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. Author manuscript; available in PMC: 2022 Jul 16.
Published before final editing as: West J Nurs Res. 2021 Jan 16:193945920986907. doi: 10.1177/0193945920986907

Sleep, Mild Cognitive Impairment, and Interventions for Sleep Improvement: An Integrative Review

Maral Torossian 1, Sarah Marie Fiske 2, Cynthia S Jacelon 3
PMCID: PMC8282804  NIHMSID: NIHMS1663987  PMID: 33455559

Abstract

Sleep disturbance in mild cognitive impairment (MCI) is associated with progression to Alzheimer’s Disease (AD), more severe AD symptoms, and worse health outcomes. The aim of this review was to examine the relationship between sleep and MCI, and the effectiveness of sleep improvement interventions for older adults with MCI or AD. An integrative review was conducted using four databases, and findings were analyzed using an iterative process. Findings from 24 studies showed that alterations in sleep increased the risk of MCI, and that the sleep quality of individuals with MCI or AD was poorer than healthy controls. Changes in brain anatomy were also observed in healthy older adults with sleep disturbances. Examined interventions were shown to be effective in improving sleep. Screening for sleep disturbances in individuals with MCI/AD is crucial to mitigate neurodegenerative or neurobehavioral risks in this population.

Keywords: sleep quality, sleep disturbances, sleep interventions, mild cognitive impairment (MCI), Alzheimer’s Disease, cognitive impairment, older adults

Background

Mild cognitive impairment (MCI) affects up to 20% of community-dwelling older individuals (Alzheimer’s Association, 2020), and is characterized by changes in memory and cognition. These changes are beyond those associated with “normal” aging, but not to the extent where daily activities are disrupted (Kirova et al., 2015). Changes in sleep occur during normal aging (Cooke & Ancoli-Israel, 2011; Djonlagic et al., 2018), but sleep disruptions are more frequent in individuals with MCI (Vaz Fragoso & Gill, 2007), and include alterations in the onset, efficiency, and duration of sleep (Hu et al., 2017). Understanding the relationship between sleep and MCI could lead to strategies that minimize manifestations of existing cognitive changes and slow cognitive decline (Cipriani et al., 2015; Peter-Derex et al., 2015) in these individuals. However, the relationship between sleep and MCI has not been systematically explored.

Researchers have proposed a continuum of behaviors from normal aging through MCI to Alzheimer’s disease (AD) (Petersen, 2004). Although MCI and AD have distinct diagnostic criteria, they share many similar challenges for self-management. Studies have shown that individuals with MCI progress to a diagnosis of dementia at a higher rate than normal peers (Pistacchi et al., 2014). MCI can be categorized into two subtypes: amnestic MCI (aMCI) – predominant impairment in memory, and non-amnestic MCI (naMCI) (Csukly et al., 2016; Grundman et al., 2004). A number of interventions including occupational therapy, cognitive training, communication strategies, physical activity, and others have been examined for their effectiveness in slowing down the progression of MCI (da Cruz Morello et al., 2017; Dias, 2018; Liang et al., 2018).

Poor sleep quality is a common, underreported problem in older individuals (Sharifi et al., 2019), as it is often considered to be a normal part of aging. Changes in sleep patterns can lead to tiredness, fatigue, and additional cognitive decline (Hu et al., 2017), as well as alterations in safety, and well-being (Dean et al., 2017; Djonlagic et al., 2018; Gooneratne & Vitiello, 2014; Peng et al., 2019). The National Sleep Foundation recommends 7–8 hours of sleep for individuals aged 65 or older for optimal mental and physical health (National Sleep Foundation, 2020). However, 40–70% of older adults do not meet the recommended sleep duration daily (Miner & Kryger, 2017). Common changes in sleep include increased sleep latency (time required to fall asleep), increased time awake after falling asleep, and an increase in number of arousals throughout the night.

