Abstract
Neuroscience findings offer promising ways to enhance performance in educational settings. Adolescents often experience sleep deprivation, impacting memory processes crucial for learning. The synaptic homeostasis hypothesis (SHY) posits that non-rapid eye movement (NREM) sleep, particularly slow wave activity (0.5–4 Hz), downscales synapses potentiated during wakefulness, facilitating post-sleep encoding. Here, we evaluate the impact of a short nap on memory encoding of a biology lesson in a classroom setting. High school students were randomly assigned to a Nap group allowed to sleep (35-min sleep opportunity) or a Control group engaging in calm activities. Afterwards, they received the lesson and were immediately tested. The Nap group showed better memory encoding, but this was not explained by NREM sleep. Instead, longer periods of NREM sleep showed a negative correlation with performance, possibly due to sleep inertia. Thus, while short naps can enhance academic performance, careful timing may help mitigate potential sleep inertia effects.
Introduction
One of the current challenges of neuroscience is to bring the results of the investigation to the real world, translating this knowledge to improve several aspects such as health, work performance, and academic outcomes. About the latter there have been several attempts to enhance academic performance in school settings by manipulating various variables. These include incorporating surprises to improve consolidation and retrieval, inducing emotional arousal after learning to enhance memory consolidation, evaluating the effects of phenomena such as memory reconsolidation on the retrieval of multiplication facts, and examining the effects of classroom exams on the long-term memory of a graphical task1,2,3,4. Sleep-related strategies have also proven to be beneficial in enhancing performance in school settings. For example, it was observed that delaying the start of high-school classes by only 0.5 to 1 h increased the hours of night sleep, class attendance, and grades in school subjects5. In addition, studies have shown that post-learning naps at school boost memory consolidation in preschoolers6, adolescents7, and 5th graders8. Moreover, in a recent study conducted by our lab, we demonstrated that applying targeted memory reactivation with odors during night sleep improved the consolidation of a scholarly history lesson in high-school9.
Sleep is a fundamental behavior well conserved across evolution10, consisting of a reversible state of inactivity and decreased response to ambiental stimuli. It occurs at regular intervals, and it is homeostatically regulated11. Interestingly, sleep-wake patterns change throughout life; for example, at the onset of adolescence, we can observe a delayed sleep pattern12. This phenomenon, coupled with early school starting time, leads to increased sleep deprivation among adolescents13,14. Thus, the positive outcome of applying sleep-related academic improvement strategies in high-school settings becomes even more relevant when considering these ontogenetic changes in sleep-wake patterns.
Sleep deprivation, as well as low quality of sleep, impairs different memory processes, with one of the most affected being memory encoding15. Furthermore, the detrimental effects of sleep deprivation on memory encoding could be related to synaptic saturation produced during long periods of wakefulness16. The synaptic homeostasis hypothesis (SHY)17,18 posits that Non-rapid eye movement (NREM) sleep, specifically slow wave activity (0.5–4 Hz, SWA), favors a global downscaling of synapses that were potentiated during preceding wakefulness, thus facilitating the encoding of information after a period of sleep. In line with the SHY hypothesis, Antonenko et al. (2013)19 demonstrated that applying transcranial slow oscillation stimulation (tSOS) oscillating at 0.75 Hz to induce SWA in healthy humans during an 80 min afternoon nap enhanced SWA and improved memory encoding of declarative materials compared to sham. However, while NREM sleep has been strongly linked to learning benefits, recent evidence suggests that sleep spindles also play a key role. Naps of 80–90 min improve learning, with benefits correlated to spindle activity, and no clear association with SWS20,21. This raises the possibility that shorter naps with minimal SWS could also support encoding, yet their effects remain largely unexplored.
Based on the above, short naps during school time could help mitigate sleep deprivation’s damage to learning capacity. This could be particularly interesting for high-school students attending full-day schools, as a short nap could prove useful in boosting codification after the morning classes. In this context, we set out to investigate whether pre-encoding short naps in a high school setting could improve memory encoding and if there were sleep architectural and oscillatory parameters related to the improvement. For that, we performed an experiment where students slept a short nap before learning a biology lesson. They were given a 35-minute opportunity to sleep in the school library. We hypothesized that students who slept before the lesson would show an improvement in the acquisition of class contents, compared to students in the Control group who remained awake, given the enhancing effect of sleep on memory encoding. We found that students who slept before the class showed better performance on the test immediately after. However, contrary to what we expected, improvement in memory encoding was not explained by time spent in NREM sleep.
