Cognition and objective sleep quality in post-COVID-19 patients

Frontiers in Psychology 01 frontiersin.org Cognition and objective sleep quality in post-COVID-19 patients AnnaCarnes-Vendrell 1, GerardPiñol-Ripoll 1*, AdrianoTarga 2, NuriaTahan 1, MarAriza 3, NeusCano 3,4, BarbaraSegura 5,6,7,8, CarmeJunque 5,6,7,8, JavierBéjar 9, CristianBarrue 9, Nautilus Project Collaborative Group and MaiteGarolera 3,10 1 Cognitive Disorders Unit, Cognition and Behaviour Study Group, Hospital Universitari Santa Maria, Lleida, Spain, 2 Translational Research in Respiratory Medicine, Hospital Universitari Arnau de VilanovaSanta Maria, IRBLleida, Lleida, Spain, 3 Clinical Research Group for Brain, Cognition and Behaviour, Consorci Sanitari de Terrassa, Terrassa, Spain, 4 Departament de Ciències Bàsiques, Universitat Internacional de Catalunya (UIC), Barcelona, Spain, 5 Medical Psychology Unit, Department of Medicine, Universitat de Barcelona, Barcelona, Spain, 6 Institute of Neurosciences, University of Barcelona, Barcelona, Spain, 7 Institut d’Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Barcelona, Spain, 8 Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain, 9 Faculty of Informatics of Barcelona (FIB), Polytechnic University of Catalonia, Barcelona, Spain, 10 Neuropsychology Unit, Consorci Sanitari de Terrassa, Terrassa, Spain In the current study, weaimed (i) to evaluate sleep quality via wrist actigraphy monitoring of nonhospitalized and hospitalized post-COVID-19 condition (PCC) participants; (ii) to correlate actigraphy measures with subjective measures of sleep quality, such as the Pittsburgh Sleep Quality Index (PSQI); and (iii) to investigate whether total sleep time or sleep efficiency could affect PCC cognitive performance. Weincluded 49 individuals with PCC from the NAUTILUS Project (NCT05307549 and NCT05307575) who were monitored for 1 week via actigraphy and who were also assessed with a comprehensive neuropsychological battery and the PSQI. Wefound that there were significant differences between nonhospitalized PCCs and hospitalized PCCs in the number of awakenings. Wealso found a correlation between the total sleep time of both measures (actigraphy and PSQI), but wedid not observe correlations between objective and subjective parameters of latency and sleep efficiency. Regarding cognition and actigraphy measures, there was a trend of statistical significance in the performance of immediate visual memory, attention span and social cognition according to sleep efficiency. In conclusion, results indicate that although the PSQI provides clinically relevant indicators of sleep, there are divergent results between self-reported and objective sleep measures (actigraphy). Furthermore, wefound a tendency toward statistical significance in cognitive performance in PCC participants according to their sleep efficiency which could indicate that is more important for cognitive function of post-COVID-19 patients than total sleep time. KEYWORDS actigraphy, cognition, post-COVID-19 condition, sleep efficiency, sleep quality, total sleep time 1 Introduction Sleep quality has been widely studied in post-COVID-19 condition (PCC) participants. It has been proven that sleep disturbances are among the most prevalent symptoms in PCC patients, along with cognitive (Ziauddeen etal., 2022; García-Sánchez etal., 2022; DelgadoAlonso etal., 2022; Matias-Guiu etal., 2022; Davis etal., 2021; Guo etal., 2022; Ariza etal., 2022) and emotional alterations (Bourmistrova etal., 2022; Vindegaard and Benros, 2020). In OPEN ACCESS EDITED BY Khaled Trabelsi, University of Sfax, Tunisia REVIEWED BY Sangchoon Jeon, Yale University, UnitedStates Jeff Dyche, James Madison University, UnitedStates *CORRESPONDENCE Gerard Piñol-Ripoll [email protected] RECEIVED 16 April 2024 ACCEPTED 21 January 2025 PUBLISHED 03 February 2025 CITATION Carnes-Vendrell A, Piñol-Ripoll G, Targa A, Tahan N, Ariza M, Cano N, Segura B, Junque C, Béjar J, Barrue C, Nautilus Project Collaborative Group and Garolera M (2025) Cognition and objective sleep quality in post-COVID-19 patients. Front. Psychol. 