Chronotype Shifts and Cognitive Performance in Stroke Survivors: Exploring the Impact of Occupational Therapy Treatment Timing on Rehabilitation Outcomes

Abstract

Importance: Stroke survivors commonly report persistent sleep disruptions during rehabilitation and postrecovery. Despite little research that explores the impact of stroke on a patient’s chronotype, no studies have explored the differences in cognitive performance between treatment timing fitted and nonfitted to the patient’s chronotype.

Objective: To characterize the sleep chronotype in patients in the subacute poststroke period, to explore cognitive task performance differences during fitted and nonfitted hours, and to examine the relationships between sleep quality and cognitive performance.

Design: Cohort design.

Setting: Health care institution.

Participants: Twenty men and women (M age = 60.2, SD = 8.6) post–first stroke.

Outcomes and Measures: The following measures were collected twice, during fitted and nonfitted chronotypic times: demographic surveys, the Munich Chronotype Questionnaire, the Montreal Cognitive Assessment (MoCA), the Rivermead Behavioral Memory Test, Second Edition (RBMT–2), a fatigue questionnaire, and of actigraphy for 4 to 5 days.

Results: Stroke survivors exhibited an earlier sleep chronotype postinjury compared with preinjury. Enhanced memory (higher RBMT–2 scores) occurred during fitted hours. No significant differences emerged in executive functions, attention tasks, and MoCA scores between fitted and nonfitted hours. The Wake After Sleep Onset index correlated with the MoCA and RBMT–2 scores during fitted hours (MoCA: r = .53, p = .021; RBMT–2: r = .51, p = .027). Number of awakenings correlated with the MoCA letter task during nonfitted hours (r = .45; p = .044).

Conclusions and Relevance: Occupational therapy practitioners should be aware of the relationship between chronotype and cognitive performance, with a focus on the importance of personalized rehabilitation strategies in stroke care.

Plain-Language Summary: The timing of cognitive assessments poststroke can influence their reliability, particularly when considering a patient's chronotype (sleep cycle). For instance, if a morning person is evaluated later in the day (nonfitted to their chronotype), the results may indicate greater cognitive decline compared with scores they might achieve in the morning. In our study, we assessed cognitive performance twice: once fitted to the patient’s chronotype and once not. Our findings revealed that memory function significantly improved during evaluations conducted at personalized times. This suggests that occupational therapy practitioners who evaluate patients during nonoptimal times may prescribe a more extensive rehabilitation plan than necessary.

The study findings revealed that memory function for stroke survivors significantly improved during cognitive assessments conducted at personalized times, highlighting the importance of personalized occupational therapy rehabilitation strategies.

Stroke rehabilitation requires a comprehensive approach that addresses both physical and cognitive recovery. One critical but often overlooked factor influencing rehabilitation outcomes is the timing of interventions in relation to the patient’s circadian rhythm. The circadian clock regulates sleep–wake cycles and other physiological processes that are crucial for optimal brain function and recovery after a stroke (Roenneberg & Merrow, 2016). Human circadian rhythms, influenced by genetic factors, determine a person’s chronotype—the preference for being active at certain times of the day—which ranges from morning types (“larks”) to evening types (“owls”; Adan et al., 2012). These chronotypes have been shown to affect cognitive performance, attention, and executive functions, all of which are essential in stroke rehabilitation (Facer-Childs et al., 2018; Schmidt et al., 2007).

Sleep disturbances are common among people recovering from a stroke, with up to 65% of stroke survivors experiencing sleep issues (Luo et al., 2023). These disturbances, which can include fragmented sleep, prolonged sleep latency, and reduced sleep quality, are thought to result from changes in circadian rhythm, often exacerbated by factors such as age, the nature of the stroke, and the physical environment (Kantermann et al., 2015). Disturbances in sleep can impair cognitive function and slow recovery, which is particularly problematic for rehabilitation outcomes (Niu et al., 2023). Stroke patients may experience shifts in their chronotype, often shifting toward an earlier sleep preference poststroke (Kantermann et al., 2015). However, the impact of these chronotype shifts on rehabilitation performance—especially during different times of the day—remains underexplored.

