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Article

Objective Sleep–Wake Findings in Patients with Post-COVID-19 Syndrome, Fatigue and Excessive Daytime Sleepiness

by
Livia G. Fregolente
1,2,*,
Lara Diem
3,
Jan D. Warncke
1,
Julia van der Meer
1,
Anina Schwarzwald
4,
Carolin Schäfer
1,
Helly Hammer
4,
Andrew Chan
4,
Robert Hoepner
4 and
Claudio L. A. Bassetti
1
1
Sleep-Wake Epilepsy Center, Department of Neurology, Inselspital, University Hospital, 3010 Bern, Switzerland
2
Graduate School for Health Sciences, University of Bern, 3012 Bern, Switzerland
3
Neurology and Neurorehabilitation Center, Luzerner Kantonsspital, 6000 Lucerne, Switzerland
4
Department of Neurology, Inselspital, University Hospital, 3010 Bern, Switzerland
*
Author to whom correspondence should be addressed.
Clin. Transl. Neurosci. 2025, 9(1), 15; https://doi.org/10.3390/ctn9010015
Submission received: 25 November 2024 / Revised: 14 January 2025 / Accepted: 12 February 2025 / Published: 5 March 2025
(This article belongs to the Section Clinical Neurophysiology)
Browse Figure
Versions Notes

Abstract

:
Sleep–wake disturbances are common in post-COVID-19 syndrome but lack extensive objective characterization. This study evaluated sleep–wake patterns in 31 patients with post-COVID-19 syndrome referred for fatigue and excessive daytime sleepiness (EDS). Assessments included questionnaires (the fatigue severity scale, the Epworth sleepiness scale, and the Beck Depression Index-II), video polysomnography (V-PSG), the multiple sleep latency test (MSLT, n = 15), and actigraphy (n = 29). Patients (70% female, mean age 45 years) had mostly mild acute SARS-CoV-2 infections and were assessed a median of 31 weeks post-infection. Fatigue (fatigue severity scale, median 6.33), sleepiness (the Epworth sleepiness scale, median 15), and depression (Beck depression inventory-II, median 20) scores were elevated. V-PSG showed moderate sleep apnea in 35.5%, increased arousal index in 77.4%, and median sleep stage percentages of NREM1 (12%), NREM2 (37%), NREM3 (19%), and REM (15.8%). MSLT revealed only 13.3% with sleep latencies under 8 min and no sleep-onset REM periods. Actigraphy indicated increased inactivity index in 96.6%, with high variability in time in bed. These findings highlight a polysomnographic and actigraphic profile of increased arousal and clinophilia, alongside moderate sleep apnea and limited objective sleepiness on MSLT. Addressing these multifactorial sleep disturbances is crucial in managing post-COVID-19 syndrome.