Addressing sleep problems in individuals with MCI is crucial. Sleep disturbance in MCI is associated with an increased risk of progression to AD, more severe AD symptoms, poor physical function, mental health, quality of life, and alterations in subjective well-being. Sleep disturbances also contribute to an increased risk for falls caregiver burden, institutionalization, and mortality risk (Miner & Kryger, 2017; Sharifi et al., 2019; Tan et al., 2018). Despite its significance, only a few systematic reviews that investigate the relationship between sleep and MCI have been conducted. In addition, these reviews have been restricted to studies measuring sleep using polysomnography (D’Rozario et al., 2020), or to case-control designs (Hu et al., 2017), or have been limited in scope given the limited keywords used, the search of only one database, and the restricted publication range of the (January 2016 – October 2017) of the included articles (Naismith & Mowszowski, 2018).

Purpose

Thus, the first aim of this integrative review was to examine the relationship between sleep (duration, quality, fragmentation) and MCI in studies using subjective and objective measures, without restriction on study methodology, and across disciplines. The second aim was to examine the effectiveness of interventions for sleep improvement in individuals with MCI or AD. Our review was guided by the following research questions: (1) What is the relationship between sleep characteristics and qualities and MCI? (2) Are the interventions aimed at improving sleep in this population effective?

Methods

Search Strategies

To answer these research questions, the authors searched for studies in CINAHL, PubMed, Web of Science, and Psych Info using the following search strategy: “MCI or mild cognitive impairment or early Alzheimer’s or early dementia” (title), “Sleep or sleep intervention” (title), and “Relationship or impact or effect” (title/abstract/topic), separated by Boolean operator “AND”. Articles were restricted to English language, without any year limitations. All articles obtained by the above-mentioned search strategy were exported to a citation manager (RefWorks), where duplicates were removed, and articles retained were screened by titles and abstracts.

Inclusion Criteria

Articles were included if they met the following inclusion criteria: English language, examined the relationship between MCI and sleep, or between sleep and common MCI symptoms (cognitive function, behavioral symptoms, functional limitation), included interventions targeted at improving sleep in individuals with MCI or AD, and involved human subjects. Studies were excluded if they examined sleep in relation to a symptom not related to MCI or AD (respiratory, gastrointestinal), or focused on specific sleep disorders (sleep apnea, sleep-disordered breathing), included animal subjects, were secondary sources, or examined sleep as a mediator and not as a primary variable.

Data Management

The authors performed an integrative review on evidence relating to sleep and MCI. An integrative review is a broad type of research review method followed when included articles include both experimental and non-experimental designs. Because of the heterogeneity in study designs, meta-analysis was not possible. Instead, the authors analyzed findings through an iterative process, (similar to the coding undertaken in qualitative studies), following these four phases: data reduction, data display (matrix), data comparison, and data presentation (Whittemore & Knafl, 2005). Articles included were organized into a matrix (data reduction phase) as suggested by Garrard (2017). Articles included in the review were examined for rigor (high/low). Rigor was ranked “high” if the article met four criteria: design was appropriate for research question, methodology was clearly described, valid and reliable measures were used, and researchers were transparent about limitations/threats to validity (Whittemore & Knafl, 2005). The authors followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline, and used PRISMA flowchart to summarize the steps taken throughout the search process (Moher et al., 2015; Shamseer et al., 2015) (Figure 1).

Figure 1.

Figure 1.

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of the articles included in the review.

Results

The search strategy yielded a total of 113 articles across databases. Sixty-seven articles were excluded for being duplicates. The titles and abstracts of 46 articles were read to assess for eligibility. Twenty-two did not meet inclusion criteria. The authors independently re-read the titles and abstracts of these articles, to confirm that these articles were appropriately excluded. The final number of articles included in the review was 25 (Figure 1).

Study designs of reviewed articles included 15 cross-sectional, three (n=3) randomized control trials, three (n=3) quasi-experimental, one (n=1) retrospective, and two (n=2) longitudinal designs. The participants for 13 studies included individuals with MCI/AD and a control comprised of cognitively intact individuals, six studies included individuals with MCI/AD only (n=6), while five studies enrolled healthy older adults (n=5) to examine changes in cognition and risk of MCI over time. Twenty-one articles addressed the first question of the review, while three addressed the second. Results are presented below based on the themes identified in the review and include: sleep characteristics in MCI/AD, sleep and risk of developing MCI, sleep and biological markers of MCI/AD, sleep and clinical outcomes of MCI/AD, and interventions to improve sleep. Risk of bias, as well as limitations of studies are discussed.

