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Keywords:

  • memory performance;
  • sleep duration;
  • sleep efficiency

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

The prevalence of insomnia increases with age. Short sleep duration is associated with deficits in cognitive performance. We hypothesized that short sleep duration and sleep quality influence cognitive performance in older adults. The study included 78 adults aged 60 years and over (72.2 ± 5.9 years). Total sleep time and sleep efficiency (total sleep time/time in bed × 100) were calculated using actigraphy. We evaluated cognitive performance with the continuous performance test-identical pairs and the number-back test. Sleep apnea was evaluated overnight with a portable home monitoring system. The accuracy of the 0-back test significantly decreased in participants with total sleep time less than 5 h compared with those with total sleep time greater than 7 h, but there was no significant difference in continuous performance test-identical pairs between the two groups. Participants with sleep efficiency <85% showed a significant decrease in 0- and 1-back test accuracy compared with those with sleep efficiency ≥85%. There were no significant differences in the accuracy of number-back tests and continuous performance test-identical pairs between apnea–hypopnea index ≥15 h−1 and apnea–hypopnea index <15 h−1 groups, or among lowest SpO2 ≥ 90%, lowest 80–90%, and lowest SpO2 < 80% groups. Age, total sleep time and sleep efficiency were significantly correlated with accuracy on the 0-back test. Age and sleep efficiency were significantly correlated with accuracy on the 1-back test. Multiple regression analysis revealed that total sleep time was independently correlated with accuracy on the 0-back test, while age was independently correlated with accuracy on the 1-back test. Our findings suggest that sleep duration and sleep quality may play a role in cognitive performance in older adults.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

Poor sleep is associated with health problems, such as obesity, diabetes mellitus, hypertension and decreased cognitive performance (Calhoun and Harding, 2010; Tworoger et al., 2006; Van Cauter and Knutson, 2008). Sleep complaints increase with age, with older adults most often complaining of difficulty initiating or maintaining sleep (Ancoli-Israel, 2009). Late-life insomnia is often attributed to medical and psychiatric disorders, as well as age-related physiological changes in sleep–wake regulation (Neikrug and Ancoli-Israel, 2010). It is important to diagnose and treat insomnia in older adults properly because poor sleep can have serious consequences, including decreased health-related quality of life (LeBlanc et al., 2007) and impaired cognitive performance (Tworoger et al., 2006).

Short sleep duration impacts health in various ways, and manifests as higher general mortality compared with people getting 7–8 h of sleep per night. Previously, we demonstrated that short sleep duration (<4 h) was associated with deficits in cognitive performance, even in young adults (Miyata et al., 2010). Cognitive performance declines with age in a part of domains, such as memory, reasoning and spatial visualization (Salthouse, 2010), but there is substantial individual variability in the magnitude of these changes (Reuter-Lorenz and Lustig, 2005). In one epidemiological study, both short (3–4 h) and long (>10 h) self-reported sleep duration were independently related to memory impairment evaluated with a delayed word recall test in older Chinese adults (Xu et al., 2011). Given that perceived sleep duration may differ from objectively measured sleep duration under the influence of personal characteristics, relying solely on self-reported measures may introduce bias (Fichten et al., 2005).

In this study, we aimed to investigate whether objective sleep duration and sleep quality are associated with cognitive performance in community-dwelling older adults.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

Study participants

A letter containing a description of the study and a request for participation were sent to community-dwelling older adults. The study enrolled 78 consecutive volunteers aged 60 years and over (72.2 ± 5.9 years). None of the participants reported recent reductions in memory or cognitive performance, and none had any impairment in basic or instrumental activities of daily living. Study participants underwent actigraphy, as well as cognitive performance and sleep apnea screening tests, and completed questionnaires. The Chubu University Ethics Review Committee approved all procedures associated with this study. All participants were informed of the study objectives and conditions, and provided written informed consent prior to beginning the study.

Actigraphy

Actigraphy was performed for seven consecutive days for all participants as previously described (Otake et al., 2011). The actigraph (Ambulatory Monitoring, New York, NY, USA) was worn around the wrist of a non-dominant hand and was set to store data in 1-min increments. We analysed actigraphic data using the algorithm supplied by the ActionW-2 clinical sleep analysis software package for Windows (Ambulatory Monitoring). Sleep and activity were scored according to the Cole–Kripke formula. We measured total sleep time (TST), sleep efficiency (calculated as TST/time in bed × 100), sleep latency and wake after sleep onset (WASO).