The timing of rehabilitation interventions, when aligned with a person’s chronotype, could optimize cognitive function and recovery outcomes. Although studies have investigated cognitive performance at different times of day for healthy people (Adan et al., 2012; Facer-Childs et al., 2018), there is a distinct gap in the literature regarding the effect of chronotype on cognitive performance in stroke patients. Specifically, there is limited research that compares cognitive performance during chronotype-fitted versus nonfitted hours. If the timing of evaluations affects cognitive assessment scores in clinical settings, it could influence the treatment plan developed by the occupational therapist and potentially alter the rehabilitation period. This study aims to address this gap by

▪ characterizing the sleep chronotype of patients in the subacute stage of stroke and assessing changes before and after the stroke,

▪ examining differences in cognitive performance during fitted versus nonfitted hours for patients poststroke, and

▪ exploring the relationships between sleep quality and cognitive performance in these patients.

By aligning rehabilitation timing with chronotype, this research has the potential to enhance the effectiveness of stroke rehabilitation, improving both cognitive and functional outcomes.

Method

Study Population

Twenty men and women ages 44 to 70 yr were recruited in a convenient sampling method from within the Neurology Department at Loewenstein Rehabilitation Hospital in Ra’anana, Israel. Inclusion criteria were the following: a diagnosis of first-time cerebrovascular accident (CVA), 6 to 12 wk from stroke, proficiency in Hebrew, normal or corrected vision, at least one fully functional hand, and the ability to comprehend and sign a consent form. The exclusion criteria were the following: significant speech impairment, neurological conditions other than stroke, psychiatric diagnoses, preexisting sleep apnea issues before the cerebral event, daily consumption of stimulating medications (e.g., Ritalin or PK-Merz), current or previous use of sleep medications (e.g., Bondormin).

Ethical approval for the study was obtained from the Helsinki Committee of Loewenstein Rehabilitation Hospital (Approval No. 0019–20-LOE) and from the Tel Aviv University Ethics Committee (Approval No. 0002272-2). We conducted a pilot study using data from the first couple of participants. Preliminary data from the primary outcome measure were analyzed with G*Power (Version 3.1.9.6). With an α level of .05 and a power of .8 for matched-pair analyses, the calculated effect size of .587 indicated a required sample size of 20 participants.

Study Tools

We used a demographic questionnaire to obtain data regarding location of CVA, time from CVA, and time from admission to the rehabilitation department. The Munich ChronoType Questionnaire (MCTQ; Roenneberg et al., 2003) assesses chronotype on the basis of sleep behavior. It consists of simple questions regarding sleep timing (separately on work and free days), allowing the computation of its key parameters: midsleep (the midpoint between sleep onset and offset) and sleep duration. To minimize the impact of social factors, such as rigid work schedules (Kantermann et al., 2007), chronotype estimation can rely on midsleep on free days (MSF; Juda et al., 2013), as implemented in our present research. Specifically, the MCTQ was completed during the weekend leave, when the patient was at home and not at the rehabilitation center (and, therefore, was not required to wake up early or go to bed at a certain time). This self-reported tool has shown diagnostic validity against the Morningness-Eveningness Questionnaire (r = .49, p < .001; Ryu et al., 2018), and MSF demonstrates a strong correlation with sleep logs and wrist actigraphy. A significant shift in the patient’s chronotype refers to a change in midsleep time that moves a patient between chronotype classifications, as defined by Roenneberg et al. (2019): Morning types have midsleep time occurring before 3:00 a.m., intermediate types have midsleep time that falls between 3:00 a.m. and 4:30 a.m., and evening types have a midsleep time occurring after 4:30 a.m. Additionally, we used the wrist-worn Actiwatch 2 (Philips Respironics) and Actigraph wGT3X-BT (ActiGraph) to record sleep characteristics. These actigraphs also exhibited a high correlation with the MCTQ (r = .73, p < .001; Santisteban et al., 2018). We assessed the cognitive abilities of the participants using two tools: first, we used the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), a cognitive screening test known for its accessibility, simplicity, and quick administration (around 10 min). It caters to diverse populations, including patients post–cerebrovascular accident. The examination comprises eight subitems, evaluating visual perception, organizational skills, identification and orientation, short-term memory, attention, language, abstraction, and orientation. The maximum score is 30, with cutoff scores of 26 and higher considered within the normal range (Nasreddine et al., 2005). The assessment has high test–retest reliability (r = .92, p < .001) and internal consistency (Cronbach’s α = .83; Nasreddine et al., 2005). Second, we used the Rivermead Behavioral Memory Test, Second Edition (RBMT–2), which identifies impairments in daily memory function and measure changes over time. Specifically, it evaluates short-term memory, long-term memory, and prospective memory through verbal, visual, and spatial tasks. Additionally, it assesses orientation in time and place. Participants receive a profile score (range = 0–24) and a total score (range = 0–12), where maximal scores indicate normal memory. The scores are categorized into four memory levels (normal, mild decline, moderate impairment, and severe impairment; Wills et al., 2000). The assessment showcased high internal consistency, test–retest reliability, parallel forms reliability, and interrater reliability. In both assessments, we used two distinct versions for the two cognitive tests. These particular assessments (MoCA and RBMT–2) were selected because of their provision of multiple versions and their lack of a learning effect.