Sleep Characteristics in MCI/AD

Phases of Sleep

The sleep cycle consists of Non-Rapid Eye Movement (NREM) and Rapid-Eye Movement (REM) sleep. NREM consists of three stages of sleep, stage 3 being the deepest stage, also known as slow wave sleep (SWS), which is known to play a role in cerebral restoration and memory processing (Iber et al., 2007). NREM is characterized by slow wave activity (SWA), sleep spindles, and K-complexes (KC), which are long delta waves that last up to one second (American Sleep Association, 2020). REM, on the other hand, accounts for 25% of sleep in humans, is characterized by increased rate and variability of autonomic function, and is believed to play an important role in memory and attention (American Sleep Association, 2020; McGrath & Cohen, 1978).

Both REM and KCs were parameters investigated in four studies examining polysomnography (PSG)-recorded sleep in individuals with MCI and AD. Individuals with MCI and aMCI had significantly shorter REM sleep (F1,47=8.1, p<0.007), and a significantly higher disruption in SWS (F1,47=9.8, p<0.003) compared to healthy controls (Hita-Yanez et al., 2012; Sanchez-Espinosa et al., 2014). In addition, REM sleep reduction was more prominent in APOE ε4 (a genetic marker for AD) carriers than non-carriers (Hita-Yanez et al., 2012). This was contrary to findings of another study, in which there was no difference in REM percentage and REM latency between those with aMCI and healthy controls (Cavuoto et al., 2019). Subjects with AD also had significantly shorter REM sleep (18.4%), as well as a higher proportion of non-REM light sleep compared to healthy controls (25.2%) and subjects with MCI (19.3%), with no difference in REM sleep latency among the three groups (Tadokoro et al., 2020). Regarding KCs, individuals with AD had a lower density of KC compared to healthy counterparts (t=3.7, p<0.01) and to those with MCI (t=3.42, p<0.05). However, there were no differences in densities of KCs in individuals with MCI compared to healthy counterparts (Reda et al., 2017). Despite inconsistencies, most studies supported that REM sleep was shorter in individuals with MCI and AD, suggesting a decrease in proportion of deep sleep. Also, individuals with AD had a shorter KC density, which was not the case in MCI. The latter group experienced more SWS disruption.

Objective vs. Subjective Sleep Measures

There was consensus across articles using objective sleep measures such as PSG, sensors, and actigraphy, that total sleep time did not significantly vary between healthy older adults and those with MCI or AD, which ranged between 6.5 and 8.5 hours among participants from various studies (Cavuoto et al., 2019; Hayes et al., 2014; Hita-Yanez et al., 2012; Sanchez-Espinosa et al., 2014; Tadokoro et al., 2020). However, the findings of one study (Yu et al., 2017) in which researchers measured sleep using the Pittsburgh Sleep Quality Index (PSQI) contradicted this consensus. In this study, individuals with MCI had significantly longer sleep durations compared to controls. Thus, there were discrepancies between objective and subjective sleep measures in this population, which suggests the importance of incorporating both measures when examining sleep in individuals with MCI or AD.

Although there was consensus regarding overall sleep time with the exception of one study, findings in terms of objective, PSG-recorded sleep measures varied. In one study, individuals with aMCI experienced more waking after sleep onset (WASO; 65.06 min) compared to healthy controls (48.41 min; Cavuoto et al., 2019). Note that, the difference in WASO did not lead to a significant difference in total sleep duration between both groups (healthy controls: 6h52min vs aMCI: 7h21min). Findings in other studies, however, showed that WASO was not significantly different between groups (Hita-Yanez et al., 2012), or was even significantly less in individuals with aMCI (F2,1078=41.6, p<0.001; Hayes et al., 2014). Similar to the study by Cavuoto et al. (2019), this difference in WASO did not result in a significantly different total sleep time (control: 8.34 hours vs aMCI: 8.50). Actigraphy recordings, on the other hand, showed that individuals with aMCI spent longer time spent in bed, and experienced more sleep fragmentation compared to healthy controls, with moderate to large effect sizes (d= 0.76 & d=1.03 respectively; Cavuoto et al., 2019).