Results
To study the effect of sleeping a short nap in the classroom on memory encoding we conducted a one-day experiment at high school (Fig. 1). Half the students took a short nap in the school library (Nap group, n = 26), sitting in chairs with their heads resting on their forearms on the table, while electroencephalography (EEG) was recorded. Sleep opportunity was 35 min long, from 14:15 to 14:50, in a dimly lit environment with soft natural light entering through polarized windows. In contrast, the other half of the students remained in the classroom, conversing calmly with their teacher (Control group, n = 50). Twenty minutes after the nap or quiet activities, she provided a biology lesson to all of the students, and they were immediately evaluated (Fig. 1a). Difference in sample size resulted from the exclusion of subjects due to technical issues with the EEG recordings, prior knowledge of the class topic, or failure to fall asleep.
Fig. 1: Experimental procedure and effect of a short nap on memory encoding.
figure 1
a The Nap group was allowed to take a short nap in the high school library while an electroencephalography was recorded (sleep opportunity was 35 min long). In contrast, the Control group remained in the classroom performing quiet activities with their teacher and classmates. Training: their teacher delivered a biology lesson using a PowerPoint presentation consisting of 17 slides, displayed on a projector screen. Each slide was shown for 1.5 min as she commented on them orally (total lecture time 25 min). The time between waking up from the nap and the start of the training was 20 min. Testing: Students resolved an evaluation consisting of 20 multiple-choice questions with four possible answers (one correct), and they had no time limit to answer. b Mean percentage of correct answers at the testing session ± SEM for the Control and Nap groups. **p ≤ 0.01.
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A short nap at school improves memory encoding
We found that the Nap group reached a significantly higher score in the testing session than the Control group (Fig. 1b, Nap group: 53.46 ± 2.58, Control group: 43.00 ± 2.01; Two-sample t-test t(74) = −3.114, p = 0.003, Cohen’s d = 0.76).
Participants in the Nap group slept on average 17.21 ± 2.01 min. The time spent in each sleep stage is shown in Table 1, and the mean power density in NREM [Stage 2 (S2)+ Stage 3 (S3)] and Stage 1 (S1) sleep are shown in Table 2.
Table 1 Sleep structure
Full size table
Table 2 Frequency analysis
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Correlations between sleep parameters and encoding
We analyzed the correlations between time percentage in NREM sleep and performance on the multiple-choice test and mean power density in NREM frequency bands of interest (Table 2) and performance. Contrary to our hypothesis, we did not find a significant correlation between the SWA power density and performance in the testing session (r = −0.03, p = 0.876). Neither between slow oscillations nor delta power density (rslow oscillations = 0.13, pslow oscillations = 0.527; rdelta = −0.13; pdelta = 0.535). Surprisingly, we found a significant negative correlation between the percentage of NREM sleep and performance (Fig. 2a, r = −0.46, p = 0.015) and a negative correlation between spindles mean power density and performance (Fig. 2b, r = −0.41, p = 0.034). These could be attributed to the effects of sleep inertia given the short period between sleep and memory encoding. However, it should be noted that none of the correlations were corrected for multiple comparisons, and as such, these results should be interpreted with caution. Furthermore, when performing the analysis using the duration in minutes spent in each sleep stage, rather than the percentage of sleep, the correlation remained significant for performance and stage 2 sleep in minutes (r = −0.52, p = 0.005), but it was not significant for S1 (r = 0.09, p = 0.659).
Fig. 2: Sleep features and memory performance.
figure 2
Significant correlations in the Nap group between score and (a) the NREM percentage, (b) spindles, and (c) the S1 percentage (exploratory analysis). Dashed lines indicate the 95% confidence interval.
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Exploratory analysis
We further performed exploratory analyses between additional sleep stages and performance. Interestingly, we found a significant positive correlation between the percentage of S1 and performance on the multiple-choice test (Fig. 2c, r = 0.42, p = 0.030). No other significant correlations were found (wakefulness percentage: r = 0.30, p = 0.135; total sleep time: r = −0.19, p = 0.187). Taking into account the significant correlation between the percentage of S1 and performance, we further analyzed the correlations between alpha and theta in S1 epochs, the two predominant frequencies of sleep stage, and performance. We found no significant correlation between either band of interest and performance (ralpha = −0.27, palpha = 0.174; rtheta = −0.24, ptheta = 0.235).