Cognitive performance tests

Continuous performance test (CPT)

The CPT measures sustained attention and vigilance. We used the CPT-identical pairs version (CPT-IP) software(Biobehavioral Technologies, Inc., NY, USA), as described previously (Cornblatt et al., 1988). A series of four-digit stimuli were presented for a period of 50 ms, with an interstimulus interval (ISI) of 950 ms. Each complete task consisted of 150 trials, of which 30 were target trials requiring a response. In this study, we measured performance by the ratio of correct responses (accuracy).

Number (n)-back test

We used the n-back test to measure working memory capacity using software that requires participants to update their mental set continually while responding to a previously seen stimulus or stimuli (i.e. numbers) using the numeric keypad of the PC, as described previously (Callicott et al., 2003). The stimulus duration was 0.4 s, and the ISI was 1.4 s; each test included 14 trials. We used 0- and 1-back conditions, and measured performance as the percentage of correct responses (accuracy, %).

Questionnaires

Pittsburgh Sleep Quality Index (PSQI)

We evaluated sleep quality using the PSQI, a questionnaire that assesses sleep quality and quantity over a 1-month period (Buysse et al., 1989). The PSQI contains 19 items in seven component domains: subjective sleep quality; sleep latency; sleep duration; habitual sleep efficiency; sleep disturbances; use of sleep medication; and daytime dysfunction. The questionnaire requires the patient to describe sleep patterns, such as typical bedtime and wake time, length of time taken to fall asleep, and actual sleep duration. The patient then answers a series of questions relating to sleep habits and quality. Component scores are based on a four-point Likert scale that ranges from Very Good (0) to Very Bad (3). The component scores are combined to produce the Global Sleep Quality Score ranging from 0 to 27. We considered participants with scores of 6 or greater to be poor sleepers.

Epworth Sleepiness Scale (ESS)

In this test, the participant rates, on a four-point scale, his/her chances of dozing in each of eight different situations that are often encountered in daily life (Johns, 1991). The total ESS score is the sum of all responses, and ranges from 0 to 24. A score of 11 or greater reflects excessive daytime sleepiness.

Sleep apnea screening

We screened for sleep apnea using the portable Apnomonitor 3 (Chest, Tokyo, Japan) with an oronasal thermistor sensor to record airflow, a pulse oximeter to record both oxygen saturation and heart rate, and a snoring sensor. For home recording, participants were instructed on how to wear the equipment and start recording. We calculated the sleep apnea–hypopnea index (AHI) as the total number of apneas and hypopneas divided by the number of hours of artifact-free recording. The lowest oxygen saturation (SpO2) value was also evaluated.

Statistical analysis

All results are presented as mean ± standard deviation (SD). We divided participants into groups based on sleep efficiency, sleep latency, WASO (Blackwell et al., 2011; Otake et al., 2011), and clinical guidelines for the use of unattended portable monitors by the Portable Monitoring Task Force of the American Academy of Sleep Medicine (Collop et al., 2007). Results of cognitive performance tests for TST, sleep latency, WASO and lowest SpO2 groups were compared using one-way analysis of variance (anova) with Bonferroni's test. We compared sleep latency and AHI between the two groups with the non-paired t-test. We performed Pearson's [age, TST, sleep latency, WASO, AHI and 3% desaturation index (DSI)] and Spearman's (sleep efficiency, lowest SpO2, ESS and PSQI) correlation analyses followed by multiple regression analysis to determine the independent parameters correlated with the CPT-IP, 0- or 1-back test. A value of < 0.05 was considered significant. Statistical analyses were performed using IBM SPSS Statistics version 19 (IBM, Chicago, IL, USA).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