1. Introduction

The global COVID-19 (SARS-CoV-2 infection) pandemic is a major public health problem, with more than 772 million confirmed cases and nearly 7 million deaths worldwide since 2020 [1]. COVID-19 is a multi-organ disease, as the viral infection affects multiple systems other than the respiratory tract, such as cardiovascular, neurological, immunological and gastrointestinal systems. There has been an increasing amount of evidence demonstrating that the disease evolves after the acute infection, with up to 87% of patients reporting residual symptoms [2]. The nomenclature and clinical picture of the so-called post-COVID-19 syndrome or long-COVID has evolved since the first description in August 2020 [2]. Clinical guidelines define post-COVID-19 syndrome as “the wide range of health consequences that can be present 4 or more weeks after infection with SARS-CoV-2” [3,4]. Post-COVID-19 syndrome occurs not only in patients with a severe acute infection, but also in those with milder disease, suggesting that post-intensive care syndrome is not the only pathophysiological mechanism behind it [5,6,7]. The incidence rate varies between reporting sources and countries, ranging from 10% after 12 weeks [8] to 76% of people at 6 months [9]. Although the true incidence rate is still unknown, it is safe to say that post-COVID-19 syndrome is a highly prevalent complication of the acute infection and represents a major public health concern.
The clinical picture of post-COVID-19 syndrome is highly variable, with a complex of symptoms including fatigue, headache, anosmia, cough, dyspnea, hair loss and sleep complaints [5,10,11]. The pathophysiology of neuropsychiatric symptoms is still debated: on the one hand, neurotoxicity following the cytokine storm and microglial activation is postulated [12]; on the other hand, it is hypothesized that the SARS-CoV-2 could enter the brain from the cribriform plate along the olfactory tract [13]. In addition, the viral effect on brain endothelial cells may lead to neuroinflammation and thrombotic changes in the central nervous system [12,14]. Chronic fatigue is known to be a common sequela of past viral epidemics [15,16,17]. Previous studies have shown a prevalence of fatigue in up to 90% of patients with post-COVID-19 syndrome months after the acute infection, making it probably the predominant symptom [18,19].
Fatigue in post-COVID-19 syndrome is not fully understood and is most likely multifactorial [20]. Sleep disturbance is a possible secondary cause of fatigue and is itself a common symptom after acute SARS-CoV-2 infection [18,21]. Despite its high prevalence of up to 26% in patients with post-COVID-19 syndrome [22,23,24], few reports have addressed how sleep–wake disturbances affect the development and course of the disease. Furthermore, objective measures of sleep in patients with post-COVID syndrome complaining about fatigue or excessive daytime sleepiness are lacking. The aim of this study was to better characterize the sleep–wake pattern using objective measures in patients with post-COVID syndrome reporting EDS and fatigue.

2. Materials and Methods

Patients with post-COVID syndrome complaining from fatigue and excessive daytime sleepiness were enrolled from the specialized outpatient clinic of the Inselspital, Bern, over a 14-month period from 02/2021 to 04/2022. Inclusion criteria were (1) post-COVID syndrome, as defined by the WHO [3], and (2) excessive daytime sleepiness (ESS ≥ 10) and fatigue (FSS ≥ 3.5). We excluded individuals with known pre-existing severe respiratory or cardiovascular diseases, as well as those with previously diagnosed primary sleep disorders. All patients consented to participate in the prospective registry of the neuroimmunological outpatient clinic and on the Bern sleep registry (KEK-Bern: Ethic no. 2017-01369 and 2022-00415).

2.1. Clinical Evaluation

The severity of COVID-19 was categorized with respect to initial symptomatology into asymptomatic, mild, moderate, severe and critical following a previously described definition [25]. Fatigue was assessed using the fatigue severity scale (FSS, 9 items, score range 0–7, cutoff ≥ 3.5) [26]. Daytime sleepiness was measured using the Epworth sleepiness scale (ESS, 8 items, score range 0–24, cutoff ≥ 10) [27]. Depression symptoms were assessed using the Beck depression inventory -II (BDI-II, 21 items, score range 0–63, cutoff ≥ 14) [28].

2.2. Sleep Investigations

Video polysomnography (V-PSG) was performed and scored according to the current technical and scoring recommendations of the American Academy of Sleep Medicine (AASM) [29] using Remlogic and Somnomedics. Recorded signals included frontal, central and occipital electroencephalogram, electrooculogram, submentalis electromyogram, oronasal thermocouple, nasal pressure transducer, electrocardiogram, thoracic and abdominal inductance plethysmography, right and left anterior tibialis electromyogram, snore microphone, body position and pulse oximetry. The severity of obstructive sleep apnea was described by the number of apneas and hypopneas per hour of sleep (apnea–hypopnea index, AHI). We defined a relevant sleep apnea as an AHI greater than 15/h.
A subgroup of patients underwent daytime testing with the multiple sleep latency test (MSLT) on the day after the PSG to document an abnormal daytime mean sleep latency, defined as <8 min on MSLT. This test consisted of 4 to 5 nap opportunities with at least 1.5 h between each nap. Patients submitted to MSLT are allowed to sleep ad libitum until 10 am. Each nap opportunity lasted 20 min or 15 min after sleep onset, according to the AASM guidelines [30].
Participants were advised to maintain a regular sleep routine for 2 weeks prior to the PSG. To monitor the sleep–wake cycle, patients wore a MotionWatch 8 [31] (Cambridge Neurotechnology, Cambridge, UK) on their non-dominant wrist for at least one week before the PSG. Accelerometer signals acquired with a sensitivity threshold of 20 counts were pre-processed and exported using MotionWare (Cambridge Neurotechnology, Cambridge, UK). Participants kept a diary of bedtime and wake time during the protocol. For each recording, estimated total sleep time (TST), time in bed (TIB), sleep efficiency (SE), and inactivity index per 24 h were determined. In addition, activity counts were collected to calculate variables related to the rest–activity rhythm. Inter-daily stability represents the similarity between consecutive 24 h periods. The relative amplitude, calculated using the mean activity of the 5 consecutive hours with the lowest activity (L5) and the mean activity of the 10 consecutive hours with the highest activity (M10), reflects the variability of the rest–activity rhythm and highlights differences between rest and active phases [32].

