Sleep Diary Research Articles

Investigating the subjective and objective efficacy of a cognitive behavioural therapy for insomnia (CBT-I)-based smartphone app on sleep: A randomised controlled trial.

Due to insufficient treatment options for insomnia, effective solutions are urgently needed. We evaluated the effects of a CBT-I-based app combining sleep training with subjective and objective sleep monitoring on (i) sleep and (ii) subjective-objective sleep discrepancies (SOSD). Fifty-seven volunteers (20-76 years; MAge = 45.67 ± 16.38; 39 female) suffering from sleep problems were randomly assigned to an experimental group (EG, n = 28) or a waitlist control group (CG, n = 29). During the 6-week app phase, the EG used the CBT-I-based programme and a heart rate sensor for daily sleep monitoring and -feedback, while the CG used sleep monitoring only. Sleep was measured (i) subjectively via questionnaires (Insomnia Severity Index, ISI; Pittsburgh Sleep Quality Index, PSQI), (ii) objectively via ambulatory polysomnography (PSG), and (iii) continuously via heart-rate sensor and sleep diaries. Data revealed interactions for ISI (p = 0.003, ƞ2 part = 0.11) and PSQI (p = 0.050, ƞ2 part = 0.05), indicating training-specific improvements in EG, yet not in CG. While PSG-derived outcomes appear to be less training-specific, a tendential reduction in wake after sleep onset (WASO) was found in EG (p = 0.061, d = 0.55). Regarding changes in SOSD, the results indicate improvements during the app phase (EG) for sleep efficiency, sleep onset latency, and WASO (p ≤ 0.022, d ≥ 0.46); for total sleep time both groups showed a SOSD reduction. The findings indicate beneficial effects of a novel smartphone app on sleep and SOSD. More scientific evaluation of such digital programmes is needed to ultimately help in reducing the gap in non-pharmacological insomnia treatment.

Cognitive behavioural therapy for insomnia reduces actigraphy and diary measured sleep discrepancy for individuals with comorbid insomnia and major depressive disorder: A report from the TRIAD study

Objective/BackgroundDiscrepancies between sleep diaries and actigraphy occur among individuals with insomnia. Cognitive behavioural therapy for insomnia (CBT-I) improves insomnia but the impact on discrepancy is unclear. This study examined CBT-I's effects on actigraphy-diary discrepancy and explored sleep-related beliefs and attitudes as a mediator. Patients/MethodsParticipants were 108 (age M±SD = 47.23 ± 12.42, 67.60 % female) adults with insomnia and major depressive disorder from the Treatment of Insomnia and Depression study. They were randomized to 7 sessions of CBT-I or sham Quasi-Desensitization Therapy for Insomnia (DTI), plus 16 weeks of antidepressants. Two weeks of actigraphy and sleep diary were collected at baseline, mid-treatment, end-treatment. Differences between sleep diary and actigraphy total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were calculated. Participants completed Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS) at baseline and mid-treatment. ResultsAt baseline, diary (versus actigraphy) TST was shorter (1.1 ± 1.41h), whilst SOL (21.64 ± 41.25min) and WASO (17.45 ± 61.99min) were longer. Mixed effects models using daily data showed that after adjusting for age and sex, participants in the CBT-I group (versus DTI) showed greater reduction in all actigraphy-diary discrepancy domains (all p-values<.01), reductions evident from mid-treatment. Group differences on actigraphy-diary discrepancy reductions in TST, SOL, and SE (not WASO) were mediated by changes in DBAS from baseline to mid-treatment (all p-values<.05). Changes in discrepancy did not mediate insomnia symptom changes (p-values>.39). ConclusionsCBT-I reduced actigraphy-diary discrepancy in individuals with comorbid insomnia and depression; this reduction was associated with improved sleep-related attitudes, a therapeutic target of CBT-I. Clinical trial registrationTRIAD (Treatment of Insomnia and Depression): Improving Depression Outcome by Adding Insomnia Therapy to Antidepressants. Prospectively registered with Clinical Trials (NCT00767624). Support (if any)MH078924, MH078961, MH079256.