16:1418602. doi: 10.3389/fpsyg.2025.1418602 COPYRIGHT © 2025 Carnes-Vendrell, Piñol-Ripoll, Targa, Tahan, Ariza, Cano, Segura, Junque, Béjar, Barrue, Nautilus Project Collaborative Group and Garolera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Brief Research Report PUBLISHED 03 February 2025 DOI 10.3389/fpsyg.2025.1418602 Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 02 frontiersin.org fact, many studies have shown that there is a reduction in sleep quality both in hospitalized and nonhospitalized patients (Akıncı and Melek Başar, 2021; Samushiya etal., 2022; Al-Ameri etal., 2022; Malik etal., 2022; Karimi etal., 2022). However, according to our previous study, there is no significant difference in the severity of the disease (CarnesVendrell etal., 2024). However, most of the studies included only subjective sleep quality measures, such as the Pittsburgh Sleep Quality Index (PSQI), because of its ease of administration and due to the fact that it requires less time. With objective measures such as wrist-actigraphy, it is possible to describe the sleep patterns of the participants in a more realistic manner. Thus, it allows for simultaneous ecological assessment of sleep with noninvasive monitoring, as opposed to polysomnography, which usually requires an overnight stay in the hospital and then an expensive sleep evaluation. According to the Academy of Sleep Medicine, the actigraphy provide useful information and it may becost-effective method for assessing specific sleep disorders (AncoliIsrael etal., 2003). To date, several studies have been published using actigraphy methods to analyze sleep patterns and circadian rhythms during the COVID-19 lockdown. In fact, a systematic review was published in 2023 to summarize the previous literature in this field (Ferreira-Souza etal., 2023). They concluded that actigraphy should beincluded as part of the sleep hygiene strategy, as it may bethe best instrument for obtaining data about sleep patterns. However, only one of the included 15 studies involved COVID-19 patients, and the remaining studies involved the general population (medical staff, children, and students, among others). There is a large amount of evidence that sleep plays a fundamental role in the regulation of emotions and adequate cognitive functioning. In fact, a lack of adequate sleep is a major source of many harmful diseases related to the heart, the brain, psychological changes, high blood pressure, diabetes, and/or weight gain (Belal et al., 2020; Salehinejad etal., 2022). Previous findings in older adults support the idea that reduced sleep quality can contribute to poor cognitive performance (Lim etal., 2013; Naismith etal., 2010; Lambiase etal., 2014; Blackwell etal., 2006; Blackwell etal., 2011; Yaffe etal., 2014). However, to our knowledge, the relationship between sleep quality and cognition in the PCC has not been studied. Therefore, in the current study, weaimed (i) to evaluate sleep quality through wrist actigraphy monitoring of nonhospitalized and hospitalized post-COVID-19 condition (PCC) participants; (ii) to correlate actigraphy measures with subjective measures of sleep quality, such as the Pittsburgh Sleep Quality Index (PSQI); and (iii) to investigate whether total sleep time or sleep efficiency could affect PCC participants’ cognitive performance. 2 Methods 2.1 Participants We included 49 participants from the Nautilus Project (ClincalTrials.gov IDs: NCT05307549 and NCT05307575), 35 of whom were nonhospitalized, which means that only showed mild COVID-19 symptoms in the acute phase and stayed at home, and 14 of whom were hospitalized (presenting moderate to severe symptoms of the disease). The samples were collected at Hospital Universitari Santa Maria (Lleida, SPAIN) and Hospital Universitari Arnau de Vilanova (Lleida, SPAIN). The inclusion criteria for the PCC group were a confirmed diagnosis of COVID-19 according to the WHO criteria with signs and symptoms of the disease observed during the acute phase, a period of at least 12 weeks after infection, and age between 18 and 65 years. The exclusion criteria were an established diagnosis of a psychiatric disorder, neurological disorder, neurodevelopmental disorder, or systemic pathology known to cause cognitive deficits before COVID-19 infection and motor or sensory alterations that could interfere with the neuropsychological assessment. 