While objective measures of sleep including onset latency, duration, and efficiency (Cavuoto et al., 2019) were not significantly different between those with aMCI and healthy controls, subjective reports of sleep varied across studies. Subjective reports of sleep onset latency (SOL), sleep time, nocturnal awakenings, ability to fall asleep after awakening, as well as overall sleep quality in individuals with MCI in two studies were significantly worse compared to self-reports of healthy older adults (Hita-Yanez et al., 2012; Yu et al., 2017). Note that, PSG recordings of the same sample in one of the two studies did not significantly correlate with any of the self-reports of sleep provided by MCI subjects (Hita-Yanez et al., 2013). The case was similar in a study involving subjects with AD and MCI, whereby subjective and objective sleep measures did not align: subjects with AD reported a significantly better sleep quality (higher PSQI scores), compared to healthy subjects and those with MCI, with no significant differences in reports of daytime sleepiness between the three groups (Tadokoro et al., 2020). However, objective sleep measures showed worse sleep measures in individuals with AD compared to the other two groups (Tadokoro et al., 2020). On the other hand, in another study, self-reported sleep quality was not significantly different between individuals with MCI, AD, and healthy controls. Note that, among the three groups, controls had a significantly lower intake of sleep medications, which could have improved the sleep qualities in individuals with AD and MCI (Rozzini et al., 2018).

Sleep efficiency in older adults diagnosed with MCI or aMCI as compared to normal controls varied across studies. In two studies, sleep efficiency in this population was similar to that of healthy controls (Cavuoto et al., 2019; Sanchez-Espinosa et al., 2014). However, in one study, individuals with MCI reported fewer awakenings at night compared to those without MCI (F2,1078=26.7, p<0.001; Hayes et al., 2014), and in a third study the sleep efficiency of individuals with MCI was worse compared to controls (Yu et al., 2017). In another study, subjects with AD reported significantly worse sleep efficiency compared to normal counterparts (t= −2.09, p< 0.05) (Hot et al., 2011). Interestingly, however, all other sleep measures, such as total sleep time, REM, and SWS were preserved (Hot et al., 2011).

To summarize, evidence suggested that there were discrepancies in findings within and between studies. Subjective reports (sleep efficiency, awakenings), as well as objective reports (WASO, sleep fragmentation), among studies were inconsistent. In addition, subjective and objective sleep measures within the same study did not align. Hence, triangulation of sleep measures is warranted, with a detailed analysis and explanation of discrepancies whenever noted.

Amnestic vs. Non-amnestic MCI

In a sub-group analysis of individuals with MCI with an average age of 75.4, 73% of individuals with good sleep quality - a score of ≤5 on the PSQI - had aMCI, while 54% of those with poor sleep quality - a score of 5 or more on PSQI- had naMCI (Rozzini et al., 2018). However, in another study, those with naMCI (mean age: 69.8) had significantly worse PSQI total and sleep disturbance scores compared to controls (t=2.03, p<0.0.3), but not to individuals with aMCI (mean age: 67.7) (Seidel et al., 2015). It might be that the difference in sleep quality between individuals with naMCI and aMCI becomes more pronounced with the advance in age. Note that, similar to the previous study, both naMCI and aMCI groups had a significantly higher use of sleep medications compared to controls, which might have affected PSQI sleep scores.

Sleep and Risk of Developing MCI/AD

Sleep disturbances may be related to the risk of developing MCI. Burke and colleagues (2018) found that individuals who experienced sleep disturbances had 1.39 times higher risk of developing MCI compared to individuals without sleep disturbances. This risk remained significant even after controlling for APOE ε4 genotype, and sleep medication use (Burke et al., 2018). A similar finding was also reported in a study including women without MCI at enrollment, in which participants wore an actigraphy for three days at baseline, and underwent a comprehensive cognitive assessment five years later (follow-up). Those who were in the lowest quartile of sleep efficiency (< 74%) had 1.53 times higher risk of developing MCI compared to their counterparts who were in the highest quartile (>86%) even after adjusting for multiple confounders (age, depression, exercise, self-reported health status, etc.). Besides actual sleep efficiency, variability in sleep efficiency as well as in total sleep time increased risk of MCI (OR=1.9 & OR=1.4 respectively) (Diem et al., 2016).