Contributions of the light environment and co-sleeping to sleep consolidation into nighttime in early infants: A pilot study

BackgroundSleep consolidation into nighttime is considered the primary goal of sleep development in early infants. However, factors contributing to sleep consolidation into nighttime remain unclear. AimTo clarify the influences of the light environment and nighttime co-sleeping on sleep consolidation into nighttime in early infants. Study designCross-sectional study. Subjects and methodsSleep–wake time and light stimulation were measured in infants for 4 consecutive days using actigraphy. The infants’ mothers were asked to complete a sleep events diary and a questionnaire about childcare, including “co-sleeping”, defined as when the infant and mother slept on the same surface throughout the night. Outcome measuresThe data were analyzed with a focus on daytime and nighttime sleep parameters. ResultsDaytime light stimulation reduced daytime “active sleep”, tended to reduce daytime sleep, and increased daytime waking. Nighttime light stimulation reduced nighttime “quiet sleep” and nighttime sleep and increased nighttime waking. Co-sleeping reduced nighttime waking, and, as a result, nighttime sleep time and sleep efficiency increased. Co-sleeping reduced daytime sleep and tended to increase daytime waking. Consequently, co-sleeping tended to increase the ratio of nighttime sleep to daytime sleep. ConclusionsThe present findings suggest that an appropriate light environment promotes daytime waking and nighttime sleep in early infants, but it does not contribute to sleep consolidation into nighttime by itself. On the other hand, co-sleeping may promote sleep consolidation into nighttime. Therefore, further methods for safe co-sleeping need to be established while avoiding risk factors for sudden unexpected death in infancy/sudden infant death syndrome.

THERANAP: AN EDUCATIONAL SLEEP HEALTH INTERVENTION FOR FRAIL OLDER ADULTS

Abstract Napping and other daytime sleep is often overlooked and poorly defined in insomnia research. Studies have shown correlations between older adults’ napping habits and increased medical co-morbidities and risks of dementia, but others report napping enhances memory consolidation and broader aspects of cognition in younger adults. Why is napping recommended for younger adults but considered problematic for older adults? In previous a research study, (18 older adults in an independent living facility: 6 men,12 women, mean age 84, SD= 7.62, range 67-96, who self-reported sleep problems) participants reported napping in qualitative interviews but denied daytime sleep on standard sleep assessments. Participants spoke about napping apologetically and expressed dread of its links to functional and cognitive decline. They stigmatized planned napping, which could potentially benefit their cognitive functioning, but seemed accepting of accidental napping, which could well indicate more serious physical or cognitive issues To address these issues, TheraNap teaches intentional napping as a therapy. This sleep health education intervention empowers sleep-deprived older adults to take control of their sleep habits by practicing intentional napping to improve overall sleep health. TheraNap includes: • Short sleep health training session with recommended napping guidelines • Two-week long planned napping routines • A sleep diary • Supplementary sleep meditations • Follow-session to assess perceptions of sleep health More research is needed into the ways napping can improve physical and cognitive function, and more education is needed for older adults, their families and caregivers, and LTC staff about the importance of sleep for older adults.

INTERACTIVE ROLE OF SLEEP AND AROUSAL ON COGNITION AND BRAIN STRUCTURE IN CHRONIC PAIN PATIENTS

Abstract Chronic widespread pain (CWP) patients experience heightened arousal, insomnia complaints, poor cognition, and diffuse brain structure alterations, but interactive relationships among symptoms remain unexplored. We assessed whether physiological arousal [assessed via heart rate variability (HRV)] moderated associations between sleep and cognition/brain structure. Adults (N=42, Mage=46.2±13.7) with CWP and insomnia (difficulty falling/staying asleep) completed 14 daily sleep diaries, 5-minute Holter monitoring [HRV root mean square of successive normal heartbeats (RMSSD), low frequency/high frequency (lf/hf) computed], cognitive tasks [Stroop (processing speed/attention/inhibition), Sternberg (working memory)], and magnetic resonance imaging. Moderation analyses examined interactive associations between sleep [total sleep time (TST), wake after sleep onset, sleep onset latency (SOL)] and HRV with cognition/gray matter volume of frontal/temporal regions. SOL and lf/hf interacted in associations with working memory and attention/processing speed. Longer SOL was associated with worse Sternberg/Stroop at lowest sympathetic predominance levels. Conversely, longer SOL was associated with better Stroop at highest lf/hf. SOL and RMSSD interacted in associations with anterior cingulate, showing longer SOL associated with lower volumes at lowest arousal levels. TST and RMSSD interacted in associations with right hippocampus, with shorter TST associated with lower volume at highest/average arousal levels. Shared dynamic physiological arousal and sleep onset hyperarousal mechanisms impact cognition and neural structure in CWP. Longer SOL may only negatively impact cognition/anterior cingulate structure up to a certain arousal threshold, with shared mechanisms potentially benefitting cognition at highest arousal levels. At higher arousal, longer TST may protect against hippocampal volume reduction. Future prospective studies are needed to inform temporal characteristics.