Control measurements
A similar percentage of participants both in the Nap and Control groups reported having sleep problems such as difficulty achieving or maintaining sleep, recurring nightmares, and few hours of sleep (Nap group: 30.8%; Control group: 28.0%, χ2 (1, N = 76) = 0.064, p = 0.801). Also, both groups presented similar hours of sleep on the night before the experiment (Nap group: 6.73 ± 0.16; Control group: 6.90 ± 0.18, t(76) = 0.605, p = 0.491). Furthermore, there was no significant correlation between hours of sleep the night before the experiment and performance (r = 0.02, p = 0.847).
To verify that both groups exhibited similar performance in school subjects, we compared quarter scholar biology grades between the Nap and Control groups. We did not find significant differences between the groups in biology performance (Nap group: 76.15 ± 3.19; Control group: 77.40 ± 1.84, t(74) = 0.362, p = 0.718). Furthermore, we did not find a significant correlation between performance in the multiple-choice exam in the experiment and quarter scholar biology grades (r = 0.14, p = 0.107).
We further analyzed the level of perceived anxiety (State-Trait Anxiety Inventory, STAI) to take this factor into account due to its effects on memory encoding. Both groups showed similar levels of auto-reported anxiety (Nap group: 32.77 ± 1.15; Control group: 34.88 ± 1.26, t(74) = 1.091, p = 0.279). Furthermore, we did not find any significant correlation between performance in the multiple-choice exam and reported levels of anxiety (r = 0.01, p = 0.884).
The analysis considering sex as a factor revealed no significant interaction between group and sex (Two-way ANOVA, F(1,72) = 0.33, p = 0.569), indicating that the effect of group was consistent across sexes. Additionally, there was no significant main effect of sex (F(1,72) = 2.80, p = 0.099), suggesting no performance differences between male and female students. However, a significant main effect of group was observed (F(1,72) = 10.62, p = 0.002), with the Nap group outperforming the Control group in encoding performance.
Discussion
Our study shows that a short nap in a high school setting is sufficient to enhance subsequent encoding of a biology lesson. It is important to emphasize the ecological nature of our study: the class was conducted and supervised by their teacher, and the students were able to sleep in their chairs, resting their heads on the table. This allows for the development of easily implementable strategies to improve academic performance without needing specialized resting places equipped with beds.
Moreover, in an exploratory analysis, we found a positive correlation between S1 sleep percentage and improvement in encoding. This is an encouraging result to suggest that naps even shorter than 20 min could benefit memory encoding. More research is needed to better understand oscillatory activity related to the improvement given by naps consisting mainly of S1 and S2 sleep. Nevertheless, Leong et al. (2023)22 recently found that a 30 min nap (in this case, sleep time was counted from the first epoch of S1) improved memory retrieval 210 min after the encoding session. However, they did not find any correlations with sleep architecture or oscillatory activity.
It is important to highlight that, despite the beneficial effects of sleep on memory encoding being largely attributed to NREM sleep17,18, other factors may also play a role in post-sleep encoding improvement. For example, there is evidence that sleep spindles improve learning after an afternoon nap20,21. Mander et al. (2011)20 showed that an 80-minute nap before learning improved encoding compared to an equivalent period of wakefulness, and the boosting of learning capacity correlated with sleep spindles. Moreover, Ong et al. (2020)21 demonstrated that a 90-minute nap pre-encoding improved learning compared to staying awake, correlated with sleep spindles during the nap, and boosted hippocampal activation during encoding. Surprisingly, neither of the two studies found correlations between episodic learning restoration and properties of SWS. This could imply that shorter naps with little to no SWS sleep could also help boost posterior encoding.
Although there is evidence of electrical activity, such as spindles being related to the enhancement of memory encoding, the potential benefits of naps shorter than 80 min in improving learning remain largely unexplored due to the attribution of learning improvement after napping to the effects of NREM sleep. Specifically regarding the role of lighter sleep stages, several studies suggest that light sleep, including both S1 and S2, is related to memory processing23,24,25,26. Interestingly, our findings regarding the potential effects of S1 sleep on encoding improvement in a high school setting arise at a time when the role of S1 in various memory processes is being increasingly recognized. For example, studies on dream incubation have shown that dream images related to incubated information can be induced during S1, and this can, in turn, enhance creativity27,28. Moreover, Lacaux et al. (2021)29 demonstrated the role of S1 sleep in gist abstraction, showing that spending time in S1 sleep tripled the chance of discovering hidden regularities vs. wakefulness or S2 sleep. Furthermore, it has recently been demonstrated S1 involvement in memory consolidation by proving that interruptions on sleep onset during S1 sleep lead to a higher forgetting rate than sleep interruptions during S230.