Procedure

The study protocol was explained to the participant by an occupational therapist. Each participant read and signed an informed consent form. Then, the participant filled out the demographic questionnaire and the MCTQ regarding his or her sleep patterns post–cerebral event and for the preceding 3 mo. Participants were then given an actigraph and instructed to wear it for 4 to 5 d. The test sessions were scheduled on the basis of the participant’s chronotype classification according to the MCTQ. The procedure included two assessment sessions using the MoCA and RBMT–2. Half of the participants underwent cognitive assessments during their chronotypically fitted hours, followed by evaluations during nonfitted hours, and the remaining half followed the reverse sequence. The selected hours in this research were as follows: For morning chronotypes, the fitted time was 0800, and the nonfitted time was 1400; for intermediate chronotypes, the fitted time was 1000, and the nonfitted time was 1400; and for evening chronotypes, the fitted time was 1700, and the nonfitted time was 0800. Additionally, in each of the groups, half of the participants commenced cognitive assessments starting from the MoCA, and the other half started from the RBMT–2. Actigraph usage concluded after the second assessment. A double-blind protocol was maintained, because both assessors and participants were unaware of the fitted and nonfitted testing times.

Data Analysis

Four sleep quality indices were extracted from actigraphy wristwatch recordings: the average of total sleep time per day (minutes); the average number of awakenings during the sleep period per day; the efficiency of sleep (total hours of sleep per day divided by hours spent in bed per day, averaged across the days and presented as a percentage); and the Wake After Sleep Onset (WASO) index, indicating the average number of minutes elapsed from the onset of sleep to the first awakening (Chen et al., 2016).

We conducted data processing using IBM SPSS Statistics (Version 27). A descriptive statistical analysis was used to characterize the study population. We used Mann–Whitney U tests to examine differences between groups, considering those commencing at fitted versus nonfitted times and those starting with different assessment sequences. To assess changes in chronotype before and after the cerebral event and to explore variations in cognitive performance at different times, we conducted Wilcoxon tests. Effect size, r, was calculated using Z and N (number of participants) according to Fritz et al. (2012): $r = \frac{Z}{\sqrt{N}}$

Furthermore, we used a χ² test to compare the participants’ chronotype distribution with that of the general population. The relationships between sleep quality metrics and cognitive performance was investigated using Spearman’s rank correlation test. Correlation coefficients between .1 and .39 were interpreted as weak; .4 to .69, as moderate; and .7 or greater, as strong (Schober et al., 2018). All tests adhere to standard statistical criteria (p < .05) to ascertain statistical significance.

Results

All 20 participants read and signed an informed consent pretrial and successfully completed the study. The research data were collected over a period of approximately 7 mo in the neurological department of the Loewenstein Rehabilitation Hospital in Ra’anana, Israel. The study population comprised 16 men (80%) and 4 women (20%). Mean age was 60.2 yr (SD = 8.6). Ten participants (50%) encountered cerebral events on the right side; 6 (30%), on the left side; and 4 (20%), in the brainstem and cerebellum. Sixteen (80%) of the participants experienced an ischemic event, whereas 4 (20%) participants experienced hemorrhage. Average time elapsed since the cerebral event was 51.6 days (SD = 6.3), and the time since admission to rehabilitation was 35.5 days (SD = 10.5).

No significant differences were found in the research measures between participants who began with an RBMT–2 assessment and those who started with a MoCA assessment (ps = .105–.912). Hence, the order of assessments did not influence the outcomes.

The sleep chronotype of the participants underwent a significant shift after the cerebral event compared with their pre-event chronotype, as indicated by a notable alteration in their midsleep time postcerebral event (r = .516, p = .021; Figure 1). Specifically, 60% of the study participants after a cerebral event exhibited an early chronotype, 35% of the study participants displayed an intermediate chronotype, and 5% exhibited a late chronotype.

Figure 1.

