Sleep is an essential physiological process that contributes to physical recovery, emotional stability, cognitive functioning, and memory consolidation. Adequate sleep duration and quality are particularly important for undergraduate medical students, who experience substantial academic demands, frequent examinations, extensive clinical learning activities, and irregular daily schedules. These factors can disrupt normal sleep patterns and adversely influence overall well-being. A global review identified sleep disturbances as a major health concern among medical students, highlighting the contribution of academic pressures, lifestyle behaviours, and irregular sleep schedules to impaired sleep quality.[1] Consequently, understanding sleep-related changes in this population is important for promoting student health and academic functioning. The prevalence of poor sleep quality among medical students has received considerable attention. Rao et al. reported a pooled prevalence of 52.7% across 57 observational studies, demonstrating that sleep disturbances affect a substantial proportion of medical students worldwide.[2] Similarly, Seoane et al. identified a significant association between sleep quality and academic performance in a systematic review and meta-analysis.[3] Persistent sleep disturbances are associated with impaired attention, reduced concentration, daytime fatigue, emotional distress, and diminished learning efficiency. Nevertheless, sleep quality is influenced by several interconnected factors, including psychological stress, physical activity, dietary habits, electronic device usage, and individual sleep behaviours. These observations emphasize the need to investigate not only subjective sleep disturbances but also their physiological correlates. The autonomic nervous system plays a central role in cardiovascular regulation and exhibits characteristic changes during different stages of sleep. Parasympathetic modulation generally predominates during non-rapid eye movement sleep, whereas rapid eye movement sleep is associated with greater autonomic fluctuations.[4,5] Heart rate variability (HRV) represents beat-to-beat variations in cardiac intervals and is widely used as a noninvasive indicator of cardiac autonomic modulation. According to established measurement standards, HRV can be assessed using time-domain and frequency-domain parameters.[6] The standard deviation of normal-to-normal intervals (SDNN) reflects overall variability during the recording period, while the root mean square of successive differences (RMSSD) predominantly represents short-term vagally mediated modulation. Frequency-domain indices, including low-frequency and high-frequency power, provide additional information regarding oscillatory components of cardiac rhythm.[7]. The Pittsburgh Sleep Quality Index (PSQI) is a validated instrument commonly used to evaluate subjective sleep quality over the preceding month. It assesses several dimensions, including sleep duration, sleep latency, habitual sleep efficiency, sleep disturbances, and daytime dysfunction.[8] Previous investigations have demonstrated associations between impaired sleep quality and altered HRV among medical students. Cvejic et al. examined the relationship between sleep disturbances and nocturnal HRV, while Guo et al. investigated the associations among sleep quality, depressive symptoms, and autonomic cardiac modulation using 24-hour HRV monitoring.[9,10] However, differences in study populations, recording conditions, and HRV assessment protocols highlight the importance of further institution-based investigations. The present study aimed to assess sleep quality and examine its association with resting heart rate and HRV among undergraduate medical students at Konaseema Institute of Medical Sciences and Research Foundation, Amalapuram, India. Specifically, the study evaluated differences in cardiovascular autonomic parameters between students with good and poor sleep quality and investigated correlations between PSQI scores and selected HRV indices. Understanding these associations could provide useful physiological insights into sleep-related cardiovascular regulation and support the development of student wellness initiatives.

