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Nature and Science of Sleep 2024The COVID-19 pandemic has influenced clinical sleep protocols with stricter hospital disinfection requirements. Facing these new rules, we tested if a new artificial...
PURPOSE
The COVID-19 pandemic has influenced clinical sleep protocols with stricter hospital disinfection requirements. Facing these new rules, we tested if a new artificial intelligence (AI) algorithm: The Nox BodySleep™ (NBS) developed without airflow signals for the analysis of sleep might assess pertinently sleep in patients with Obstructive Sleep Apnea (OSA) and chronic insomnia (CI) as a control group, compared to polysomnography (PSG) manual scoring.
PATIENTS-METHODS
NBS is a recurrent neural network model that estimates Wake, NREM, and REM states, given features extracted from activity and respiratory inductance plethysmography (RIP) belt signals (Nox A1 PSG). Sleep states from 139 PSG studies (CI N = 72; OSA N = 67) were analyzed by NBS and compared to manually scored PSG using positive percentage agreement, negative percentage agreement, and overall agreement metrics. Similarly, we compared common sleep parameters and OSA severity using sleep states estimated by NBS for each recording and compared to manual scoring using Bland-Altman analysis and intra-class correlation coefficient.
RESULTS
For 127,170 sleep epochs, an overall agreement of 83% was reached for Wake, NREM and REM states (92% for REM states in CI patients) between NBS and manually scored PSG. Overall agreement for estimating OSA severity was 100% for moderate-severe OSA and 91% for minimal OSA. The absolute errors of the apnea-hypopnea index (AHI) and total sleep time (TST) were significantly lower for the NBS compared to no scoring of sleep. The intra-class correlation was higher for AHI and significantly higher for TST using the NBS compared to no scoring of sleep.
CONCLUSION
NBS gives sleep states, parameters and AHI with a good positive and negative percentage agreement, compared with manually scored PSG.
PubMed: 38911319
DOI: 10.2147/NSS.S431650 -
Journal of Alzheimer's Disease Reports 2024We examined associations between objective sleep duration and cognitive status in older adults initially categorized as cognitively non-impaired (CNI, = 57) or...
We examined associations between objective sleep duration and cognitive status in older adults initially categorized as cognitively non-impaired (CNI, = 57) or diagnosed with mild cognitive impairment (MCI, = 53). On follow-up, 8 years later, all participants underwent neuropsychiatric/neuropsychological evaluation and 7-day 24-h actigraphy. On re-assessment 62.7% of participants were cognitively declined. Patients who developed dementia had significantly longer night total sleep time (TST) than persons with MCI who, in turn, had longer night TST than CNI participants. Objective long sleep duration is a marker of worse cognitive status in elderly with MCI/dementia and this association is very strong in older adults.
PubMed: 38910938
DOI: 10.3233/ADR-230203 -
PloS One 2024To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of...
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
Topics: Humans; Male; Female; Aged; Middle Aged; Adult; Magnetic Resonance Imaging; Brain; Aged, 80 and over; Adolescent; Aging; Young Adult; Memory; Cognition; Cross-Sectional Studies; Neuroimaging; Research Design; Data Collection
PubMed: 38905233
DOI: 10.1371/journal.pone.0306006 -
Sensors (Basel, Switzerland) Jun 2024To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset,...
OBJECTIVE
To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia.
METHODS
A total of 153 patients with insomnia (I, 83 females, mean age + SD = 41.37 + 16.19 years) and 121 healthy controls (HC, 78 females, mean age + SD = 36.99 + 14.91 years) wore an actigraph for one week. Insomnia was classified into sleep onset insomnia (SOI), maintenance insomnia (MaI), mixed insomnia (MixI) and negative misperception insomnia (NMI). To study their activity-based prospective memory performance, all the participants were required to push the actigraph event marker button twice, at bedtime (task 1) and at get-up time (task 2).
RESULTS
Only patients with maintenance and mixed insomnia had a significantly lower accuracy in the activity-based prospective memory task at get-up time compared with the healthy controls.