Sleep midpoint boxplot, according to the Munich ChronoType Questionnaire (MCTQ), before and after the cerebral event (N = 20).

Differences were found in cognitive performance on the RBMT–2 between fitted and nonfitted hours (Table 1). The memory index was better during the fitted hours, compared with nonfitted hours, for 6 (30%) of the study participants. Six (30%) of the participants shifted from a category of mild memory impairment during nonfitted hours to a category of normal memory during fitted hours. The rest of the participants did not change category, but some shifted in score within the same category. Specifically, 7 participants (35%) advanced to a higher score within the same category, whereas 4 (20%) exhibited a decrease of up to 2 points within their respective categories. Finally, 5 (25%) participants maintained their scores during the fitted and nonfitted times. In addition, cognitive functions as assessed by the MoCA, measures of attention, executive function, and the overall score showed no significant differences between fitted and nonfitted hours (Table 1).

Table 1.

Medians and IQRs of Cognitive Performance Measures During Fitted and Nonfitted Hours

Cognitive Performance Measure	Mdn (IQR)		Z	p	Effect Size
Cognitive Performance Measure	Nonfitted Hours	Fitted Hours	Z	p	Effect Size
MoCA subscales
Executive Function	4.0 (2.0–4.7)	4.0 (3.0–5.0)	−1.51	.117	.34
Attention: Digits	2.0 (1.0–2.0)	2.0 (1.0–2.0)	−1.13	.250	.25
Attention: Letters	1.0 (1.0–1.0)	1.0 (1.0–1.0)	−1.00	.500	.22
Attention: Serial Subtraction	3.0 (2.0–3.0)	3.0 (2.0–3.0)	−0.38	.500	.08
Total score	23.5 (21.0–26.7)	24.5 (21.0–27.0)	−0.60	.281	.13
RBMT–2	19.0 (17.2–20.0)	20.0 (17.2–22.0)	−1.70	.048	.38

Note. N = 20. IQR = interquartile range; MoCA = Montreal Cognitive Assessment; RBMT–2 = Rivermead Behavioral Memory Test, Second Edition.

The average total sleep duration for participants was 8.1 hr per night (SD = 2.6). The average number of awakenings was 30.2 times per night (SD = 13.2). Sleep efficiency averaged 75.9% (SD = 16.8). As depicted in Table 2, there were overall six significant correlations between the sleep quality measures and cognitive performance measures during fitted hours but only one significant correlation during nonfitted hours. Specifically, a weak negative correlation was observed between the total sleep duration and sleep efficiency with the overall score on the MoCA and the memory measure during fitted sleep hours. In other words, high total sleep duration and low sleep efficiency were associated with decreased cognitive performance in the assessed measures. Conversely, during nonfitted sleep hours, a weak positive correlation was found between WASO and cognitive measures; that is, longer WASO times were associated with higher overall MoCA scores and RBMT–2 scores. Additionally, a weak negative correlation was found between the number of awakenings and one of the attention measures (letters) during nonfitted sleep hours. This implies that more awakenings were associated with reduced attention.

Table 2.

Spearman’s Correlation Coefficients Between Sleep Quality Measures and Cognitive Performance Measures During Fitted and Nonfitted Hours

Cognitive Performance Measure	WASO		Sleep Efficacy		Awakenings		Total Sleep Duration
Cognitive Performance Measure	NF	F	NF	F	NF	F	NF	F
MoCA subscale
Executive Function	.28	.14	−.26	−.24	−.17	−.11	−.23	−.24
Attention: Digits	.12	.20	−.09	−.30	−.21	.16	.12	−.30
Attention: Letters	−.14	.19	.23	.00	–.45*	−.25	.27	.25
Attention: Serial Subtraction	−.15	.20	.01	−.23	.03	.11	−.39	−.20
Total score	.26	.53*	-.31	−.51*	.08	.09	−.42	−.46*
RBMT–2
Memory	.25	.51*	−.19	−.55*	.32	.43	−.26	−.48*

Note. N = 15. Boldface indicates statistically significant correlation. F = fitted hours; MoCA = Montreal Memory Test; NF = nonfitted hours; RBMT–2 = Rivermead Behavioral Memory Test, Second Edition; WASO = Wake After Sleep Onset.

p < .05.