Although the beneficial effects of sleep periods before learning are mainly attributed to the role of NREM sleep, contrary to our expectations, we found that the longer the periods of NREM sleep, the lower the performance in the testing session. Furthermore, we also found a negative correlation between sleep spindles and performance. This phenomenon could be explained by the effects of sleep inertia after the nap due to the interval between waking from the nap and the start of the training session being only 20 min long. Despite the detrimental effects of sleep inertia in different memory processes, and literature consistently showing a 30-minute interval or more is necessary to resolve them31, various reasons motivated our decision to apply a shorter interval between waking from the nap and the training session. Firstly, we had limited time to experiment to interfere to a minimal extent with regular high school activities. In this context, we opted to dedicate 25 min to the training session consisting of a biology class prepared and given by their teacher, so the training would be the most representative in extension, quantity, and quality of what a real school class would be like. We made sure to dedicate enough time to the testing session, allowing students to complete the multiple-choice questions at their own pace to mitigate the detrimental effects of stress on their performance32. Finally, we considered that the requisite of having to wait 30 min after a nap to resume scholarly activities would be a considerable impediment to applying naps during school time, so shorter periods before resuming scholarly activities after waking could be more easily integrated into the scholar structure. However, due to the lack of sleepiness score scales collection, we are limited in our ability to bolster our interpretation of sleep inertia effects in this context.
In addition to these considerations, a potential limitation of our study was that the device used to obtain sleep electrical recordings only has a single channel, which may introduce errors in determining sleep stages. However, the confusion matrix analyzed by Pretel et al. (2024)33 indicates accurate detection of sleep phases. An additional factor to take into account was the lack of intra-subject comparison, with analyses conducted only between groups. To address it, performance in quarterly biology scores was assessed, revealing no significant differences and assuming comparable levels of learning capacity among groups. Furthermore, the presence of experimenters in the library, even at a distance, along with the awareness of participating in a study, may have led to increased pressure to settle down and fall asleep, potentially increasing sleep onset latency. Moreover, ecological studies, such as those conducted in schools, introduce additional complexities due to uncontrolled environments compared to laboratory settings. Despite efforts to optimize experimental protocols, factors like timing between classes, breaks, and local or international examination days can impact study outcomes. Nevertheless, despite numerous uncontrollable variables, valuable insights can still be obtained from ecological studies.
Finally, a further limitation of our study was that the Nap group experienced a change in context between napping (in the library) and learning (in the classroom), whereas the Control group remained in the classroom the entire time. Although changes in context can reduce interference34, we attribute the encoding improvement to sleep. When comparing the test scores between the Control group and participants in the Nap group who did not reach S1 sleep, we found no significant differences (Control group: 43.00 ± 2.01, Nap group participants who did not reach S1: 48.00 ± 9.56; two-sample t-test, t(53) = 0.715, p = 0.478).
Overall, our findings suggest that short naps can enhance academic performance by boosting memory encoding while requiring minimal adjustments to the learning environment in schools. However, it is important to take into account that if naps are implemented as a routine in schools to improve academic performance, it is advisable to wait at least 30 min after the nap before students attend their next class. Furthermore, our results also suggest that even periods of rest shorter than 20 min could yield positive outcomes, and in this case, the interval between waking from the nap and encoding may not be necessary. Further studies are needed to examine the effects of shorter naps, or even resting periods without S2 or S3, on memory encoding. Overall, our study provides novel and ecological evidence supporting the application of short napping periods in schools.