LOOK AHEAD SLEEP: STUDY DESIGN AND BASELINE CHARACTERISTICS

Abstract Older adults with obesity and type 2 diabetes (T2D) are at elevated risk for mild cognitive impairment (MCI) and dementia. Compared with younger persons, older adults are more likely to have sleep-disordered breathing (SDB), which is also common among adults with T2D and obesity and is associated with higher risk of dementia. The Look AHEAD Sleep (LA-Sleep) study investigates links of SDB and circadian rest/activity rhythms with cognitive performance, MCI, and dementia in older adults with T2D and obesity/overweight enrolled in Look AHEAD Aging (LA-A). LA-Sleep participants completed cognitive measures at baseline and were randomized to sleep measurement in the first (2022-2023) or second year (2023-2024). Sleep measures include assessments of SDB (WatchPAT), circadian rest/activity rhythms (ActiGraph) and daily sleep diary. Of those who enrolled in LA-A, 62.8% consented to LA-Sleep. To date in 203 participants have provided data from WatchPAT and 107 provided actigraphy data. Participants were a mean (±SD) age of 76.1±5.4 y, had a mean BMI of 34.2±6.7 kg/m2, 56.7% identified as female, and 75.4% were Non-Hispanic White, 12.3% Non-Hispanic Black, and 6.9% Hispanic. Approximately 90.2% had &amp;gt;high school education. Of participants with obesity in 2018-2020, 27.9% (41/147) had an apnea hypopnea index (AHI) of ≥15 to &amp;lt;30/h (moderate SDB) and 20.4% (30/147) had ≥30 events/h (severe SDB). Mean actigraphy-estimated total sleep time was 7.1±1.4 h, and mean wake after sleep onset was 62.5±31.0 min/night. Among adults with T2D and overweight/obesity, a high proportion have SDB and sleep fragmentation, potentially increasing risk for dementia.

UNVEILING THE COMPLEX REALITIES: UNDERSTANDING SLEEP AND FATIGUE CHARACTERISTICS OF END-OF-LIFE CAREGIVERS

Abstract This mixed methods multi-case study explores sleep and fatigue characteristics among end-of-life caregivers. The research sample encompasses cases aiding individuals with advanced (n=4) and chronic illnesses (n=1), including various caregiver demographics such as gender, age groups (19-64 and 65+), and health conditions. Data collection involves comprehensive surveys capturing demographics, care recipient symptom intensity, caregiver depressive symptoms, caregiving stress, caregiver sleep quality, and caregiver fatigue level. The study integrates data from a 30-day Personal Sleep Monitoring Device (PSMD) and a 14-day sleep journal, enriched by insights from semi-structured interviews and field notes. The outcomes indicate a PSQI (Pittsburgh Sleep Quality Index) score of 6.1, accompanied by 7.44 hours of sleep recorded by the PSMD. There were approximately 20.5 nocturnal awakenings within 30 days, with average fatigue at 55.28 and daily fatigue at 49.66. Objective sleep outcomes were consistent among caregivers, while their own sleep ratings ranged from “Fairly Bad” to “Very Good.” Themes for each case were formulated to delve into the context of these sleep and fatigue characteristics. These themes encompass adaptive leadership, linchpin roles, emotional labor management, self-reliance, and vigilance. A prevailing overarching theme is the perception of “Not having control.” A significant finding underscores the dynamic influence of care recipients’ daily statuses on caregivers’ sleep quality and fatigue levels, leading to unpredictable outcomes. This study offers profound insights into end-of-life caregivers’ intricate journeys, highlighting individual differences and the dynamic nature of caregiving roles, while recognizing the importance of tailored interventions for enhancing caregiver’s well-being and caregiving effectiveness.

The Impact of Weighted Blanket Use on Adults with Sensory Sensitivity and Insomnia.

This study's purpose was to determine the impact of weighted blanket use on moderate to severe insomnia in adults with sensory sensitivity greater than the average population. For this study, a four-week, single-case, multiple-participant ABA study design was used. Through convenience sampling, four participants scoring 15 or greater on the Insomnia Severity Index (ISI), which categorizes them as having moderate to severe insomnia, and much more than most people in sensory sensitivity on the Adolescent/Adult Sensory Profile were recruited. First, seven-day baseline sleep data was gathered, followed by two weeks of weighted blanket use, concluding with a seven-day withdrawal phase. Additional outcome measures included: Tuck and Snooze Survey, Consensus Sleep Diary Morning, and Additional Sleep Diary Questions. Data analysis included visual analysis, mean comparisons, Tau-U calculations, and pre- to post-ISI category comparisons. All participants' ISI scores were categorized as one level less severe postintervention. All participants demonstrated increased sleep quality, and three participants showed an increase in sleep duration based on individual mean comparisons between baseline and intervention phases. Weighted blankets appear beneficial in reducing insomnia severity in adults with much more than the average population sensory sensitivity. In addition, those with self-reported anxiety may have increased benefit from this intervention.