Discussion

The main outcomes of the research indicates significant differences in the midsleep time among participants, demonstrating a shift to earlier chronotypes in the postcerebral event period compared with their pre-event status, as reported in the MCTQ. Additionally, cognitive performance, specifically in memory assessment (with the RBMT–2), was significantly better during the fitted hours. Moreover, a weak positive correlation was found between the WASO score and the overall score in the MoCA and the total score in the RBMT–2 during the fitted hours. A negative correlation was identified between the number of awakenings and one of the attentions subitems in the MoCA during the nonfitted hours.

The distribution of chronotype categories of our participants before the CVA is similar to that reported in the healthy population (Adan et al., 2012). After the cerebral event, they transitioned to become early chronotypes, consistent with previous studies that reported a shift to earlier chronotypes in the early months after a cerebral event (Kantermann et al., 2015). The transition to morning chronotypes after a cerebral event could be interpreted as an acceleration of aging processes. Studies have shown that patients with ischemic stroke exhibit a biological age older than that of healthy control participants, evident even after only 3 mo poststroke (Egorova et al., 2019). Consequently, the disparity between chronological age and biological age observed in patients poststroke can serve as an indicator of age acceleration (Fernández-Pérez et al., 2023). This measure has proven predictive of various age-related health outcomes and mortality risks (Ryan, 2021). It has also been linked to an elevated risk of cardiovascular diseases (Roetker et al., 2018), underscoring its utility as a marker of aging and biological health status (Fernández-Pérez et al., 2023).

Our main finding shows that the memory abilities, measured using the RBMT–2, were significantly better during the fitted hours compared with nonfitted hours. Overall, 30% of the study participants demonstrated an elevation in their final scores during their fitted hours, transitioning from the mild memory impairment category to the normal memory category. Previous research has established a connection between attentional skills and various memory functions (Angelopoulou & Drigas, 2021; Naveh-Benjamin et al., 2000). As attention levels declined, a parallel decrease in memory performance was noted (Naveh-Benjamin et al., 2000). Consequently, it can be deduced that, during memory assessments, participants managed to mobilize higher attention levels during their fitted hours compared with nonfitted hours, facilitating superior information processing.

Unlike the aforementioned findings, there were no differences in MoCA scores between the testing hours. It should be noted that we intentionally selected the MoCA and the RBMT–2 because of their widespread use as clinical assessment tools in rehabilitation practice. These assessments were chosen to align with real-world clinical scenarios and to ensure the study’s applicability to routine occupational therapy evaluations. The MoCA serves as a quick cognitive screening tool, but it may not possess the sensitivity to detect subtle attentional changes influenced by chronotype, unlike the RBMT–2, which provides a more detailed assessment of memory performance. Beyond the inherent characteristics of these assessment tools, a potential neurophysiological explanation for this differential effect is that memory functions—particularly episodic and prospective memory, as assessed by the RBMT–2—are more susceptible to circadian influences compared with the broad cognitive domains screened by the MoCA. Research suggests that circadian rhythms modulate hippocampal-dependent memory processes, with optimal retrieval occurring during peak alertness periods (Schmidt et al., 2007). In contrast, general cognitive screening tools such as the MoCA assess a wider range of functions that may not be as strongly influenced by time-of-day variations. Additionally, brain regions involved in memory, such as the hippocampus and prefrontal cortex, exhibit diurnal fluctuations in neural efficiency (Smies et al., 2022), which could contribute to the improved performance observed in RBMT–2 scores during fitted hours. The observed enhancement in memory during fitted times may, therefore, reflect participants’ ability to engage higher attentional resources, indirectly benefiting memory tasks assessed by the RBMT–2.

Another finding of this study relates to the relations between sleep quality and cognitive performance after cerebral events. The results revealed that a longer duration between sleep onset and first awakening was associated with higher scores in both MoCA and RBMT–2. Also, more nocturnal awakenings correlated with decreased performance in an attention-letters task. These finding emphasize the critical role of sleep in poststroke rehabilitation, influencing both neuroplasticity and recovery outcomes. Quality sleep supports the brain’s ability to reorganize and form new neural connections, which are essential for regaining motor and cognitive functions (Brunetti et al., 2022). Conversely, sleep disturbances such as insomnia, sleep-disordered breathing, or fragmented sleep can impair neuroplasticity, slow motor recovery, exacerbate cognitive deficits, and reduce engagement in physical therapy (Brunetti et al., 2022). Additionally, poor sleep increases fatigue, stress, anxiety, and depression, all of which hinder rehabilitation progress. Some unexpected correlations were observed: We found negative correlations of sleep efficiency and total sleep hours with the overall scores of the MoCA and RBMT–2. These associations contradict previous findings that link sleep quality in patients postcerebral event to cognitive tasks that involve attention, memory, and executive functions (Pink, 2018). A possible explanation for these unexpected findings might be the use of wrist actigraphy in nonoptimal hospital conditions (shared rooms, artificial lighting, noises), potentially influencing sleep quality and measurement reliability and, consequentially, resulting in some unexpected correlations.