However, both studies had limitations. Findings of the former study (Burke et al., 2018) were based on one item related to sleep in the Neuropsychiatric Inventory Questionnaire (NPI-Q), which is not a tool used primarily for sleep measurement. In the latter study, risk of MCI was based on sleep patterns examined five years prior to the cognitive examination, which imposes a threat to reliability of findings of MCI due to long time interval.

Sleep and Biological Markers of MCI/AD

Several measures were used to investigate the association between sleep and biomarkers of MCI and AD. Biomarkers for these studies included amyloid beta (Aβ), total tau, and phosphorylated tau (p-tau), levels in the brain (through PET imaging), cerebrospinal fluid (CFS), and blood plasma. The relationship between sleep and changes in MCI- or AD-related anatomical structures of the brain were also examined.

PET Imaging Biomarkers

Researchers examined the association between sleep parameters, primarily NREM sleep slow wave activity (SWA), and AD biomarkers in the brain. Based on PET imaging, all-night NREM SWA decreased with an increase in tau composite in the brain (F1,27=11.46, p=0.002), specifically in the entorhinal, parahippocampal, orbital frontal, precuneus, inferior parietal, and inferior temporal regions. For Aβ composite, however, this inverse relationship was significant only for 1–2 Hz NREM SWA (F1, 28=4.48, p=0.043) (Lucey et al., 2019).

Other sleep parameters measured in the study included total sleep time (TST), sleep latency, REM onset, number of arousals, WASO, and time in each stage. Sleep latency and REM onset latency measured by electroencephalography (EEG) were significantly and positively correlated with PET Aβ load (F1,29 =4.4, p=0.045 & F1,30 =12.5, p=0.001 respectively). However, none of the self-reported parameters were associated with Aβ load. Of the measured sleep parameters, only total sleep time (TST; F1,28 = 5.99, p=0.021) and self-reported napping (F1,27 = 9.28, p=0.005) were positively associated with tau pathology (Lucey et al., 2019).

CSF Biomarkers

A number of biomarkers in the CSF, such as amyloid beta (Aβ42), tau, p-tau, have been associated with AD. In the same study (Lucey et al., 2019), tau/Aβ42 (F1,12=5.33, p=0.04) and p-tau/Aβ42 (F1,11=5.73, p=0.035), but not Aβ42, were inversely related to all-night NREM SWA. (Lucey et al., 2019). None of the other sleep parameters showed any association with CSF tau/Aβ42 (Lucey et al., 2019).

Plasma Aβ Concentrations

A number of sleep parameters were examined in a study, including, but not limited to, total sleep time, sleep latency, sleep efficiency, SWS, and REM (Sanchez-Espinosa et al., 2014). SWS was significantly correlated with Aβ42 (r=0.57, p=0.02) in subjects with aMCI, but not in controls. No other parameter was associated with plasma Aβ42 in any of the groups (Sanchez-Espinosa et al., 2014).

Changes in Brain Anatomy

Healthy individuals with a PSQI score of 5 or more (poor sleepers), had significantly lower cortical volumes in aMCI-related regions, including the left amygdala, hippocampi, and bilateral parietal lobules at FDR=0.1, compared to those with a better sleep quality (PSQI scores ≤5) (Alperin et al., 2019). In terms of cortical thinning, “poor sleepers” had cortical thinning in the right superior frontal gyrus, frontal pole, and medial orbitofrontal cortex, which are also aMCI-related regions of interest. In addition, shorter REM sleep in subjects with MCI was significantly associated with thinner posterior cingulate, left precuneus, as well as bilateral post-central gyri (Sanchez-Espinosa et al., 2014). Note that, some naMCI-related regions also varied between the two groups. The left insular cortex and left middle temporal gyrus had less volume in poor sleepers, while the right fusiform gyrus, the left supramarginal gyrus, and the bilateral insular cortices were significantly thinner in poor sleepers (Alperin et al., 2019).