Baseline characteristics of participants are shown in Table 1. The accuracy of the 0-back test significantly decreased in participants with TST < 5 h compared with those with TST > 7 h (90.6 ± 12.0% versus 97.9 ± 4.5%, = 0.024), but there was no significant difference in the accuracy of the 1-back test between the two groups (Table 2). Participants with <85% sleep efficiency showed a significant decrease in accuracy compared with those with ≥85% sleep efficiency on both the 0-back (90.0 ± 11.9% versus 97.3 ± 7.7%, = 0.003) and 1-back (51.4 ± 22.3% versus 64.6 ± 25.0%, = 0.017) tests. There was no significant difference in accuracy between the two groups on the CPT-IP (Table 3). Participants with sleep latency ≥30 min showed decreased accuracy on the 0- and 1-back tests compared with participants with <15 min or 15–30 min sleep latency, but this difference was not significant. These groups showed no significant difference in accuracy on the CPT-IP (Table 3). Participants with WASO longer than 30 min tended to have lower accuracy on the 0- and 1-back tests than those with WASO ≤ 5 min or 5–30 min, but these differences were also not significant. There was no significant difference in accuracy on the CPT-IP among the three groups (Table 3).

Table 1. The characteristics, actigraphy and sleep apnea screening data, cognitive performance tests, PSQI and ESS of all subjects
  1. AHI, apnea–hypopnea index; CPT-IP, continuous performance test-identical pairs; DSI, 3% desaturation index; ESS, Epworth Sleepiness Scale; PSQI, Pittsburgh Sleep Quality Index; SpO2, oxygen saturation; TST, total sleep time; WASO, wake after sleep onset.

Age (years)72.2 ± 5.9
Sex: male (%)16 (20.5)
Body mass index (kg m−2)22.4 ± 2.4
Dyslipidemia (n, %)39 (50.0)
Hypertension (n, %)52 (66.7)
Diabetes mellitus (n, %)13 (16.7)
Cardiovascular diseases (n, %)17 (21.8)
Orthopedic problems (n, %)21 (26.9)
Insomnia (n, %)2 (2.6)
Medication (n, %)56 (70.9)
Sleep drugs (n, %)21 (26.6)
Current smoker (n, %)14 (17.7)
Current drinker (n, %)26 (33.3)
TST (min)317.2 ± 105.8
Sleep efficiency (%)83.4 ± 13.0
Sleep latency (min)24.2 ± 35.6
WASO (min)27.6 ± 30.5
AHI (h−1)13.1 ± 11.6
DSI (h−1)3.8 ± 4.1
Lowest SpO2 (%)84.6 ± 5.8
CPT-IP: accuracy0.685 ± 0.221
0-back test: accuracy (%)94.0 ± 10.4
1-back test: accuracy (%)58.5 ± 24.6
PSQI5.9 ± 3.0
ESS5.8 ± 4.2
Table 2. Results of cognitive performance tests and questionnaires in each TST group
 TST
<5 h5–7 h>7 h
  1. CPT-IP, continuous performance test-identical pairs; TST, total sleep time.

  2. a

    < 0.05, versus >7 h.

n 262131
CPT-IP: accuracy0.639 ± 0.2250.677 ± 0.2120.729 ± 0.221
0-back test: accuracy (%)90.6 ± 12.0a92.1 ± 12.797.9 ± 4.5
1-back test: accuracy (%)54.9 ± 22.860.8 ± 26.459.8 ± 25.0
Table 3. Results of cognitive performance tests and questionnaires in each sleep efficiency, sleep latency and WASO group
 Sleep efficiencySleep latencyWASO
≥85%<85%<15 min<30 min≥30 min≤5 min≤30 min>30 min
  1. CPT-IP, continuous performance test-identical pairs version; WASO, wake after sleep onset.

  2. a

    < 0.05.

N 4236461415162622
CPT-IP: accuracy0.687 ± 0.2050.683 ± 0.2420.690 ± 0.2140.655 ± 0.2340.660 ± 0.2450.671 ± 0.2610.671 ± 0.1750.692 ± 0.235
0-back test: accuracy (%)97.3 ± 7.790.0 ± 11.9a94.8 ± 10.793.8 ± 12.291.9 ± 8.093.8 ± 12.595.9 ± 8.692.9 ± 10.3
1-back test: accuracy (%)64.6 ± 25.051.4 ± 22.3a57.0 ± 23.663.3 ± 26.152.9 ± 25.054.5 ± 23.766.2 ± 25.047.7 ± 23.1

There were no significant differences in accuracy on the CPT-IP, 0- and 1-back tests between the two AHI groups. The lowest SpO2 ≥ 90% group tended to have greater accuracy on the CPT-IP than the <80% or 80–90% groups. The lowest SpO2 groups had no significant differences in accuracy on the 0- and 1-back tests (Table 4).