The study is subject to certain limitations, including a relatively small sample size of 20 participants, which may not fully capture the diversity of patients’ postcerebral events. However, it is noteworthy that the effect size of our main finding was .516, signifying a medium effect. Technical challenges were encountered during the actigraphy data collection of 15 participants, and we recognize the possibility of spontaneous improvement during the acute subphase of rehabilitation (Pink 2018). Another limitation of the study may have been the actigraphy data, which were recorded in the rehabilitation department. Factors such as hospital routines, noise, excessive exposure to artificial light, inappropriate humidity and temperature, reduced physical activity (Truksinas et al., 2022), and depression and anxiety poststroke, might have affected the sleep patterns during weekdays (Park & Kyong, 2023). Consequently, weekday actigraphy data might not fully represent patients’ habitual sleep patterns outside of hospitalization. However, in this study, we believe that this had no significant influence on the patients’ chronotype, because the classification was based on free days (weekend leave when the patient was at home and not required to wake up early or go to bed at a certain time). Thus, even before the cerebral event, the vast majority of patients were people in the working-age group who maintained a structured daily schedule, rising in the morning and working until the afternoon. Their chronotype was likely influenced by both environmental factors (e.g., work schedules) and genetic predisposition, which is estimated to account for approximately 50% of chronotype variability (Kalmbach et al., 2017). However, the shift toward an earlier chronotype observed poststroke suggests that, despite the strong genetic component, circadian regulation can be altered by neurological and physiological changes after brain injury. Finally, we acknowledge a potential influence of stroke-related factors and undetected preexisting conditions on sleep abnormalities, despite excluding patients with diagnosed sleep disorders before the neurological event.

Furthermore, the study underscores the significance of comparing the timing chosen for the repetition of cognitive assessments, with a specific emphasis on memory (as measured with the RBMT–2). Hence, we are confident that synchronizing the treatment schedule with a patient’s chronotype holds the potential for enhancing memory assessment performance. Previous studies also have shown that it is crucial to synchronize cognitive performance with chronotype (Facer-Childs et al., 2018; Wiłkość-Dębczyńska & Liberacka-Dwojak, 2023). In the present study, several associations were identified between cognitive functions and sleep metrics. Moreover, evidence from various studies suggests that sleep disorders can manifest in both the acute and chronic phases after a cerebral event (Campos et al., 2005; Khazaei et al., 2022; Niu et al., 2023). Consequently, we recommend that occupational therapists address sleep-related concerns and work toward enhancing sleep conditions for hospitalized patients during the subacute rehabilitation period. Moreover, we recommend that they provide guidance to patients and their caregivers in executing instrumental activities of daily living tasks, such as driving, financial and medical management, and work and learning, during the hours when patients are more attentive on the basis of their personal chronotype (Paradee et al., 2005; Wiłkość-Dębczyńska & Liberacka-Dwojak, 2023).

Implications for Occupational Therapy Practice

Stroke survivors often experience ongoing sleep disturbances during recovery. Our main question was whether the timing of cognitive evaluations of occupational therapy compromise their accuracy. Therefore, we assessed cognitive performance twice, once aligned with the patient’s chronotype and once not aligned. The findings acquired in our preliminary small sample size of 20 participants, with a moderate effect size, have the following implications for occupational therapy practice:

▪ Occupational therapy practitioners should be aware that the memory function is lower during evaluations conducted at nonpersonalized timings compared to personified timings.

▪ The occupational therapist’s choice of evaluation timing may influence the reliability of the cognitive assessment and thereby the devised treatment plan.

Conclusion

In conclusion, occupational therapists should recognize the distinctive features of patients undergoing rehabilitation and prioritize the customization of rehabilitation treatments. This approach aligns with the global shift toward personalized medicine. We recommend an investigation of research questions within the framework of diverse populations with cognitive impairments, including patients with acquired head injuries. Future studies should also investigate the adaptability of these findings to diverse rehabilitation settings globally, considering variations in treatment schedules and cultural differences. Additionally, there is potential value in expanding the assessment to encompass motor performance at various times, taking into account the chronotype of participants within populations experiencing motor impairments, such as those with orthopedic injuries and spinal cord injuries.