2.2 Procedure Participants were recruited at the Cognitive Disorder Unit of Hospital Universitari Santa Maria (Lleida, SPAIN) and Hospital Universitari Arnau de Vilanova (Lleida, SPAIN). Weobtained written informed consent from all of the participants before inclusion. We collected data on sociodemographic characteristics, previous comorbidities and COVID-19 symptoms in the first session. At the second visit, a neuropsychological assessment was performed. Different cognitive domains were assessed with an extensive and comprehensive neuropsychological battery that was described in a previous study (Carnes-Vendrell etal., 2024). All of the evaluations were performed by trained neuropsychologists. The participants were also asked to complete different questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), which allowed us to assess sleep quality. Finally, the participants were monitored for 7 days with a wrist-mounted actigraph (Actiwatch 2, Philips Respironics, Murrysville, PA). Weobtained the following variables: total sleep time (hours), time in bed (hours), sleep efficiency (%, defined as the ratio between total sleep time and the time spent in bed), sleep latency (minutes, defined as the time spent awake until the first sleep episode), time spent awake after sleep onset (WASO (minutes), and number of awakenings). The participants’ anonymity and confidentiality were guaranteed. The Scientific Ethics Committee of the Hospital Universitari Arnau de Vilanova approved both the study and the consent procedure (CEIC 2384), as did the Drug Research Ethics Committee (CEIm) of Consorci Sanitari de Terrassa (CEIm code: 02–20–107-070) and the Ethics Committee of the University of Barcelona (IRB00003099). Additionally, the investigation was conducted in accordance with the latest version of the Declaration of Helsinki. 2.3 Statistical analysis Descriptive analyses were performed on PCC patients (nonhospitalized vs. hospitalized). For categorical variables, frequencies and percentages were obtained, and for quantitative variables, the means and standard deviations or medians and interquartile ranges (IQRs) were obtained. For the sociodemographic and clinical profiles, quantitative variables were compared between severity groups by using Student’s t test or the Mann–Whitney test, according to normality (verified by using the Shapiro–Wilk test). For categorical variables, groups were compared by using Pearson’s chi-squared tests (or the Fisher’s exact test, if applicable). Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 03 frontiersin.org To analyze the relationship between the PSQI score and the actigraphy parameters (sleep latency, sleep efficiency and total sleep time), Spearman’s rho correlations were used, given the nonnormality of the parameters. To analyze the relationships between cognitive parameters and actigraphy parameters (total sleep time and sleep efficiency), nonparametric Pearson’s chi-squared tests were used (or the Fisher’s exact test was applied, if appropriate). Both total sleep time and sleep efficiency were dichotomized according to the median, and cognition variables were converted into dichotomous variables depending on whether the result indicated cognitive impairment (−1 standard deviation from the mean). The statistical significance level that was used in the analyses was 5% (α = 0.05). All of the analyses were performed with IBM SPSS statistics 26. 3 Results Of the 49 PCC patients, 35 were not hospitalized, and the mean (M) age was 48.80 years (standard deviation [SD]: 8.75); 14 were hospitalized, with a mean age of 55.14 years (SD: 5.86), and this difference was significant (p = 0.016). In the nonhospitalized-PCC group, the majority of participants were female (77.11%) and had more years of education (M: 14.79; SD: 2.33) (p = 0.042). Participants in the hospitalized PCC group had more comorbidities, such as high blood pressure (42.9%), dyslipidaemia (35.7%), and obesity (42.9%). Table1 shows the clinical and sociodemographic characteristics of the sample. In terms of sleep quality, the hospitalized PCC participants obtained a mean PSQI total score of 9.50 (SD: 4.50), and the TABLE1 Clinical and sociodemographic characteristics of the sample. Non-hospitalitzed PCC Hospitalized PCC p value n= 35 n= 14 Age (years) (SD) 48.80 (8.75) 55.14 (5.86) 0.016* Female (%) 77.11% 42.9% 0.021* Years of education (SD) 14.79 (2.33) 13.14 (2.85) 0.042* Days since COVID-19 (SD) 453.91 (287.82) 363.21 (134.52) 0.141 MoCA (SD) 25.79 (2.58) 25.43 (2.38) 0.65 BMI (SD) 26.56 (5.34) 28.66 (3.97) 0.192 Tobacco smoking (%) 5.9% 14.3% 0.338 Alcohol consumption (%) 50.0% 21.4% 0.068 Previous comorbidities Heart disease (%) 0.0% 0.0% - Respiratory disease (%) 14.7% 0.0% 0.13 Chronic kidney disease (%) 0.0% 0.0% - High blood pressure (%) 11.8% 42.9% 