Study Design and Setting A cross-sectional observational study was conducted in the Departments of Physiology and Pharmacology, Konaseema Institute of Medical Sciences and Research Foundation (KIMS & RF), Amalapuram, Andhra Pradesh, India, between April 2025 and March 2026. The study was designed to assess sleep quality and examine its association with resting heart rate and heart rate variability (HRV) among undergraduate medical students. Study Population and Sample Size The study included 100 undergraduate medical students aged 18–25 years from different academic years. Students who provided informed consent and were willing to undergo sleep quality assessment and cardiovascular autonomic evaluation were considered eligible for participation. The sample size was 100 participants. Inclusion Criteria 1. Undergraduate medical students aged 18–25 years. 2. Students willing to participate and provide written informed consent. 3. Students eligible to undergo resting electrocardiographic recording and HRV assessment. Exclusion Criteria 1. Students with known cardiovascular diseases or cardiac arrhythmias. 2. Students with acute febrile illness at the time of assessment. 3. Students with known neurological disorders affecting autonomic function. 4. Students receiving medications known to influence heart rate or cardiac autonomic modulation. Assessment of Sleep Quality Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), a standardized questionnaire evaluating subjective sleep quality during the preceding month.[8] The questionnaire consists of seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. The global PSQI score ranges from 0 to 21, with higher scores indicating poorer sleep quality. Participants were classified into two groups based on their global PSQI scores: • Good sleep quality: PSQI score ≤5. • Poor sleep quality: PSQI score >5. Assessment of Cardiac Autonomic Modulation Cardiac autonomic modulation was evaluated through resting heart rate and HRV analysis. Participants were assessed in a quiet environment under standardized resting conditions. Following a period of supine rest, electrocardiographic signals were recorded, and a five-minute segment was selected for HRV analysis. Measurements were interpreted according to established recommendations for short-term HRV assessment.[6,7,11] The following cardiovascular parameters were evaluated: • Resting heart rate: Expressed in beats per minute. • SDNN: Standard deviation of normal-to-normal RR intervals, expressed in milliseconds. • RMSSD: Root mean square of successive differences between normal RR intervals, expressed in milliseconds. • High-frequency power: Spectral power within 0.15–0.40 Hz, expressed in normalized units. • LF/HF ratio: Ratio of low-frequency power (0.04–0.15 Hz) to high-frequency power (0.15–0.40 Hz). These parameters were used to examine differences in cardiac autonomic modulation between students with good and poor sleep quality. The LF/HF ratio was interpreted cautiously and was not considered a direct indicator of sympathetic activity.[12]. Statistical Analysis Data were summarized using descriptive and inferential statistical methods. Categorical variables were expressed as frequencies and percentages, while continuous variables were presented as means and standard deviations. Differences in resting heart rate and HRV parameters between students with good and poor sleep quality were assessed using Welch’s independent-samples t-test. Pearson’s correlation coefficient was used to evaluate associations between global PSQI scores and selected cardiovascular autonomic parameters. All statistical tests were two-sided, and a p-value <0.05 was considered statistically significant. Ethical Considerations The study was approved by the Institutional Ethics Committee of Konaseema Institute of Medical Sciences and Research Foundation, Amalapuram, Andhra Pradesh, India. Written informed consent was obtained from all participants before enrolment. Participation was voluntary, and confidentiality was maintained through anonymized study records. All study procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki.