Behavioural Parameters of Circadian Rhythm Are Not Correlated with Dim Light Melatonin Onset: An Observational Study on Healthy Volunteers.

Dim light melatonin onset (DLMO) is considered the most reliable marker of the circadian rhythm phase in humans. DLMO may moderately correlate with sleep onset and sleep offset time. There are no sufficient data about the correlations between DLMO and clinical scales assessing sleep quality and daytime symptoms of poor night sleep. The aim of the study was to determine the association between DLMO and basic sleep parameters from actigraphy and sleep diaries, as well as the association between DLMO and the following insomnia clinical scales: the Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and chronotype questionnaires: Morningness-Eveningness Questionnaire (MEQ) and Composite Scale of Morningness (CSM). Participants of the study were healthy volunteers. Sleep parameters were measured by sleep diaries and actigraphy, and the following clinical scales: the AIS, ISI, and ESS, and chronotype questionnaires: MEQ and CSM. DLMO was calculated based on plasma melatonin concentration. The blood samples were collected hourly at five time points between 20:00 and 00:00 during the session in dim red light (<50 lux). Melatonin concertation was determined by LC-MS/MS. Twenty-one volunteers participated in the study. DLMO was calculated in 12 participants. There was a significant correlation between DLMO and ISI (r = 0.60, p = 0.038) and ESS (r = 0.61, p = 0.034). The correlation coefficient between the DLMO and the AIS was also high, however insignificant (r = 0.57, p = 0.054). There were no significant correlations between DLMO and chronotype scales MEQ and CSM. DLMO did not correlate with sleep onset and sleep offset; however, DLMO correlated with the Sleep Fragmentation Index (SFI) (r = 0.67, p = 0.017). DLMO is associated with poorer sleep maintenance, a stronger feeling of insomnia, and sleepiness during the day. Simultaneously, chronotype pattern and circadian rhythm parameters do not correlate with DLMO. Biological circadian rhythm does not reflect the real-life sleep-wake rhythm, indicating that the lifestyle is more often disconnected from the biological clock.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study.

Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Improved sleep, cognitive processing and enhanced learning and memory task accuracy with Yoga nidra practice in novices

Complementary and Alternative medicine is known to have health benefits. Yoga nidra practice is an easy-to-do practice and has shown beneficial effects on stress reduction and is found to improve sleep in insomnia patients. Effect of yoga nidra practice on subjective sleep is known but its effect on sleep and cognition objectively is not documented. The aim of the study was to study the effect of yoga nidra practice on cognition and sleep using objective parameters. 41 participants were enrolled, and baseline sleep diary (SD) collected. Participants volunteered for overnight polysomnography (PSG) and cognition testing battery (CTB) comprising of Motor praxis test, emotion recognition task (ERT), digital symbol substitution task, visual object learning task (VOLT), abstract matching (AIM), line orientation task, matrix reasoning task, fractal-2-back test (NBACK), psychomotor vigilance task and balloon analog risk task. Baseline CTB and after one and two weeks of practice was compared. Power spectra density for EEG at central, frontal, and occipital locations during CTB was compared. Repeat SD and PSG after four weeks of practice were done. After yoga nidra practice, improved reaction times for all cognition tasks were seen. Post intervention compared to baseline (95%CI; p-value, effect size) showed a significant improvement in sleep efficiency of +3.62% (0.3, 5.15; p = 0.03, r = 0.42), -20min (-35.78, -5.02; p = 0.003, d = 0.84) for wake after sleep onset and +4.19 μV2 (0.5, 9.5; p = 0.04, r = 0.43) in delta during deep sleep. Accuracy increased in VOLT (95% CI: 0.08, 0.17; p = 0.002, d = 0.79), AIM (95% CI: 0.03, 0.12; p = 0.02, d = 0.61) and NBACK (95% CI: 0.02, 0.13; p = 0.04, d = 0.56); ERT accuracy increased for happy, fear and anger (95% CI: 0.07, 0.24; p = 0.004, d = 0.75) but reduced for neutral stimuli (95% CI: -0.31, -0.12; p = 0.04, r = 0.33) after yoga nidra practice. Yoga Nidra practice improved cognitive processing and night-time sleep.