Moreover, the severity of sleep disturbances in older adults with MCI were negatively correlated with the volume of the left amygdala (r= −0.525, p<0.001) and left hippocampus (r= −0.491, p=0.001). It is noteworthy, however, that the main purpose of this study was to examine the association between mood and anxiety-related neuropsychiatric symptoms and reduced medial temporal volume (Yuen et al., 2019). Accordingly, sleep was measured using the NPI-Q, in which sleep comprises only one item. Hence, these findings should be interpreted with caution.

Sleep Disturbances and Clinical Outcomes of MCI

The relationship between sleep disturbances and clinical outcomes, both cognitive and behavioral, was examined in a number of studies. There was consensus that worse sleep was associated with poorer immediate memory, delayed recall, and worse cognitive function. Sleep also had a significant association with behavioral symptoms commonly manifested in MCI and AD. Findings from the studies in the literature review are presented below.

Cognitive Outcomes

SWS arousal and REM sleep percentage did not predict cognitive function in individuals with aMCI or healthy controls. However, sub-analysis of subjects with aMCI showed that REM sleep predicted immediate memory in APOE ε4 carriers (Hita-Yanez et al., 2012), but not in APOE ε4 non-carriers. In addition, greater variability in time allotted to sleep in subjects with aMCI was associated with lower scores on memory recall (r= −0.48, p< 0.05; Westerberg et al., 2010). In subjects with AD, a faster theta frequency during SWS was associated with better delayed cued recall (r=0.64, p<0.05; Hot et al., 2011). Furthermore, KC density was significantly decreased in the AD group compared to those with MCI and healthy controls, and was positively correlated with mini-mental state examination (MMSE) scores (r=0.38, p=0.003), suggesting that cognitive status worsens with a decline in KC density (Reda et al., 2017).

Behavioral Outcomes

In the early stages of AD (Clinical Dementia Rating = 0.5), those with sleep disturbances had significantly more prevalent behavioral and psychological symptoms in dementia (BPSD), including anxiety, euphoria, disinhibitions, and aberrant motor behaviors (X2= 10.62, X2=24.69, X2= 18.95, and X2= 10.85 respectively, p<0.05) compared to those without sleep disturbances (Kabeshita et al., 2017). Sleep disturbances also predicted BPSD at the early stages of AD (β= 0.32, p<0.001), but not this did not hold true for those in more advanced stages (Kabeshita et al., 2017).

In terms of the relationship between sleep and depression, findings among studies did not align. In one study, sleep was significantly correlated with (r=0.412, p<0.001), and predicted depression (β=0.39, p<0.001) in individuals with MCI (McKinnon et al., 2019). This relationship was bidirectional, as depression was also a factor contributing to worse sleep quality. This was supported in a study in which individuals with MCI who had past or current major depression (MD) had significantly worse scores on total PSQI scores, and particularly PSQI items on sleep quality, latency, efficiency, and daytime dysfunction, compared to subjects with MCI without depression, and to healthy individuals (Naismith et al., 2011). However, in another study on subjects with aMCI, sleep measures such as arousal and WASO were not significantly correlated with depression, despite using the same depression tools as the former study (Geriatric Depression Scale; Naismith et al., 2010). The latter study had a sample size of n=15, which would explain insignificant findings. Another explanation could be the use of different sleep measures (PSQI vs WASO), whereby worse subjective rather than objects reports of sleep quality tend to be pronounced in individuals with depression. Demographics such as age, gender, and years of education are unlikely to be reason behind the difference in findings, as these characteristics were similar between samples of both studies.

Interventions to Improve Sleep

Interventions for sleep improvement may slow the progression from MCI to AD, and help individuals self-manage their health and well-being. Four research studies in this review examined the effectiveness of various sleep interventions in individuals with MCI and AD. Findings showed promising results, and are presented below.