Methods
Participants
102 healthy 15 to 17-year-old high-school students (62 females and 40 males) from six different courses from the same school (who shared the same teacher) volunteered for the study. None of the participants reported having any history of neuropsychiatric disorders, use of drugs, being sick, or taking any medication during the experiment. Their parents or legal guardians signed a written informed consent approved by the Human Ethics Committee of the Universidad de Buenos Aires (Buenos Aires, Argentina) before the students participated in the study. Data from 24 subjects was excluded from the analysis due to one of the following reasons: technical difficulties with the electroencephalographic recordings (7 subjects), knowing about the topic of the class due to personal interest (1 subject), and being unable to fall asleep (16 subjects). Of these 16 subjects, 5 never reached S1, and 11 never reached S2. Finally, data from 1 treatment and 1 control subject was excluded from the analysis because they reported sleeping 0.5 and 2 h, respectively the night before the experiment, whereas the other participants reported sleeping between 4.5 and 10 hs (6.82 ± 0.12). Outliers in reported sleep duration were identified and removed using the ROUT test with a Q threshold of 5%. Thus, the final sample was 76 participants (50 females and 26 males).
Experimental design
The experiment was conducted in one day. Both the protocol and the consent were approved by the Human Ethics Committee Facultad de Medicina (FMED), University of Buenos Aires (UBA), Buenos Aires, Argentina, (Res. (CD) n° 4081/04), following the principles expressed in the Declaration of Helsinki. One week before the beginning of the experiment, all participants were informed that they would be taking part in a memory experiment, which would include a nap in the school library. Superficial electrodes were placed on each participant for adaptation.
In each course, students were randomly assigned to one of two groups, the Nap or the Control groups. The specific group allocations (nap or wake) were only disclosed immediately before the session, ensuring that participants were unaware of their group until just before the experiment began. Electrodes were placed in Cz, forehead, and mastoid in both groups. The Nap group was allowed to take a short nap in the school library while an electroencephalography took place (using Baby-Blue, a 3-EEG wireless system developed by Pretel et al. (2024)33). The library was equipped with polarized windows that allowed soft, natural light to enter, so the environment was not completely dark even when the artificial lights were turned off. The students were seated at large tables, both next to each other and facing opposite sides of the table, ensuring no physical contact between participants. The time between lights off/on was 35 min, and the nap took place at 14.15. The researcher and 1 assistant, along with the school preceptor, remained in a mezzanine overlooking the library to ensure a quiet environment and compliance with the nap instruction. Meanwhile, the Control group remained in the classroom conversing with their teacher. After the nap or calm activities, both the Nap group and the Control group participated in a scheduled school break in the schoolyard to minimize sleep inertia before returning to the classroom together. Twenty minutes after waking up from the nap or after quiet activities, both groups received a Biology lesson (training), and immediately afterward, they solved an exam about the lecture (testing session), as described below. Finally, they completed the State-Trait Anxiety Inventory (STAI) as a control measure35.
The training consisted of a biology lesson about the beginning and evolution of the universe and the chemistry of carbon, their biology teacher provided the lesson. As a tool to provide the lecture, she used a PowerPoint presentation of 17 slides, shown to the students in the classroom using a projector. Each slide had text and images and was presented for about 1.5 min as she commented on them orally. The training session took 25 min long.
Immediately after training, students resolved a multiple-choice evaluation consisting of 20 questions with four possible answers to each question and one correct answer, as a measure of the encoding level (testing session). Students had no time limit to solve the exam, and the answers to all the exam questions were among the information given on the slides.
Control measurements
The Socio-Demographic questionnaires consisted of answering questions about age, gender, scholarly performance, sleep problems, and hours of sleep the night before the experiment. Furthermore, we evaluated psychological measures using the State-Trait Anxiety Inventory (STAI)35.
Experimental groups
Subjects were randomly assigned to two conditions: the “Nap” and the “Control” groups. Although we did not register brain electrical activity of participants in the Control group, electrode placement was also performed in this group to maintain similar conditions in both groups.
After electrode placement participants in the Nap group (n = 26, 20 females, 6 males) slept a nap in the school library, sitting in chairs, with their heads resting on their forearms on the table while an electroencephalography took place. The time between lights off/on was 35-min. They were trained 20 min after waking from the short nap. Finally, they were tested immediately after training.
Meanwhile, participants in the Control group (n = 50, 30 females, 20 males) chatted calmly between themselves and their teacher. They were trained and tested together with the Nap group.
Sleep data
Sleep electrical recordings were obtained using a Baby-Blue open-hardware portable 1-channel EEG device33. Sleep latency was measured since turning on the equipment, immediately afterward lights were off and participants tried to conciliate sleep.