The study comprised 100 undergraduate medical students. Women represented 56.0% and men 44.0%. Students aged 21–23 years formed the largest age category (54.0%). Second-year students accounted for 27.0%, followed by third-year (26.0%), first-year (25.0%), and final-year students (22.0%). Demographic characteristics and sleep classification are presented in Table 1. Table 1. Demographic Characteristics and Sleep Quality of Participants (N = 100) Characteristic n (%) Age group, years 18–20 38 (38.0) 21–23 54 (54.0) 24–25 8 (8.0) Sex Male 44 (44.0) Female 56 (56.0) Year of study First year 25 (25.0) Second year 27 (27.0) Third year 26 (26.0) Final year 22 (22.0) Sleep quality Good sleep quality: PSQI ≤5 42 (42.0) Poor sleep quality: PSQI >5 58 (58.0) Poor sleep quality was represented in 58 students (58.0%); 42 (42.0%) had good sleep quality. As shown in Table 2, poor sleepers had a higher resting heart rate (79.8 ± 8.6 versus 72.4 ± 7.2 beats/min; p < 0.001). SDNN and RMSSD were lower by 12.4 ms and 15.4 ms, respectively. Mean age did not differ significantly between groups (p = 0.353). Table 2. Comparison of Autonomic Function Parameters According to Sleep Quality Parameter Good sleep (n = 42) Mean ± SD Poor sleep (n = 58) Mean ± SD p value Age, years 20.8 ± 1.5 21.1 ± 1.7 0.353 PSQI score 3.4 ± 1.1 8.6 ± 2.2 <0.001 Resting heart rate, beats/min 72.4 ± 7.2 79.8 ± 8.6 <0.001 SDNN, ms 55.6 ± 12.8 43.2 ± 11.6 <0.001 RMSSD, ms 47.8 ± 13.6 32.4 ± 10.8 <0.001 High-frequency power, normalized units 44.2 ± 9.4 33.6 ± 8.8 <0.001 Low-frequency/high-frequency power ratio 1.42 ± 0.48 2.18 ± 0.72 <0.001 The frequency-domain comparisons in Table 2 showed lower normalized high-frequency power among poor sleepers (33.6 ± 8.8 versus 44.2 ± 9.4; p < 0.001). Their low-frequency/high-frequency ratio was higher (2.18 ± 0.72 versus 1.42 ± 0.48; p < 0.001). These observations describe spectral differences without classifying participants as having autonomic dysfunction. As summarized in Table 3, PSQI scores correlated positively with resting heart rate (r = 0.42) and the low-frequency/high-frequency ratio (r = 0.48). Negative correlations were represented for SDNN (r = −0.46), RMSSD (r = −0.52), and normalized high-frequency power (r = −0.44). All corresponding p values were <0.001. RMSSD showed the largest absolute correlation coefficient. Table 3. Correlation Between PSQI Score and Autonomic Function Parameters (N = 100) Autonomic parameter Pearson’s r p value Resting heart rate 0.42 <0.001 SDNN −0.46 <0.001 RMSSD −0.52 <0.001 High-frequency power −0.44 <0.001 Low-frequency/high-frequency power ratio 0.48 <0.001

The present study examined the association between subjective sleep quality and cardiovascular autonomic modulation among 100 undergraduate medical students. Poor sleep quality was observed in 58.0% of participants and was associated with significantly higher resting heart rate and lower heart rate variability (HRV) parameters, including SDNN, RMSSD, and normalized high-frequency power. Furthermore, Pittsburgh Sleep Quality Index (PSQI) scores demonstrated significant negative correlations with SDNN and RMSSD, with the strongest inverse association observed for RMSSD (r = −0.52). These findings suggest that impaired sleep quality is associated with differences in cardiac autonomic modulation among medical students, highlighting the potential physiological implications of inadequate sleep. The prevalence of poor sleep quality observed in the present study was consistent with findings from previous investigations involving medical students. Rao et al. reported a pooled prevalence of 52.7%, while Binjabr et al. documented a prevalence of 55.64% in their systematic review and meta-analysis.[2,14] The slightly higher prevalence observed in the present study could reflect differences in academic demands, examination schedules, lifestyle habits, and individual sleep behaviours. Variations in geographical settings, participant characteristics, and sleep assessment methods also contribute to differences in reported prevalence. These observations emphasize the importance of identifying sleep disturbances among undergraduate medical students and incorporating appropriate sleep health education into institutional wellness programmes. Students with poor sleep quality demonstrated significantly lower SDNN and RMSSD values compared with those reporting good sleep quality. These time-domain HRV parameters provide useful information regarding cardiac autonomic modulation, particularly short-term vagally mediated activity. Reduced RMSSD is generally associated with lower cardiac parasympathetic modulation, while SDNN reflects overall variability during the recording period.[7,11] The accompanying increase in resting heart rate further supports the presence of differences in cardiovascular autonomic regulation between the groups. However, HRV parameters are influenced by several physiological factors, including respiratory patterns, physical activity, emotional state, and intrinsic heart rate. Therefore, these findings should be interpreted as associations rather than definitive evidence of autonomic dysfunction. The relationship between sleep and autonomic regulation is supported by established physiological mechanisms. Normal sleep involves characteristic changes in autonomic activity across different sleep stages. Non-rapid eye movement sleep is generally associated with increased parasympathetic modulation and cardiovascular stability, whereas rapid eye movement sleep is characterized by greater autonomic fluctuations.