2.3. Data Availability

The anonymized data that support the findings of this study are available from the corresponding author upon reasonable request.

2.4. Statistical Analysis

Overall, descriptive statistics were presented. Due to the exploratory, hypothesis-generating nature of this study, sample size calculations were not performed. Frequencies and percentages were used for categorical variables and medians and interquartile ranges (IQRs) were used for continuous variables, given non-normal distributions of some variables. Comparison of characteristics of participants who were submitted to MSLT to those who did not, as well as of patients with and without relevant sleep apnea were made using chi-square and Kruskal–Wallis tests. Two-sided alpha = 0.05 was used to assess statistical significance. Analyses were performed using R software v4.1.1 [33], summary tables were generated using the gtsummary v1.7.1 [34] package.

3. Results

A total of 31 patients were included (24 [77%] women, with a median age of 47 years, interquartile range (IQR) = 32–53, BMI 25.4 kg/m2, IQR = 23.5–28.4) (Table 1). Clinical evaluation was conducted on average 31 weeks (IQR = 24–45) after diagnosis of SARS-CoV-2 infection. A total of 2/28 patients (7.1%) reported severe/critical symptoms on the acute infection. Regarding the validated questionnaires, patients had high FSS (median 6.33, IQR 5.6–6.4), ESS (median 15, IQR 13–17) and BDI-II scores (median 20, IQR 14–28). A total of 22/28 patients (78.6%) had BDI-II scores > 13. There was no correlation between ESS or FSS with BDI-II scores (Supplementary Figure S1).
Results from V-polysomnography (PSG, n = 31) and the multiple sleep latency test (MSLT, n = 15) are reported in Table 2. Of note, sleep efficiency was reduced (under 85%) in 15 participants (48.4%), (median 82%, IQR 73–92), as the arousal index (median 29, IQR 16–46) and rapid-eye-movement (REM) sleep latency (median 151 min, IQR 85–232) were increased, respectively, in 24 (77.4%) and 21 (67.7%) participants. On average, the apnea hypopnea index (AHI) was 8/h (IQR 4–22) and the oxygen desaturation index (ODI) 3/h (IQR 2–10). No patient had an increased PLMS index (median 2, IQR 0–7). Figure 1 shows the distribution of sleep stages of the 31 participants.
Participants who were submitted to daytime tests (n = 15) did not differ in age, gender, BMI or performance in questionnaires concerning fatigue, daytime sleepiness and depression (FSS, ESS, BDI-II) to those who only went through a V-PSG (Supplementary Tables S1 and S2). Due to the local methodological differences, patients who are submitted to MSLT are allowed to sleep ad libitum until 10 am, whereas in the V-PSG protocol patients are woken up at 6 am. We observed a difference on average total sleep time (467 and 336 min, respectively, p < 0.001). Total wake time was also higher in patients submitted to MSLT (19% and 7%, respectively, p = 0.03). Conversely, there was a significant difference in sleep efficiency (83% and 90%, respectively, p = 0.04). The mean sleep latency of the MSLT of the cohort was 12.2 min (IQR 9.4–15.6). 2/15 (13.3%) participants had a mean sleep latency < 8 min. There were no sleep-onset rapid-eye-movement (REM) sleep period (SOREMP).
11/31 (35.5%) patients had a relevant sleep apnea, as defined by AHI greater than 15/h (Table 3). Patients with sleep apnea had a higher BMI (p = 0.026). There were no significant differences in other demographic (including severity of COVID-19 symptoms) or questionnaires characteristics, although there was a higher, but non-significant average age in the sleep apnea group (51 and 44, respectively, p = 0.06). Patients with sleep apnea had a higher oxygen desaturation index (ODI, p < 0.001), higher percentage of non-REM-sleep 1 (NREM1, p = 0.006) and a higher arousal index (p = 0.001). There was no significant difference in total sleep time, sleep efficiency, PLMS index or percentage of the other sleep stages. Patients with sleep apnea had a tendency to fall asleep earlier in the MSLT. The mean sleep latency in this group was 10.2 min (IQR 8.3–12.1) and 13.1 min (IQR 10.8–16) in patients without sleep apnea, but the results were not significant (p = 0.2).
A total of 29 patients wore an actigraph (Motionwatch) for at least one week, and 17 (59%) of them wore the actigraphy for 2 weeks (Table 4). The estimated time in bed was on average 8.2 (IQR 7.9–9.3) hours, and the variability between the longest and shortest value was 5.2 h (IQR 4–6.6). The estimated sleep efficiency was 82.8% (IQR 79.3–86.6). The median of the inactivity index in 24 h was high (40%, IQR 37–44) and all but 1 patient (96.6%) had an inactivity index higher then 33%. The nonparametric analysis showed a relative amplitude of 0.9 (IQR 0.86–0.94) and an inter-daily stability index of 0.5 (IQR 0.4–0.5).