Impact of Sleep Duration, Quality, and Chronotype on Learning and Academic Performance: A Cross-Sectional Study Among First Year Medical Students of a Tertiary Care Institute.

Introduction The link between sleep and cognitive processes, such as memory and learning, continues to be one of the most intriguing and perplexing theories. Undergraduate medical students in their first year are particularly vulnerable to sleep disturbances. Academic achievement and learning have been linked to sleep patterns, which include not only the quantity and quality of sleep but also the timing of sleep in relation to the natural sleep onsets, or chronotypes. There have been conflicting reports on the outcomes of sleep and relatively fewer researches focused on the impact of chronotypes on learning and academic achievement among medical students. The current study thus sought to determine the chronotypes of medical students, evaluate the quantity and quality of sleep, and determine the impact of these factors on learning and academic performance. Methods The study was conducted in the Department of Physiology, All India Institute of Medical Sciences (AIIMS) Gorakhpur, India. Sleep health was assessed in 167 first-year medical students using the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), morningness-eveningness questionnaire (MEQ), and sleep log books. Learning and memory assessment was performed using Raven's progressive matrices test. Grade point average (GPA) was used to assess their academic performance. The relationship of sleep scores with GPA and RPM scores were obtained by linear regression analysis. One-way analysis of variance (ANOVA) and unpaired t-test were used to investigate other comparisons among categories of chronotypes and those of mean GPA. A p-value of <0.05 was considered as significant. Results The mean GPA and RPM scores obtained in the groups with PSQI ≥ 5 (2.67± 1.1, 49.51 ±6.24, respectively) and PSQI < 5 (3.15± 0.59, 54.73± 4.01, respectively) and those in the group with ESS ≥ 10 (2.72± 1.17, 50.97± 5.92, respectively) and ESS < 10 (3.15 ± 0.6, 54.18± 3.91, respectively)varied with statistically significant differences (p < 0.05). Statistically significant R-squared values for the relationship of PSQI and ESS scores with RPM and GPA scores were obtained. No correlation between academic grades and chronotype was found. Poor GPA scores were found to be associated with reduced mean sleep duration for one week before the exams. Conclusion Learning and academic performance are negatively impacted by poor sleep quality and daytime sleep dysfunction. No definite evidence for the association of sleep chronotypes with the learning and memory could be attained. Higher test performance is more closely linked to the average sleep length over a duration of time preceding the exams.

The effect of a neonatal sleep intervention on maternal postpartum hypertension: a randomized trial

In nonpregnant adults, poor sleep is associated with higher blood pressure. Poor sleep is common in the postpartum period and is often attributed to infant caretaking needs. However, its effects on cardiovascular health in individuals with a hypertensive disorder of pregnancy are unknown. This study aimed to determine the effect of a neonatal sleep intervention on maternal postpartum blood pressure in individuals with a hypertensive disorder of pregnancy. In this single-institution pilot randomized controlled trial from July 2021 to March 2022, 110 individuals with a hypertensive disorder of pregnancy were randomized to receive a neonatal sleep intervention (SNOO responsive bassinet) plus usual care of safe sleep education (n=54) or usual care alone (n=56). Remote follow-up visits were conducted at 1 week, 6 weeks, and 4 months after delivery and involved blood pressure and weights, sleep and mood questionnaires, and self-reported infant and maternal sleep logs. Based on institutional data, the sample size had 80% power to detect a 4.5-mm Hg difference in the primary outcome of mean arterial pressure at 6 weeks after delivery. Baseline characteristics were similar between the arms. At 1 week after delivery, the intervention arm had lower mean arterial pressure and less antihypertensive medication use than the control arm (99±10 vs 103±7 mm Hg [P=.04] and 23% vs 35% [P=.15], respectively). At 6 weeks after delivery, mean arterial pressure was similar between arms (93±8 vs 94±8 mm Hg; P=.54), but there was a lower rate of antihypertensive use in the intervention arm (15% vs 26%; P=.19). Scores from maternal sleep and mood questionnaires at 6 weeks after delivery and self-reported infant and maternal sleep duration at 6 weeks and 4 months after delivery were similar between arms (P>.05). The SNOO responsive bassinet as a neonatal sleep intervention did not result in improved mean arterial pressure at 6 weeks after delivery after hypertensive disorders of pregnancy.

What difference does sleep make? Continuous glucose monitoring metrics during fixed-overnight time versus sleep periods among older adults with type 1 diabetes.