In a randomized control trial, researchers examined the effectiveness of a 20-week physical activity program on sleep and cognitive outcomes in individuals with MCI (Bademli et al., 2018). The program consisted of rhythmic exercises for 20 minutes, and free walking for 40 minutes. Findings in this study indicated that, after the completion of the physical activity program, individuals in the experimental group had a significantly better sleep quality (lower PSQI scores) compared to controls (U=2.45, p<0.001). Not only did the program improve sleep, but also cognitive function (U=3.25, p=0.001). In another randomized control trial involving sleep hygiene education, physical activity, and bright light therapy (Falck et al., 2020), older individuals in the intervention group reported a significantly better subjective sleep quality (measured using PSQI) compared to those in the control group. However, objective sleep measures (sleep efficiency, duration, fragmentation, WASO, and sleep latency) were not significantly different between the two groups.

“Sleep Well Think Well” (SWTW) group program, was another intervention which was examined in terms of effectiveness in improving sleep quality in individuals with MCI. The program used cognitive behavioral therapy (CBT)-derived principles, and consisted of face-to-face meetings and phone calls between participants and psychologists with expertise in sleep. Participants were given sleep-related educational materials (Naismith et al., 2019). Individuals in the SWTW group had significantly improved sleep quality (lower PSQI scores) at the end of the intervention as compared to controls (F=5.6, p-0.023), with a large effect size (d=0.83). However, other sleep measures, such as daytime sleepiness, TST, WASO, and sleep efficiency did not differ between the two groups.

In another study, sleep medications “Zolpidem” and “Trazodone” eliminated the increased risk of MCI in individuals experiencing sleep disruptions (Burke et al., 2018). This effect was observed in APOE ε4 carriers as well, whereby those not using these sleep medications had a higher hazard for MCI compared to non-carriers. This did not hold true in APOE ε4 carriers who used trazodone and zolpidem (Burke et al., 2018). Note that, both sleep measures and medication use were self-reported, and this was a secondary data analysis. Hence, there was a substantial risk of recall bias, and causality cannot be established.

Lastly, Papalambros et al. (2019), investigated the effectiveness of acoustic stimulation in improving sleep in individuals with aMCI. This intervention increased sleep onset by ~15%, and SWA by >10%, which, in turn, was associated with improved overnight word recall (r=0.78, p<0.012) (Papalambros et al., 2019). However, similar to the results of the SWTW intervention, other sleep measures (TST, sleep efficiency, sleep latency, etc.) did not significantly vary between real and sham stimulation groups.

Discussion

This integrative review was the first of which we are aware to examine the relationship between sleep and MCI in studies using both subjective and objective measures, and the effectiveness of sleep interventions in individuals with MCI or AD. Findings of the review suggest that individuals with MCI or AD experience changes in sleep characteristics and cycles, including shorter REM sleep, longer NREM light sleep, and disruption of SWS, with no changes in REM latency, and total sleep time. In addition, individuals with MCI and aMCI in these studies reported poor sleep efficiency, sleep onset latency, nocturnal awakenings, and poor sleep quality. Findings were contradictory regarding changes in WASO, sleep fragmentation, and sleep efficiency. Also, objective measures within studies did not align with self-reports of participants (Hita-Yanez et al., 2013; Tadokoro et al., 2020). The relationship between sleep and depression was also contradictory. There was consensus, however, that sleep disturbance was significantly associated with memory impairment in APOE ε4 carriers, and worse BPSD in the early stages of AD. Sleep disturbances increased the risk of MCI; this was supported by elevated AD biomarkers (tau, Aβ) and changes in MCI-related regions in the brain.

Regarding the second research question, interventions involving physical activity, CBT, and acoustic stimulation were shown to be effective in improving sleep. Besides, use of sleep medications was shown to minimize risk of MCI in those with sleep disturbances. Thus, more research is needed to validate these findings, and examine the impact of combining more than one of these interventions on different sleep measures.