4. Discussion

This study is the larger single-center cohort to characterize sleep–wake changes with V-PSG, MSLT, actigraphy and standardized questionnaires in patients with post-COVID-19 syndrome reporting excessive daytime sleepiness. Although all patients had an increased subjective sleepiness as measured by the ESS, this finding could only be objectified with the MSLT in 13.3% of patients. In contrast, a history of mild acute SARS-CoV-2 infection, female gender, fatigue, depression, and changes in sleep architecture were frequently found within our cohort.
The discrepancy between subjective and objective sleepiness is probably multifactorial. Although some individuals had a decreased sleep latency on the multiple sleep latency test, we could not replicate the findings of this recent study in four patients reporting excessive daytime sleepiness and none of our subjects had narcolepsy-like symptoms or SOREMPs [24]. Although the MSLT is considered by the International Classification of Sleep Disorders as one of the most important examinations to diagnose central disorders of hypersomnolence [35], it is noteworthy that it has its shortcomings. In particular, in patients in the narcolepsy borderland (central disorders of hypersomnolence other than narcolepsy type 1) [36], the test-to-test variability is high and it may not be the ideal tool for diagnosing these conditions [37,38,39]. Moreover, it has already been shown that MSLT abnormalities with multiple SOREMPs may occur in up to 13% of randomly selected individuals from the normal population [40]. Another important confounding factor in our population is the high fatigue scores on the FSS. Although fatigue and sleepiness per se are two distinct symptoms, they are often confused by patients and even physicians [41]. Additionally, both sleepiness and fatigue may co-occur in post-COVID syndrome [19,22]. Psychiatric comorbidities such as depression may also play a role, as recent literature suggests that mood disorders affect sleep quality in patients, particularly in women [42,43,44].
Furthermore, other sleep disorders such as obstructive sleep apnea (OSA) certainly influence the complex relationship between fatigue and sleepiness in patients with post-COVID syndrome. Indeed, we found that patients with a relevant sleep apnea had slightly lower multiple sleep latencies in the MSLT. Despite previous reports [45], we did not find patients with REM-Sleep behavior disorder or periodic limb movement in sleep. Approximately one third of the cohort had a relevant sleep apnea, as defined by an AHI greater than 15/h, which is higher than the reported 6–17% in the general population [46]. Since our population is predominantly female and relatively young, we would rather expect lower sleep disordered breathing prevalence in our population as compared to the overall population. Compared to patients without relevant sleep apnea, patients with OSA were older and had a higher BMI, which are known risk factors for OSA [35,46]. On the other hand, the OSA prevalence in our cohort is much lower than the reported prevalence of 49 of the 67 patients hospitalized with acute respiratory distress syndrome due to COVID-19 [47]. The association between OSA and outcomes of acute SARS-CoV-2 infection has been well documented [48,49]. How OSA influences the development of post-COVID syndrome is still debated [50]. A recent large electronic health registry study found a 1.41–3.93 increased odds of developing post-COVID syndrome on patients with previous OSA [51]. The physiopathology is still unclear, but based on the understanding that repeated nocturnal deoxygenation and hypoxia leads to a proinflammatory and prothrombotic state [52], it is speculated that OSA may worsen not only acute SARS-CoV-2 infection but also post-COVID syndrome outcomes [53]. A follow-up study on the treatment effects of OSA on post-COVID syndrome would shed a light on this issue.
In addition, actigraphy showed a tendency towards high night-to-night variability, high inactivity index and relatively low inter-daily stability during the monitoring. These findings suggest clinophilia and poor sleep hygiene, which may contribute to and be affected by increased fatigue in a bidirectional relationship [54]. Our results are consistent with the reports of 65 subjects 3 months after hospitalization due to COVID-19 infection, which showed a reduced inter-daily stability of 0.59 and a mean sleep efficiency of 84.6% [55]. Clinophilia is a symptom usually associated with mood disorders [43], and in our cohort, a large percentage (78.6%) of patients reported depressive symptoms.
The mechanisms through which post-COVID-19 syndrome affects the sleep wake cycle are still hypothetical. Speculated pathomechamisms include autoimmunity, endothelial abnormalities, and immune dysregulation, which may lead to leading to brain involvement and the manifestation of fatigue and neuropsychiatric symptoms [56]. A recent study demonstrated dysfunctional neurological signaling characterized by reduced serotonin levels through the vagus nerve in post-COVID-19 syndrome [57]. This finding not only adds a neurobiological dimension to our understanding of post-COVID-19 complications, but also raises therapeutic considerations [57], as targeting the serotonin modulation may hold promise as a potential path with sleep–wake implications. As research in this area advances, a deeper understanding of these intricate mechanisms will be crucial for developing targeted and effective interventions to alleviate the burden of sleep-related symptoms in post-COVID-19 patients.
This exploratory study has limitations. The small sample size and the referral bias inherent in our single-center study represent the main ones, as only patients with post-COVID-19 syndrome who complained of sleep disturbances and excessive daytime sleepiness underwent sleep–wake assessment. Due to the retrospective nature of this exploratory analysis, there was no control group in this study. The tertiary center setting may have led to an overrepresentation of severe cases. The variability in the application of MSLT reflects the real-world nature of our study, where logistical constraints, patient preferences, and differing protocols within our center influenced the decision to perform daytime testing. These factors highlight the need for more standardized approaches in future research to ensure consistency and improve the comparability of results across studies.
We highlighted in patients with post-COVID-19 syndrome reporting excessive daytime sleepiness and fatigue a polysomnographic and actigraphic pattern of hyperarousal and clinophilia, as well as a high frequency of moderate sleep apnea, suggesting a multifactorial etiology for the complaints. These findings address a critical gap in the current understanding of post-COVID-19 syndrome, providing novel insights into the interplay of subjective and objective disturbances. By identifying these patterns, this study underscores the importance of joining forces between specialized units like neuro-immunology and sleep–wake clinics to enable an interdisciplinary and multimodal diagnostic approach. This opens new perspectives on the management of patients of post-COVID-19 syndrome. Further study with larger sample sizes and control groups are needed to confirm these observations and to identify predictors of objective sleep–wake changes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ctn9010015/s1, Figure S1: Relationship between depressive symptoms with subjective sleepiness and fatigue; Table S1: Comparison of patients with and without MSLT; Table S2: Comparison normal and abnormal MSLT.