Hypoglycaemia during sleep is a common and clinically important issue for people living with insulin-treated diabetes. Continuous glucose monitoring devices can help to identify nocturnal hypoglycaemia and inform treatment strategies. However, sleep is generally inferred, with diabetes researchers and physicians using a fixed-overnight period as a proxy for sleep-wake status when analysing and interpretating continuous glucose monitoring data. No study to date has validated such an approach with established sleep measures. Continuous glucose monitoring and research-grade actigraphy devices were worn and sleep diaries completed for 2 weeks by 28 older adults (mean age 67 years [SD 5]; 17 (59%) women) with type 1 diabetes. Using continuous glucose monitoring data from a total of 356 nights, fixed-overnight (using the recommended period of 00:00 hours-06:00 hours) and objectively-measured sleep periods were compared. The fixed-overnight period approach missed a median 57 min per night (interquartile range: 49-64) of sleep for each participant, including five continuous glucose monitoring-detected hypoglycaemia episodes during objectively-measured sleep. Twenty-seven participants (96%) had at least 1 night with continuous glucose monitoring time-in-range and time-above-range discrepancies both ≥ 10 percentage points, a clinically significant discrepancy. The utility of fixed-overnight time continuous glucose monitoring as a proxy for sleep-awake continuous glucose monitoring is inadequate as it consistently excludes actual sleep time, obscures glycaemic patterns, and misses sensor hypoglycaemia episodes during sleep. The use of validated measures of sleep to aid interpretation of continuous glucose monitoring data is encouraged.

Sleep-wake state discrepancy among cancer survivors with insomnia symptoms.

To evaluate the discrepancy and correlation between sleep-wake measures (i.e., time in bed (TIB), total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE%)) reported on sleep diary and measured by actigraphy among cancer survivors with insomnia symptoms; and examine the influences of sociodemographic and clinical variables on these measurement differences. A heterogenous sample of cancer survivors with insomnia symptoms (n = 120; M age = 63.7 ± 10.1; female = 58.3%) was included. Seven consecutive days of sleep diary and actigraphic data were obtained along with information on demographic, sleep, and mental health symptoms. Bland-Altman plot, Pearson correlation coefficient, concordance correlation coefficient, and mixed linear model approach were used to conduct the analysis. Self-reported TIB, SOL, and WASO were longer than measured by actigraphy (TIB: 8.6min. (95% CI, 3.7, 13.5; p < .001); SOL: 14.8min. (95% CI, 9.4, 20.2; p < .0001); and WASO: 20.7min. (95% CI, 9.4, 20.2; p < .0001), respectively); and self-reported TST and SE% were shorter than measured by actigraphy (TST: 6.8min. (95% CI, -18.7, 5.13); and SE%: 0.7% (95%CI, -3.0, 2.0), respectively), but were not statistically significant. Sex, higher insomnia severity, and poor sleep quality were associated with discrepancy between several sleep-wake measures. Subjective and objective sleep-wake measures may present discrepant finding among cancer survivors with symptoms of insomnia. Future research is needed to validate appropriate sleep-wake assessment, and better understand factors that influence the discrepancy that exists between measures among this population. Clinical trials identifier: NCT03810365. Date of registration: January 14, 2019.

Monitoring Daytime Napping in Community Dwelling Older Adults Using Contactless Sleep Technologies

AbstractBackgroundIncreased daytime napping and excessive sleepiness are associated with cognitive decline in older adults, especially in people living with dementia (PLWD) [1]. Subjective assessments of naps are burdensome and maybe unreliable in PLWD and hence there is a need for technologies that provide objective longitudinal assessment of the incidence and duration of naps. Here we compare two contactless sleep technologies (CSTs) against sleep diary and actigraphy for monitoring daytime napping in community dwelling non‐demented older adults.MethodTwo under‐mattress CSTs (Withings Sleep Analyser [WSA] and Emfit QS [Emfit]) along with actigraphy (Actiwatch Spectrum [AWS]) were deployed in the home of 17 older adults for a period of 14 days ( = 65 years; Mean Age ± SD = 72 ± 4.49; 6 Women). The ground truth nap information was collected using an extended consensus sleep diary that included additional information about naps (timing, duration, and location). We analyzed the agreement of the napping events and duration estimated by WSA, Emfit and AWS against the sleep diary reported events.ResultThe CSTs only detected in‐bed naps whilst the AWS detected both in‐bed and not‐in‐bed naps. Although all the compared devices detected spurious naps unreported in the sleep diary, it was highest in AWS (81% of total naps detected) followed by Emfit (63%) and WSA (16%) as shown in Figure 1. Among the CSTs, WSA accurately detected more in bed naps while registering less spurious naps compared to Emfit but had lower duration agreement to sleep diary (Figure 2). Further, when the contribution of daytime naps to 24‐h total sleep time was computed, the WSA estimate (12.8±6.1%) was closest to the sleep diary estimate (12.9±9.1%) followed by AWS (15.6±9.9%) and Emfit (17.2±11.1%).ConclusionCSTs, with their ability to provide both contextual location information and objective measures of napping, such as timing and duration, offer a reliable and unobtrusive alternative to traditional methods such as sleep diary and actigraphy for long‐term round‐the‐clock monitoring of sleep in older adults.