0.016* Dyslipidemia (%) 14.7% 35.7% 0.103 Diabetes mellitus (%) 2.9% 7.1% 0.508 Obesity (%) 17.6% 42.9% 0.067 Chronic liver disease (%) 0.0% 0.0% - Chronic pain (%) 2.9% 0.0% 0.517 Quality of sleep PSQI total score 8.70 (3.20) 9.50 (4.50) 0.803 Poor quality of sleep (>5) 81.8% 78.6% 0.796 Actigraphy variables Total sleep time, hours (Mdn, IQR) 6.73 (5.93–7.48) 6.78 (6.15–7.27) 0.851 Time in bed, hours (Mdn, IQR) 8.27 (7.77–8.72) 7.96 (7.67–8.60) 0.432 Sleep efficiency, % (Mdn, IQR) 84.99 (77.69–88.24) 85.62 (81.21–87.45) 0.298 Sleep latency, minutes (Mdn, IQR) 14.00 (9.00–22.00) 15.00 (12.00–23.00) 0.565 WASO, minutes (Mdn, IQR) 45.00 (27.00–61.00) 44.00 (25.00–52.00) 0.507 Number of awakenings (Mdn, IQR) 36.33 (28.71–43.86) 25.84 (18.67–34.71) 0.008* Unless otherwise specified, results are presented as mean (standard deviation). Mdn, median; IQR, interquartile range. Bold values mean level of statistical significance = *p < 0.05, **p < 0.01, ***p < 0.001. PCC, Post-COVID-19 Condition; MoCA, Montreal Cognitive Assessment; BMI, Body Mass Index; WASO, wake after sleep onset. Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 04 frontiersin.org nonhospitalized PCC participants obtained a mean score of 8.70 (SD: 3.20); however, this difference was not significant (p = 0.803). The nonhospitalized-PCC group had a percentage of participants with a score above 5 on the PSQI which indicates poorer sleep quality (81.8% vs. 78.6%, respectively), with no significant differences between the groups (p = 0.796) (Table1). According to the actigraphy variables, there were only significant differences in the number of awakenings (p = 0.008) between nonhospitalized PCCs (M: 36.33; IR: 28.71–43.86) and hospitalized PCCs (M: 25.84; IR: 18.67–34.71) (Table1). Wedid not observe significant differences in the remaining actigraphy variables, such as sleep latency and efficiency, time in bed or total sleep time, between the nonhospitalized and hospitalized PCC patients. Regarding the correlation between subjective and objective measures of sleep variables, weevaluated the correlation of sleep latency, sleep efficiency and total sleep time according to the PSQI and actigraphy. Wefound a correlation between total sleep time and both measures (p = 0.009; Rho Spearman: −0.447) in the nonhospitalized PCC group (Table2). This negative correlation should beinterpreted considering that the higher the PSQI score (which indicates worse sleep quality), the fewer the hours of total sleep time according to the actigraph. Finally, weevaluated the cognition parameters according to the total sleep time and sleep efficiency of the whole sample (Table3). We did not observe significant differences in cognition between groups in terms of total sleep time or sleep efficiency. There was a trend toward statistical significance in the performance of immediate visual memory (p = 0.054), attention span (p = 0.056) and social cognition (p = 0.062) according to sleep efficiency; specifically, a lower sleep efficiency corresponded to a worsened cognitive performance. 4 Discussion Our study demonstrated significant differences only in the number of awakenings between nonhospitalized and hospitalized PCC patients. Therefore, for the remaining actigraph parameters, PCC participants had the same results regardless of the severity of PCC. When wecorrelated the wrist-actigraphy results with selfreported variables (PSQI), wefound a significant correlation with total sleep time. Finally, when we analyzed the relationships between cognitive performance and total sleep time and sleep efficiency, wefound a tendency toward statistical significance in visual memory, attention and social cognition according to sleep efficiency. Some previous research has shown similar results in terms of subjective and objective measurements of sleep quality. Different studies that were performed only with nonhospitalized PCC participants have obtained results similar to our results for the actigraph parameters (Reid etal., 2024; Henríquez-Beltrán etal., 2022). In addition, our results are comparable to those of previous studies that included only samples of hospitalized PCC patients (Benítez etal., 2022; Targa etal., 2022; Jackson etal., 2023; HenríquezBeltrán etal., 2022). To our knowledge, there is only one study that included nonhospitalized and hospitalized PCC participants and compared the actigraphy parameters between them. In that study, the authors