Author Contributions

Conceptualization, L.G.F. and C.L.A.B.; methodology, L.G.F., L.D., R.H. and C.L.A.B.; software, L.G.F. and J.v.d.M.; formal analysis, L.G.F. and J.v.d.M.; investigation, L.G.F., L.D., J.D.W., J.v.d.M., A.S., C.S. and H.H.; resources, C.L.A.B.; data curation, L.G.F., L.D., J.D.W., J.v.d.M., A.S., C.S. and H.H.; writing—original draft preparation, L.G.F.; writing—review and editing, L.D., J.D.W., J.v.d.M., A.S., C.S., H.H., A.C., R.H. and C.L.A.B.; visualization, L.G.F.; supervision, R.H. and C.L.A.B.; project administration, C.L.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of University of Bern (KEK-Bern: Ethic no. 2017-01369 and 2022-00415).

Informed Consent Statement

All patients consented to participate in the prospective registry of the neuroimmunological outpatient clinic and on the Bern sleep registry.

Data Availability Statement

The anonymized data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors acknowledge all sleep technicians and physicians working on the Sleep Wake Epilepsy Center Inselspital for their work on recording the sleep examinations.

Conflicts of Interest

Livia G. Fregolente declares no conflict of interest related to this manuscript. Lara Diem received travel grants from Merck, Biogen, Roche and Bayer Schweiz. She also received speaker’s honoraria from Biogen, Novartis, Lundbeck, Merck and Swiss Ice Hockey. All conflicts are not related to this work. Jan D. Warncke declares no conflict of interest related to this manuscript. Julia Van der Meer declares no conflict of interest related to this manuscript. Anina Schwarzwald declares no conflict of interest related to this manuscript. Carolin Schäfer declares no conflict of interest related to this manuscript. Helly Hammer has received speaker/advisor honorary from Merck, Biogen, Janssen, Teva. She received research support within the last 5 years from Biogen. She received travel grants from Biogen, Roche, Janssen, Merck. All conflicts are not related to this work. Andrew Chan received honoraria for boards/speaker activities from Actelion (Janssen/J&J), Alexion, Almirall, Biogen, Celgene (BMS), Genzyme, Horizon Merck KGaA (Darmstadt, Germany), Novartis, Roche, and Teva, all for hospital research funds. Research support from Biogen, CSL Behring, Genzyme, Roche and UCB; All conflicts are not related to this work. Robert Hoepner received speaker/advisor honorary from Merck, Novartis, Roche, Biogen, Alexion, Sanofi, Janssen, Bristol-Myers Squibb, Teva/Mepha and Almirall. He received research support within the last 5 years from Roche, Merck, Sanofi, Biogen, Chiesi, and Bristol-Myers Squibb. He also received research grants from the Swiss MS Society, the SITEM Insel Support Fund and is a member of the Advisory Board of the Swiss and International MS Society. He also serves as deputy Editor-in-Chief for Journal of Central Nervous System disease. All conflicts are not related to this work. Claudio L. A. Bassetti declares no conflict of interest related to this manuscript.

Abbreviations

AHIApnea–Hypopnea Index
BDIBeck Depression Inventory
BMIBody Mass Index
ESSEpworth Sleepiness Scale
FSSFatigue Severity Scale
IQRInterquartile Range
MSLTMultiple Sleep Latency Test
NREMNon-Rapid-Eye-Movement Sleep
OSAObstructive Sleep Apnea
PLMSPeriodic Limb Movement of Sleep
REMRapid-Eye-Movement Sleep
SARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2
SOREMPSleep-Onset REM Period
SpO2Oxygen Desaturation