Monitoring Daytime Napping in Community Dwelling Older Adults Using Contactless Sleep Technologies

AbstractBackgroundIncreased daytime napping and excessive sleepiness are associated with cognitive decline in older adults, especially in people living with dementia (PLWD) [1]. Subjective assessments of naps are burdensome and maybe unreliable in PLWD and hence there is a need for technologies that provide objective longitudinal assessment of the incidence and duration of naps. Here we compare two contactless sleep technologies (CSTs) against sleep diary and actigraphy for monitoring daytime napping in community dwelling non‐demented older adults.MethodTwo under‐mattress CSTs (Withings Sleep Analyser [WSA] and Emfit QS [Emfit]) along with actigraphy (Actiwatch Spectrum [AWS]) were deployed in the home of 17 older adults for a period of 14 days (≥65 years; Mean Age ± SD = 72 ± 4.49; 6 Women). The ground truth nap information was collected using an extended consensus sleep diary that included additional information about naps (timing, duration, and location). We analyzed the agreement of the napping events and duration estimated by WSA, Emfit and AWS against the sleep diary reported events.ResultThe CSTs only detected in‐bed naps whilst the AWS detected both in‐bed and not‐in‐bed naps. Although all the compared devices detected spurious naps unreported in the sleep diary, it was highest in AWS (81% of total naps detected) followed by Emfit (63%) and WSA (16%) as shown in Figure 1. Among the CSTs, WSA accurately detected more in bed naps while registering less spurious naps compared to Emfit but had lower duration agreement to sleep diary (Figure 2). Further, when the contribution of daytime naps to 24‐h total sleep time was computed, the WSA estimate (12.8±6.1%) was closest to the sleep diary estimate (12.9±9.1%) followed by AWS (15.6±9.9%) and Emfit (17.2±11.1%).ConclusionCSTs, with their ability to provide both contextual location information and objective measures of napping, such as timing and duration, offer a reliable and unobtrusive alternative to traditional methods such as sleep diary and actigraphy for long‐term round‐the‐clock monitoring of sleep in older adults.

Relationship between metabolic syndrome, sleep quality and cognitive impairment

AbstractBackgroundMetabolic syndrome is a risk factor for cardiovascular diseases, which have been related to cognitive impairment and Alzheimer disease. Sleep disorders in older adults have a negative impact on cognitive performance. However, a link between metabolic syndrome, sleep, and cognitive decline has not been established.MethodA retrospective cross‐sectional study was carried out. Information was collected from clinical histories about demographic, metabolic parameters, sleep diaries, neurocognitive evaluation and neuroimaging studies. For statistical analyses, chi‐square tests of association were included for categorical variables (age, sex, educational level, social economy status (SES), hypertension, diabetes, BMI, depression, anxiety, smoking, sedentary lifestyle) for each memory problem. A binary logistic regression model was used to predict the probabilities of memory problems. Which was built including each variable independently.ResultsThe logistic regression revealed that women and low educational level were associated with increased memory difficulties over time (odds ratio [OR] = 5.3, p &lt; 0.001). Depression was associated with increased odds of memory problems (OR = 3.8, p &lt;0.001). When the sleep quality variable was added, it was observed that the poor sleep quality partially reduced the impact of age, gender, and depression on memory. However, all continued to present an OR greater than 1. Of the medical conditions, type 2 diabetes and hypertension showed a significant interaction (OR = 1.7, p &lt; 0.001).ConclusionWe believe that there is an association between metabolic state, sleep quality and cognitive impairment. in such a way that those with metabolic disorders and poor sleep quality have decreased cognitive performance.

