Participants aged ≥55 years were recruited from the community (eg, newspaper ads and health fairs) and completed phone interview screenings including a brief medical review, the telephone interview for cognitive status (TICS) [32], and the Modified Clinical Dementia Rating [33]. Participants were excluded from the study if they obtained a TICS score of ≤27 (the equivalent of a Mini-Mental State Examination of 24) [32] and a Modified Clinical Dementia Rating score >0, which would indicate cognitive impairment. Individuals with diagnosed sleep disorders (eg, chronic insomnia and sleep apnea) and current use of sleep medications or aids (eg, zolpidem and doxepin) were also excluded. Self-report of minor sleep complaints (eg, occasional difficulty falling asleep, staying asleep, or waking too early) were not considered exclusionary criteria, as these subthreshold sleep complaints are common in older adults and reflect normal sleep in a cognitively healthy population [3,4]. In addition, participants were screened for depression and excluded if they scored >10 on the Geriatric Depression Scale-Short Form [34], as well as other cognitive domains, including attention, verbal memory, language, and executive functioning (Table 1), to determine a cognitively healthy participant group.

In this study, 73 cognitively healthy older adults met the study criteria with, at least, 6 nights of actigraph data and <75% of EMA questions answered. The 73 participants (19 males, 54 females) had a mean age of 67.64 (SD 9.59) years. Table 1 provides the descriptive data of the sample. This study was part of a larger longitudinal study on cognition and aging; as such, all participants completed a 3-hour battery of cognitive tests and questionnaires; scores were compared with normative data, and participants whose scores fell ≥1.5 SDs below the mean were excluded from the sample (see Table 1 for average cognitive performances of the sample). After completing cognitive testing, participants wore an actigraph for 1 week while also completing EMA measures (ie, phone-based questions 4 times daily).

This study was approved for human subjects by the Washington State University Institutional Review Board under a study entitled “Activities of Daily Living, Executive Functioning and Aging” (Institutional Review Board Number 12606-011).

Participants wore actigraphs for an average of 7.47 nights (SD 0.40). Measures of SE (mean 91.93% [SD 5.02]), SOL (mean 20.30 [SD 16.23] minutes), and WASO (mean 38.10 [SD 28.34] minutes) were consistent with cognitively healthy older adult samples in other studies [36]. Longer SOL (r=−.361, P=.002), but not SE and WASO (r=−.096 to.020, P=.002), correlated with older age. Table 2 presents actigraph data for the participant sample.

On average, participants completed EMA questions for 7.96 (SD 0.44) days and answered an average of 79.52% morning, 75.35% midday, 81.49% afternoon, and 83.03% evening phone calls. Older age exhibited small correlations with EMA reports of greater sleepiness or drowsiness at the morning time block (ρ=−.265, P=.03) and more negative mood at the morning time block (ρ=−.238, P=.04). Figure 1 shows the mean values of the EMA data.

Correlations of evening EMA data (Day A, Time 4) with the EMA data the following day (Day B, Times 1-4) revealed that prior evening self-reports of mood, fatigue, sleepiness or drowsiness, and perceived thinking abilities generally correlated with EMA reports for identical questions the next morning, midday, afternoon, and evening (ρ=.389-.747, P=.002-.001; Table 3). All EMA questions generally correlated with each other at all time blocks (ρ=.335-.794, P=.001), except mood and perceived thinking abilities with fatigue and sleepiness or drowsiness at the evening time block (Table 3).

This study was an exploratory approach to examine relationships between objectively measured sleep quality (eg, WASO, SOL, and SE) and self-report of mood, fatigue, sleepiness or drowsiness, and perceived thinking abilities in a cognitively healthy older adult sample using actigraphy and EMA phone-based self-reports.

Older age was related to EMA reports of greater sleepiness or drowsiness and negative mood in the morning. Relationships between participants’ age and EMA questions of fatigue and perceived thinking abilities did not achieve significance. When the EMA data were compared between the previous night and the following day, reports of fatigue were markedly lower in the morning; however, EMA reports of sleepiness or drowsiness remained stable from the evening to morning and midday time blocks the following day. It is possible that the EMA questions of fatigue and sleepiness or drowsiness are not measuring identical constructs and that the wording of these 2 questions affected the reporting by participants (ie, “current” vs “past 2 hours”).