Recordings were scored offline as wake, sleep stages 1–3, following EEG standard criteria by Rechtschaffen & Kales (1968)36, but without electromyography (EMG) nord electrooculogram (EOG). Sleep onset was defined as the first occurrence of either a sleep spindle or a K-complex. Artifacts were defined in S1 as EEG signals above 200 µV of amplitude due to the lack of EMG to perform artifact remotion. Artifact-free NREM 30-second epochs were divided into consecutive 10 s blocks that overlapped 5 s in time. Each block was tapered by a single Hanning window before applying Fast Fourier Transformation, which resulted in block power spectra. Power spectra were then averaged across all blocks. Mean power density over Cz electrode for sleep periods was determined for the frequency bands of interest (SWA: 0.5–4; slow oscillation: 0.5–1 Hz; delta: 1–4 Hz; theta: 4–8 Hz; alpha: 8–13 Hz; spindles: 12–15 Hz).
Statistical analysis
Data were statistically analyzed with SPSS version 25 (IBM Corporation). We calculated the percentage of correct answers during the testing session (score) for the Nap and Control groups and then compared this percentage between the two groups with a two-tailed t-test (alpha was set at 0.05). We report Cohen’s d as an effect size estimate.
We also performed Pearson correlations between the percentage of NREM sleep and the score achieved during the testing session. Furthermore, we performed Pearson correlations between scores on the testing session and mean power density in the frequency bands of interest (SWA, slow oscillation, delta, theta, alpha, spindles) during NREM sleep.
Furthermore, we conducted exploratory correlation analyses between performance and the percentage of S1 and wakefulness, as well as performance and total sleep time. Additionally, given the significant correlation found between S1 percentage and performance, we examined correlations between alpha and theta mean power density during S1 and performance. We additionally conducted an exploratory analysis to evaluate whether the effect of the nap on encoding performance remained when considering sex as a factor. For this, we performed a two-way ANOVA with ‘sex’ and ‘group’ as between-subjects factors, each with two levels: male vs. female and Nap vs. Control. The alpha level was set at 0.05.
We conducted independent two-tailed t-tests to compare quarter scholar biology grades, the STAI index score, and hours of sleep the night before the experiment between the Nap and Control groups. Furthermore, a chi-square test was used to compare the percentage of participants reporting sleep problems between groups. Pearson’s correlation analyses were performed between multiple-choice test performance and quarter scholar biology grades, hours of sleep the night before the experiment, as well as between anxiety levels and multiple-choice test performance.
Data availability
The datasets analyzed during the current study are available in the Zenodo repository, [https://zenodo.org/records/11225542].
References
Ballarini, F., Martínez, M. C., Díaz Perez, M., Moncada, D. & Viola, H. Memory in elementary school children is improved by an unrelated novel experience. PLoS One 8, e66875 (2013).
CASPubMedPubMed CentralGoogle Scholar
Nielson, K. A. & Arentsen, T. J. Memory modulation in the classroom: selective enhancement of college examination performance by arousal induced after lecture. Neurobiol. Learn. Mem. 98, 12–16 (2012).
PubMedGoogle Scholar
Katzoff, A., Zigdon, N. M. & Ashkenazi, S. Difficulties in retrieval multiplication facts: the case of interference to reconsolidation. Trends Neurosci. Educ. 20, 100137 (2020).
PubMedGoogle Scholar
Lopes da Cunha, P., Ramírez Butavand, D., Chisari, L. B., Ballarini, F. & Viola, H. Exams at classroom have bidirectional effects on the long-term memory of an unrelated graphical task. npj Sci. Learn. 3, 19 (2018).
CASPubMedPubMed CentralGoogle Scholar
Wheaton, A. G., Chapman, D. P. & Croft, J. B. School start times, sleep, behavioral, health, and academic outcomes: a review of the literature. J. Sch. Health 86, 363–381 (2016).
PubMedPubMed CentralGoogle Scholar
Kurdziel, L., Duclos, K. & Spencer, R. M. Sleep spindles in midday naps enhance learning in preschool children. Proc. Natl Acad. Sci. USA 110, 17267–17272 (2013).
CASPubMedPubMed CentralGoogle Scholar
Lemos, N., Weissheimer, J. & Ribeiro, S. Naps in school can enhance the duration of declarative memories learned by adolescents. Front. Syst. Neurosci. 8, 103 (2014).
PubMedPubMed CentralGoogle Scholar
Cabral, T. et al. Post-class naps boost declarative learning in a naturalistic school setting. npj Sci. Learn. 3, 14 (2018).
