Postoperative Sleep Quality and Autonomic Nervous Function in Thyroid Surgery Patients: A Wearable Holter-Based Observational Study

Postoperative Sleep Quality and Autonomic Nervous Function in Thyroid Surgery Patients: A Wearable Holter-Based Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Postoperative Sleep Quality and Autonomic Nervous Function in Thyroid Surgery Patients: A Wearable Holter-Based Observational Study Yang Shan, Xinyuan Zhang, Reziya Aierken, Kun Xu, Dali Meng, Zhengming Deng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839788/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Heart rate variability (HRV) reflects the function of the autonomic nervous system. The associations of perioperative HRV dynamics with sleep among patients undergoing thyroidectomy remain unclear. Methods: This retrospective study included 46 patients with continuous HRV monitoring (SDNN, SDANN, RMSSD, pNN50, LFnu, HFnu, LF/HF) from 1 day before to 3 days after surgery. Sleep groups were defined via the Pittsburgh Sleep Quality Index. Results: Postoperatively, SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF decreased, whereas HFnu increased. According to the 24-h analyses, SDNN on POD2 and pNN50/RMSSD from the day of surgery to POD2 were significantly lower in the sleep-disorder group. During the daytime, the SDNN values on POD2–3 and pNN50 on surgery day–POD3, as well as the RMSSD values on surgery day and POD2–3, were significantly lower. At night, pNN50 and RMSSD on surgery day–POD2 remained lower than those in the normal sleep group. Conclusions: Perioperative sleep disturbance linked to vagal impairment and reduced regulatory responsiveness. HRV monitoring could assist in personalized recovery prediction and management. Figures Figure 1 Figure 2 Synopsis HRV was used to assess perioperative autonomic nervous system function. Post-thyroidectomy patients presented altered ANS activity, indicating a stress response. Poor sleep quality was associated with reduced autonomic stability. 1. Introduction Preoperative sleep disturbances are reported in up to 60% of surgical patients, with preoperative insomnia and anxiety identified as significant risk factors for postoperative sleep impairment [ 1 ]. Patients' sleep quality directly affects their postoperative rehabilitation process, thus perioperative sleep issues have garnered increasing clinical attention. Thyroid disorders are among the most prevalent endocrine diseases and are closely associated with sleep disturbances. This relationship may stem from hormone-mediated emotional changes (e.g., anxiety, depression), somatic symptoms related to thyroid dysfunction, disruptions in circadian rhythms [ 2 ], etc. Moreover, circadian rhythm disturbances can dysregulate the hypothalamic–pituitary–adrenal (HPA) axis, impair thyroid function, and even contribute to the development of thyroid malignancies [ 3 ]. Surgery remains the primary treatment for many thyroid conditions; however, perioperative anxiety, postoperative pain, physiological stress, and environmental factors can further compromise sleep quality in these patients. Therefore, optimizing sleep in patients undergoing thyroid surgery is essential to improve recovery outcomes. In 1997, Danish professor Kehlet first proposed the concept of enhanced recovery after surgery (ERAS), which emphasizes evidence-based perioperative strategies aimed at reducing surgical stress, minimizing complications, improving nutritional status, and expediting recovery. The ERAS protocol has likewise been adopted in the field of thyroid surgery in China, where patients typically experience marked clinical improvement within 2 to 3 days postoperatively. In recent years, our team—under the leadership of Professor Zhiwei Jiang—has integrated intelligent wearable devices into ERAS protocols to monitor perioperative vital signs and clinical indicators continuously to optimize clinical management. HRV is currently recognized internationally as an important method for evaluating autonomic nervous system function. Wearable HRV devices have been successfully applied to monitor[ 4 ] perioperative traumatic stress levels. HRV is a non-invasive method for assessing autonomic nervous control. HRV reflects the beat-to-beat variation in R–R intervals, modulated by sympathetic and parasympathetic input to the sinoatrial node. Lower HRV indicates impaired autonomic cardiovascular control and less responsiveness and adaptability[ 5 ] of the autonomic nervous system underlying cardiovascular disease. Surgical interventions can elicit significant autonomic dysregulation due to stress responses. Our previous studies have demonstrated that ERAS protocols attenuate these stress responses and facilitate autonomic recovery in surgical patients [ 6 , 7 ]. However, no studies to date have investigated autonomic dysfunction or perioperative stress levels in thyroidectomy patients via HRV analysis. Emerging evidence suggests a strong link between HRV and sleep–wake rhythms [ 8 , 9 ], with HRV patterns being predictive of sleep stage transitions [ 10 ]. Sleep plays a pivotal role in autonomic homeostasis; sleep disorders often present as increased heart rate and reduced HRV during nocturnal periods, likely owing to a state of hyperarousal. This exploratory study aimed to characterize circadian patterns and fluctuations in HRVs via a non-invasive wearable device capable of continuous monitoring. We sought to evaluate perioperative autonomic nervous function in patients undergoing thyroidectomy and to elucidate the impact of perioperative sleep quality on physiological stress and postoperative recovery. 2. Methods 2.1 Patients Patients who underwent thyroid surgery in the Department of General Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, from October 1, 2024, to August 30, 2025, were retrospectively included. Wearable devices continuously monitored HRV throughout the perioperative period. Owing to the retrospective nature of the study, informed consent from patients was waived. The inclusion criteria were as follows: (1) aged between 18 and 85 years, irrespective of sex; (2) hospitalized for thyroid surgery; and(3) American Society of Anesthesiologists (ASA) physical status classification I–III. The exclusion criteria were as follows: (1) patients with cognitive impairment, such as intellectual disability or psychiatric disorders; (2) patients with severe cardiac, pulmonary, hepatic, or renal dysfunction; (3) patients with current use of hypnotic medications; and (4) patients with severe postoperative complications, including hemorrhage, severe hypocalcemia, postoperative delirium, thyroid storm, etc. This study was designed as a retrospective, observational, and exploratory investigation. Ethical approval was obtained from the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine. All included patients received perioperative care based on the ERAS protocol, which included the following: (1) comprehensive preoperative education and psychological counseling; (2) multimodal analgesia; (3) early postoperative ambulation; (4) early initiation of enteral nutrition; (5) shortened preoperative fasting time and administration of oral carbohydrate drinks before