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Ctn 09 00015 g001
Figure 1. Distribution of sleep stages in patients with post-COVID syndrome. The box plot illustrates the distribution of sleep stages (NREM1, NREM2, NREM3, REM, and Wake) as a percentage of total recording time. Each box represents the interquartile range (IQR), with the median indicated by the line inside the box. Whiskers extend to 1.5 times the IQR, and outliers are plotted individually. The figure provides insights into the variation and prevalence of different sleep stages during the recording period. NREM = non-rapid-eye-movement sleep; REM = rapid-eye-movement sleep. N = 31.
Figure 1. Distribution of sleep stages in patients with post-COVID syndrome. The box plot illustrates the distribution of sleep stages (NREM1, NREM2, NREM3, REM, and Wake) as a percentage of total recording time. Each box represents the interquartile range (IQR), with the median indicated by the line inside the box. Whiskers extend to 1.5 times the IQR, and outliers are plotted individually. The figure provides insights into the variation and prevalence of different sleep stages during the recording period. NREM = non-rapid-eye-movement sleep; REM = rapid-eye-movement sleep. N = 31.
Ctn 09 00015 g001
Table 1. Patients Characteristics.
Table 1. Patients Characteristics.
DemographicsN = 31 1
Age (years)47 (32, 53)
Gender
Male23% (7/31)
Female77% (24/31)
BMI (kg/m2)25.4 (23.5, 28.4)
Time since COVID-19 infection (weeks)31 (24, 45)
Duration of acute infection (days)14 (10, 21)
Severe Symptoms7.1% (2/28)
QuestionnairesN = 31
Epworth sleepiness scale (ESS)15.00 (13.00, 17.00)
Fatigue severity scale (FSS)6.33 (5.65, 6.44)
Beck depression inventory (BDI-II)20 (14, 28)
1 Median (IQR); % (n/N). Missing: time since COVID-19 infection; duration of acute infection; severe symptoms; BDI; BMI: body mass index.
Table 2. Polysomnography and MSLT.
Table 2. Polysomnography and MSLT.
PolysomnographyN = 31
Apnea–hypopnea index (/h)8 (4, 22)
Apnea–hypopnea index NREM (/h)6 (3, 22)
Apnea–hypopnea index REM (/h)13 (5, 24)
Apnea index (/h)0.4 (0.1, 1.2)
Oxygen desaturation index (/h)3 (2, 10)
Mean SpO2 (%)94.70 (93.75, 95.45)
Time SpO2 < 90% (min)0.0 (0.0, 0.4)
Sleep latency (min)12 (5, 32)
Total sleep time (min)366 (321, 452)
Sleep efficiency (%)86 (73, 93)
REM latency (min)144 (79, 218)
Arousal index (/h)29 (16, 48)
Mean heart rate (bpm)64 (57, 71)
PLMS (/h)2 (0, 7)
NREM1 (% TRT)12 (6, 17)
NREM2 (% TRT)37 (28, 45)
NREM3 (% TRT)19 (16, 26)
REM (% TRT)15.8 (10.1, 19.8)
Wake (% TRT)12 (6, 27)
MSLTN = 15
Mean sleep latency in MSLT (min)12.2 (9.4, 15.6)
Maximal NREM stage in MSLT
NREM113% (2/15)
NREM267% (10/15)
NREM320% (3/15)
SOREMP in MSLT0% (0/15)