Look AHEAD Sleep: Study Design and Baseline Characteristics

AbstractBackgroundOlder adults with obesity (body mass index [BMI] ≥30 kg/m2) and type‐2 diabetes (T2D) have increased risks for mild cognitive impairment (MCI) and dementia. Sleep‐disordered breathing (SDB) is common in older adults and those with T2D and obesity, and has been linked to increased risk of Alzheimer’s disease (AD) and Alzheimer’s disease‐related dementias (ADRD). SDB may represent a unique and modifiable pathway that elevates the risk of cognitive decline among patients with T2D and obesity. Yet, few studies have examined associations between SDB and cognitive outcomes among persons with T2D and obesity. Addressing this gap is critical as the prevalence of metabolic disorders is increasing, placing a growing population at risk for MCI, and AD/ADRD. Look AHEAD Sleep (LA‐Sleep) was designed to study associations of SDB and circadian rhythms with cognitive function in older adults with a history of these conditions. Here, we report initial baseline characteristics of LA‐Sleep cohort.MethodParticipants enrolled in Look AHEAD Aging (LA‐A), a continuation of the Look AHEAD cohort study, were eligible to participate in LA‐Sleep. Participants completed cognitive assessments and were randomized to have a sleep assessment in either Year 1 (2022‐2023) or 2 (2023‐2024). Year 1 participants completed sleep questionnaires and received in‐home sleep assessment devices (WatchPAT and ActiGraph) and a sleep diary by mail.ResultOf 1,522 who consented to LA‐A, 952 agreed to LA‐Sleep and to date, 161 have provided year 1 WatchPAT (n = 161) or Actigraph (n = 107) data. There were more females (55.3%) than males; the average (±SD) age was 76.1±5.4 y; average BMI was 34.3±7.0; and 10.6% of the sample was Non‐Hispanic Black, 6.2% Hispanic, and 76.4% Non‐Hispanic White. Most had more than a high school education (90.1%). Of those with obesity in 2018‐2020, 20.0% had severe SDB (apnea hypopnea index ≥30). Overall mean actigraphic‐estimated sleep duration was 7.1±1.4 hours, sleep efficiency (total sleep time/time in bed) was 87.0%, and wake‐after‐sleep‐onset was 62.5±31.0 minutes/night.ConclusionAmong an older adult cohort with T2D and overweight or obesity, a high proportion of participants have severe SDB and high levels of sleep fragmentation, placing them at increased risk for cognitive impairment.

Indexing the Predictive Utility of Real‐World Sleep to Discriminate Early Alzheimer’s Disease

AbstractBackgroundSleep dysfunction is one of earliest symptoms of Alzheimer’s disease (AD) and can accelerate cognitive decline, increasing risk of AD. Emerging technologies, like actigraphy, allow for continuous monitoring of real‐world sleep. Our goal is to determine the predictive utility of real‐world sleep dysfunction features for indexing warning signs of incipient dementia like mild cognitive impairment (MCI).MethodSubjects were classed as cognitively neurotypical (N = 31) or impaired: MCI (n = 51) or AD (N = 7) based on NIA‐AA diagnostic criteria, as evaluated by a neurologist from clinical exam, neuropsychological, and demographic assessments. (Table 1) Each subject was tracked for 3 months with wrist‐worn actigraphy. Self‐reported sleep diaries validated real‐world sleep data. Sleep duration and quality were estimated across means and standard deviations (SD) of: Total Sleep Time (TST), Sleep Latency (SL), Sleep Efficiency (SE), Wakefulness After Sleep Onset (WASO), and Awakening Count (AC). An optimal logistic regression model, adjusting for demographics (age, gender, education), was quantified and used to rank the explanatory value of sleep covariates to discriminate impaired vs. typical aging using stepwise selection.ResultAcross 6,530 validated, nightly sleep periods (mean = 73.4 ± 17.0 sleep periods per subject), we found best discrimination of impaired vs. typical aging, ranked from best to least discrimination (Table 2), for average sleep efficiency [SE] (b = 3.58, p = 0.03), nightly wakefulness [WASO] (b = 3.42, p = 0.04), variability of daily sleep quality [SE] (b = 1.65, p = 0.001), and awakening frequency [AC] (b = ‐0.79, p = .03). TST did not discriminate impaired from typical aging.ConclusionResults align with prior literature, demonstrating that sleep quality is an optimal feature to track early warning signs of dementia from real‐world sleep. Critically, SE shows the most promise for discriminating early decline while TST did not show predictive utility in this study. Digital fingerprints of sleep behavior in the real world offer unique added value to neurobiological biomarker evidence of preclinical disease, improving early detection of AD risk to bolster interventions aimed at treating AD—‐even its earliest stages.