surgery; and (6) avoidance of routine urinary catheterization. Surgical drainage tubes were routinely placed and removed as early as possible on the basis of drainage output. All study procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki. 2.2 Data collection HRV monitoring was performed via a long-term wearable Holter device (TES010, THOTH (SUZHOU) Medical Technology Co., Ltd.). Upon hospital admission, the researcher cleaned the skin of the precordial area with an alcohol swab and attached the registered smart device to the patient’s chest (FIG. 1 ①). HRV and heart rate data were then monitored throughout the procedure from one day before surgery (Pre) to postoperative day 3 (POD3). After data collection, statistical analysis was performed via ECG Data Analysis and Decision Aid(Computer program) (FIG. 1 ②-⑤). Sleep quality was evaluated via the Pittsburgh Sleep Quality Index (PSQI) both pre- and on POD3. PSQI was developed in 1989 by Dr. Buysse and colleagues at the University of Pittsburgh. This scale is used to assess sleep quality in patients with sleep or psychiatric disorders, as well as in the general population. Over the years, the PSQI has been widely applied in sleep research and clinical practice worldwide. According to Buysse et al., a PSQI total score of 5 serves as the cutoff point, with a sensitivity of 89.6% and a specificity of 86.5% for distinguishing individuals with poor sleep quality from those with normal sleep[ 11 ]. The reliability and validity of the scale have been well established. In China, Liu Xianchen et al. proposed a cutoff score of PSQI > 7, yielding a sensitivity of 98.3% and a specificity of 90.2% for differentiating cases from normal subjects[ 12 ]. 2.3 HRV analysis The HRV parameters included both time-domain and frequency-domain indices. Time-domain analysis indicators were acquired in hourly units and covered all data within the hour: the standard deviation of all NN intervals (SDNN), which reflects the overall HRV level and is regulated by both sympathetic and parasympathetic nerves; Standard deviation of the average NN intervals over 5-minute segments (SDANN), which represents the long-term global HRV variability. A reduction in the SDANN indicates attenuated long-term autonomic fluctuations. Root mean square of successive differences (RMSSD) primarily reflects parasympathetic (vagal) activity and is sensitive to the mean heart rate. Percentage of successive NN intervals differing by more than 50 ms (pNN50), which also reflects vagal tone and is commonly used as a supplementary index to the RMSSD. Frequency domain analysis indicators were acquired every hour, and a 5-minute data segment was selected each time. Low-frequency power (LF, 0.04–0.15 Hz), which reflects both sympathetic and parasympathetic modulation and indicates overall autonomic balance. High-frequency power (HF, 0.15–0.40 Hz) primarily represents parasympathetic (vagal) activity and is influenced by respiratory sinus arrhythmia (RSA); Low-frequency to high-frequency ratio (LF/HF), which indicates the sympathovagal balance, represents the relative dominance of sympathetic versus parasympathetic activity. LFnu and HFnu represent low- and high-frequency components expressed in normalized units, reflecting the relative contributions of sympathetic and parasympathetic modulation, respectively. 2.4 Statistical analysis Statistical analysis was performed using SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA), and data visualization was conducted using GraphPad Prism version 10 (GraphPad Software, San Diego, CA, USA). HRV data without group stratification were tested for normality via the Shapiro–Wilk test. Variables following a normal distribution were expressed as means ± standard deviations (mean ± SD), whereas nonnormally distributed variables were presented as medians and interquartile ranges (Median [Q1, Q3]). The Friedman test was used for within-group comparisons of repeated measures, with Wilcoxon signed-rank tests applied for post hoc pairwise comparisons of HRV parameters, and multiple comparisons were adjusted via the Holm‒Bonferroni procedure. After the variables were grouped on the basis of postoperative PSQI scores, all the HRV variables were expressed as medians (IQRs). Between-group comparisons were conducted via the Wilcoxon rank-sum test (Mann–Whitney U test). A two-sided p value ≤ 0.05 was considered statistically significant. 3. Results 3.1 Patient characteristics A total of 64 patients met the inclusion criteria. Among them, 3 had a history of cardiac disease, 5 had invalid data, 4 received sedative-hypnotics during the perioperative period, 1 developed postoperative bleeding, and 5 developed postoperative hypocalcemia. Ultimately, 46 patients were included in the final statistical analysis. All patients received surgical treatment from the same clinical team. The cohort consisted of 15 males and 31 females, with a median age of 57.00 (46.75, 62.50) years. The median body mass index (BMI) was 24.55 (22.88, 25.84) kg/m². Among them, 14 patients underwent total thyroidectomy, and 32 underwent Non-total thyroidectomy. 3.2 Perioperative HRV circadian rhythm Significant differences in SDNN, LFnu, HFnu, and LF/HF were observed across time points. The SDNN on the day of surgery differed significantly from the preoperative value. LFnu, HFnu, and LF/HF on the day of surgery and POD1-2 were significantly different from those at baseline, whereas other pairwise comparisons revealed no significant differences. Differences in the SDANN, pNN50, and RMSSD were not statistically significant. Overall, SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF exhibited a postoperative decreasing trend, whereas only HFnu showed an increasing trend. Table 1 Perioperative 24-hour HRV distribution Pre Surgery Pod1 Pod2 Χ 2 p SDNN(ms) 111.05(100.39, 133.37) 104.45(88.01, 123.65) a 103.84(92.10, 118.17) 108.82(92.91, 124.72) 11.679 0.009 SDANN(ms) 102.94(85.19, 120.56) 91.25(80.65, 109.86) 92.12(81.52, 105.90) 95.96(81.54, 112.66) 6.771 0.080 PNN50(%) 4.91(1.99, 8.96) 4.39(1.58, 9.68) 3.88(1.67, 9.82) 4.92(2.14, 9.79) 2.455 0.484 RMSSD(ms) 31.53(23.21, 43.32) 29.29(21.40, 40.99) 31.40(21.30, 37.37) 30.39(23.80, 39.97) 7.214 0.065 LFnu(%) 63.03(58.09, 71.61) 55.05(46.82, 62.71) a 52.47(47.15, 59.82) a 57.96(51.46, 63.80) a 36.460 <0.001 HFnu(%) 36.22(30.41, 41.57) 43.85(37.31, 52.18) a 46.41(40.20, 51.21) a 42.23(36.13, 48.39) a 29.874 <0.001 LF/HF 1.83(1.41, 2.41) 1.27(0.91, 1.68) a 1.13(0.94, 1.49) a 1.38(1.07, 1.77) a 33.671 <0.001 Note: The Friedman test was used for the comparison of HRVs at multiple time points within the group, the Wilcoxon signed rank test was used for pairwise comparisons after statistical significance, and multiple comparisons were adjusted via the Holm‒Bonferroni procedure. a P < 0.05 vs Pre. 3.3 Comparison of HRV indices under different sleep conditions Patients were classified into Group 2 (poor sleep, ≥ 8) and Group 1 (good sleep, < 8) according to their postoperative Pittsburgh Sleep Quality Index scores. As shown in Table 2 , there were no differences between the two groups in terms of age, sex, BMI, surgical method, or preoperative HRV parameters. Table 2 Baseline characteristics of the