PubMedPubMed CentralGoogle Scholar
Vidal, V. et al. Odor cueing during sleep improves consolidation of a history lesson in a school setting. Sci. Rep. 12, 10350 (2022).
CASPubMedPubMed CentralGoogle Scholar
Vorster, A. P. & Born, J. Sleep and memory in mammals, birds and invertebrates. Neurosci. Biobehav. Rev. 50, 103–119 (2015).
PubMedGoogle Scholar
Rasch, B. & Born, J. About sleep’s role in memory. Physiol. Rev. 93, 681–766 (2013).
CASPubMedPubMed CentralGoogle Scholar
Crowley, S. J., Acebo, C. & Carskadon, M. A. Sleep, circadian rhythms, and delayed phase in adolescence. Sleep. Med. 8, 602–612 (2007).
PubMedGoogle Scholar
Gradisar, M., Gardner, G. & Dohnt, H. Recent worldwide sleep patterns and problems during adolescence: a review and meta-analysis of age, region, and sleep. Sleep. med. 12, 110–118 (2011).
PubMedGoogle Scholar
Gradisar, M. & Crowley, S. J. Delayed sleep phase disorder in youth. Curr. Opin. Psychiatry 26, 580–585 (2013).
PubMedPubMed CentralGoogle Scholar
Cousins, J. N. & Fernández, G. The impact of sleep deprivation on declarative memory. Prog. Brain Res. 246, 27–53 (2019).
PubMedGoogle Scholar
Tononi, G. & Cirelli, C. Sleep and synaptic down-selection. Eur. J. Neurosci. 51, 413–421 (2020).
PubMedGoogle Scholar
Tononi, G. & Cirelli, C. Sleep and synaptic homeostasis: a hypothesis. Brain Res. Bull. 62, 143–150 (2003).
PubMedGoogle Scholar
Tononi, G. & Cirelli, C. Sleep function and synaptic homeostasis. Sleep. Med. Rev. 10, 49–62 (2006).
PubMedGoogle Scholar
Antonenko, D., Diekelmann, S., Olsen, C., Born, J. & Mölle, M. Napping to renew learning capacity: enhanced encoding after stimulation of sleep slow oscillations. Eur. J. Neurosci. 37, 1142–1151 (2013).
PubMedGoogle Scholar
Mander, B. A., Santhanam, S., Saletin, J. M. & Walker, M. P. Wake deterioration and sleep restoration of human learning. Curr. Biol. 21, R183–R184 (2011).
CASPubMedPubMed CentralGoogle Scholar
Ong, J. L., Lau, T. Y., Lee, X. K., van Rijn, E. & Chee, M. W. L. A daytime nap restores hippocampal function and improves declarative learning. Sleep 43, zsaa058 (2020).
PubMedPubMed CentralGoogle Scholar
Leong, R. L. F. et al. Influence of mid-afternoon nap duration and sleep parameters on memory encoding, mood, processing speed, and vigilance. Sleep 46, zsad025 (2023).
PubMedPubMed CentralGoogle Scholar
Wyatt, J. K., Bootzin, R. R., Anthony, J. & Bazant, S. Sleep onset is associated with retrograde and anterograde amnesia. Sleep 17, 502–511 (1994).
CASPubMedGoogle Scholar
Wyatt, J. K., Bootzin, R. R., Allen, J. J. & Anthony, J. L. Mesograde amnesia during the sleep onset transition: replication and electrophysiological correlates. Sleep 20, 512–522 (1997).
CASPubMedGoogle Scholar
Lahl, O., Wispel, C., Willigens, B. & Pietrowsky, R. An ultra-short episode of sleep is sufficient to promote declarative memory performance. J. Sleep. Res. 17, 3–10 (2008).
PubMedGoogle Scholar
Genzel, L., Kroes, M. C., Dresler, M. & Battaglia, F. P. Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes? Trends Neurosci. 37, 10–19 (2014).
CASPubMedGoogle Scholar
Horowitz, A. H., Cunningham, T. J., Maes, P. & Stickgold, R. Dormio: a targeted dream incubation device. Conscious Cogn. 83, 102938 (2020).
PubMed CentralGoogle Scholar
Horowitz, A. H., Esfahany, K., Gálvez, T. V., Maes, P. & Stickgold, R. Targeted dream incubation at sleep onset increases post-sleep creative performance. Sci. Rep. 13, 7319 (2023).