Table 4. Results of cognitive performance tests and questionnaires in AHI and lowest SpO2 groups
 AHILowest SpO2
<15 h−1≥15 h−1<80%<90%≥90%
  1. AHI, apnea–hypopnea index; CPT-IP, continuous performance test-identical pairs; SpO2, oxygen saturation.

n 4830145110
CPT-IP: accuracy0.657 ± 0.2470.731 ± 0.1670.633 ± 0.2440.665 ± 0.2240.837 ± 0.096
0-back test: accuracy (%)93.0 ± 11.995.5 ± 7.496.4 ± 6.193.0 ± 11.895.7 ± 6.9
1-back test: accuracy (%)59.8 ± 25.156.4 ± 23.957.6 ± 28.457.6 ± 24.657.1 ± 17.7

Single correlation analysis revealed that age was significantly related to CPT-IP accuracy (= −0.276, = 0.015). Age, TST and sleep efficiency were significantly correlated with accuracy on the 0-back test (age, = −0.324, < 0.001; TST, = 0.270, = 0.017; sleep efficiency, = 0.354, = 0.001). Age and sleep efficiency were significantly correlated with accuracy on the 1-back test (age, = −0.556, < 0.001; sleep efficiency, = 0.240, = 0.035). Multiple regression analysis showed that TST was independently correlated with accuracy on the 0-back test (β = 0.267, = 0.033), while age was independently correlated with accuracy on the 1-back test (β = −0.468, < 0.001). None of the other factors, such as sleep efficiency, sleep latency, WASO, AHI, DSI, lowest SpO2, ESS and PSQI, showed significant correlations with 0- or 1-back test accuracy (Table 5).

Table 5. Simple and multiple regression analysis
 CPT-IP0-back test1-back test
SimpleMultipleSimpleMultipleSimpleMultiple
r P β P r P β P r P β P
  1. AHI, apnea–hypopnea index; CPT-IP, continuous performance test-identical pairs; DSI, 3% desaturation index; ESS, Epworth Sleepiness Scale; PSQI, Pittsburgh Sleep Quality Index; SpO2, oxygen saturation; TST, total sleep time; WASO, wake after sleep onset.

Age−0.2760.015−0.2740.058−0.324<0.001−0.0930.499−0.556<0.001−0.468<0.001
TST0.1520.1850.2100.1570.2700.0170.2670.0330.1070.3520.0180.885
Sleep efficiency0.0040.972−0.2600.1440.3540.0010.0010.9940.2400.0350.2930.052
Sleep latency0.0620.597−0.0880.586−0.0730.534−0.0720.6430.1180.3130.2520.067
WASO0.0120.925−0.1090.513−0.1320.298−0.0760.639−0.1680.1840.0490.728
AHI0.1370.2310.0750.6440.0940.4130.1860.240−0.0710.5370.0910.505
DSI0.1150.3260.1520.427−0.1430.221−0.3480.064−0.2020.082−0.2060.205
Lowest SpO2−0.1160.321−0.0930.606−0.0290.805−0.1260.4700.0160.895−0.0760.619
ESS0.0220.851−0.0060.968−0.0500.662−0.0610.6380.2480.029−0.0850.454
PSQI0.0930.4180.0660.6240.0850.4600.1720.202−0.0120.9190.1510.197

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

We found that short sleep duration and diminished sleep efficiency decreased memory capacity, while AHI and lowest SpO2 did not influence memory-back performances in our participants. Sleep duration was independently associated with memory. Short sleep duration and poor sleep quality might have adverse effects on neural bases of memory in older adults.

Sleep duration, aging and memory

Total sleep time was correlated with 0-back test accuracy, while age was correlated with 1-back test accuracy in this study. This difference could be interpreted as a task characteristic: the 0-back test reflects simple attention and short-term memory; and the 1-back test reflects working memory capacity, in particular, executive function (Callicott et al., 2003; Craik, 2002). In general, working memory tasks significantly decline with normal aging (Dobbs and Rule, 1989). Middle-aged and older adults perform more poorly than younger adults on working memory tests (Reuter-Lorenz and Lustig, 2005), and there are load-dependent age effects (Jaeggi et al., 2009). The 1-back test requires a heavier mental workload of working memory than the 0-back test. According to increase in mental workload of the n-back test, age may have a stronger effect on the cognitive state (0-back test: accuracy 94.0 ± 10.4% versus 1-back test: accuracy 58.5 ± 24.6%; Table 1). Further evaluation is needed to clarify these points.