patients. Variables Normal sleep group(n = 27) Sleep disorder group(n = 19) Χ 2 /T/Z P Age(year) 51.63 ± 14.79 57.79 ± 6.43 -1.922 0.062 Sex 0.016 1 Male 9 6 Female 18 13 BMI(kg/m2) 24.68(22.86, 28.58) 24.22(22.89, 25.39) Surgical method 0.02 1 Non-total thyroidectomy 19 13 Total thyroidectomy 8 6 Preoperative HRV SDNN(ms) 118.89(100.53, 138.14) 108.21(98.71, 118.89) -1.116 0.265 SDANN(ms) 103.52(86.83, 123.04) 96.63(84.91, 106.21) -1.428 0.153 pNN50(%) 6.97(3.10, 12.43) 3.02(1.32, 8.39) -1.83 0.067 RMSSD(ms) 34.27(24.08, 43.92) 29.77(21.87, 37.06) -1.16 0.246 LFnu(%) 64.41(59.03, 68.23) 60.84(55.93, 72.78) -0.424 0.672 HFnu(%) 35.33(31.05, 40.90) 38.13(28.50, 43.59) -0.536 0.592 LF/HF 1.83(1.44, 2.38) 1.71(1.35, 2.51) -0.536 0.592 Preoperatively, no significant differences in HRV parameters were observed between the two groups (P > 0.05). According to the 24-h HRV analysis, the SDNN on postoperative day (POD) 2 was significantly lower in the sleep-disorder group than in the normal-sleep group, and the pNN50 and RMSSD values on the day of surgery and POD1-2 were also significantly lower in the sleep-disorder group. During the daytime (06:00–22:00), the SDNN on POD2–3 was significantly lower in the sleep-disorder group; the pNN50 was significantly lower on the day of surgery and POD1–3; and the RMSSD was significantly lower on the day of surgery and POD2–3. At night (22:00–06:00), the pNN50 and RMSSD values on the day of surgery and POD1–2 remained lower in the sleep-disorder group. 4. Discussion 4.1 HRV and perioperative sleep disturbance In our study, the SDNN and SDANN increased sharply during the daytime on the day of surgery, indicating autonomic adaptation to surgical stress and greater overall HRV fluctuations. Almost all HRV indices, including SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF, tended to decrease postoperatively, whereas HFnu increased, suggesting impaired autonomic function—particularly vagal activity—and an imbalance between sympathetic and parasympathetic modulation. Low pNN50 and RMSSD combined with high HFnu may reflect elevated basal vagal tone but reduced regulatory responsiveness, with compensatory vagal activation counteracting increased postoperative sympathetic activity. Patients with poor sleep presented lower postoperative SDNN, pNN50, and RMSSD values, indicating that sleep disturbance is associated with reduced vagal activity and delayed autonomic recovery from stress. On the day of surgery, the sleep-disorder group presented a lower HFnu, higher LFnu, and LF/HF, suggesting higher stress levels with sympathetic dominance and vagal suppression, although these differences were not statistically significant. Surgery and general anesthesia not only alter patients’ subjective sleep quality but also disrupt circadian rhythms. Previous studies have demonstrated measurable perioperative changes in sleep architecture and timing, including prolonged sleep latency on the night before surgery, reduced total sleep time on the first postoperative night, an earlier sleep midpoint, and marked increases in sleep inertia on POD1, indicating deteriorated sleep quality [ 13 ]. Postoperative sleep disturbance adversely affects patient outcomes, including delayed recovery, cognitive impairment, increased pain sensitivity, and cardiovascular events [ 14 ]. Surgical stress impairs autonomic nervous system function, which plays a key role in the neuro-endocrine–immune regulatory network, and intact autonomic function facilitates postoperative recovery. Prior studies have shown a close relationship between sleep and autonomic regulation: sympathetic activity predominates during the daytime, whereas parasympathetic activity is more prominent at night, reflecting circadian rhythmicity [ 6 ]. During non-rapid eye movement (NREM) sleep, sympathetic activity significantly decreases while vagal activity increases, whereas sympathetic activity increases during rapid eye movement (REM) sleep, as confirmed by robust experimental evidence [ 15 ]. Normal sleep prepares the body for the next day’s activity, whereas sleep disturbances repeatedly activate the HPA axis, elevate circulating catecholamines, and trigger inflammatory signaling, affecting cytokine release [ 16 ]. The suprachiasmatic nucleus (SCN) regulates rhythmic glucose production and utilization, as well as insulin secretion and sensitivity, via the autonomic nervous system; thus, circadian disruption can impair glucose metabolism and potentially exacerbate postoperative insulin resistance [ 17 ]. Consequently, persistent sleep disturbances may compromise host resilience, reducing the ability to cope with surgical stress. Patients with sleep disturbances exhibit higher sleep reactivity, meaning that their sleep is more easily disrupted by stressors [ 18 ]. In our study, some patients reported perioperative circadian disruption, manifested as total insomnia on the night of surgery and daytime drowsiness and lethargy. Research has shown that on the first day of recovery after sleep deprivation, both slow-wave sleep and rapid eye movement (REM) sleep increase, with REM rebound sometimes persisting until the third day, highlighting the severity of sleep loss [ 19 ]. In our study, patients with normal sleep exhibited even increased postoperative vagal activity, possibly because pNN50 and HF are strongly influenced by respiratory sinus arrhythmia (RSA), and patients may have compensated by extra daytime sleep. The residual effects of anesthesia, analgesic use, and prolonged bed rest postoperatively may also contribute. 4.2 Advantages and Limitations This study focused on perioperative HRV changes in patients undergoing thyroid surgery and grouped them on the basis of postoperative sleep quality, which filled the current research gap on the correlation between autonomic nervous system dynamics and sleep status in this specific surgical population, both domestically and internationally. In this study, HRV indices were collected during consecutive circadian periods from the day before surgery to the third day after surgery, which comprehensively revealed the dynamic changes in autonomic nervous system function. However, several limitations should be noted. First, as an observational study, this investigation was subject to potential confounding variables—such as medication use, intraoperative and day-of-surgery events, and psychological stressors—that were not fully controlled and may have influenced HRV outcomes. Second, sleep quality was assessed solely via subjective questionnaires, without objective measurements such as polysomnography or actigraphy, which may limit the accuracy of sleep evaluation. Third, the relatively small sample size (n = 46) constrained the statistical power of the analyses, necessitating the use of non-parametric tests. Future studies should aim to expand the sample size and incorporate objective sleep assessments to validate and extend these findings. 