CONCLUSION
The results show that maintenance and mixed insomnia involve an impaired activity-based prospective memory performance, while sleep onset and negative misperception insomnia do not seem to be affected. This pattern of results suggests that the fragmentation of sleep may play a role in activity-based prospective memory efficiency at wake-up in the morning.
Topics: Humans; Female; Sleep Initiation and Maintenance Disorders; Male; Adult; Memory, Episodic; Middle Aged; Actigraphy; Sleep
PubMed: 38894403
DOI: 10.3390/s24113612 -
Sensors (Basel, Switzerland) May 2024Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of...
Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.
Topics: Humans; Dermatitis, Atopic; Machine Learning; Actigraphy; Wrist; Male; Female; Adult; Algorithms; Pruritus; Wearable Electronic Devices; Quality of Life; Sleep; Middle Aged
PubMed: 38894155
DOI: 10.3390/s24113364 -
Scientific Reports Jun 2024Previous studies on sleep state misperception have objectively evaluated sleep status in special environments using polysomnography. There is a paucity of data from...
Previous studies on sleep state misperception have objectively evaluated sleep status in special environments using polysomnography. There is a paucity of data from studies that evaluated habitual sleep status in home environments. The present study aimed to investigate sleep state misperception in the home environment of patients with chronic insomnia using a lumbar-worn actigraphy to identify sleep habits associated with sleep state misperception severity. Thirty-one patients and 42 healthy volunteers were included in the insomnia and non-insomnia group, respectively. Participants recorded subjective assessments in sleep diaries, objective assessments with an actigraphy worn for 14 days, and self-assessments using questionnaires. Both groups had similar objective sleep ratings; however, insomnia group had significantly worse subjective ratings (total sleep time, wake after sleep onset, and sleep onset latency). A significant correlation was found between subjective and objective total sleep time scores in non-insomnia group but not in insomnia group. Insomnia group had earlier bedtimes, significantly longer bedtimes, and impaired daytime functioning (Sheehan Disability Scale score); additionally, they underestimated their total sleep time, particularly with earlier bedtimes and longer laying durations. Monitoring the sleep status and habits of individuals in home environments could be instrumental in identifying key points for targeted interventions on sleep hygiene and cognitive behavioral therapy for insomnia.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Male; Female; Middle Aged; Adult; Sleep; Actigraphy; Surveys and Questionnaires; Polysomnography; Sleep Quality; Habits
PubMed: 38886489
DOI: 10.1038/s41598-024-64355-3 -
Journal of Medical Internet Research Jun 2024Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human... (Comparative Study)
Comparative Study
Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities.
BACKGROUND
Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms.
OBJECTIVE
This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA).
METHODS
A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the "Staff Hours" app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data's nested structure.
RESULTS
Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night's midpoint of sleep (β=-0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA's activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night.
CONCLUSIONS
This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies.
Topics: Humans; Smartphone; Exercise; Actigraphy; Adult; Female; Male; Sleep; Middle Aged
PubMed: 38885499
DOI: 10.2196/56144 -
International Journal of Women's Health 2024To investigate the associations between anxiety symptoms in midlife women and sleep features later in life, the aim is to test the hypothesis that poor sleep, as...
PURPOSE
To investigate the associations between anxiety symptoms in midlife women and sleep features later in life, the aim is to test the hypothesis that poor sleep, as measured by each of six individual dimensions (4 objective actigraphy measures, 2 self-reports) of sleep health, is associated with higher levels of anxiety symptoms in midlife women.
PARTICIPANTS AND METHODS
The participants in this longitudinal analysis included women from the SWAN Sleep I Study, a subcohort of the community-dwelling midlife women participating in the core Study of Women's Health Across the Nation (SWAN), which was initiated in 1996. Of the 370 participants enrolled in the Sleep Study, 270 were included in the analytic sample, and 100 who did not meet the inclusion criteria were excluded. Baseline measures of six dimensions of multidimensional sleep health (actigraphy measures: efficiency, duration, mid-sleep timing, regularity; self-report measures: alertness, satisfaction) were obtained between 2003 and 2005, corresponding to SWAN core annual/biennial assessments 5-8. Associations of each dimension with self-reported anxiety symptoms (Generalized Anxiety Disorder - 7-item scale; GAD-7), collected during visits 12 (2009-2011), 13 (2011-2013), and 15 (2015-2017), were examined using mixed models. The GAD-7 outcome was measured both continuously and as a categorical variable due to its skewed distribution.