Median (IQR); % (n/N). MSLT: multiple sleep latency test; NREM: non-rapid-eye-movement sleep; REM: rapid-eye-movement sleep; SpO2: oxygen desaturation; PLMS: periodic limb movement of sleep; SOREMP: sleep-onset REM period; TRT: total recording time.
Table 3. Comparison of patients with and without a relevant sleep apnea.
Table 3. Comparison of patients with and without a relevant sleep apnea.
VariablesAHI > 15/h, N = 11 1AHI < 15/h, N = 20 1p-Value 2
Age (years)51 (47, 55)44 (30, 52)0.060
Gender 0.7
Male27% (3/11)20% (4/20)
Female73% (8/11)80% (16/20)
Severe symptoms12% (1/8)5.0% (1/20)0.5
BMI (kg/m2)28.1 (26.1, 31.4)25.2 (22.9, 26.2)0.026
FSS6.11 (5.55, 6.38)6.42 (5.77, 6.47)0.3
ESS15.00 (14.00, 16.50)14.00 (12.00, 17.00)0.3
BDI-II16 (10, 20)22 (16, 31)0.056
Apnea–hypopnea index (/h)24 (22, 40)5 (4, 8)<0.001
Apnea–hypopnea index REM (/h)39 (14, 46)9 (4, 18)0.010
Oxygen desaturation index (/h)15 (10, 32)2 (1, 3)<0.001
Sleep latency (min)14 (3, 19)12 (8, 38)0.14
Total sleep time (min)366 (328, 447)364 (321, 444)>0.9
Sleep efficiency (%)88 (75, 93)85 (71, 93)0.5
NREM1 (% TRT)24 (12, 33)10 (6, 13)0.006
NREM2 (% TRT)34 (20, 43)38 (30, 46)0.3
NREM3 (% TRT)19 (15, 21)20 (16, 27)0.5
REM (% TRT)10.9 (6.7, 17.2)15.9 (11.1, 20.5)0.094
Wake (% TRT)12 (7, 26)12 (6, 29)0.8
Arousal index (/h)46 (34, 55)17 (14, 27)0.001
PLMS (/h)3 (0, 7)2 (0, 7)0.9
Mean sleep latency in MSLT (min)10.2 (8.3, 12.1)13.1 (10.8, 16.0)0.2
1 Median (IQR). 2 Wilcoxon rank sum test; Fisher’s exact test; % (n/N). MSLT: multiple sleep latency test; NREM: non-rapid-eye-movement sleep; REM: rapid-eye-movement sleep; SpO2: oxygen desaturation; PLMS: periodic limb movement of sleep; SOREMP: sleep-onset REM period; TRT: total recording time.
Table 4. Actigraphy.
Table 4. Actigraphy.
VariablesN = 29 1
Bedtime (HH:mm)21:51:00 to 00:54:00
Get up time (HH:mm)04:22:00 to 09:51:00
Time in bed (hours)8.23 (7.92, 9.27)
Variability of time in bed (hours)5.20 (4.00, 6.60)
Sleep duration (hours)8.15 (7.85, 9.07)
Sleep efficiency (%)82.8 (79.3, 86.6)
Mean wake bout (minutes)00:01:45 to 00:03:27
Inactivity index (%)40 (37, 44)
Relative amplitude0.90 (0.86, 0.94)
Inter-daily stability0.47 (0.39, 0.54)
L523:00:00 to 03:00:00
M1004:00:00 to 13:00:00
Duration (days)
7–1312 (41%)
>1317 (59%)
1 Range; Median (IQR); n (%). L5: mean activity of the 5 consecutive hours with the lowest activity; M10: mean activity of the 10 consecutive hours with the highest activity.
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Fregolente, L.G.; Diem, L.; Warncke, J.D.; van der Meer, J.; Schwarzwald, A.; Schäfer, C.; Hammer, H.; Chan, A.; Hoepner, R.; Bassetti, C.L.A. Objective Sleep–Wake Findings in Patients with Post-COVID-19 Syndrome, Fatigue and Excessive Daytime Sleepiness. Clin. Transl. Neurosci. 2025, 9, 15. https://doi.org/10.3390/ctn9010015