Consistent with the initial hypotheses, EMA reports of fatigue and sleepiness or drowsiness were related to previous night’s sleep. Specifically, poorer SE was related to greater sleepiness or drowsiness the next morning. However, WASO and SOL were not significant predictors of EMA measures of sleepiness or drowsiness and fatigue the following morning. Furthermore, the TST was removed from the regression analyses owing to a limited relationship with any of the dependent measures, and no evidence of contributing to the overall model. Sleep measures were not strongly related to mood and perceived thinking abilities; this relationship was likely affected by the minimal variation in the EMA data for these questions (ie, mostly average reports).

Findings of predictive relationships between sleep the previous night and EMA measures the next morning were inconsistent. Poorer SE was associated with increased levels of sleepiness or drowsiness at the morning time block and levels of fatigue at the morning and midday time blocks. These findings support the research of McCrae et al [17] who found relationships between greater self-reported sleep problems with subjective complaints of daytime fatigue in older adults. This study expands these findings to include an objective assessment of sleep (actigraphy) as predictors of subjective daytime fatigue.

Previous night’s sleep predicted only morning or midday EMA reports but did not predict EMA reports of fatigue and sleepiness or drowsiness at afternoon and evening time blocks. Other daytime factors (eg, naps and consumption of caffeine) may mitigate the impact of the previous night’s sleep on energy levels later in the day. For example, one study found that greater variability in daytime naps was associated with poorer health status [37], and it is recommended that future research consider the implication of naps on cognitive and functional abilities.

Previous night’s sleep did not predict EMA reports of mood and perceived thinking abilities the next day. Although the literature suggests that self-reported poor sleep is associated with depression and decreased functional status [38], this study found that objective sleep was not directly associated with mood. Our findings support the research of previous studies [30] that subjective, but not objective, sleep quality was associated with self-reported affect (eg, poorer sleep quality correlated with more negative affect). Of note, participants in this study were screened for depressive symptoms at the outset; thus, relationships with mood may be affected by the baseline levels of emotional symptoms for the participants. Interestingly, our results contrast the findings of Russell et al [31] who found that subjective but not objective sleep measures predicted self-reported fatigue the following morning, as our objective sleep measures did predict reports of next-morning fatigue; however, this study was in a generally healthy older adult sample, whereas others evaluated patients with chronic fatigue syndrome [31], suggesting differences in detections within clinical populations.

The study sample was predominantly female, highly educated, and racially homogenous, which may limit the generalization of findings to other demographic groups. The number of participants excluded from the initial sample was substantial as a result of study requirements; larger sample sizes, as well as comparisons of healthy groups to those with sleep disorders or sleep medications, would be useful to assess the influence of medical and pharmaceutical impact on sleep and daily functioning. By nature of the EMA data, there were many individual variables and, as a result, a large number of analyses were conducted in this study, increasing the possibility of false-positive findings. To compensate for this, we used more stringent P values and reduced variables in the models to only those identified in initial correlation analyses (ie, removal of actigraphic TST and EMA daily activity performance). Regarding EMA limitations, the type of telephone number (eg, home vs cell phone) may have influenced the data collection, such that individuals who provided home phone numbers may not have been home to respond to the phone calls; the qualitative nature of missed response items is worthy of further investigation. In addition, the time blocks chosen for this study were based on focus-group information; however, this may not have accurately captured variation in wake-up time (ie, participants who would normally wake up sooner or later than the phone call period). Sensor-based assessment, such as using actigraphy or wrist-based fitness devices, could be a way to prompt EMA questions within a designated time of waking up, thus adjusting to the individual chronology of participants.

Future research should consider longitudinal effects of sleep and perceived functioning on actual cognitive and everyday performance. For example, if maintaining consistent sleep patterns is found to be more predictive of perceived functioning and, thus, contributing to actual cognitive performance, this could inform treatments for sleep disruption. Given the prevalence of naps as individuals age [39], it would be beneficial to include the influence of napping in models of sleep and daily functioning (eg, pre- and postnap EMA reports). Furthermore, research on sleep and neurodegenerative disease has explored the sleep profiles of those with mild cognitive impairment [40,41] and dementia [42], as well as neurological changes in poor sleepers with cognitive deficits [43]. Additional research efforts should investigate whether poor sleep is a robust contributor or risk factor for cognitive decline, as monitoring changes in sleep could be beneficial for the treatment of neurodegenerative disease.