RESULTS
No statistically significant associations were found between any of the six baseline sleep health dimensions and the GAD-7 score after adjustment for covariates.
CONCLUSION
The reasons for the lack of support for our hypothesis, despite previous evidence supporting an association between sleep and anxiety, are unclear. There is considerable overlap between anxiety and sleep symptoms, which may complicate the interpretation of our the findings. Thus, the failure to identify associations is likely multifactorial, and more studies with shorter follow-up intervals are warranted to better understand these relationships.
PubMed: 38884052
DOI: 10.2147/IJWH.S455834 -
Frontiers in Psychology 2024Online education has become a crucial component of teachers' professional development, and universities incorporate innovative pedagogical approaches to enhance...
INTRODUCTION
Online education has become a crucial component of teachers' professional development, and universities incorporate innovative pedagogical approaches to enhance teachers' training. These approaches have proven invaluable, particularly during the COVID-19 pandemic. This study investigates the impact of online versus face-to-face learning environments on sleep quality, physical activity, and cognitive functioning among physical education students.
METHODS
Utilizing a unique methodological approach that combines wrist actigraphy, the Pittsburgh Sleep Quality Index, and the Cambridge Neuropsychological Test Automated Battery, we provide a comprehensive assessment of these variables. Over 4 weeks, 19 male students participated in alternating online and face-to-face class formats.
RESULTS
Our results reveal no significant differences in sleep quality or cognitive function between learning environments. However, notable findings include significant differences in Paired Associates Learning and weekday step counts in the face-to-face setting.
DISCUSSION
These insights suggest that while online learning environments may not adversely affect sleep or cognitive functions, they could impact certain aspects of physical activity and specific cognitive tasks. These findings contribute to the nuanced understanding of online learning's implications and can inform the design of educational strategies that promote student well-being.
PubMed: 38882507
DOI: 10.3389/fpsyg.2024.1397588 -
European Journal of Sport Science Jun 2024The aim of this study was to investigate sleep-wake behavior and gain insights into perceived impairment (sleep, fatigue, and cognitive function) of athletes competing...
The aim of this study was to investigate sleep-wake behavior and gain insights into perceived impairment (sleep, fatigue, and cognitive function) of athletes competing in two international multi-day adventure races. Twenty-four athletes took part across two independent adventure races: Queensland, Australia and Alaska, USA. Individual sleep periods were determined via actigraphy, and racers self-reported their perceived sleep disturbances, sleep impairment, fatigue and cognitive function. Each of these indices was calculated for pre-, during- and post-race periods. Sleep was severely restricted during the race period compared to pre-race (Queensland, 7:46 [0:29] vs. 2:50 [1:01]; Alaska, 7:39 [0:58] vs. 2:45 [2:05]; mean [SD], hh:mm). As a result, there was a large cumulative sleep debt at race completion, which was not 'reversed' in the post-race period (up to 1 week). The deterioration in all four self-reported scales of perceived impairment during the race period was largely restored in the post-race period. This is the first study to document objective sleep-wake behaviors and subjective impairment of adventure racers, in the context of two geographically diverse, multi-day, international adventure races. Measures of sleep deprivation indicate that sleep debt was extreme and did not recover to pre-race levels within 1 week following each race. Despite this objective debt continuing, perceived impairment returned to pre-race levels quickly post-race. Therefore, further examination of actual and perceived sleep recovery is warranted. Adventure racing presents a unique scenario to examine sleep, performance and recovery.
PubMed: 38874812
DOI: 10.1002/ejsc.12143