Footnotes

Acknowledgments

This work was supported by Loewenstein Rehabilitation Center (grant no. KM600010335). The work is based on Ms. Ayelet Hersch’s master’s thesis.

References

Adan

Archer

S. N.

Hidalgo

M. P.

Di Milia

Natale

, & Randler

(2012). Circadian typology: A comprehensive review. Chronobiology International, 29, 1153–1175. https://doi.org/10.3109/07420528.2012.719971

Angelopoulou

, & Drigas

(2021). Working memory, attention and their relationship: A theoretical overview. Research, Society and Development, 10, e46410515288. https://doi.org/10.33448/rsd-v10i5.15288

Brunetti

Rollo

Broccolini

Frisullo

Scala

, & Della Marca

(2022). Sleep and stroke: Opening our eyes to current knowledge of a key relationship. Current Neurology and Neuroscience Reports, 22, 767–779. https://doi.org/10.1007/s11910-022-01234-2

Campos

T. F.

Diógenes

F. P.

França

F. R.

Dantas

R. C. S.

Araujo

J. F.

, & Menezes

A. A. L.

(2005). The sleep-wake cycle in the late stage of cerebral vascular accident recovery. Biological Rhythm Research, 36, 109–114. https://doi.org/10.1080/09291010400028799

Chen

J. H.

Lauderdale

D. S.

, & Waite

L. J.

(2016). Social participation and older adults’ sleep. Social Science and Medicine, 149, 164–173. https://doi.org/10.1016/j.socscimed.2015.11.045

Egorova

Liem

Hachinski

, & Brodtmann

(2019). Predicted brain age after stroke. Frontiers in Aging Neuroscience, 11, 348. https://doi.org/10.3389/fnagi.2019.00348

Facer-Childs

E. R.

Boiling

, & Balanos

G. M.

(2018). The effects of time of day and chronotype on cognitive and physical performance in healthy volunteers. Sports Medicine—Open, 4, 47. https://doi.org/10.1186/s40798-018-0162-z

Fernández-Pérez

Jiménez-Balado

Lazcano

Giralt-Steinhauer

Rey Álvarez

Cuadrado-Godia

, … Jiménez-Conde

(2023). Machine learning approximations to predict epigenetic age acceleration in stroke patients. International Journal of Molecular Sciences, 24, 2759. https://doi.org/10.3390/ijms24032759

Fritz

C. O.

Morris

P. E.

, & Richler

J. J.

(2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141, 2–18. https://doi.org/10.1037/a0024338

10.

Juda

Vetter

, & Roenneberg

(2013). The Munich ChronoType Questionnaire for shift-workers (MCTQShift). Journal of Biological Rhythms, 28, 130–140. https://doi.org/10.1177/0748730412475041

11.

Kalmbach

D. A.

Schneider

L. D.

Cheung

Bertrand

S. J.

Kariharan

Pack

A. I.

, & Gehrman

P. R.

(2017). Genetic basis of chronotype in humans: Insights from three landmark GWAS. Sleep, 40, zsw048. https://doi.org/10.1093/sleep/zsw048

12.

Kantermann

Juda

Merrow

, & Roenneberg

(2007). The human circadian clock’s seasonal adjustment is disrupted by daylight saving time. Current Biology, 17, 1996–2000. https://doi.org/10.1016/j.cub.2007.10.025

13.

Kantermann

Meisel

Fitzthum

Penzel

Fietze

, & Ulm

(2015). Changes in chronotype after stroke: A pilot study. Frontiers in Neurology, 5, 287–287. https://doi.org/10.3389/fneur.2014.00287

14.

Khazaei

Ayubi

Khazaei

, & Afrookhteh

(2022). Sleep quality and related determinants among stroke patients: A cross-sectional study. Iranian Journal of Psychiatry, 17, 84–90.

15.

Luo

Liu

Zhuge

, & Zhu

(2023). Sleep quality after stroke: A systematic review and meta-analysis. Medicine, 102, e33777. https://doi.org/10.1097/MD.0000000000033777

16.

Nasreddine

Z. S.

Phillips

N. A.

Bédirian

Charbonneau

Whitehead

Collin

, … Chertkow

(2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53, 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x

17.