5. Conclusion Our study revealed that perioperative sleep disturbance is associated with vagal impairment and reduced regulatory responsiveness. Perioperative sleep is a multi-system and multi-dimensional important variable that exerts a fundamental influence on neurological, immune, metabolic, and psychological recovery. Improving perioperative sleep is thus a key strategy to increase surgical safety and optimize rehabilitation outcomes. The ERAS management model can reduce risk factors for postoperative sleep disturbance, thereby improving perioperative sleep; however, no ERAS protocol directly targeting perioperative sleep disturbance currently exists. Perioperative sleep status may serve as a predictor of the postoperative stress response and recovery capacity. Given the potential side effects of pharmacological treatments, perioperative sleep interventions should prioritize non-pharmacologic approaches, with pharmacologic support used judiciously. Current non-drug therapies include music therapy, psychological counseling, acupuncture, transcranial magnetic stimulation, respiratory training[ 20 ], and hypoxia therapy[ 21 ], among others. To promote enhanced recovery after surgery, perioperative sleep management should adopt individualized and multimodal strategies tailored to each patient’s needs. Declarations Data availability statement Data available on request due to privacy/ethical restrictions. Conflicts of interest The authors declare that they have no conflicts of interest. Funding Sources The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge the financial support received for this research article. This study was funded by the Jiangsu Province 14th Five-Year Medical Key Discipline in Traditional Chinese Surgery (No. ZDXK202251). Author Contributions Zhiwei Jiang and Guanwen Gong conceived and designed the study. Yang Shan and Xinyuan Zhang were responsible for the completion of the experiment and wrote the manuscript. Aierken Reziya analyzed the data. Dali Meng, Zhengming Deng, and Yuliang Zhang reviewed and criticized the article. Kun Xu, Ling He, and Hui Cheng contributed to the data collection and cohort sorting of thyroid surgery patients for the retrospective analysis. All the authors read, edited, and approved the manuscript. Ethics Approval Ethical approval for this study was obtained from the Ethics Committee of Jiangsu Provincial Hospital of Traditional Chinese Medicine. Clinical trial number: not applicable. Consent to Participate Owing to the retrospective nature of the study, informed consent from patients was waived. Consent for Publication Additional informed consent was obtained from all individual participants for whom identifying information was included in this article. 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuliang","middleName":"","lastName":"Zhang","suffix":""},{"id":546937365,"identity":"43d64433-f1a3-4ffb-a4db-be4bba1833b6","order_by":8,"name":"Hui Cheng","email":"","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Cheng","suffix":""},{"id":546937369,"identity":"5495dc26-8970-4339-a391-fd55ae65973f","order_by":9,"name":"Guanwen Gong","email":"","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guanwen","middleName":"","lastName":"Gong","suffix":""},{"id":546937371,"identity":"562b388e-6649-45bb-acd6-26a3927a5a4b","order_by":10,"name":"Zhiwei Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxmYILcfG3v/gwIcKCTl5YrUY8/OcYXw444yFsWEDkbYlzpyRw2zM2VaRyHCAgFLmduZnD7/8Ocy44UDuMWnGeRIJjA3MDx/dwOswNnNjGZ7DzAYHzqVJF26TyGNnYDM2zsHvFzNpCYnbbAYHG8ykZ26TKGZs4GGTxq+F/Zu0hMFtHoPDQL28cyQSGw4Q1MJjJvkh4baEZBuPsTFvA3FayqQZDvw34OdhS3w445iEsWEzAb8Y9h/fJvnjT1p9m/zjAwc+1NTJybM3P3yMV0sDMKB5UISY8SgHAVDyYPxBQNEoGAWjYBSMcAAAUe9L9DaUIE0AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-10-12 09:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7839788/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7839788/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96286777,"identity":"8afce227-e93a-4a00-9e46-77502f0c6d7a","added_by":"auto","created_at":"2025-11-19 12:06:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7189786,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptshanyang.docx","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/a37082bfb23f0e3b30c293ea.docx"},{"id":96286770,"identity":"fa9bbbfe-e98d-4838-b3c0-d0c4700da5cd","added_by":"auto","created_at":"2025-11-19 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12:06:03","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78082,"visible":true,"origin":"","legend":"","description":"","filename":"7df2c0d5ce4a4fe0b1ad6f5b3b03b2ca1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/901fb2a41b59546031b9763a.xml"},{"id":96286779,"identity":"e9b0dcff-4fbc-4b01-957f-6a1b77129fae","added_by":"auto","created_at":"2025-11-19 12:06:03","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87173,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/aae779798141d213a46040bc.html"},{"id":96286771,"identity":"f02527d7-e635-497c-bd12-0843a44f12d7","added_by":"auto","created_at":"2025-11-19 12:06:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":913262,"visible":true,"origin":"","legend":"\u003cp\u003eHRV data collection procedure\u003c/p\u003e\n\u003cp\u003e① Wearable Holter ECG recorder; ② time domain and frequency domain analysis during surgery; ③ Poincaré scatter plot during operation, during the daytime, and at night from left to right; ④ The whole N‒Ninterval scatter plot, the operation period is marked with a box; and ⑤ HRV tendency graphof the whole process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/27530428059303da83b1fdda.png"},{"id":96286774,"identity":"91ff5f34-ae24-45fb-b7e0-a8822375d9e4","added_by":"auto","created_at":"2025-11-19 12:06:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2809043,"visible":true,"origin":"","legend":"\u003cp\u003eHRV distribution across postoperative sleep quality groups\u003c/p\u003e\n\u003cp\u003eNote: Changes in HRVin patients undergoing thyroid surgery during the perioperative period. Patients were divided into group 2 (≥8points) with poor sleep and group 1 (\u0026lt;8 points) with good sleep. 1a--7a refer to the whole-day data, 1b--7b refer to the daytime data (6:00--22:00), and 1c--7c refer to the nighttime data (22:00--6:00). N1=27, N2=19. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/f6c669b8fa4cdaaf6bcdc2d3.png"},{"id":107655081,"identity":"bb4f068a-5c16-4f31-adb0-72d464567bb9","added_by":"auto","created_at":"2026-04-23 15:41:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3577920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7839788/v1/616cd633-de9d-4433-9fed-bf81d6d4ff40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Postoperative Sleep Quality and Autonomic Nervous Function in Thyroid Surgery Patients: A Wearable Holter-Based Observational Study","fulltext":[{"header":"Synopsis","content":"\u003cp\u003eHRV was used to assess perioperative autonomic nervous system function. Post-thyroidectomy patients presented altered ANS activity, indicating a stress response. Poor sleep quality was associated with reduced autonomic stability.