Avalilação PREreview de Within and between sleep and cognition: associations in older community dwelling individuals

Authors of the review

Name: Stephanie R. U, ORCID: https://orcid.org/0000-0002-6220-4661

• Bio: Stephanie U is a PhD candidate whose work investigates the role of circadian rhythms in Alzheimer’s Disease resilience in older adults in the Brain Resilience Study. She has a background in Psychology, Neuroscience, and Population Health.

Name: Aina E. Roenningen, ORCID: https://orcid.org/0009-0009-6070-4951

• Bio: Aina Roenningen is an international PhD candidate exploring relationships between sleep and cognition in older adults recruited in the Brain Resilience Study. Aina’s research background is in Neuroscience and Psychology,

Name: Leanne Rokos, ORCID: https://orcid.org/0009-0006-6640-400X

• Bio: Leanne Rokos is a Research Technician with a PhD in Medical Science and a background in computational modeling of early developmental brain networks.

Summary of the preprint

In this manuscript, the authors aimed to investigate how inter- and intra-individual variations in sleep contribute to variations in cognitive performance in a sample of 35 community dwelling, cognitively intact older adults. Sleep data was collected using actigraphy (Actiwatch Spectrum, Philips Respironics) and daily sleep diaries completed for up to 14 consecutive days. Participants also completed a battery of cognitive assessments daily over the 14 days using an electronic tablet. The tests measured different cognitive functions such as working memory, reaction time, attention, verbal fluency, and problem solving. At the end of the study, participants underwent a 10-hour Polysomnography recording. To examine the associations, statistical analyses included intraclass correlation coefficient analysis, Principal Component Analysis (PCA), and a mixed model analysis. Sleep measures were reduced using PCA into four components: sleep duration, sleep efficiency, subjective sleep quality, and nap effect. Mixed models were used to study between-person effects (differences in mean) and within-person effects (deviations from individuals’ mean). Overall, the results showed that longer sleep duration and greater sleep efficiency, measured with actigraphy, were associated with faster reaction times in both between- and within-individual analyses and fewer errors in between-individual analyses. No significant associations were observed between sleep and other cognitive domains such as learning, visual memory, verbal reasoning, and verbal fluency. The authors argue that the relationship between sleep and cognition may be an important topic in dementia research for predicting dementia onset and development. Their work provides an important foundation for future research investigating individual differences in sleep-cognition associations.

1. Big picture comments

1. Good or excellent things

The study investigated an important question in neuroscience, aging, and cognition research by examining the relationship between sleep and cognitive performance in older adults.

The study assessed sleep using objective (actigraphy and polysomnography) and subjective measures (sleep diaries), alongside daily cognitive assessments across multiple cognitive domains. The use of different methods of sleep data collection, in addition to the comprehensive cognitive testing battery, is a key strength of this study’s design. The duration of data collection (~14 days) is another strength, compared to only collecting 7 days of data, which is often the duration of data collection in other studies.

The authors had a clear and relevant research question and the overall rationale for the study was well developed. Existing literature was effectively used to contextualize the findings, with the discussion relating the results to previous studies and considering them from multiple perspectives.

The study design allowed for repeated measurements, enabling the authors to examine naturally occurring variation in sleep and cognition within individuals. The findings also have potential implications for future research and clinical populations.

They demonstrated high adherence with ~95% of planned actigraphy and cognitive observations available, supporting the feasibility of the study design.

1. Things to be improved

Introduction 

Many of the statements on sleep and cognition variations in older adults imply resilience, however, do not explicitly mention the term resilience. This framing could help explain their topic on intra– and inter-individual variations in sleep and cognition.

Although aims are expressed in the introduction, the authors could consider explicitly stating their hypotheses to further clarify the expected relationships or outcomes of the study.

The exploratory nature of the research could also be emphasized early on instead of stating it as a limitation (see line 437).

Methods 

Although the authors state that a full description of methods is presented in a different paper, the article would benefit from an explicit description of inclusion/exclusion criteria regardless. The eligibility criteria should be more specific by clearly listing cut-off points for inclusion/exclusion in the study.

Since the sleep (i.e., actigraphy and sleep diaries) and cognitive assessments were completed in the participants’ home environment, descriptions of how participant compliance was ensured should be addressed.

A clearer rationale of why sleep-apn(o)ea was assessed with polysomnography (PSG) following the 14 days of sleep and cognitive assessment should be stated in the methods and the introduction. There are also further analyses the authors could examine with this PSG data including power spectra analysis and PSG comparisons to their actigraphy/sleep diary data.

Results 

The authors could provide more detail about the rationale for their choice of statistical analyses and software, including the use of both R and SAS.