[4,5] Disturbances in sleep duration, continuity, and quality can influence these regulatory processes. The significantly lower HRV parameters observed among poor sleepers are consistent with this physiological relationship. Nevertheless, the resting HRV measurements employed in the present study do not directly assess sleep architecture or establish the mechanisms underlying these associations. Previous investigations among medical students have reported comparable relationships between sleep disturbances and HRV. Cvejic et al. identified associations between poorer sleep quality, psychological distress, and reduced nocturnal HRV.[9] Similarly, Guo et al. investigated the relationships among sleep quality, depressive symptoms, and cardiac autonomic modulation using 24-hour HRV monitoring.[10] These findings suggest that sleep disturbances and psychological factors are closely interconnected with cardiovascular autonomic regulation. Differences in recording duration, measurement protocols, and participant characteristics should be considered when comparing results across studies. The present findings add to the existing evidence by examining subjective sleep quality alongside short-term resting HRV measurements among undergraduate medical students. Frequency-domain analysis demonstrated significantly lower normalized high-frequency power and a higher low-frequency/high-frequency (LF/HF) ratio among students with poor sleep quality. Reduced high-frequency power is compatible with lower vagally mediated cardiac modulation under appropriate recording conditions. However, interpretation of the LF/HF ratio requires caution. Billman emphasized that this ratio does not reliably represent sympathovagal balance and should not be considered a direct measure of sympathetic nervous system activity.[12] Accordingly, the observed differences in spectral parameters are best interpreted alongside resting heart rate and time-domain HRV measures rather than as evidence of isolated sympathetic predominance. Correlation analysis revealed significant associations between PSQI scores and cardiovascular autonomic parameters. Higher PSQI scores were associated with increased resting heart rate and LF/HF ratio, whereas SDNN, RMSSD, and high-frequency power demonstrated inverse relationships. RMSSD exhibited the strongest correlation, suggesting that short-term vagally mediated HRV was particularly associated with subjective sleep quality in the study population. Nevertheless, the moderate magnitude of these correlations indicates that sleep quality accounts for only part of the variability in cardiovascular autonomic measures. Other potential influences include academic stress, psychological distress, physical fitness, caffeine consumption, body composition, and individual physiological characteristics. The findings have implications for student health and future physiological research. Sleep quality assessment could form part of broader institutional health promotion initiatives, particularly in academically demanding environments. Educational programmes addressing sleep hygiene, regular sleep schedules, physical activity, and stress management deserve consideration. However, the cross-sectional design does not establish whether improving sleep quality produces measurable changes in cardiovascular autonomic regulation. Further prospective investigations incorporating objective sleep measurements, repeated HRV recordings, and adjustment for relevant confounding variables are necessary to determine the temporal and clinical significance of these associations. Limitations The cross-sectional design limits causal interpretation of the observed associations. The single-institution setting and sample size of 100 students restrict generalizability. Sleep quality was assessed using a subjective questionnaire, introducing potential recall and reporting bias. Resting HRV measurements do not reflect overnight autonomic changes.

The present study identified a significant association between poor sleep quality and altered cardiovascular autonomic modulation among undergraduate medical students. Students reporting poor sleep quality exhibited higher resting heart rates and lower SDNN, RMSSD, and high-frequency HRV power compared with good sleepers. Increasing PSQI scores were associated with reduced HRV, particularly RMSSD, indicating a relationship between subjective sleep disturbances and short-term vagally mediated cardiac modulation. These findings emphasize the importance of sleep health awareness among medical students. Nevertheless, causal relationships cannot be established from the cross-sectional design. Further longitudinal studies incorporating objective sleep assessment, repeated HRV measurements, and adjustment for relevant confounding factors are recommended.

