMSSD, LF, LF/HF, and LFnu significantly correlate with the 24-h averages of HRV indices. Conclusion: The circadian rhythms of the HRV indices over 24 hours can, to some extent, predict the severity of stress, emotion and sleep conditions in female populations under stress.
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