The contradiction between subjective and objective sleep measures within studies (Hita-Yanez et al., 2013; Tadokoro et al., 2020) highlights the importance of incorporating both subjective and objective measures when examining a complex phenomenon like sleep in older adults with cognitive impairment. Also, confounders such as use of sleep medication (Rozzini et al., 2018; Seidel et al., 2015), stress, anxiety, educational background, comorbidity, and existing sleep disorders (Hot et al., 2011) were not considered when examining differences in sleep measures between individuals with MCI/AD and healthy controls. Lastly, sleep in some studies was measured using tools (NPI) that are not intended for sleep measurement (Kabeshita et al., 2017), or using PSG recordings of one night, which does not constitute a sleep pattern (Hita-Yanez et al., 2012; Reda et al., 2017). These limitations may have limited the validity of the findings.

Thus, future research that examines the above-mentioned inconsistencies, addresses the limitations identified in these studies, and validates findings in RCTs is recommended. Also, a number of studies measured sleep using PSG in a lab, or in a controlled setting. This highlights the need for further research to validate findings in different settings, including homes, hospitals, and long-term care facilities, and in a more diverse population, to ensure generalizability of findings. Particularly, examining sleep in a population with MCI/AD and comorbidities would be valuable to understand the interplay between comorbidities and sleep in individuals with MCI/AD, which was not addressed in any of the studies included in the integrative review. Finally, given the promising findings of sleep medications in improving sleep, and the vulnerability of older adults to their side effects (Burke et al., 2018), more research is needed to establish “best practices” for their use such that risks do not outweigh benefits. Researchers must therefore refer to the Beers criteria medication list when prescribing medications to older adults, as in many cases, medications affective for younger adults may be inappropriate for the geriatric population (Duke Clinical Research Institute, 2020).

In addition to their relevance to researchers interested in this area, findings of this review are of value to health care providers (HCPs) including nurses, physicians, psychologists, neurologists caring for individuals with MCI/AD. Our results highlight the importance of screening for sleep disturbances in individuals with MCI/AD, or those genetically predisposed to AD, in order to prevent or mitigate memory problems and BPSD in this population. Monitoring sleep quality amongst individuals with MCI may not only be a helpful way to monitor for the development of AD, but also a non-invasive way in which healthcare professionals may screen for the development of AD early on (Lucey et al., 2019), as sleep moderates the relationship between AD biomarkers (Aβ) and memory. In other words, improving the sleep of individuals with early AD can help improve their memory (Wilckens et al., 2018). In addition, HCPs are encouraged to recommend physical activity programs, CBT, and acoustic stimulation to individuals with MCI/AD experiencing sleep disturbances. Regarding sleep medications, HCPs should carefully weigh the risks and benefits prior to prescribing them, and closely monitor individuals for any side effects.

A limitation of this review is the exclusion of grey literature, which could have resulted in publication bias, and excluded pertinent research studies. Also, it is noteworthy mentioning that MCI and AD have different etiologies despite sharing similar characteristics, and not all individuals with MCI progress to AD. However, the strengths of the review lie in the number of searched databases (n=4), the clear and explicit research questions, search strategy, and the inclusion/exclusion criteria of articles. The authors also followed the PRISMA reporting guideline, provided a flow chart of study selection, a list of included studies, and clearly described the criteria used for quality appraisal of individual studies. Moreover, study selection and quality appraisal were conducted separately by two of the authors, which further enhances rigor.

This review examined the relationship between sleep and MCI/AD, and the effectiveness of interventions targeted at sleep improvement in this population. Findings indicated that several sleep characteristics are altered in this population, and that sleep disturbances increase one’s risk of developing MCI/AD. Four interventions for sleep improvement were discussed, all of which showed promising findings. Further research is recommended to validate and generalize findings, and address inconsistencies in findings. Findings of this integrative review are of value to health care providers across disciplines, who are well positioned to detect sleep disturbance early on, and make needed referrals for sleep improvement, to delay or prevent undesired outcomes of MCI/AD.

Acknowledgments

Funding Statement: Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number P20NR016599. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of Conflicting Interests

The authors declare that there is no conflict of interest.

Contributor Information

Maral Torossian, University of Massachusetts Amherst.

Sarah Marie Fiske, University of Massachusetts Amherst.

Cynthia S. Jacelon, University of Massachusetts Amherst.

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