1. Azad MC, Fraser K, Rumana N, Abdullah AF, Shahana N, Hanly PJ, et al. Sleep disturbances among medical students: a global perspective. J Clin Sleep Med. 2015;11(1):69-74. doi:10.5664/jcsm.4370. PMID: 25515274. https://pubmed.ncbi.nlm.nih.gov/25515274/ 2. Rao WW, Li W, Qi H, Hong L, Chen C, Li CY, et al. Sleep quality in medical students: a comprehensive meta-analysis of observational studies. Sleep Breath. 2020;24(3):1151-65. doi:10.1007/s11325-020-02020-5. PMID: 32072469. https://pubmed.ncbi.nlm.nih.gov/32072469/ 3. Seoane HA, Moschetto L, Orliacq F, Orliacq J, Serrano E, Cazenave MI, et al. Sleep disruption in medicine students and its relationship with impaired academic performance: a systematic review and meta-analysis. Sleep Med Rev. 2020;53:101333. doi:10.1016/j.smrv.2020.101333. PMID: 32485517. https://pubmed.ncbi.nlm.nih.gov/32485517/ 4. Tobaldini E, Nobili L, Strada S, Casali KR, Braghiroli A, Montano N. Heart rate variability in normal and pathological sleep. Front Physiol. 2013;4:294. doi:10.3389/fphys.2013.00294. PMID: 24137133. https://pubmed.ncbi.nlm.nih.gov/24137133/ 5. Chouchou F, Desseilles M. Heart rate variability: a tool to explore the sleeping brain? Front Neurosci. 2014;8:402. doi:10.3389/fnins.2014.00402. PMID: 25565936. https://pubmed.ncbi.nlm.nih.gov/25565936/ 6. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93(5):1043-65. PMID: 8598068. https://pubmed.ncbi.nlm.nih.gov/8598068/ 7. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. doi:10.3389/fpubh.2017.00258. PMID: 29034226. https://pubmed.ncbi.nlm.nih.gov/29034226/ 8. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213. doi:10.1016/0165-1781(89)90047-4. PMID: 2748771. https://pubmed.ncbi.nlm.nih.gov/2748771/ 9. Cvejic E, Huang S, Vollmer-Conna U. Can you snooze your way to an ‘A’? Exploring the complex relationship between sleep, autonomic activity, wellbeing and performance in medical students. Aust N Z J Psychiatry. 2018;52(1):39-46. doi:10.1177/0004867417716543. PMID: 28649873. https://pubmed.ncbi.nlm.nih.gov/28649873/ 10. Guo X, Su T, Xiao H, Xiao R, Xiao Z. Using 24-h heart rate variability to investigate the sleep quality and depression symptoms of medical students. Front Psychiatry. 2022;12:781673. doi:10.3389/fpsyt.2021.781673. PMID: 35058822. https://pubmed.ncbi.nlm.nih.gov/35058822/ 11. Laborde S, Mosley E, Thayer JF. Heart rate variability and cardiac vagal tone in psychophysiological research: recommendations for experiment planning, data analysis, and data reporting. Front Psychol. 2017;8:213. doi:10.3389/fpsyg.2017.00213. PMID: 28265249. https://pubmed.ncbi.nlm.nih.gov/28265249/ 12. Billman GE. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol. 2013;4:26. doi:10.3389/fphys.2013.00026. PMID: 23431279. https://pubmed.ncbi.nlm.nih.gov/23431279/ 13. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. doi:10.1371/journal.pmed.0040296. PMID: 17941714. https://pubmed.ncbi.nlm.nih.gov/17941714/ 14. Binjabr MA, Alalawi IS, Alzahrani RA, Albalawi OS, Hamzah RH, Ibrahim YS, et al. The worldwide prevalence of sleep problems among medical students by problem, country, and COVID-19 status: a systematic review, meta-analysis, and meta-regression of 109 studies involving 59427 participants. Curr Sleep Med Rep. 2023:1-19. doi:10.1007/s40675-023-00258-5. [Epub ahead of print]. PMID: 37359215. https://pubmed.ncbi.nlm.nih.gov/37359215/