From an open science perspective, it would be great if the authors are open and willing to share their scripts. The authors did not include the code in their supplementary materials section or their data analysis plan section and did not fully justify why both R and SAS were used for their data analysis. R is a great program to use for reproducibility because it is freely available and accessible. SAS is an older, more expensive program that has different coding structures. Using both software packages may therefore create additional barriers to reproducing the analyses, particularly for researchers seeking to replicate the results using the raw data.

It would be interesting to explore and discuss sex differences between the participants, even if they were just exploratory.

Given the large number of statistical comparisons, the authors could consider reporting corrected results alongside the uncorrected results.

For clarity, the authors could consider stating all measures presented in the results section in the methods section (e.g., MMSE findings).

Discussion

Four principal components were identified in Line 344 to account for 54% of the variance. From the authors' and reader's perspective, this seems quite low. A discussion of this finding, including further literature supporting or conflicting their finding and perhaps what may account for the additional 46%, would help the reader to better understand the analysis and how to interpret the results.

The limitations section could mention concerns around subjective measures of sleep. For example, literature demonstrates how subjective sleep estimates tend to be inaccurate. This would likely impact their Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores and should be mentioned as a limitation. Also, the authors should consider mentioning more explicitly in the limitations of how the small number of participants restricts the generalizability of the results.

The authors did not include a Future Directions section or any comments about their next steps in the Discussion section. The authors could more clearly highlight the potential applications of the approach and mention how they plan to extend the exploratory findings to a different population or context, increase their sample size, or change the study to continue investigating their research questions. It would be extremely interesting from the readers' perspective to know what the authors plan to do next.

The manuscript could further position its findings relative to recent work using a similar within-/between-person framework. For example, Buxton et al. (2025) found that within-person variation in wake after sleep onset was associated with processing speed, whereas sleep duration was not.

Overall 

The overall language throughout the article could be improved to enhance readers’ comprehension of the topic and the measures used. Specifically, more concise and succinct sentences would be beneficial.

The rationale of this study is well supported by the authors’ review of the existing literature. However, a thorough review of more recent literature could provide additional context for the rationale and to situate their findings within the current research landscape.

1. Small picture comments

1. Good or excellent things

This manuscript highlights the feasibility of conducting remote and longitudinal sleep and cognitive assessments in community-dwelling older adults. The manuscript notes that the analyses as exploratory and acknowledges the lack of multiple-comparison correction. The authors conducted multiple analyses and included many figures that were both informative and visually pleasing. It was great to read that practice effects were considered with time-in study included as a covariate in the models. The sample also had a relatively even representation of females and males.

The use of actigraphy as one of the sleep measures is a strength, as it is relatively uncommon in research studies involving older adults. Specifically, use of both objective and subjective sleep measures (i.e., actigraphy and sleep diaries) provides a more comprehensive characterization of participants’ sleep.

1. Things to be improved

A description of the sample demographics (e.g., race, ethnicity) would help to better characterize the study population and allow readers to assess the generalizability of the findings.

The authors could also benefit from clearly distinguishing findings based on visual inspection from statistically tested results.

For ease of understanding, short descriptions of the cognitive tasks and sleep measures presented in the tables would be helpful.

The authors used many abbreviations for scientific methods and longer words which would be helpful to define the first time it is mentioned in the article. One example is the use of EEG instead of Electroencephalography. It would be recommended to first mention it as Electroencephalography (EEG), then refer to it as EEG to prevent reader confusion.

When downloading the full text article, the figures would also benefit from some size reduction as they appear very large in the PDF.

For readability, the authors could consider using standard capitalization for variable names rather than presenting them in all capital letters.

Figure 1: The authors could consider using a colour-blind-friendly colour palette in Figure 1 (i.e., instead of red/green).

Lines 185–186: The authors could clarify who reviewed the data for completeness/integrity and what constituted “nonsensical data,” with an example if appropriate.

Lines 188–189: The authors could clarify how the other derived sleep metrics were calculated, instead of providing the formula for only sleep efficiency.

Line 256: There is a minor typographical error: “nor” should be “not.”

Closing remarks

This is a thoughtful and interesting exploratory study with a strong focus on inter -and intra-individual associations between sleep and cognition. Overall, this topic is important because daily and weekly variations in sleep and cognition in older adults need to be better understood, particularly in the context of resilience and dementia research. The study design including multiple facets of sleep and cognitive data provides a comprehensive approach to examining these associations. The paper demonstrates the feasibility and the value of ecological design for future studies with larger samples, longer monitoring periods, and potentially experimental sleep manipulation. Providing additional details about their analyses and variables, as well as access to their code, would further improve the transparency and reproducibility of the work. We look forward to reading the authors’ future work.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.