Sleep quality and cognitive performance

We showed that objective sleep duration and sleep efficiency were related to cognitive performance decline in older adults. Sleep quality was significantly associated with poor cognitive performance as measured using the Mini-Mental State Examination and Trail Making Test part B in older women (Blackwell et al., 2006). Sleep problems are related to quality of life in older adults, and sleep quantity and quality tend to decrease with age (Cirelli, 2012). In older women, good (PSQI < 6) and poor (PSQI ≥ 6) sleepers significantly differ on tests of working memory, attentional set shifting and abstract problem solving (Nebes et al., 2009). Motor-sequence learning, but not verbal declarative memory, displays loss of sleep-dependent consolidation with aging. Improving sleep through behavioral or pharmacological treatments may enhance cognition and performance in older adults (Pace-Schott and Spencer, 2011). Our findings suggest that poor sleep quality may be a marker for memory impairment. Sleep evaluation using actigraphy in older adults provides important information for the early detection and prevention of sleep-related cognitive impairment.

Sleep and sustained attention

We did not observe a relationship between sleep duration or sleep quality and sustained attention. Sustained attention is impaired by excessive daytime sleepiness in sleep disorders and sleep-disordered breathing (SDB) with hypersomnia (Van Schie et al., 2012). In our study, the ESS of most participants was within normal limits (mean ESS, 5.8 ± 4.2). Further studies involving older adults with moderate to severe SDB with hypersomnia will help clarify the relation between sleep and sustained attention.

Sleep apnea and cognitive performance

Reports on the relationship between SDB and cognitive performance have been inconsistent. Indeed, SDB impaired cognitive performance in older adults in some studies (Yaffe et al., 2011), while it did not in others (Sforza et al., 2010). One prospective study showed that older women with SDB were more likely to develop mild cognitive impairment or dementia (Yaffe et al., 2011). However, a cross-sectional study found that the impact of undiagnosed SDB on cognitive performance was limited in generally healthy older adults, and only slightly affected severe cases (Sforza et al., 2010). In our study, SDB was not independently correlated with cognitive performance. Older adults with AHI ≥ 15 h−1 or lowest SpO2 < 90% did not show significant differences in cognitive performance compared with those with AHI < 15 h−1 or lowest SpO2 ≥ 90%. SDB included both patients with middle-age- and elderly-onset SDB. These two patient types show different clinical characteristics. Elderly-onset obstructive sleep apnea syndrome (OSAS) may have a smaller impact on physiological changes associated with OSAS than middle-age-onset OSAS (Bliwise, 2011). In a previous study, we observed differences in electroencephalographic and cardiac arousal, and the pattern of SDB between middle-aged and older patients with SDB (Noda et al., 1995, 2000). Age-dependent increases in the incidence of SDB may not directly influence the hemodynamics leading to severe cardiovascular complications or cognitive dysfunction (Noda et al., 1998). We evaluated sleep apnea with portable home monitoring systems in this study. Polysomnographic measurements may provide a pathophysiological explanation for the connection between sleep parameters, including arousal and cognitive performance, in older adults.

Limitations

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References

Although our results suggest that short sleep duration impairs memory, our study included a high percentage of female participants. In aged populations, the prevalence of mild cognitive impairment is higher in men than women (Petersen et al., 2010). We showed that TST independently impaired short-term memory, but it remains unclear why TST did not affect working memory. More complex information processing that includes short-term memory may explain the differences in working n-back task results. Given the cross-sectional design, future studies will be needed to confirm the causal relationship between sleep-related factors and cognitive impairment, and include an adequate number of male participants in prospective cohorts.

In conclusion, our findings suggest that short sleep duration is associated with decreased memory performance. Sleep might play an important role in individual differences in cognitive performance in older adults.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Acknowledgement
  9. Conflict of Interest
  10. References
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