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003ePreoperative sleep disturbances are reported in up to 60% of surgical patients, with preoperative insomnia and anxiety identified as significant risk factors for postoperative sleep impairment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients' sleep quality directly affects their postoperative rehabilitation process, thus perioperative sleep issues have garnered increasing clinical attention. Thyroid disorders are among the most prevalent endocrine diseases and are closely associated with sleep disturbances. This relationship may stem from hormone-mediated emotional changes (e.g., anxiety, depression), somatic symptoms related to thyroid dysfunction, disruptions in circadian rhythms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], etc. Moreover, circadian rhythm disturbances can dysregulate the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis, impair thyroid function, and even contribute to the development of thyroid malignancies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Surgery remains the primary treatment for many thyroid conditions; however, perioperative anxiety, postoperative pain, physiological stress, and environmental factors can further compromise sleep quality in these patients. Therefore, optimizing sleep in patients undergoing thyroid surgery is essential to improve recovery outcomes.\u003c/p\u003e\u003cp\u003eIn 1997, Danish professor Kehlet first proposed the concept of enhanced recovery after surgery (ERAS), which emphasizes evidence-based perioperative strategies aimed at reducing surgical stress, minimizing complications, improving nutritional status, and expediting recovery. The ERAS protocol has likewise been adopted in the field of thyroid surgery in China, where patients typically experience marked clinical improvement within 2 to 3 days postoperatively. In recent years, our team\u0026mdash;under the leadership of Professor Zhiwei Jiang\u0026mdash;has integrated intelligent wearable devices into ERAS protocols to monitor perioperative vital signs and clinical indicators continuously to optimize clinical management. HRV is currently recognized internationally as an important method for evaluating autonomic nervous system function. Wearable HRV devices have been successfully applied to monitor[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] perioperative traumatic stress levels.\u003c/p\u003e\u003cp\u003eHRV is a non-invasive method for assessing autonomic nervous control. HRV reflects the beat-to-beat variation in R\u0026ndash;R intervals, modulated by sympathetic and parasympathetic input to the sinoatrial node. Lower HRV indicates impaired autonomic cardiovascular control and less responsiveness and adaptability[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] of the autonomic nervous system underlying cardiovascular disease. Surgical interventions can elicit significant autonomic dysregulation due to stress responses. Our previous studies have demonstrated that ERAS protocols attenuate these stress responses and facilitate autonomic recovery in surgical patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, no studies to date have investigated autonomic dysfunction or perioperative stress levels in thyroidectomy patients via HRV analysis. Emerging evidence suggests a strong link between HRV and sleep\u0026ndash;wake rhythms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with HRV patterns being predictive of sleep stage transitions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sleep plays a pivotal role in autonomic homeostasis; sleep disorders often present as increased heart rate and reduced HRV during nocturnal periods, likely owing to a state of hyperarousal. This exploratory study aimed to characterize circadian patterns and fluctuations in HRVs via a non-invasive wearable device capable of continuous monitoring. We sought to evaluate perioperative autonomic nervous function in patients undergoing thyroidectomy and to elucidate the impact of perioperative sleep quality on physiological stress and postoperative recovery.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patients\u003c/h2\u003e\u003cp\u003ePatients who underwent thyroid surgery in the Department of General Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, from October 1, 2024, to August 30, 2025, were retrospectively included. Wearable devices continuously monitored HRV throughout the perioperative period. Owing to the retrospective nature of the study, informed consent from patients was waived.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were as follows: (1) aged between 18 and 85 years, irrespective of sex; (2) hospitalized for thyroid surgery; and(3) American Society of Anesthesiologists (ASA) physical status classification I\u0026ndash;III.\u003c/p\u003e\u003cp\u003eThe exclusion criteria were as follows: (1) patients with cognitive impairment, such as intellectual disability or psychiatric disorders; (2) patients with severe cardiac, pulmonary, hepatic, or renal dysfunction; (3) patients with current use of hypnotic medications; and (4) patients with severe postoperative complications, including hemorrhage, severe hypocalcemia, postoperative delirium, thyroid storm, etc.\u003c/p\u003e\u003cp\u003eThis study was designed as a retrospective, observational, and exploratory investigation. Ethical approval was obtained from the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine. All included patients received perioperative care based on the ERAS protocol, which included the following: (1) comprehensive preoperative education and psychological counseling; (2) multimodal analgesia; (3) early postoperative ambulation; (4) early initiation of enteral nutrition; (5) shortened preoperative fasting time and administration of oral carbohydrate drinks before surgery; and (6) avoidance of routine urinary catheterization. Surgical drainage tubes were routinely placed and removed as early as possible on the basis of drainage output. All study procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\u003cp\u003eHRV monitoring was performed via a long-term wearable Holter device (TES010, THOTH (SUZHOU) Medical Technology Co., Ltd.). Upon hospital admission, the researcher cleaned the skin of the precordial area with an alcohol swab and attached the registered smart device to the patient\u0026rsquo;s chest (FIG. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e①). HRV and heart rate data were then monitored throughout the procedure from one day before surgery (Pre) to postoperative day 3 (POD3). After data collection, statistical analysis was performed via ECG Data Analysis and Decision Aid(Computer program) (FIG. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e②-⑤).