did not observe a significant difference among PCC participants according to the severity of the disease (Tański etal., 2024). Thus, our results confirm the absence of a relationship between the severity of PCC and actigraphy parameters. Most of the previous studies that also used wrist-actigraphy and the PSQI did not perform correlation analyses. Wedemonstrated a correlation between objective and subjective measurements in terms of total sleep time. Tański etal. (2024) also found the same correlation, but they used the Epworth Sleepiness Scale (ESS), which is used to assess daytime sleepiness. Their results also demonstrated correlations between the ESS score and total time in bed and wakefulness after sleep onset. Given the absence of previous studies that analyze the correlation between objective and subjective sleep data, it is not possible to draw any conclusions other than those described. Perhaps the fact that self-reported and subjective measures do not enjoy the same reliability as objective ones can make that many researchers do not consider analyzing this correlation. Finally, we investigated whether cognitive performance is related to actigraphy parameters such as total sleep time and sleep efficiency. Based on previous studies that demonstrated the importance of good-quality sleep for good cognitive functioning, weanalyzed these two variables. Unexpectedly, wedid not observe significant results. However, this may bedue to the small sample size because we did observe a tendency toward statistical significance in visual memory, attention and social cognition according to sleep efficiency. To our knowledge, only one previous recent study has analyzed cognitive performance and sleep quality in COVID-19 patients. The authors also found that lower sleep efficiency was associated with lower attention and processing speed (Reid etal., 2024). In studies focusing on the role of sleep for cognition but not in COVID-19 population, it is said that sleep is crucial for cognition, specially in elderly people. Sleep TABLE2 Correlations between objective and subjectives sleep measures. Total PCC sample Non-hospitalized PCC Hospitalized PCC n= 49 n= 35 n= 14 Sleep latency −0.074 −0.034 −0.208 p= 0.622 p= 0.850 p= 0.475 Sleep efficiency −0.013 −0.009 0.017 p= 0.930 p= 0.960 p= 0.955 Total sleep time −0.396 −0.447 −0.370 p= 0.006** p= 0.009** p= 0.193 Spearman’s Rho correlation. Bold values mean level of statistical significance = *p < 0.05, **p < 0.01, ***p < 0.001. Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 05 frontiersin.org fragmentation and absence of deep sleep has been associated with worse cognitive function in older people (Lim etal., 2013; Naismith etal., 2010; Yaffe etal., 2014; Targa etal., 2021). The strengths of the study include the use of both objective and self-reported measures of sleep quality, such as the PSQI and wristactigraphy. In addition, weperformed a comprehensive cognitive assessment, which allowed us to explore the possible effects of sleep quality on all cognitive domains. However, several limitations should beconsidered. First, our relatively small sample size may have limited our ability to achieve statistically significant results. Second, the absence of a healthy control group prevented us from extending our conclusions to the general population. Finally, wecannot forget that this is a cross-sectional study; thus, a longitudinal follow-up would be necessary to observe the evolution of sleep quality and its TABLE3 Group comparison of cognitive performance in PCC participants according to total sleep time and sleep efficiency. Total sleep time Sleep efficiency Memory RAVLT total score Chi-square 1.742 0.17 Sig. 0.187 0.68 RAVLT immediate recall Chi-square 1.38 0.294 Sig. 0.24 0.588 RAVLT delayed recall Chi-square 1.557 0.34 Sig. 0.212 0.56 ROCF immediate recall Chi-square 3.714 3.714 Sig. 0.099 0.054 ROCF delayed recall Chi-square 0.903 0.903 Sig. 0.342 0.342 Attention and processing speed Digit span forward Chi-square 1.557 3.657 Sig. 0.212 0.056 Digit span backward Chi-square 1.002 1.002 Sig. 0.609 0.609 Digit Symbol Chi-square 2.002 2.002 Sig. 0.490 0.490 TMT A Chi-square 0.903 0.903 Sig. 0.342 0.342 TMT B Chi-square 0.003 0.003 Sig. 1.000 1.000 Executive functions Stroop color word Chi-square 0.523 0.294 Sig. 0.47 0.588 Verbal fluency_P Chi-square 0.405 0.005 Sig. 0.725 1.000 Verbal fluency_M Chi-square 3.329 3.329 Sig. 0.110 0.068 Verbal fluency_R Chi-square 0.504 0.699 Sig. 0.702 0.375 Language Semantic fluency Chi-square 0.091 0.091 Sig. 1.000 1.000 BNT Chi-square 0.699 0.004 Sig. 0.463 1.000 Praxis ROCF copy trial Chi-square 0.008 0.294 Sig. 0.928 0.588 Social cognition Eye test Chi-square 0.525 3.496 Sig. 0.469 0.062 Pearson’s Chi square results. Level of statistical significance = *p < 0.05, **p < 0.01, ***p < 0.001. PCC, Post-COVID-19 Condition; RAVLT, Rey Auditory Verbal Learning Test; ROCF, Rey–Osterrieth Complex Figure Test; TMT, Trail Making Test; BNT, Boston Naming Test. Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 06 frontiersin.org alterations in PCC participants to determine how they evolve over time. Despite the end of the pandemic, COVID-19 is still present in the population, and the disease may have a long-lasting impact on health due to persistent COVID-19 symptoms, such as sleep and emotional disturbances, as well as cognitive impairment. Our results confirm that PCC participants suffer from poor sleep quality, which has been corroborated both with objective and subjective measures, and that this may affect their cognitive performance, especially in memory, attention, and social cognition tasks. Although our study did not show significant results for many variables, as weexpected, webelieve that it is relevant because of its implications. Therapeutic strategies focused on sleep quality may imply improvements in different areas, such as cognition and psychological processes. Data availability statement The raw data supporting the conclusions of this article will bemade available by the authors, without undue reservation. Ethics statement The Scientific Ethics Committee of the Hospital Universitari Arnau de Vilanova approved both the study and the consent procedure (CEIC 2384), as did the Drug Research Ethics Committee (CEIm) of Consorci Sanitari de Terrassa (CEIm code: 02-20-107-070) and the Ethics Committee of the University of Barcelona (IRB00003099). The patients/participants provided written informed consent to participate in the study. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author contributions AC-V: Conceptualization, Methodology, Project administration, Validation, Writing – original draft. GP-R: Funding acquisition, Writing – review & editing. AT: Formal analysis, Methodology, Writing– review & editing. NT: Project administration, Writing– review & editing. MA: Conceptualization, Supervision, Writing– review & editing. NC: Conceptualization, Methodology, Project administration, Writing– review & editing. BS: Writing– review & editing. CJ: Writing– review & editing. JB: Writing– review & editing. CB: Writing– review & editing. MG: Conceptualization, Funding acquisition, Investigation, Writing– review & editing. Group members of Nautilus Project Collaborative Group Vanesa Arauzo and Jose A. Bernia, Consorci Sanitari de Terrassa (CST), Terrassa, Spain. Marta Balague-Marmaña and Berta Valles-Pauls, Hospital Sant Joan Despí Moisès Broggi, Consorci Sanitari Integral. Jesús Caballero, Hospital Universitari Arnau de Vilanova, Lleida, Spain. Ester Gonzalez-Aguado and Carme Tayó-Juli, Consorci Sanitari Alt Penedès-Garraf, Vilafranca de Penedés, Barcelona, Spain. Eva Forcadell-Ferreres and Silvia Reverte-Vilarroya, Hospital Verge de la Cinta, Tortosa, Tarragona, Spain. Susanna Forné, Fundació Sant Hospital de la Seu d’Urgell, La Seu d’Urgell, Lleida, Spain. Anna Bartes-Plans and Jordina MuñozPadros, Consorci Hospitalari de Vic, Vic, Barcelona, Spain. Jose A. Muñoz-Moreno and Anna Prats-Paris, Servei de Malalties Infeccioses, Fundació Lluita contra les Infeccions – Hospital Universitari Germans Trias iPujol, Badalona, Barcelona, Spain. Inmaculada Rico and Nuria Sabé, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain. Marta Almeria and Laura Casas, Hospital Universitari Mútua Terrassa, Terrassa, Barcelona, Spain. Maria José Ciudad and Anna Ferré, Badalona Serveis Assistens, Badalona, Barcelona, Spain. Tamar Garzon and Manuela Lozano, Institut d’Assistència Sanitària, Girona, Spain. Marta Cullell and Sonia Vega, Fundació Salut Empordà, Figueres, Girona, Spain. Sílvia Alsina, Fundació Hospital de Puigcerdà, Puigcerdà, Girona, Spain. Maria J. Maldonado-Belmonte and Susana Vazquez-Rivera, Hospital Universitario Central de la Cruz Roja San José y Santa Adela, Madrid, Spain. Eva Baillès and Sandra Navarro, Servei Andorrà d’Atenció Sanitària (SAAS), Andorra. Ayoze González Hernández, Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias. Yaiza Molina, Clínica Universitaria de Psicología, Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias. Victoria Olive, Occupational Health Care Service, Hospital Clínic Barcelona. Silvia Cañizares, Section of Clinical Psychology of Health, Clinical Institute of Neurosciences, Hospital Clinic of Barcelona. Department of Clinical Psychology and Psychobiology, Universitat de Barcelona. Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE00053) and the La Marató de TV3 Foundation (202111–30–31-32) to MG, the Instituto de Salud Carlos III de Madrid (PI22/01687, ISCIII) and the Agency for Management of University and Research Grants (2021SGR 00761) to GP-R. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could beconstrued as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may beevaluated in this article, or claim that may bemade by its manufacturer, is not guaranteed or endorsed by the publisher. Carnes-Vendrell et al. 10.3389/fpsyg.2025.1418602 Frontiers in Psychology 07 frontiersin.org References Akıncı, T., and Melek Başar, H. (2021). Relationship between sleep quality and the psychological status of patients hospitalised with COVID-19. Sleep Med. 80, 167–170. doi: 10.1016/j.sleep.2021.01.034 Al-Ameri, L. T., Hameed, E. K., and Maroof, B. S. (2022). Sleep quality in COVID-19 recovered patients. Sleep Sci. 15, 168–171. doi: 10.5935/1984-0063.20220037 Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., and Pollak, C. P. (2003). The role of Actigraphy in the study of sleep and circadian rhythms. Sleep 26, 342–392. doi: 10.1093/sleep/26.3.342 Ariza, M., Cano, N., Segura, B., Adan, A., Bargalló, N., Caldú, X., et al. (2022). Neuropsychological impairment in post-COVID condition individuals with and without cognitive complaints. Front. Aging Neurosci. 14:1029842. doi: 10.3389/ fnagi.2022.1029842 Belal, H. M., Bin, F. A., Ansari, M. A., Khan, A., Alkahtani, F., Khan, H., et al. (2020). Progress in detection of insomnia sleep disorder: a comprehensive review. Curr. Drug Targets 22, 672–684. doi: 10.2174/1389450121666201027125828 Benítez, I. D., Moncusí-Moix, A., Vaca, R., Gort-Paniello, C., Minguez, O., Santisteve, S., et al. (2022). Sleep and circadian health of critical COVID-19 survivors 3 months after hospital discharge. Crit. Care Med. 50, 945–954. doi: 10.1097/ CCM.0000000000005476 Blackwell, T., Yaffe, K., Ancoli-Israel, S., Redline, S., Ensrud, K. E., Stefanick, M. L., et al. (2011). Association of Sleep Characteristics and Cognition in older communitydwelling men: the MrOS sleep study. Sleep 34, 1347–1356. doi: 10.5665/SLEEP.1276 Blackwell, T., Yaffe, K., Ancoli-Israel, S., Schneider, J. L., Cauley, J. A., Hillier, T. A., et al. (2006). Poor sleep is associated with impaired cognitive function in older women: the study of osteoporotic fractures. J. Gerontol. Ser. A Biol. Med. Sci. 61, 405–410. doi: 10.1093/gerona/61.4.405 Bourmistrova, N. W., Solomon, T., Braude, P., Strawbridge, R., and Carter, B. (2022). Long-term effects of COVID-19 on mental health: a systematic review. J. Affect. Disord. 299, 118–125. doi: 10.1016/j.jad.2021.11.031 Carnes-Vendrell, A., Piñol-Ripoll, G., Ariza, M., Cano, N., Segura, B., Junque, C., et al. (2024). Sleep quality in individuals with post-COVID-19 condition: relation with emotional, cognitive and functional variables. Brain Behav. Immun. Health 35:100721. doi: 10.1016/j.bbih.2023.100721 Davis, H. E., Assaf, G. S., McCorkell, L., Wei, H., Low, R. J., Yochai, R., et al. (2021). Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 38:101019. doi: 10.1016/j.eclinm.2021.101019 Delgado-Alonso, C., Valles-Salgado, M., Delgado-Álvarez, A., Yus, M., Gómez-Ruiz, N., Jorquera, M., et al. (2022). Cognitive dysfunction associated with COVID-19: a comprehensive neuropsychological study. J. Psychiatr. Res. 150, 40–46. doi: 10.1016/J.JPSYCHIRES.2022.03.033 Ferreira-Souza, L. F., Julianelli-Peçanha, M., Coelho-Oliveira, A. C., Reis-Silva, A., Moura-Fernandes, M., Trindade-Gusmão, L., et al. (2023). Impacts of COVID-19 pandemic on sleep quality evaluated by wrist Actigraphy: a systematic review. J. Clin. Med. 12:1182. doi: 10.3390/jcm12031182 García-Sánchez, C., Calabria, M., Grunden, N., Pons, C., Arroyo, J. A., Gómez-Anson, B., et al. (2022). Neuropsychological deficits in patients with cognitive complaints after COVID-19. Brain Behav. 