AMA Style

Fregolente LG, Diem L, Warncke JD, van der Meer J, Schwarzwald A, Schäfer C, Hammer H, Chan A, Hoepner R, Bassetti CLA. Objective Sleep–Wake Findings in Patients with Post-COVID-19 Syndrome, Fatigue and Excessive Daytime Sleepiness. Clinical and Translational Neuroscience. 2025; 9(1):15. https://doi.org/10.3390/ctn9010015

Chicago/Turabian Style

Fregolente, Livia G., Lara Diem, Jan D. Warncke, Julia van der Meer, Anina Schwarzwald, Carolin Schäfer, Helly Hammer, Andrew Chan, Robert Hoepner, and Claudio L. A. Bassetti. 2025. "Objective Sleep–Wake Findings in Patients with Post-COVID-19 Syndrome, Fatigue and Excessive Daytime Sleepiness" Clinical and Translational Neuroscience 9, no. 1: 15. https://doi.org/10.3390/ctn9010015

APA Style

Fregolente, L. G., Diem, L., Warncke, J. D., van der Meer, J., Schwarzwald, A., Schäfer, C., Hammer, H., Chan, A., Hoepner, R., & Bassetti, C. L. A. (2025). Objective Sleep–Wake Findings in Patients with Post-COVID-19 Syndrome, Fatigue and Excessive Daytime Sleepiness. Clinical and Translational Neuroscience, 9(1), 15. https://doi.org/10.3390/ctn9010015

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