\u003c/p\u003e\u003cp\u003eSleep quality was evaluated via the Pittsburgh Sleep Quality Index (PSQI) both pre- and on POD3. PSQI was developed in 1989 by Dr. Buysse and colleagues at the University of Pittsburgh. This scale is used to assess sleep quality in patients with sleep or psychiatric disorders, as well as in the general population. Over the years, the PSQI has been widely applied in sleep research and clinical practice worldwide. According to Buysse et al., a PSQI total score of 5 serves as the cutoff point, with a sensitivity of 89.6% and a specificity of 86.5% for distinguishing individuals with poor sleep quality from those with normal sleep[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The reliability and validity of the scale have been well established. In China, Liu Xianchen et al. proposed a cutoff score of PSQI\u0026thinsp;\u0026gt;\u0026thinsp;7, yielding a sensitivity of 98.3% and a specificity of 90.2% for differentiating cases from normal subjects[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 HRV analysis\u003c/h2\u003e\u003cp\u003eThe HRV parameters included both time-domain and frequency-domain indices. Time-domain analysis indicators were acquired in hourly units and covered all data within the hour: the standard deviation of all NN intervals (SDNN), which reflects the overall HRV level and is regulated by both sympathetic and parasympathetic nerves; Standard deviation of the average NN intervals over 5-minute segments (SDANN), which represents the long-term global HRV variability. A reduction in the SDANN indicates attenuated long-term autonomic fluctuations. Root mean square of successive differences (RMSSD) primarily reflects parasympathetic (vagal) activity and is sensitive to the mean heart rate. Percentage of successive NN intervals differing by more than 50 ms (pNN50), which also reflects vagal tone and is commonly used as a supplementary index to the RMSSD. Frequency domain analysis indicators were acquired every hour, and a 5-minute data segment was selected each time. Low-frequency power (LF, 0.04\u0026ndash;0.15 Hz), which reflects both sympathetic and parasympathetic modulation and indicates overall autonomic balance. High-frequency power (HF, 0.15\u0026ndash;0.40 Hz) primarily represents parasympathetic (vagal) activity and is influenced by respiratory sinus arrhythmia (RSA); Low-frequency to high-frequency ratio (LF/HF), which indicates the sympathovagal balance, represents the relative dominance of sympathetic versus parasympathetic activity. LFnu and HFnu represent low- and high-frequency components expressed in normalized units, reflecting the relative contributions of sympathetic and parasympathetic modulation, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA), and data visualization was conducted using GraphPad Prism version 10 (GraphPad Software, San Diego, CA, USA). HRV data without group stratification were tested for normality via the Shapiro\u0026ndash;Wilk test. Variables following a normal distribution were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), whereas nonnormally distributed variables were presented as medians and interquartile ranges (Median [Q1, Q3]). The Friedman test was used for within-group comparisons of repeated measures, with Wilcoxon signed-rank tests applied for post hoc pairwise comparisons of HRV parameters, and multiple comparisons were adjusted via the Holm‒Bonferroni procedure. After the variables were grouped on the basis of postoperative PSQI scores, all the HRV variables were expressed as medians (IQRs). Between-group comparisons were conducted via the Wilcoxon rank-sum test (Mann\u0026ndash;Whitney U test). A two-sided p value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e\u003cp\u003eA total of 64 patients met the inclusion criteria. Among them, 3 had a history of cardiac disease, 5 had invalid data, 4 received sedative-hypnotics during the perioperative period, 1 developed postoperative bleeding, and 5 developed postoperative hypocalcemia. Ultimately, 46 patients were included in the final statistical analysis. All patients received surgical treatment from the same clinical team. The cohort consisted of 15 males and 31 females, with a median age of 57.00 (46.75, 62.50) years. The median body mass index (BMI) was 24.55 (22.88, 25.84) kg/m\u0026sup2;. Among them, 14 patients underwent total thyroidectomy, and 32 underwent Non-total thyroidectomy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Perioperative HRV circadian rhythm\u003c/h2\u003e\u003cp\u003eSignificant differences in SDNN, LFnu, HFnu, and LF/HF were observed across time points. The SDNN on the day of surgery differed significantly from the preoperative value. LFnu, HFnu, and LF/HF on the day of surgery and POD1-2 were significantly different from those at baseline, whereas other pairwise comparisons revealed no significant differences. Differences in the SDANN, pNN50, and RMSSD were not statistically significant. Overall, SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF exhibited a postoperative decreasing trend, whereas only HFnu showed an increasing trend.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerioperative 24-hour HRV distribution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePod1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePod2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111.05(100.39, 133.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.45(88.01, 123.65)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.84(92.10, 118.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108.82(92.91, 124.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDANN(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102.94(85.19, 120.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.25(80.65, 109.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.12(81.52, 105.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.96(81.54, 112.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNN50(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.91(1.99, 8.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.39(1.58, 9.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88(1.67, 9.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.92(2.14, 9.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.53(23.21, 43.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.29(21.40, 40.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.40(21.30, 37.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.39(23.80, 39.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFnu(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.03(58.09, 71.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.05(46.82, 62.71)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.47(47.15, 59.82)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.96(51.46, 63.80)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHFnu(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.22(30.41, 41.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.85(37.31, 52.18)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.41(40.20, 51.21)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.23(36.13, 48.39)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF/HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83(1.41, 2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27(0.91, 1.68)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13(0.94, 1.49)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.38(1.07, 1.77)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: The Friedman test was used for the comparison of HRVs at multiple time points within the group, the Wilcoxon signed rank test was used for pairwise comparisons after statistical significance, and multiple comparisons were adjusted via the Holm‒Bonferroni procedure. \u003csup\u003ea\u003c/sup\u003eP \u0026lt; 0.05 vs Pre.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Comparison of HRV indices under different sleep conditions\u003c/h2\u003e\u003cp\u003ePatients were classified into Group 2 (poor sleep, \u0026ge;\u0026thinsp;8) and Group 1 (good sleep, \u0026lt;\u0026thinsp;8) according to their postoperative Pittsburgh Sleep Quality Index scores. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there were no differences between the two groups in terms of age, sex, BMI, surgical method, or preoperative HRV parameters.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal sleep group(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSleep disorder group(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e/T/Z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.63\u0026thinsp;\u0026plusmn;\u0026thinsp;14.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.68(22.86, 28.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.22(22.89, 25.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-total thyroidectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal thyroidectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative HRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118.89(100.53, 138.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.21(98.71, 118.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDANN(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103.52(86.83, 123.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.63(84.91, 106.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epNN50(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.97(3.10, 12.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.02(1.32, 8.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.27(24.08, 43.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.77(21.87, 37.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFnu(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.41(59.03, 68.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.84(55.93, 72.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHFnu(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.33(31.05, 40.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.13(28.50, 43.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF/HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.83(1.44, 2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.71(1.35, 2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePreoperatively, no significant differences in HRV parameters were observed between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). According to the 24-h HRV analysis, the SDNN on postoperative day (POD) 2 was significantly lower in the sleep-disorder group than in the normal-sleep group, and the pNN50 and RMSSD values on the day of surgery and POD1-2 were also significantly lower in the sleep-disorder group. During the daytime (06:00\u0026ndash;22:00), the SDNN on POD2\u0026ndash;3 was significantly lower in the sleep-disorder group; the pNN50 was significantly lower on the day of surgery and POD1\u0026ndash;3; and the RMSSD was significantly lower on the day of surgery and POD2\u0026ndash;3. At night (22:00\u0026ndash;06:00), the pNN50 and RMSSD values on the day of surgery and POD1\u0026ndash;2 remained lower in the sleep-disorder group.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 HRV and perioperative sleep disturbance\u003c/h2\u003e\u003cp\u003eIn our study, the SDNN and SDANN increased sharply during the daytime on the day of surgery, indicating autonomic adaptation to surgical stress and greater overall HRV fluctuations. Almost all HRV indices, including SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF, tended to decrease postoperatively, whereas HFnu increased, suggesting impaired autonomic function\u0026mdash;particularly vagal activity\u0026mdash;and an imbalance between sympathetic and parasympathetic modulation. Low pNN50 and RMSSD combined with high HFnu may reflect elevated basal vagal tone but reduced regulatory responsiveness, with compensatory vagal activation counteracting increased postoperative sympathetic activity. Patients with poor sleep presented lower postoperative SDNN, pNN50, and RMSSD values, indicating that sleep disturbance is associated with reduced vagal activity and delayed autonomic recovery from stress. On the day of surgery, the sleep-disorder group presented a lower HFnu, higher LFnu, and LF/HF, suggesting higher stress levels with sympathetic dominance and vagal suppression, although these differences were not statistically significant.\u003c/p\u003e\u003cp\u003eSurgery and general anesthesia not only alter patients\u0026rsquo; subjective sleep quality but also disrupt circadian rhythms. Previous studies have demonstrated measurable perioperative changes in sleep architecture and timing, including prolonged sleep latency on the night before surgery, reduced total sleep time on the first postoperative night, an earlier sleep midpoint, and marked increases in sleep inertia on POD1, indicating deteriorated sleep quality [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Postoperative sleep disturbance adversely affects patient outcomes, including delayed recovery, cognitive impairment, increased pain sensitivity, and cardiovascular events [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Surgical stress impairs autonomic nervous system function, which plays a key role in the neuro-endocrine\u0026ndash;immune regulatory network, and intact autonomic function facilitates postoperative recovery. Prior studies have shown a close relationship between sleep and autonomic regulation: sympathetic activity predominates during the daytime, whereas parasympathetic activity is more prominent at night, reflecting circadian rhythmicity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. During non-rapid eye movement (NREM) sleep, sympathetic activity significantly decreases while vagal activity increases, whereas sympathetic activity increases during rapid eye movement (REM) sleep, as confirmed by robust experimental evidence [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Normal sleep prepares the body for the next day\u0026rsquo;s activity, whereas sleep disturbances repeatedly activate the HPA axis, elevate circulating catecholamines, and trigger inflammatory signaling, affecting cytokine release [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The suprachiasmatic nucleus (SCN) regulates rhythmic glucose production and utilization, as well as insulin secretion and sensitivity, via the autonomic nervous system; thus, circadian disruption can impair glucose metabolism and potentially exacerbate postoperative insulin resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, persistent sleep disturbances may compromise host resilience, reducing the ability to cope with surgical stress.\u003c/p\u003e\u003cp\u003ePatients with sleep disturbances exhibit higher sleep reactivity, meaning that their sleep is more easily disrupted by stressors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our study, some patients reported perioperative circadian disruption, manifested as total insomnia on the night of surgery and daytime drowsiness and lethargy. Research has shown that on the first day of recovery after sleep deprivation, both slow-wave sleep and rapid eye movement (REM) sleep increase, with REM rebound sometimes persisting until the third day, highlighting the severity of sleep loss [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In our study, patients with normal sleep exhibited even increased postoperative vagal activity, possibly because pNN50 and HF are strongly influenced by respiratory sinus arrhythmia (RSA), and patients may have compensated by extra daytime sleep. The residual effects of anesthesia, analgesic use, and prolonged bed rest postoperatively may also contribute.