12:e2508. doi: 10.1002/BRB3.2508 Guo, P., Ballesteros, A. B., Yeung, S. P., Liu, R., Saha, A., Curtis, L., et al. (2022). COVCOG 1: factors predicting physical, neurological and cognitive symptoms in long COVID in a community sample. A first publication from the COVID and cognition study. Front. Aging Neurosci. 14:4922. doi: 10.3389/fnagi.2022.804922 Henríquez-Beltrán, M., Labarca, G., Cigarroa, I., Enos, D., Lastra, J., Nova-Lamperti, E., et al. (2022). Sleep health and the circadian rest-activity pattern four months after COVID-19. J. Bras. Pneumol. 48:3:398. doi: 10.36416/1806-3756/ e20210398 Jackson, C., Stewart, I. D., Plekhanova, T., Cunningham, P. S., Hazel, A. L., Al-Sheklly, B., et al. (2023). Effects of sleep disturbance on Dyspnoea and impaired lung function following hospital admission due to COVID-19in the UK: a prospective multicentre cohort study. Lancet Respir. Med. 11, 673–684. doi: 10.1016/ S2213-2600(23)00124-8 Karimi, S., Derakhshan, M., and Tondro, A. (2022). Evaluation of the relationship between stress and severity of COVID-19 symptoms and sleep quality in COVID-19 patients. Maedica 17, 129–133. doi: 10.26574/maedica.2022.17.1.129 Lambiase, M. J., Gabriel, K. P., Kuller, L. H., and Matthews, K. A. (2014). Sleep and executive function in older women: the moderating effect of physical activity. J. Gerontol. A Biol. Sci. Med. Sci. 69, 1170–1176. doi: 10.1093/gerona/glu038 Lim, A. S. P., Kowgier, M., Lei, Y., Buchman, A. S., and Bennett, D. A. (2013). Sleep fragmentation and the risk of incident Alzheimer’s disease and cognitive decline in older persons. Sleep 36, 1027–1032. doi: 10.5665/sleep.2802 Malik, M., Atiq, A., Tahir, M. J., Akhtar, F. K., Malik, M. I., Hassan, W., et al. (2022). Comparison of sleep quality among COVID-19 patients and non-COVID-19 population in Pakistan: a cross sectional study during the COVID-19 pandemic. Ann. Med. Surg. 78:3894. doi: 10.1016/j.amsu.2022.103894 Matias-Guiu, J. A., Delgado-Alonso, C., Díez-Cirarda, M., Martínez-Petit, A., Oliver-Mas, S., Delgado-álvarez, A., et al. (2022). Neuropsychological predictors of fatigue in post-COVID syndrome. J. Clin. Med. 11:3886. doi: 10.3390/ jcm11133886 Naismith, S. L., Rogers, N. L., Hickie, I. B., Mackenzie, J., Norrie, L. M., and Lewis, S. J. G. (2010). Sleep well, think well: sleep-wake disturbance in mild cognitive impairment. J. Geriatr. Psychiatry Neurol. 23, 123–130. doi: 10.1177/0891988710363710 Reid, K. J., Ingram, L. T., Jimenez, M., Orban, Z. S., Abbott, S. M., Grimaldi, D., et al. (2024). Impact of sleep disruption on cognitive function in patients with postacute sequelae of SARS-CoV-2 infection: initial findings from a neuro-COVID-19 clinic. Sleep Adv. 5:2. doi: 10.1093/sleepadvances/zpae002 Salehinejad, M. A., Azarkolah, A., Ghanavati, E., and Nitsche, M. A. (2022). Circadian disturbances, sleep difficulties and the COVID-19 pandemic. Sleep Med. 91, 246–252. doi: 10.1016/j.sleep.2021.07.011 Samushiya, M. A., Kryzhanovsky, S. M., Ragimova, A. A., Berishvili, T. Z., Chorbinskaya, S. A., and Ivannikova, E. I. (2022). Psychoemotional disorders and sleep impairments in patients with COVID-19. Neurosci. Behav. Physiol. 52, 231–235. doi: 10.1007/s11055-022-01229-z Tański, W., Tomasiewicz, A., and Jankowska-Polańska, B. (2024). Sleep disturbances as a consequence of long COVID-19: insights from Actigraphy and Clinimetric examinations-an uncontrolled prospective observational pilot study. J. Clin. Med. 13:839. doi: 10.3390/jcm13030839 Targa, A. D. S., Benítez, I. D., Dakterzada, F., Carnes, A., Pujol, M., Jorge, C., et al. (2021). Sleep profile predicts the cognitive decline of mild-moderate Alzheimer’s disease patients. Sleep 44:117. doi: 10.1093/sleep/zsab117 Targa, A. D. S., Benítez, I. D., González, J., Torres, G., Santisteve, S., Vaca, R., et al. (2022). Sleep and circadian health 6 months after critical COVID-19 disease. Respirology 27, 1083–1088. doi: 10.1111/resp.14390 Vindegaard, N., and Benros, M. E. (2020). COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav. Immun. 89, 531–542. doi: 10.1016/j.bbi.2020.05.048 Yaffe, K., Falvey, C. M., and Hoang, T. (2014). Connections between sleep and cognition in older adults. Lancet Neurol. 13, 1017–1028. doi: 10.1016/ S1474-4422(14)70172-3 Ziauddeen, N., Gurdasani, D., O’Hara, M. E., Hastie, C., Roderick, P., Yao, G., et al. (2022). Characteristics and impact of long COVID: findings from an online survey. PLoS One 17:4331. doi: 10.1371/journal.pone.0264331