Naveh-Benjamin

Craik

F. I. M.

Perretta

J. G.

, & Tonev

S. T.

(2000). The effects of divided attention on encoding and retrieval processes: The resiliency of retrieval processes. Quarterly Journal of Experimental Psychology, Section A: Human Experimental Psychology, 53, 609–625. https://doi.org/10.1080/027249800410454

18.

Niu

Liu

Zeng

, & Shi

(2023). Sleep quality and cognitive function after stroke: The mediating roles of depression and anxiety symptoms. International Journal of Environmental Research and Public Health, 20, 2410. https://doi.org/10.3390/ijerph20032410

19.

Paradee

C. V.

Rapport

L. J.

Hanks

R. A.

, & Levy

J. A.

(2005). Circadian preference and cognitive functioning among rehabilitation inpatients. Clinical Neuropsychologist, 19, 55–72. https://doi.org/10.1080/13854040490524173

20.

Park

K. M.

, & Kyong

(2023). Disturbance of sleep and circadian rhythm in hospitalized patients. Chronobiology in Medicine, 5, 7–17. https://doi.org/10.33069/cim.2023.0003

21.

Pink

D. H.

(2018). When: The scientific secrets of perfect timing. Riverhead Books.

22.

Roenneberg

, & Merrow

(2016). The circadian clock and human health. Current Biology, 26, R432–R443. https://doi.org/10.1016/j.cub.2016.04.011

23.

Roenneberg

Pilz

L. K.

Zerbini

, & Winnebeck

E. C.

(2019). Chronotype and social jetlag: A (self-) critical review. Biology, 8, 54–73. https://doi.org/10.3390/biology8030054

24.

Roenneberg

Wirz-Justice

, & Merrow

(2003). Life between clocks: Daily temporal patterns of human chronotypes. Journal of Biological Rhythms, 18, 80–90. https://doi.org/10.1177/0748730402239679

25.

26.

Roetker

N. S.

Pankow

J. S.

Bressler

Morrison

A. C.

, & Boerwinkle

(2018). Prospective study of epigenetic age acceleration and incidence of cardiovascular disease outcomes in the ARIC study (Atherosclerosis Risk in Communities). Circulation. Genomic and Precision Medicine, 11, e001937. https://doi.org/10.1161/CIRCGEN.117.001937

27.

Ryan

C. P.

(2021). “Epigenetic clocks”: Theory and applications in human biology. American Journal of Human Biology, 33, e23488. https://doi.org/10.1002/ajhb.23488

28.

Ryu

Joo

E. Y.

Choi

S. J.

, & Suh

(2018). Validation of the Munich Chronotype Questionnaire in Korean older adults. Psychiatry Investigation, 15, 775–782. https://doi.org/10.30773/pi.2018.04.09

29.

Santisteban

J. A.

Brown

T. G.

, & Gruber

(2018). Association between the Munich Chronotype Questionnaire and wrist actigraphy. Sleep Disorders, 2018, 5646848. https://doi.org/10.1155/2018/5646848

30.

Schmidt

Collette

Cajochen

, & Peigneux

(2007). A time to think: Circadian rhythms in human cognition. Cognitive Neuropsychology, 24, 755–789. https://doi.org/10.1080/02643290701754158

31.

Schober

Boer

, & Schwarte

L. A.

(2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia, 126, 1763–1768. https://doi.org/10.1213/ANE.000000000 0002864

32.

Smies

C. W.

Bodinayake

K. K.

, & Kwapis

J. L.

(2022). Time to learn: The role of the molecular circadian clock in learning and memory. Neurobiology of Learning and Memory, 193, 107651. https://doi.org/10.1016/j.nlm.2022.107651

33.

Truksinas

Frange

da Paz

Vago

E. L.

Andersen

M. L.

Tufik

, & Santos Coelho

F. M.

(2022). Circadian preference and stroke characteristics: A descriptive study. Chronobiology International, 39, 644–652. https://doi.org/10.1080/07420528.2021.2021228

34.

Wiłkość-Dębczyńska

, & Liberacka-Dwojak

(2023). Time of day and chronotype in the assessment of cognitive functions. Postepy Psychiatrii i Neurologii, 32, 162–166. https://doi.org/10.5114/ppn.2023.132032

35.

Wills

Clare

Shiel

, & Wilson

B. A.

(2000). Assessing subtle memory impairments in the everyday memory performance of brain injured people: Exploring the potential of the Extended Rivermead Behavioural Memory Test. Brain Injury, 14, 693–704.