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Advantages and Limitations\u003c/h2\u003e\u003cp\u003eThis study focused on perioperative HRV changes in patients undergoing thyroid surgery and grouped them on the basis of postoperative sleep quality, which filled the current research gap on the correlation between autonomic nervous system dynamics and sleep status in this specific surgical population, both domestically and internationally. In this study, HRV indices were collected during consecutive circadian periods from the day before surgery to the third day after surgery, which comprehensively revealed the dynamic changes in autonomic nervous system function. However, several limitations should be noted. First, as an observational study, this investigation was subject to potential confounding variables\u0026mdash;such as medication use, intraoperative and day-of-surgery events, and psychological stressors\u0026mdash;that were not fully controlled and may have influenced HRV outcomes. Second, sleep quality was assessed solely via subjective questionnaires, without objective measurements such as polysomnography or actigraphy, which may limit the accuracy of sleep evaluation. Third, the relatively small sample size (n\u0026thinsp;=\u0026thinsp;46) constrained the statistical power of the analyses, necessitating the use of non-parametric tests. Future studies should aim to expand the sample size and incorporate objective sleep assessments to validate and extend these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study revealed that perioperative sleep disturbance is associated with vagal impairment and reduced regulatory responsiveness. Perioperative sleep is a multi-system and multi-dimensional important variable that exerts a fundamental influence on neurological, immune, metabolic, and psychological recovery. Improving perioperative sleep is thus a key strategy to increase surgical safety and optimize rehabilitation outcomes. The ERAS management model can reduce risk factors for postoperative sleep disturbance, thereby improving perioperative sleep; however, no ERAS protocol directly targeting perioperative sleep disturbance currently exists. Perioperative sleep status may serve as a predictor of the postoperative stress response and recovery capacity. Given the potential side effects of pharmacological treatments, perioperative sleep interventions should prioritize non-pharmacologic approaches, with pharmacologic support used judiciously. Current non-drug therapies include music therapy, psychological counseling, acupuncture, transcranial magnetic stimulation, respiratory training[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and hypoxia therapy[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], among others. To promote enhanced recovery after surgery, perioperative sleep management should adopt individualized and multimodal strategies tailored to each patient\u0026rsquo;s needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eData available on request due to privacy/ethical restrictions.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding Sources\u003c/p\u003e\n\u003cp\u003eThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge the financial support received for this research article. This study was funded by the Jiangsu Province 14th Five-Year Medical Key Discipline in Traditional Chinese Surgery (No. ZDXK202251).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eZhiwei Jiang and Guanwen Gong conceived and designed the study. Yang Shan and Xinyuan Zhang were responsible for the completion of the experiment and wrote the manuscript. Aierken Reziya analyzed the data. Dali Meng, Zhengming Deng, and Yuliang Zhang reviewed and criticized the article. Kun Xu, Ling He, and Hui Cheng contributed to the data collection and cohort sorting of thyroid surgery patients for the retrospective analysis. All the authors read, edited, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003eEthics Approval\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Jiangsu Provincial Hospital of Traditional Chinese Medicine. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eOwing to the retrospective nature of the study, informed consent from patients was waived.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eAdditional informed consent was obtained from all individual participants for whom identifying information was included in this article.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, we used ChatGPT for translation. After using this tool, we reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eButris N, Tang E, Pivetta B, et al. 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J Physiol Sci. 2025;75(1):100002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jphyss.2024.100002\u003c/span\u003e\u003cspan address=\"10.1016/j.jphyss.2024.100002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7839788/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7839788/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: Heart rate variability (HRV) reflects the function of the autonomic nervous system. The associations of perioperative HRV dynamics with sleep among patients undergoing thyroidectomy remain unclear.\nMethods: This retrospective study included 46 patients with continuous HRV monitoring (SDNN, SDANN, RMSSD, pNN50, LFnu, HFnu, LF/HF) from 1 day before to 3 days after surgery. Sleep groups were defined via the Pittsburgh Sleep Quality Index.\nResults: Postoperatively, SDNN, SDANN, pNN50, RMSSD, LFnu, and LF/HF decreased, whereas HFnu increased. According to the 24-h analyses, SDNN on POD2 and pNN50/RMSSD from the day of surgery to POD2 were significantly lower in the sleep-disorder group. During the daytime, the SDNN values on POD2–3 and pNN50 on surgery day–POD3, as well as the RMSSD values on surgery day and POD2–3, were significantly lower. At night, pNN50 and RMSSD on surgery day–POD2 remained lower than those in the normal sleep group.\nConclusions: Perioperative sleep disturbance linked to vagal impairment and reduced regulatory responsiveness. HRV monitoring could assist in personalized recovery prediction and management.","manuscriptTitle":"Postoperative Sleep Quality and Autonomic Nervous Function in Thyroid Surgery Patients: A Wearable Holter-Based Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 12:05:58","doi":"10.21203/rs.3.rs-7839788/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c54e7e60-8cb6-4ce1-af49-c133afb26207","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T15:40:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 12:05:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7839788","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7839788","identity":"rs-7839788","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}