CASPubMedPubMed CentralGoogle Scholar
Lacaux, C. et al. Sleep onset is a creative sweet spot. Sci. Adv. 7, eabj5866 (2021).
PubMedPubMed CentralGoogle Scholar
Lacaux, C., Andrillon, T., Arnulf, I. & Oudiette, D. Memory loss at sleep onset. Cereb. Cortex 3, tgac042 (2022).
Google Scholar
Tassi, P. & Muzet, A. Sleep inertia. Sleep. Med. Rev. 4, 341–353 (2000).
PubMedGoogle Scholar
Payne, J. D. et al. Stress administered prior to encoding impairs neutral but enhances emotional long-term episodic memories. Learn. Mem. 14, 861–868 (2007).
PubMedPubMed CentralGoogle Scholar
Pretel, M. R., Vidal, V., Kienigiel, D., Forcato, C. & Ramele, R. A low-cost and open-hardware portable 3-electrode polysomnography device. HardwareX 19, e00553 (2024).
PubMedPubMed CentralGoogle Scholar
Smith, S. M. & Vela, E. Environmental context-dependent memory: A review and meta-analysis. Psychon. Bull. Rev. 8, 203–220 (2001).
CASPubMedGoogle Scholar
C. Spielberger, R. Gorsuch, R. Lushene, P. Vagg, G. Jacobs, Manual for the state-trait anxiety inventory. (Consulting Psychologists Press, 1983).
A. Rechtschaffen, A. Kales, eds. A manual of standardized terminology, techniques and scoring system of sleep stages in human subjects. (Brain Information Service/Brain Research Institute, University of California, 1968).
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Acknowledgements
We thank Quilmes High School for providing the facilities to conduct this experiment. This study received no funding.
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Authors and Affiliations
Laboratorio de Sueño y Memoria, Depto. de Ciencias de la Vida, Instituto Tecnológico de Buenos Aires (ITBA), Capital Federal, Buenos Aires, Argentina
Vanessa Vidal, Matias R. Pretel, Lucila Capurro, Leonela M. Tassone, Malen D. Moyano & Cecilia Forcato
Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Capital Federal, Buenos Aires, Argentina
Vanessa Vidal, Luis I. Brusco & Fabricio M. Ballarini
Quilmes High School, Quilmes, Buenos Aires, Argentina
Romina G. Malacari
Centro de Neuropsiquiatría y Neurología de la Conducta (CENECON), Facultad de Medicina, Universidad de Buenos Aires (UBA), Capital Federal, Buenos Aires, Argentina
Luis I. Brusco
Instituto de Biología Celular y Neurociencias “Prof. E. De Robertis” (IBCN), Facultad de Medicina, Universidad de Buenos Aires (UBA), Capital Federal, Buenos Aires, Argentina
Fabricio M. Ballarini
Departamento de Ciencias de la Vida, Instituto Tecnológico de Buenos Aires (ITBA), Capital Federal, Buenos Aires, Argentina
Fabricio M. Ballarini
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Vanessa Vidal
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2. Matias R. Pretel
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3. Lucila Capurro
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4. Leonela M. Tassone
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5. Malen D. Moyano
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6. Romina G. Malacari
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7. Luis I. Brusco
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8. Fabricio M. Ballarini
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9. Cecilia Forcato
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Contributions
V.V., F.M.B and C.F. made substantial contributions to the conception and design of the work; V.V., L.I.B., F.M.B. and C.F. performed the research; V.V., R.M.P., L.C., L.M.T. and R.G.M. acquired the data; V.V. performed the statistical analysis; L.C., M.D.M and C.F. performed the EEG analysis; V.V. and C.F. wrote the paper; V.V., M.R.P, M.D.M, R.G.M., L.I.B., F.M.B. and C.F contributed to revising it critically; C.F contributed in project administration.
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Correspondence to Cecilia Forcato.
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Competing interests
C.F. is a co-founder of NeuroAcoustics, a company focused on closed-loop auditory stimulation during sleep. However, NeuroAcoustics was not involved in funding, designing, or conducting this study, and the research does not use or evaluate any technology developed by the company.
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Vidal, V., Pretel, M.R., Capurro, L. et al. Short naps improve subsequent learning in a high school setting. npj Sci. Learn. 10, 15 (2025). https://doi.org/10.1038/s41539-025-00307-4
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Received:19 November 2024
Accepted:07 March 2025
Published:22 March 2025
DOI:https://doi.org/10.1038/s41539-025-00307-4
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